text
stringlengths 254
1.16M
|
|---|
---
title: Colloidal Nanoparticles Isolated from Duck Soup Exhibit Antioxidant Effect
on Macrophages and Enterocytes
authors:
- Ligen Xu
- Mingcai Duan
- Zhaoxia Cai
- Tao Zeng
- Yangying Sun
- Shuang Cheng
- Qiang Xia
- Changyu Zhou
- Jun He
- Lizhi Lu
- Daodong Pan
journal: Foods
year: 2023
pmcid: PMC10000818
doi: 10.3390/foods12050981
license: CC BY 4.0
---
# Colloidal Nanoparticles Isolated from Duck Soup Exhibit Antioxidant Effect on Macrophages and Enterocytes
## Abstract
Food-derived colloidal nanoparticles (CNPs) have been found in many food cooking processes, and their specific effects on human health need to be further explored. Here, we report on the successful isolation of CNPs from duck soup. The hydrodynamic diameters of the obtained CNPs were 255.23 ± 12.77 nm, which comprised lipids ($51.2\%$), protein ($30.8\%$), and carbohydrates ($7.9\%$). As indicated by the tests of free radical scavenging and ferric reducing capacities, the CNPs possessed remarkable antioxidant activity. Macrophages and enterocytes are essential for intestinal homeostasis. Therefore, RAW 264.7 and Caco-2 were applied to establish an oxidative stress model to investigate the antioxidant characteristics of the CNPs. The results showed that the CNPs from duck soup could be engulfed by these two cell lines, and could significantly alleviate 2,2′-Azobis(2-methylpropionamidine) dihydrochloride (AAPH)-induced oxidative damage. It indicates that the intake of duck soup is beneficial for intestinal health. These data contribute to revealing the underlying functional mechanism of Chinese traditional duck soup and the development of food-derived functional components.
## 1. Introduction
Food-grade nanoparticles, such as nanoemulsions and liposomes, have been successfully developed with excellent stability and efficacy [1,2]. Soups can produce colloidal nanoparticles (CNPs) in the case of self-assembly, which have been observed in soups made with clams [3], Chinese medicine [4], and pig bones [5]. Nutrients migrate from food raw materials to water, and then form self-assembling particles between molecules that interact with each other covalently and noncovalently during heating, ranging in size from the nanometer to the micron scale. Soups have rich flavors and complex components, and the CNPs in them change the degree of digestion and the absorption of nutrients in raw materials [6,7], whereas further research is needed to investigate the ingestion and functioning of these nanoparticles.
Macrophages are immune cells that serve a variety of purposes. They are widely distributed throughout the body and serve as significant study subjects for cellular phagocytosis, cellular immunity, and molecular immunology. Macrophages have a strategic role in intestinal homeostasis and intestinal physiology [8]. RAW 264.7 is considered to be one of the best models of macrophages, and it has several uses in the study of inflammation, immunity, apoptosis, and tumor research [9,10]. The colonic mucosal epithelium is the fulcrum that maintains intestinal homeostasis, and these barrier-forming cells can precisely control redox signaling and thus avoid tissue damage [11]. The interactions between food and intestine have been studied using the Caco-2 cell model, which is well adapted for this purpose [12].
Oxidative stress is caused by the imbalance between oxidation and antioxidation in biological systems. It usually leads to the excess accumulation of reactive oxygen species (ROS) and induces the damage of cellular components and cell apoptosis [13,14]. A large number of studies on oxidative stress and anti-inflammatory processes have conducted out in Caco-2 cells or RAW 264.7 cells, confirming the representativeness of such cell models [15,16,17]. Therefore, studying the interactions of food-derived CNPs with both macrophages and enterocytes should be appropriate for revealing the effects of CNPs on intestinal health.
Duck meat, as a quality and nutritious meat resource, is becoming more and more well-liked by consumers worldwide, especially in Asia [18]. In China, old duck is often used as the raw material for duck soup, and is it believed to have a curative effect on inflammation. The aged ducks (500 days of age) were found to have a significant antioxidant capacity, with abundant metabolites [19]. However, the CNPs in duck soup, and their biological effects on intestine have not been characterized yet.
To further understand the bioactivity of duck soup, the CNPs were isolated from duck soup, and their interactions with Caco-2 cells and RAW 264.7 cells were investigated to reveal the antioxidant effects of these CNPs on the intestinal tract. Therefore, this work should expand our knowledge of the biological function mechanism of food soup, and contribute to the development of gastrointestinal protection.
## 2.1. Materials
All the analytical-grade reagents used were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China), including sodium dihydrogen phosphate, glucose, sodium chloride, sodium hydroxide, and disodium hydrogen phosphate. Triglyceride kits, BCA protein assay kits, total antioxidant capacity colorimetric (T-AOC) assay kits (FRAP method), 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS), cell counting kit-8 (CCK-8), and 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) were provided by Sangon Biotech Co., Ltd. (Shanghai, China). The following products were purchased from Sigma-Aldrich Co., Ltd. (Shanghai, China): Hoechst 33342 staining solution, dimethyl sulfoxide (DMSO), DiBAC4[3] staining solution, 2,2′-Azobis(2-methylpropionamidine) dihydrochloride (AAPH), and penicillin–streptomycin (100×, Sterile). The following products were obtained from Invitrogen Co., Ltd. (Carlsbad, CA, USA): Fetal bovine serum (FBS), $25\%$ Pancreatin + Ethylene Diamine Tetraacetic Acid (EDTA), Dulbecco’s modified minimal essential medium (DMEM), MitoSOX Red staining solution, phosphate buffered saline (PBS), Hank’s balanced salt solution (HBSS), and minimal essential medium (MEM). The RAW 264.7 and Caco-2 cell lines were procured from the BeNa Culture Collection (Su Zhou, China). The HiPrep $\frac{16}{60}$ Sephacryl S-500HR (1.0 × 120 cm) column was purchased from General Electric Company (Fairfield, CT, USA). Cell culture flasks, 96-well plates (black transparent flat bottom), and 24-well plates were purchased from Corning Company (Corning, NY, USA).
## 2.2. The Preparation of Duck Soup
Fresh 600-day-old Sheldrake carcasses (Huaying, Xinyang, China) were purchased. The duck breast meat was cut into square pieces with a side length of about 2 cm, blanched in boiling water to remove blood, and then washed on the surface in clean water. Meat pieces were cooked in deionized water (meat/water, w/v, 1:3) for 3 h at 100 °C, and heated with an induction cooker (300 W), during which the water loss caused by cooking was supplemented according to the liquid level. The duck soup was filtered twice with eight layers of cotton gauze to remove solids, and then stored at −40 °C for future use [6].
## 2.3. Separation of the CNPs from the Duck Soup
The CNPs of duck soup were separated according to a reported method, with some modifications [6]. The duck soup was filtered through a 0.45 μm filter membrane after being centrifuged at 400× g for 10 min. Four milliliters of duck soup supernatant were separated using a pre-equilibrated chromatographic column equipped with AKTA avant150 (General Electric Company, Fairfield, Connecticut). The concentration of phosphate buffer was adjusted to 0.02 M. The flow rate was 1 mL/min. The automatic collector was set at 4 mL/tube, and the UV monitor was set at 280 nm. The eluent at each stage isolated from the column was labeled as Fn (n for the scientific count), depending on the peak time.
Each Fn (1 mL) was gently injected into the sample pool of dynamic light scattering (DLS) (Malvern, UK) at 25 °C for measurement. The viscosity and refractive index (RI) were 0.8872 and 1.330, respectively [5]. The CNPs were selected from the eluents based on the polymer dispersity index (PDI), the hydrodynamic diameter, light scattering, and the ζ-potential. Each determination was repeated three times.
## 2.4. The Morphologies of the CNPs
The CNPs were dripped onto the copper net covered with Formvar films; the excess solution was slowly wiped off with filter paper along the edge of the copper mesh, and dripped with $1\%$ uranyl acetate for dyeing. Morphologies were observed under a transmission electron microscope (TEM) at 80 kV.
## 2.5. Major Compositions Analysis of the CNPs
The major compositions (lipids, proteins, and carbohydrates) of the obtained CNPs were detected [20]. A protein detection was performed according to the method of BCA protein quantitative kits. An anthrone-sulfuric acid test was used to assess the number of polysaccharides in CNPs. According to the method provided by the kits, the content of triglyceride in the sample was measured using the GPO-PAP enzyme assay. The absorbance measurement was conducted with a multifunctional microplate reader (Infinite 200 PRO, TECAN, Switzerland). All of the above indexes were tested 3 times.
## 2.6. Determination of Antioxidant Activities
The ABTS free radical scavenging capacity of CNPs was evaluated based on a published method, with slight modifications [21]. The ABTS powder was weighed and prepared to 7 mM, and the potassium persulfate reagent was weighed and prepared to 140 mM. Then, 5 mL of ABTS solution was mixed with 88 μL potassium persulfate solution and placed away from light for 12–16 h. The mixture was then diluted 50 times, with distilled water as the ABTS+ reserve solution. The ABTS+ reserve solution (200 μL) and the sample solution (50 μL) were absorbed and added into the enzyme-labeled plate. The absorbance was measured at 734 nm after standing for 10 min in a dark environment at room temperature, and the measurement was repeated 3 times. The reported method was used to test the ferric reducing antioxidant power (FRAP) of the CNPs [20]. The 100 mM FeSO4·7H2O solution was diluted with deionized water to 0.15, 0.30, 0.60, 0.90, 1.20, and 1.50 mM as the standard for calibration curves. Samples (5 μL) and FeSO4·7H2O standard (5 μL) were added into 96-well plates in equal quantities, then FRAP solution reagent (5 μL) was added into each well and incubated at 37 °C for 5 min, and distilled water (5 μL) was used as the control. Finally, absorbance was measured at the wavelength of 593 nm, and test temperature was set at 37 °C, and the measurement was repeated 3 times. The DPPH radical scavenging ability of CNPs was tested, the published method was slightly modified (the ratio of DPPH solution to sample solution was 1:1, 100 µL) [22]. The 0.1 mM DPPH solution (100 μL) and sample solution (100 μL) were absorbed, then added to 96-well plates and mixed. After being kept in dark for 30 min, the plate was placed on the enzyme label analyzer, the absorbance was recorded at 517 nm, and the measurement was repeated 3 times.
## 2.7. Toxicity Test of the CNPs on Cells
Using the CCK-8 kit, the toxicity of CNPs to Raw 264.7 and Caco-2 cells was investigated. The cells (100 µL, 5 × 104 cell/well) were inoculated into 96-well plates and incubated overnight in incubators (37 °C, $5\%$ CO2). Each well of the cells was incubated for 24 h with 100 μL of CNPs at different concentrations, and mixed with 10 μL of CCK-8 solution for 4 h. The absorbance values were measured at 450 nm, and the cell viability was calculated by referring to Gao et al. [ 23]. Each determination was repeated three times.
## 2.8. Observation of the Uptake of CNPs by Raw 264.7 Cells and Caco-2 Cells
Nile red (1 µg/mL) was mixed with CNPs (1 mg/mL) and incubated for one hour at 40 °C. The filtrate was extracted via centrifugation at 4000× g for 5 min. The retained particles were washed with HBSS and centrifuged, and repeated several times until no red fluorescence was observed in the filtrate. The remaining particles were re-suspended in HBSS for use.
The cell suspension was inoculated at 5 × 104 cell/well in 24-well plates, and incubated overnight (37 °C, $5\%$ CO2). Then, the medium was removed and HBSS was used to wash the cells twice. The cells were fixed with $4\%$ paraformaldehyde and stained with Hoechst 33342 (1–10 µg/mL).
The Hoechst 33342-stained cells and Nile Red-tagged colloidal particles were mixed and incubated for 3 h. Fluorescence was observed via inversed fluorescent microscope (IX-53, Olympus, Japan). The excitation and emission wavelengths of Nile red were 549 nm and 628 nm, respectively. The excitation and emission wavelengths of Hoechst 33342 were 346 and 460 nm, respectively. The instrument provided software for observation under a unified background, and the whole experiment was carried out three times in a dark environment [20].
## 2.9. Detection of Cell Membrane Potential and Mitochondrial Superoxide
DiBAC4 [3] staining solution (5 µm) and Mito-sox Red staining solution (2.5 µm) were applied for the determination of cell membrane potential and mitochondrial superoxide, respectively, using HBSS as the solvent. The procedures were as follows: 200 μL of 5 × 104 cell/well cells were seeded in a black 96-microwell plate and cultured overnight in an incubator (37 °C, $5\%$ CO2). The staining solution was added to each well at a dosage of 100 µL, and the excess staining solution was cleared after a certain period of incubation (30 min for DiBAC4 [3] and 10 min for Mito-Sox Red). Then, 100 µL of various concentrations (100 µg/mL, 500 µg/mL, and 1000 µg/mL) of the CNPs, and HBSS (control) were added, and then 50 µL AAPH (6.4 µm) was added and incubated for 30 min. Finally, 510 nm and 580 nm were chosen as the excitation and emission wavelengths, respectively, and the fluorescence intensity was observed under an inverted fluorescence microscope. Each determination was repeated three times.
## 2.10. Statistical Analysis
The data were presented as mean ± standard deviation. Statistical differences were examined via a one-way analysis of variance (ANOVA) combined with Duncan multiple comparison. The significance level was set at $p \leq 0.05.$ Graphs were performed by Origin 2019 (Origin Lab, Northampton, MA, USA).
## 3.1. Isolation and Properties of the CNPs
Three eluents, F1, F2, and F3, were separated and collected in the range of 100 to 160 min, among which F1 in the range of 100 to 120 min had a stronger signal of light scattering intensity than other eluents (Figure 1A). As shown in Table 1, the average hydrodynamic diameters of F1, F2, and F3 were 255 nm, 220 nm, and 147 nm, respectively, and the light scattering intensity in F1 was roughly three times greater than in F2. It has been indicated that larger particle sizes of CNPs are more efficiently phagocytosed by macrophages [24]. The minimum PDI of F1 indicated a narrow sample size distribution, while the particles in F2 and F3 may not have a uniform size. The maximum negative ζ-potential of F3 indicated a greater ionic bond interaction with the chromatographic gel, resulting in a delay in separation. The TEM micrograph of F1 confirmed that the particles contained in F1 had a uniform spheroid shape (Figure 1B). Thus, the representative F1 was chosen to learn the nano-functional properties of the CNPs from duck soup.
## 3.2. Major Components and Antioxidant Activities of CNPs
The CNPs obtained after 3 h of continuous simmering of the duck soup had a lipids content of $51.2\%$, followed by $30.8\%$ of proteins and $7.9\%$ of sugars (Table 2). It has been found that the constituent proteins in the particles are mainly associated with antioxidant activity [25], and that the second most abundant protein in CNPs offers the possibility of antioxidant activity. Protein extracts from duck meat have been shown to have a good ability in antioxidant and free radical scavenging [26]. The comparison revealed that the composition of CNPs in the duck soup was similar to that of porcine bone soup [20], but significantly different from that of freshwater clam [23], which contained $60\%$ carbohydrates. This may be due to the differential compositions between clams and duck meat, indicating that the formation of CNPs in the soup should be closely associated with the ingredients of raw materials.
It is common practice to utilize spectrophotometric techniques to assess food antioxidant potential, including the determinations of ABTS and DPPH, both of which involve the scavenging of free radicals [27]. Another method monitoring the iron ion reducing capacity is expressed as FRAP, and a high FRAP value indicates a stronger antioxidant activity [28]. As the CNP concentration increased, the antioxidant capacity showed an increasing trend in Figure 2, demonstrating that CNPs had a powerful antioxidant capability, but the effect of the high concentration of CNPs on cells needs to be explored.
## 3.3. Cytotoxicities of CNPs
Some nanoparticles that are used as food additives are toxic to Caco-2 cells, disrupting the cell tight junction permeability barrier and exacerbating the intestinal barrier injury inflammatory response caused by oxidative stress [29,30]. Similarly, it has been found that SiO2 CNPs have cytotoxic effects on macrophages at high concentrations [31]. However, self-assembled nanoparticles derived from porcine bone and freshwater clam had a protective effect on cells [23,32].
There was no significant difference in cell activity between the CNP treatments and the control, indicating that CNPs had no significant toxicity to Caco-2 cells (Figure 3A) or Raw 264.7 cells (Figure 3B), and could significantly promote the growth and proliferation of these cells when the concentration was 50–300 µg/mL. However, when the concentration of CNPs gradually increased; as can be seen from Figure 3, the cell activity showed a trend of decline.
## 3.4. Interactions of Caco-2 Cells and Raw 264.7 Cells with CNPs
Nile red is a lipophilic fluorescent dye that can be used for CNPs containing abundant lipids, and its reliability has been widely verified [7].
As observed in Figure 4, after incubation, almost every cell nucleus was wrapped in the CNPs, and all regions of the cell except the nuclear region emitted a red fluorescence, indicating that the CNPs were not only attached to the cytoplasmic membrane, but also engulfed by the cells. The obtained CNPs from duck soup have been proven with significant antioxidant capacity, and here, their absorption by cells through the endocytic pathway implies the potential to improve the antioxidant capacities of cells.
## 3.5. Determination of Membrane Potential and Mitochondrial Superoxide Content in Cells
Oxidative stress is caused by an imbalance between reactive oxygen species (ROS) production and the antioxidant capacities of cells, which is a cellular state that is characterized by an excessive production of ROS [33,34]. ROS are produced by aerobic cells during metabolism, and the overproduction of ROS can cause cellular damage to intestinal epithelial cells [35,36]. Therefore, the body’s antioxidant system needs exogenous antioxidants to effectively avoid the occurrence of oxidative stress. According to several reports, the reduction in oxidative stress prevents intestinal barrier deterioration and lowers inflammatory reactions inside the gut [37,38,39]. AAPH is a free radical initiator that can release hydroxyl radicals upon the stimulation of cells, thus causing oxidative stress and some damage to cell membranes. High concentrations of AAPH can severely damage cells, causing oxidative stress and further activating uncoupling proteins on the mitochondria, leading to a decrease in mitochondrial respiration rate and thus reducing intracellular free radical levels [40,41].
In the absence of AAPH-induced cell damage (AAPH-), the fluorescence intensity of the groups in Caco-2 cells with additional CNPs was comparable to that of the control group, with no discernible differences based on the green fluorescence of DiBAC4[3] in Figure 5A. The relative fluorescence units (RFU) of Caco-2 cells were found to be much lower than those of the control group when the concentration of CNPs was 1000 µg/mL, as shown in Figure 5B. In Raw 264.7 cells, it was noticed that the fluorescence intensities of the groups with additional CNPs did not change substantially from that of the control group, based on the green fluorescence of DiBAC4[3] in Figure 5C. Moreover, the RFU of the groups added with various concentrations of CNPs did not differ noticeably from the control group in Figure 5D.
When the AAPH inducer was added to the cells, as shown in Figure 5 (AAPH+), the green fluorescence in Caco-2 cells and Raw 264.7 cells was extinguished, while a decrease in RFU could be observed. However, the fluorescence was significantly restored by the addition of CNPs, probably due to the alleviation of cellular damage caused by AAPH radicals, which significantly restored the cellular membrane potential and thus counteracted the hyperpolarized state of the cellular membrane caused by extracellular hydrogen peroxide radicals. For Caco-2 cells, the presence of AAPH has been reported to cause an increase in cell permeability, which can be reduced by CNPs [32]. CNPs can also protect the macrophage cytoplasm and membrane from AAPH-induced oxidative damage [20]. Therefore, it can be inferred that CNPs in the appropriate concentration range extracted from duck soup would rather protect than damage the membranes of Caco-2 and Raw 264.7 cells under oxidative stress.
The mitochondrion is the main site of ROS production in cells, and also the target organ of cellular oxidative stress damage [34]. Mito-Sox *Red is* a specific fluorescent indicator for the detection of reactive oxygen ROS levels, and its fluorescence intensity is proportional to the ROS concentration.
As shown in Figure 6A,C, there was no difference in the red fluorescence when Caco-2 cells and Raw 264.7 cells ingested the CNPs (AAPH-). As shown in Figure 6B,D, the fluorescence intensities of Caco-2 cells and Raw 264.7 cells were not significantly different from those of the control group, indicating that the CNPs in duck soup had almost no effect on mitochondrial reactive oxygen radicals. When the cells were subjected to AAPH radical-induced damage (AAPH+), as observed in Figure 6A,C, the fluorescence of Caco-2 cells and Raw 264.7 cells almost disappeared, and the strong fluorescence could hardly be seen under the microscope, which indicated that AAPH radicals could resist the oxygen respiration in mitochondria and the production of ROS. When CNPs were added to co-incubate with the cells, the red fluorescence in the cells was significantly restored compared to the control group, counteracting some of the inhibition of mitochondrial ROS by AAPH and increasing the production of ROS, indicating that CNPs could relieve the oxidative stress of cells. In Figure 6B, different concentrations of CNPs could restore the intracellular fluorescence intensity of Caco-2 cells compared to the control group, except for 1000 µg/mL of CNPs, which had no such effect. As shown in Figure 6D, compared with the AAPH group, different concentrations of CNPs significantly increased the intracellular fluorescence intensity of Raw 264.7 cells and promoted ROS proliferation in mitochondria. It was speculated that when the concentration of CNPs exceeds 1000 µg/mL, it might cause toxic damage to the cells, which in turn impaired the mitochondrial function, which was consistent with the detection of the effects of CNPs on the cell membrane potential. The experimental results showed that 100 µg/mL and 500 µg/mL CNPs could effectively maintain mitochondrial oxygen respiration and shield cells from the oxidative harm brought on by hydrogen peroxide radicals.
Interestingly, duck meat is considered to have a pyretolysis effect on the body in Chinese folk and Chinese medicine, and duck soup is highly popular [42]. Further investigations revealed that the consumption of duck meat reduced energy metabolism in rats [43]. In this study, CNPs extracted from duck soup benefited the growth of macrophages and intestinal epithelial cells, and had the effect of alleviating the oxidative stress of the cells, which has implications for explaining the potential antioxidant benefits of duck meat and soup. It is worth mentioning that further tests should be carried out in mice to validate the function of CNPs in duck soup.
## 4. Conclusions
In conclusion, this study successfully extracted bioactive colloidal nanoparticles from duck soup, verifying their antioxidant activity. In a suitable concentration range, the CNPs were able to interact directly with RAW 264.7 cells and Caco-2 cells, and alleviate their cellular damage when exposed to oxidative stress. This study will contribute to the extraction and application of food-derived CNPs for better efficacy, and promote new attempts at nanotechnology in the food field.
## References
1. Zhao L., Temelli F.. **Preparation of anthocyanin-loaded liposomes using an improved supercritical carbon dioxide method**. *Innov. Food Sci. Emerg. Technol.* (2017) **39** 119-128. DOI: 10.1016/j.ifset.2016.11.013
2. Yang T., Zheng J., Zheng B.S., Liu F., Wang S., Tang C.H.. **High Internal Phase Emulsions Stabilized by Starch Nanocrystals**. *Food Hydrocoll.* (2018) **82** 230-238. DOI: 10.1016/j.foodhyd.2018.04.006
3. Yu Z., Gao G., Wang H., Ke L., Luo S.. **Identification of protein-polysaccharide nanoparticles carrying hepatoprotective bioactives in freshwater clam (**. *Int. J. Biol. Macromol.* (2020) **151** 781-786. DOI: 10.1016/j.ijbiomac.2020.02.105
4. Zhou J., Gao G., Chu Q., Wang H., Rao P., Ke L.. **Chromatographic isolation of nanoparticles from Ma-Xing-Shi-Gan-Tang decoction and their characterization**. *J. Ethnopharmacol.* (2014) **151** 1116-1123. DOI: 10.1016/j.jep.2013.12.029
5. Ke L., Wang H., Gao G., Rao P., He L., Zhou J.. **Direct interaction of food derived colloidal micro/nano-particles with oral macrophages**. *npj Sci. Food* (2017) **1** 3. DOI: 10.1038/s41538-017-0003-3
6. Zou J., Xu M., Zou Y., Yang B.. **Chemical compositions and sensory characteristics of pork rib and Silkie chicken soups prepared by various cooking techniques**. *Food Chem.* (2021) **345** 128755. DOI: 10.1016/j.foodchem.2020.128755
7. He W., Bu Y., Wang W., Zhu W., Li X., Li J., Zhang Y.. **Effects of Thermoultrasonic Treatment on Characteristics of Micro-Nano Particles and Flavor in Greenland Halibut Bone Soup**. *Soc. Sci. Electron. Publ.* (2021) **79** 105785. DOI: 10.2139/ssrn.3915526
8. De Schepper S., Verheijden S., Aguilera-Lizarraga J., Viola M.F., Boesmans W., Stakenborg N., Voytyuk I., Schmidt I., Boeckx B., Dierckx de Casterle I.. **Self-Maintaining Gut Macrophages Are Essential for Intestinal Homeostasis**. *Cell* (2018) **175** 400-415. DOI: 10.1016/j.cell.2018.07.048
9. Xu C., Lu Z., Luo Y., Liu Y., Cao Z., Shen S., Li H., Liu J., Chen K., Chen Z.. **Targeting of NLRP3 inflammasome with gene editing for the amelioration of inflammatory diseases**. *Nat. Commun.* (2018) **9** 4092. DOI: 10.1038/s41467-018-06522-5
10. Jing W., Zhang X., Sun W., Hou X., Yao Z., Zhu Y.. **CRISPR/CAS9-Mediated Genome Editing of miRNA-155 Inhibits Proinflammatory Cytokine Production by RAW264.7 Cells**. *BioMed Res. Int.* (2015) **2015** 326042. DOI: 10.1155/2015/326042
11. Campbell E.L., Colgan S.P.. **Control and dysregulation of redox signalling in the gastrointestinal tract**. *Nat. Rev. Gastroenterol. Hepatol.* (2019) **16** 106-120. DOI: 10.1038/s41575-018-0079-5
12. Ding X., Hu X., Chen Y., Xie J., Ying M., Wang Y., Yu Q.. **Differentiated Caco-2 cell models in food-intestine interaction study: Current applications and future trends**. *Trends Food Sci. Technol.* (2021) **107** 455-465. DOI: 10.1016/j.tifs.2020.11.015
13. Kang K.A., Lee K.H., Chae S., Zhang R., Jung M.S., Lee Y., Kim S.Y., Kim H.S., Joo H.G., Park J.W.. **Eckol isolated from Ecklonia cava attenuates oxidative stress induced cell damage in lung fibroblast cells**. *FEBS Lett.* (2005) **579** 6295-6304. DOI: 10.1016/j.febslet.2005.10.008
14. Ngo D.N., Kim M.M., Kim S.K.. **Protective effects of aminoethyl-chitooligosaccharides against oxidative stress in mouse macrophage RAW 264.7 cells**. *Int. J. Biol. Macromol.* (2012) **50** 624-631. DOI: 10.1016/j.ijbiomac.2012.01.036
15. An J., Yang C., Li Z., Finn P.W., Perkins D.L., Sun J., Bai Z., Gao L., Zhang M., Ren D.. **In vitro antioxidant activities of Rhodobacter sphaeroides and protective effect on Caco-2 cell line model**. *Appl. Microbiol. Biotechnol.* (2019) **103** 917-927. DOI: 10.1007/s00253-018-9497-0
16. Kang H., Lee Y., Bae M., Park Y.K., Lee J.Y.. **Astaxanthin inhibits alcohol-induced inflammation and oxidative stress in macrophages in a Sirtuin 1-dependent manner**. *J. Nutr. Biochem.* (2020) **85** 108477. DOI: 10.1016/j.jnutbio.2020.108477
17. Wijeratne S., Cuppett S.L.. **Soy Isoflavones Protect the Intestine from Lipid Hydroperoxide Mediated Oxidative Damage**. *J. Agric. Food Chem.* (2007) **55** 9811-9816. DOI: 10.1021/jf071752g
18. Biswas S., Banerjee R., Bhattacharyya D., Patra G., Das A.K., Das S.K.. **Technological investigation into duck meat and its products—A potential alternative to chicken**. *World’s Poult. Sci. J.* (2019) **75** 609-620. DOI: 10.1017/S004393391900062X
19. Liu C., Pan D., Ye Y., Cao J.. **(1)H NMR and multivariate data analysis of the relationship between the age and quality of duck meat**. *Food Chem.* (2013) **141** 1281-1286. DOI: 10.1016/j.foodchem.2013.03.102
20. Wang H., Gao G., Ke L., Zhou J., Rao P., Jin Y., He L., Wan J., Wang Q.. **Isolation of colloidal particles from porcine bone soup and their interaction with murine peritoneal macrophage**. *J. Funct. Foods* (2019) **54** 403-411. DOI: 10.1016/j.jff.2019.01.021
21. Nenadis N., Wang L.F., Tsimidou M., Zhang H.Y.. **Estimation of Scavenging Activity of Phenolic Compounds Using the ABTS+ Assay**. *J. Agric. Food Chem.* (2004) **52** 4669-4674. DOI: 10.1021/jf0400056
22. Yang H., Su W., Wang L., Wang C., Wang C.. **Molecular structures of nonvolatile components in the Haihong fruit wine and their free radical scavenging effect**. *Food Chem.* (2021) **353** 129298. DOI: 10.1016/j.foodchem.2021.129298
23. Gao G., Wang H., Zhou J., Rao P., Ke L., Lin J.J., Sun Pan B., Zhang Y., Wang Q.. **Isolation and Characterization of Bioactive Proteoglycan-Lipid Nanoparticles from Freshwater Clam (Corbicula fluminea Muller) Soup**. *J. Agric. Food Chem.* (2021) **69** 1610-1618. DOI: 10.1021/acs.jafc.0c02402
24. He C., Hu Y., Yin L., Tang C., Yin C.. **Effects of particle size and surface charge on cellular uptake and biodistribution of polymeric nanoparticles**. *Biomaterials* (2010) **31** 3657-3666. DOI: 10.1016/j.biomaterials.2010.01.065
25. Chi C.F., Cao Z.H., Wang B., Hu F.Y., Li Z.R., Zhang B.. **Antioxidant and functional properties of collagen hydrolysates from Spanish mackerel skin as influenced by average molecular weight**. *Molecules* (2014) **19** 11211-11230. DOI: 10.3390/molecules190811211
26. Chen Y., Kong L., Wang S.. **Image recognition of automatic evisceration of Cherry Valley ducks and biological activities of protein extracts isolated from the duck meat**. *J. Food Process Eng.* (2018) **41** e12805. DOI: 10.1111/jfpe.12805
27. Floegel A., Kim D.-O., Chung S.-J., Koo S.I., Chun O.K.. **Comparison of ABTS/DPPH assays to measure antioxidant capacity in popular antioxidant-rich US foods**. *J. Food Compos. Anal.* (2011) **24** 1043-1048. DOI: 10.1016/j.jfca.2011.01.008
28. Bendif H., Boudjeniba M., Djamel Miara M., Biqiku L., Bramucci M., Caprioli G., Lupidi G., Quassinti L., Sagratini G., Vitali L.A.. **Rosmarinus eriocalyx: An alternative to Rosmarinus officinalis as a source of antioxidant compounds**. *Food Chem.* (2017) **218** 78-88. DOI: 10.1016/j.foodchem.2016.09.063
29. Pedata P., Ricci G., Malorni L., Venezia A., Cammarota M., Volpe M.G., Iannaccone N., Guida V., Schiraldi C., Romano M.. **In vitro intestinal epithelium responses to titanium dioxide nanoparticles**. *Food Res. Int.* (2019) **119** 634-642. DOI: 10.1016/j.foodres.2018.10.041
30. Zhao Y., Tang Y., Liu S., Jia T., Zhou D., Xu H.. **Foodborne TiO**. *Foods* (2021) **10**. DOI: 10.3390/foods10050986
31. Hadipour Moghaddam S.P., Saikia J., Yazdimamaghani M., Ghandehari H.. **Redox-Responsive Polysulfide-Based Biodegradable Organosilica Nanoparticles for Delivery of Bioactive Agents**. *ACS Appl. Mater. Interfaces* (2017) **9** 21133-21146. DOI: 10.1021/acsami.7b04351
32. Gao G., Zhou J., Jin Y., Wang H., Ding Y., Zhou J., Ke L., Rao P., Chong P.H., Wang Q.. **Nanoparticles derived from porcine bone soup attenuate oxidative stress-induced intestinal barrier injury in Caco-2 cell monolayer model**. *J. Funct. Foods* (2021) **83** 104573. DOI: 10.1016/j.jff.2021.104573
33. Dayem A.A., Choi H.Y., Kim J.H., Cho S.G.. **Role of oxidative stress in stem, cancer, and cancer stem cells**. *Cancers* (2010) **2** 859-884. DOI: 10.3390/cancers2020859
34. Newsholme P., Cruzat V.F., Keane K.N., Carlessi R., de Bittencourt P.I.. **Molecular mechanisms of ROS production and oxidative stress in diabetes**. *Biochem. J.* (2016) **473** 4527-4550. DOI: 10.1042/BCJ20160503C
35. Liu Y., Liu Y., Zang J., Abdullah A.A.I., Li Y., Dong H.. **Design Strategies and Applications of ROS-Responsive Phenylborate Ester-Based Nanomedicine**. *ACS Biomater. Sci. Eng.* (2020) **6** 6510-6527. DOI: 10.1021/acsbiomaterials.0c01190
36. Cilla A., Rodrigo M.J., Zacarias L., De Ancos B., Sanchez-Moreno C., Barbera R., Alegria A.. **Protective effect of bioaccessible fractions of citrus fruit pulps against H2O2-induced oxidative stress in Caco-2 cells**. *Food Res. Int.* (2018) **103** 335-344. DOI: 10.1016/j.foodres.2017.10.066
37. Cho Y.E., Song B.J.. **Pomegranate prevents binge alcohol-induced gut leakiness and hepatic inflammation by suppressing oxidative and nitrative stress**. *Redox Biol.* (2018) **18** 266-278. DOI: 10.1016/j.redox.2018.07.012
38. Hasegawa T., Mizugaki A., Inoue Y., Kato H., Murakami H.. **Cystine reduces tight junction permeability and intestinal inflammation induced by oxidative stress in Caco-2 cells**. *Amino Acids* (2021) **53** 1021-1032. DOI: 10.1007/s00726-021-03001-y
39. Xiang J., Yang C., Beta T., Liu S., Yang R.. **Phenolic Profile and Antioxidant Activity of the Edible Tree Peony Flower and Underlying Mechanisms of Preventive Effect on H(2)O(2)-Induced Oxidative Damage in Caco-2 Cells**. *Foods* (2019) **8**. DOI: 10.3390/foods8100471
40. Brand M.D., Affourtit C., Esteves T.C., Green K., Lambert A.J., Miwa S., Pakay J.L., Parker N.. **Mitochondrial superoxide: Production, biological effects, and activation of uncoupling proteins**. *Free Radic. Biol. Med.* (2004) **37** 755-767. DOI: 10.1016/j.freeradbiomed.2004.05.034
41. Forbes-Hernandez T.Y., Giampieri F., Gasparrini M., Mazzoni L., Quiles J.L., Alvarez-Suarez J.M., Battino M.. **The effects of bioactive compounds from plant foods on mitochondrial function: A focus on apoptotic mechanisms**. *Food Chem. Toxicol.* (2014) **68** 154-182. DOI: 10.1016/j.fct.2014.03.017
42. **Medicinal Food: The Chinese Perspective**. *J. Med. Food* (1998) **1** 117-122. DOI: 10.1089/jmf.1998.1.117
43. Feng X., Chen L., Zhuang S., Li C., Yan Z., Xu X., Zhou G.. **Effect of duck meat consumption on thyroid hormone concentrations and energy metabolism of Sprague-Dawley rats**. *Appetite* (2013) **69** 94-101. DOI: 10.1016/j.appet.2013.04.027
|
---
title: Maximum Heart Rate- and Lactate Threshold-Based Low-Volume High-Intensity Interval
Training Prescriptions Provide Similar Health Benefits in Metabolic Syndrome Patients
authors:
- Dejan Reljic
- Fabienne Frenk
- Hans Joachim Herrmann
- Markus Friedrich Neurath
- Yurdagül Zopf
journal: Healthcare
year: 2023
pmcid: PMC10000820
doi: 10.3390/healthcare11050711
license: CC BY 4.0
---
# Maximum Heart Rate- and Lactate Threshold-Based Low-Volume High-Intensity Interval Training Prescriptions Provide Similar Health Benefits in Metabolic Syndrome Patients
## Abstract
Exercise is an integral part of metabolic syndrome (MetS) treatment. Recently, low-volume high-intensity interval training (LOW-HIIT) has emerged as a time-efficient approach to improving cardiometabolic health. Intensity prescriptions for LOW-HIIT are typically based on maximum heart rate (HRmax) percentages. However, HRmax determination requires maximal effort during exercise testing, which may not always be feasible/safe for MetS patients. This trial compared the effects of a 12-week LOW-HIIT program based on: (a) HRmax (HIIT-HR), or (b) submaximal lactate threshold (HIIT-LT), on cardiometabolic health and quality of life (QoL) in MetS patients. Seventy-five patients were randomized to HIIT-HR (5 × 1 min at 80–$95\%$ HRmax), HIIT-LT (5 × 1 min at 95–$105\%$ LT) groups, both performed twice weekly on cycle ergometers, or a control group (CON). All patients received nutritional weight loss consultation. All groups reduced their body weight (HIIT-HR: −3.9 kg, $p \leq 0.001$; HTT-LT: −5.6 kg, $p \leq 0.001$; CON: −2.6 kg, $$p \leq 0.003$$). The HIIT-HR and HIIT-LT groups similarly, improved their maximal oxygen uptake (+3.6 and +3.7 mL/kg/min, $p \leq 0.001$), glycohemoglobin (−$0.2\%$, $$p \leq 0.005$$, and −$0.3\%$, $p \leq 0.001$), homeostasis model assessment index (−1.3 units, $$p \leq 0.005$$, and −1.0 units, $$p \leq 0.014$$), MetS z-score (−1.9 and −2.5 units, $p \leq 0.001$) and QoL (+10 points, $$p \leq 0.029$$, and +11 points, $$p \leq 0.002$$), while the CON did not experience changes in these variables. We conclude that HIIT-LT is a viable alternative to HIIT-HR for patients who are not able/willing to undergo maximal exercise testing.
## 1. Introduction
The metabolic syndrome (MetS) is a pathology defined by the presence of several cardiometabolic disorders, including obesity (in particular excess abdominal fat), hypertension, dyslipidemia, hyperglycemia and insulin resistance [1]. The occurrence of MetS has significantly risen worldwide during the past decades [2], with the latest estimates suggesting that globally, ~$13\%$ to ~$31\%$ of adults are affected [3]. Recently, it has been reported that COVID-19 pandemic-related measures like quarantines, social distancing and lockdowns have further contributed to the spread of MetS [4,5]. This trend is alarming because MetS is associated with an increased risk of several serious secondary diseases, such as cardiovascular disease [6], different cancers [7], all-cause mortality [6] and diminished quality of life (QoL) [8]. Additionally, recent observations indicate that excess body weight and the existence of cardiometabolic risk factors constitute an increased risk of developing a critical or lethal disease progression following a COVID-19 infection [9,10]. Therefore, effective therapeutic measures for MetS treatment are probably more urgent now than ever before.
Dietary adaptations, particularly a reduction in caloric intake, and an increase in physical activity are cornerstones in obesity and MetS treatment [11]. While caloric intake restriction is of paramount importance to achieve weight loss [12], it has been demonstrated that physical activity independently lowers the risk of developing several chronic health conditions and premature death, regardless of body mass index (BMI) [13]. It has been suggested that the level of cardiorespiratory fitness (CRF), objectified by the determination of maximal oxygen uptake (VO2max), is a major outcome for predicting cardiovascular and all-cause mortality, more significant than other well-established health risk factors like obesity, elevated blood pressure or nicotine abuse [14]. Despite a plethora of evidence on the wide range of health benefits associated with regular exercise, a large part of the global population [15], particularly obese individuals [16], do not meet the minimum physical activity guidelines of 75 min of vigorous-intensity or 150 min of moderate-intensity aerobic activity per week [17]. Over the last decade, surveys have consistently shown that time constraints are among the most frequently reported obstacles to regular exercise, both in the general population [18,19] as well as in clinical cohorts [20,21].
Thus, in the past few years, there has been an increasing scientific interest in designing and evaluating less time-consuming exercise approaches for preventing and treating chronic health conditions [22]. In this regard, low-volume high-intensity interval training (LOW-HIIT) has appeared as an innovative exercise modality to elicit comparable or even greater improvements in CRF and cardiometabolic outcomes in comparison to traditional continuous endurance training [23,24,25,26]. By definition, LOW-HIIT is a particular subtype of interval training involving a total duration of ≤10 min of intense interval bouts of ≥$80\%$ of maximum heart rate (HRmax), embedded in an overall exercise session of ≤30 min (when initial warm-up, recovery between intervals, and cool-down are added up) [23,24]. Recent research from our laboratory [27,28,29,30] and other researchers [31] has demonstrated that LOW-HIIT can effectively improve several cardiometabolic risk factors as well as subjective measures, such as QoL, in obese MetS patients.
Prescriptions for physical exercise are typically based on four main components: frequency, intensity, time, and type of exercise, also referred to as the FIIT principle [32]. Among these, intensity is considered the most important element of the physiological responses to exercise [32]. As with other cardiovascular training types, exercise intensity for LOW-HIIT is most commonly prescribed based on percentages of HRmax. For a rough estimation of exercise intensity, HRmax can be calculated using different formulas [33], most frequently via the “220—age” equation [34]. However, due to high interindividual variability in heart rate (HR) values [34,35], it is rather recommended to directly measure HRmax during an exhaustive exercise test in order to obtain more precise results. Although our own studies [27,28,29,30] and data from other research groups [31,36,37] indicate that guideline-based cardiopulmonary exercise testing (CPET) [38] is generally safe and tolerable in clinical settings, maximal exhaustion may be contraindicated in certain patient populations. Furthermore, in some individuals, true HRmax may not reached due to peripheral muscular fatigue or a lack of motivation.
Alternatively, exercise intensity can be prescribed using specific physiological thresholds based on ventilatory or blood lactate responses during incremental exercise. Ventilatory (VT) and lactate thresholds (LT) reflect specific submaximal metabolic inflection points of respiratory variables and blood lactate concentration [39]. Ventilatory thresholds and LT have been traditionally used to design training programs and predict performance in endurance and team sports athletes [40,41,42], but threshold-based exercise intensity prescription is also an interesting approach in clinical settings because it does not require maximal effort, making it potentially safer for high-risk patients. Additionally, it has been reported that threshold-based intensity prescriptions for traditional endurance training regimens were superior in improving VO2max in sedentary healthy individuals [43] and cardiometabolic risk factors in MetS patients [44], when compared to intensity prescriptions based on percentages of HRmax. In this context, endurance training programs involving exercise intensities at or above the LT were found to be particularly effective in lowering blood pressure in type 2 diabetic patients [45] and visceral fat in obese women with MetS [46]. Furthermore, it has been suggested that threshold-based training seems to be related with a lower instance of non-responders to exercise compared to training programs based on maximum values [47]. However, to our knowledge, no research has yet been undertaken to compare the effects of a LOW-HIIT program using HRmax- versus LT-based exercise intensity prescriptions on cardiometabolic health status in a clinical setting.
Thus, the main objective of this investigation was to compare the effects of a 12-week LOW-HIIT intervention either prescribed based on: (a) percentages of HRmax (HIIT-HR) or, (b) LT (HIIT-LT), on various cardiometabolic health indices and QoL in a cohort of obese patients diagnosed with MetS. We hypothesized that both LOW-HIIT protocols would improve cardiometabolic health status and QoL compared with a physically inactive control group but, on the basis of previous research using traditional endurance training regimens [43,44,47], we expected HIIT-LT to provide superior improvements than HIIT-HR.
## 2.1. Study Design
The present investigation was a sub-group analysis of a larger clinical trial examining the impact of various exercise modalities on multiple health outcomes in MetS patients. Other parts of this research project have been previously published elsewhere [27,28,29,30]. The present sub-study presents previously unpublished data from the HIIT-LT group and a sub-sample of the HIIT-HR and a non-exercising control group (CON) that additionally received capillary blood sampling during the CPET for the measurement of lactate concentrations.
In the overall trial, patients were allocated at random to different interval training protocols that were performed 2 times per week for a duration of 12 weeks or to the CON. All patients were provided with standard care nutritional consultation to support their weight reduction. Analogous to the main trial, the key outcome of this investigation was VO2max. Further outcomes of interest were cardiometabolic risk indices, body composition and QoL. The sample size determination and randomization process applied in the main trial were reported elsewhere [29]. Briefly, sample size calculation was based on the previous work of Reljic et al. [ 48], indicating a large effect of LOW-HIIT on VO2max ($d = 0.97$), that resulted in an estimated number of 16 patients per group to yield a statistical power of $95\%$. To account for dropouts, the aim was to recruit at least 25 patients for randomization into each group. Randomization was preceded by stratification according to VO2max, gender, age and BMI using the software MinimPy version 3.0 [49] to reduce the heterogeneity of patients’ main characteristics between groups. The randomization was conducted by a co-worker not engaged in the acquisition and analysis of the data.
All patients were fully informed about the objectives and procedures of the study, which conformed to the Helsinki Declaration, and gave their written consent before being included in the study. The study protocol was approved by the Medical Ethical Committee of the Friedrich-Alexander University Erlangen-Nürnberg (approval number: 210_17B) and registered at ClinicalTrials.gov (ID-number: NCT03306069).
## 2.2. Patients
Patients were recruited via flyers that were posted in medical practices and newspaper advertisements. In a first step, all interested persons were screened for eligibility by phone call or personal visit. The inclusion criteria were: age ≥18 years, a self-reported mainly physically inactive lifestyle as defined previously [50] and clinical diagnosis of MetS as classified by the International Diabetes Federation [51,52]. Criteria for exclusion were: pregnancy, clinical diagnosis of heart disease, oncological diseases, substantial musculoskeletal disorders or other major health limitations that may constitute contraindications to safe participation in exercise. All patients agreed not to change their usual lifestyle habits, apart from the study intervention. Patients were required to attend at least $75\%$ of the scheduled 24 LOW-HIIT sessions to be included in the final analysis.
## 2.3. Health Examinations
One week before starting the intervention, patients received the baseline examination, including several standardized assessments and measurements as described in detail below. The second examination was conducted during the week following the termination of the LOW-HIIT intervention with a minimum 3-day interval between the last training session.
Patients were instructed to appear overnight-fasted, to abstain from alcohol and to avoid strenuous physical activities for at least 24 h prior to each examination. If patients were required to take medication, care was taken to ensure that it was taken at the same time of day for both examinations. Prior to the second examination and regularly during the intervention period, patients were asked whether there had been any changes in the type of medication or dosage taken. The pre- and post-intervention examinations were scheduled at a similar daytime (08:00–08:30 a.m.) to minimize potential circadian bias and lasted approximately 2–3 h for each patient.
All measurements were carried out under stable and standardized laboratory conditions (temperature: 22–24 °C, and humidity: 30–$50\%$) within the examination rooms of the Hector-Center for Nutrition, Exercise and Sports at the University Hospital Erlangen and in the standardized order described below. During the examinations, the patients were dressed in their casual clothes, with an exception for the anthropometric measurements, which were performed in underwear without shoes, and the CPET, for which the patients wore a sport dress or comparable clothing. All examinations were performed by a team of highly experienced personnel, consisting of two study nurses (>5 years of work experience) and an exercise physiologist and physician (>10 years of work experience) who were assisted by two medical students. All the staff involved in the data collection were blinded to the assignment of the patient’s group.
## 2.3.1. Hydration Testing
After arriving at the research center, patients were first asked to provide a urine sample for a routine screening for urinary tract infections, kidney disorders and diabetes, and for measuring urine specific gravity (USG). Urine sample analyses were conducted within 30 min of collection using Multistix® 10 SG dipsticks (Siemens HealthCare, Erlangen, Germany).
## 2.3.2. Determination of Blood Pressure and Resting Heart Rate
Following urine collection, patients entered a quiet experimental room and after 5 min rest, resting HR (HRrest) and blood pressure were recorded with an automatic upper arm blood pressure monitor (M5 professional, Omron, Mannheim, Germany) [53]. According to recent guidelines [54], systolic (SBP) and diastolic (DBP) blood pressure were measured twice at both upper arms at intervals of 60 s and the average value from the side with the higher blood pressure was recorded. In addition, mean arterial blood pressure (MAB) was estimated according to the following formula [55]:MAB = DBP + ($\frac{1}{3}$ [SBP − DBP]).
## 2.3.3. Blood Collection
After blood pressure measurements, patients remained in the sitting position and venous blood samples were taken from the antecubital area. The blood collection tubes (Sarstedt, Nürmbrecht, Germany) were immediately further prepared and forwarded to the central laboratory of the University Hospital Erlangen for measurement of the serum concentrations of glucose, triglycerides, total cholesterol, low-density (LDL) and high-density lipoprotein cholesterol (HDL) using a photometrical determination method (Clinical Chemistry Analyzer AU700 or AU5800, Beckman Coulter, Brea, CA, USA), glycated hemoglobin A1c (HbA1c) using turbidimetric immunoassays (COBAS Integra 400, Roche Diagnostics, Mannheim, Germany) and insulin using a chemiluminescence assay (Liaison XL, DiaSorin, Saluggia, Italy). The homeostasis model assessment index (HOMA-index) was calculated according to the following formula [56]:Homeostasis model assessment-index = (insulin × glucose)/405.
## 2.3.4. Anthropometric Measurements
For standardization reasons, patients were asked again to empty their bladder, if necessary, before the measurements. Anthropometric evaluation included measurement of body weight and determination of body composition. More specifically, body weight, fat mass (FM), body fat percentage (FM%), fat free mass (FFM) and total body water (TBW) were determined using a multi-frequency segmental bioelectrical impedance analysis device (seca mBCA 515, Seca, Hamburg, Germany) with confirmed validity [57]. Patients’ waist circumference was measured in the upright position with a flexible tape (Seca, Hamburg, Germany) to the nearest millimeter, at the approximate midpoint between the last touchable rib and the upper iliac crest, as previously described [50].
## 2.3.5. Determination of the Metabolic Syndrome Severity Score
Metabolic syndrome severity was assessed according to the MetS z-score. The score was calculated using sex-specific equations based on HDL, triglycerides, glucose, waist circumference and MAB, as previously suggested [58]:Males: [(40 − HDL)/9.0] + [(triglycerides − 150)/81.0] + [(glucose − 100)/11.3] + [(waist circumference−102)/7.7] + [(MAB − 100)/9.1] Females: [(50 − HDL)/14.1] + [(triglycerides − 150)/81.0] + [(glucose − 100)/11.3] + [(waist circumference − 88)/9.0] + [(MAB − 100)/9.1]
## 2.3.6. Cardiopulmonary Exercise Testing
Cardiopulmonary exercise testing was performed on a stationary electronically braked cycle ergometer (Corival cpet, Lode, Groningen, The Netherlands) using two different standard exercise protocols [59,60]. Both protocols commenced with a brief familiarization period, followed by measurements from the resting 12-lead electrocardiogram (ECG, custo cardio 110, custo med, Ottobrunn, Germany), blood pressure and respiratory variables. Subsequently, the HIIT-HR and the CON performed a continuously incrementing ramp protocol, beginning at a workload of 50 W and then increasing by 1 W every 5 s (females) and 1 W every 4 s (males), respectively. Using this approach, maximal exertion was typically achieved within 8–12 min, as generally recommended for ramp protocols [59,60]. The HIIT-LT group performed a step incremental test, with a starting workload of 50 W, followed by a stepwise increase in the load by 25 W (females) and 30 W (males), respectively, every 3 min, as recommended to quantify the LT in untrained individuals [60,61]. With the step incremental test, maximum exertion was typically achieved within 10–14 min. Both protocols were performed with a constant cadence ranging between 60–80 rpm until volitional exhaustion.
During all CPET, exercise ECG was permanently monitored (custo cardio 110, custo med, Ottobrunn, Germany) and blood pressure was measured every 2 min with a standard cuff sphygmomanometer (ERKA, Bad Tölz, Germany). An open-circuit breath-by-breath spiroergometric system (Metalyzer 3B-R3, Cortex Biophysik, Leipzig, Germany) was used to continuously measure oxygen uptake (VO2) and carbon dioxide output (VCO2). At rest, immediately after termination of the exercise and at the 1st, 3rd and 5th min of recovery, 20 µL of capillary blood was sampled from the hyperemized earlobe to measure blood lactate concentrations. In the step incremental test (HIIT-LT group), capillary blood samples were additionally drawn within the last 20 s of each workload stage in order to determine the LT. Blood samples were immediately placed in collection tubes containing a hemolyzing solution, and subsequently measured in our laboratory using an enzymatic-amperometric method (LabTrend, BST Bio Sensor Technology, Berlin, Germany). Upon termination of the exercise, perceived exertion was requested from each patient using the 6–20 Borg scale [62]. Patients had to fulfill a minimum of two of the following criteria [63] in order to assume that maximum exertion had been achieved: a plateau in VO2, reaching a peak respiratory exchange ratio (RERmax) of ≥1.1, a peak blood lactate level of ≥8.0 mmol/L, an age predicted HRmax of ≥$90\%$ (according to the equation: 220—age) [34] and a perceived exertion value of ≥19 on the Borg scale [62].
## 2.3.7. Determination of Lactate and Ventilatory Thresholds
The lactate threshold was defined at the workload when blood lactate concentration had reached ≥4 mmol/L, as first established by Mader et al. [ 64] and later justified by Heck et al. [ 65]. Since then, the 4 mmol/L LT is also widely referred to as the “onset of lactate accumulation” (OBLA), and broadly used in exercise physiology and practice for performance diagnostics and training prescription [66]. Although it is clear that lactate accumulation does not occur suddenly at a sharp point but rather continuously in a transition zone [67], it is well accepted that the LT frequently corresponds with a blood lactate level of ~4 mmol/L in untrained individuals [68]. Moreover, the fixed 4 mmol/L LT was found to have high reproducibility and predictability in cycling endurance performance [69] and to be useful in prescribing exercise intensity for MetS patients [70]. Determination of HR (HRLT) and workload (WLT) at the 4 mmol/L LT was performed by applying the software Winlactat version 5.5.2.9 (Mesics, Münster, Germany). First (VT1) and second (VT2, also termed the respiratory compensation point, RCP) ventilatory thresholds were determined independently through visual inspection by two investigators from plots of VCO2 and VO2 (the V-slope method) [59]. In case of any discrepancy, a consensus was achieved by discussion. Heart rate and workload (WVT1 and WVT2) at both VTs were identified using an automated software (MetaSoft Studio, Cortex Biophysik, Leipzig, Germany).
## 2.4. Assessment of Self-Reported Quality of Life
Self-reported QoL was measured with the validated EuroQol Group questionnaire (EQ-5D-5L) [71]. The questionnaire consists of the simple EQ visual analogue scale (VAS) ranging from 0–100 (higher ratings imply better QoL) and the EQ-5D index, composed of 5 sub-categories (mobility, self-care, usual activities, pain/discomfort, anxiety/depression, each categorized into 5 severity levels). The values of the 5 sub-categories are transformed into a single variable, with a score of 1.0 representing perfect subjective health and a score of 0 representing the poorest possible health status, respectively [71]. The questionnaires were completed by the patients in a separate waiting lounge at the Hector-Center for Nutrition, Exercise and Sports. Any questions or uncertainties about the questionnaire could be resolved immediately with the investigators.
## 2.5. Monitoring of Daily Nutrition and Nutritional Counseling
Before study enrolment and during the final intervention week, patients were instructed to track their daily food intake over a duration of 3 successive days before each of the two examinations, with the help of a standardized 24 h nutrition protocol (Freiburger Ernährungsprotokoll; Nutri-Science, Freiburg, Germany). After delivery, the protocols were evaluated by a registered dietitian using the software PRODI 6 expert (Nutri-Science, Freiburg, Germany). In addition, patients’ resting metabolic rate (RMR) was estimated using the Harris–Benedict equation [72], as follows:Males: RMR (kcal/day) = 66.5 + 13.8 × weight (kg) + 5.0 × size (cm]) − 6.8 × age (years) Females: RMR (kcal/day) = 655 + 9.6 × weight (kg) + 1.8 × size (cm) − 4.7 × age (years) Based on the food record analysis, anthropometric data and the estimated RMR, patients received individual consultation during a personal conversation with a dietitian to support their weight loss. The dietary recommendations were made in accordance with the current obesity treatment guidelines, targeting a daily calorie reduction of 500 kcal [73]. Furthermore, patients were advised to consume at least 1.0 g/kg of protein per day to counteract a loss of muscle mass during caloric restriction, as previously recommended [74]. After the consultation, patients were provided with handouts, including recipes and nutrient lists, to increase adherence and to support them in the home-based implementation of the nutritional recommendations.
## 2.6. LOW-HIIT Protocols
Patients allocated to the exercise groups performed 2 supervised sessions per week of LOW-HIIT on electronically braked cycle ergometers (Corival cpet, Lode, Groningen, The Netherlands) in our exercise center with a minimum of 1 day recovery between sessions for a total of 12 weeks (24 sessions in total). In order to maximize adherence, patients had the option to schedule their sessions individually during the exercise center’s opening hours. The structure of the LOW-HIIT intervention was in accordance with the protocol introduced by Reljic et al. [ 48].
In brief, the protocol commenced with a short low-intensity warm-up period of 2 min. Subsequently, patients performed 5 vigorous interval bouts of 1 min duration (by accelerating the cadence and/or increasing the ergometer watt load) divided by 1 min recovery phases. After the fifth interval bout, the protocol concluded with a cool-down of 3 min duration at low intensity, corresponding to an accumulated total duration of 14 min/session. In the HIIT-HR group, patients were instructed to reach a minimum exercise intensity of 80–$85\%$ HRmax during the intervals for the first 4 weeks. The target intensity during intervals was progressively increased as follows: week 5–8: 85–$90\%$ HRmax and week 9–12: 90–$95\%$ HRmax. In the HIIT-LT group, the initial minimum exercise intensity to be achieved during intervals was set at a HR corresponding to 95–$100\%$ of the LT for the first 4 weeks and then elevated to a HR corresponding to 100–$105\%$ of the LT (week 5–12). Patients were equipped with a chest strap HR monitor (Acentas, Hörgertshausen, Germany) in every exercise session, allowing them to follow their HR in real-time on a screen. The HR responses were recorded in every session and later analyzed using the software Heart Rate Monitoring Team System (Acentas, Hörgertshausen, Germany). Average power output and energy expenditure were recorded from the cycle ergometer’s digital displays after each session. Certified sports- and physiotherapists monitored every single session to ensure that the imposed level of exercise intensity was reached.
## 2.7. Statistical Analysis
A priori sample size calculation was conducted using the software G*Power (Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany). Data analyses were performed using the software package SPSS version 24.0 software (IBM Corp., Armonk, NY, USA). Initially, data normality was analyzed using the Shapiro–Wilk test. If the data were normally distributed, a 2 × 2 repeated measures ANOVA was conducted to examine the data for both the main effects (group and time) and interaction effects (group × time). In case of significant results, Holm–Sidak post hoc tests for multiple comparisons were performed. Significant main effects of time were followed by separate post hoc paired t-tests for each group. Levene’s test was utilized to check and verify the homogeneity of variance. If no normal distribution of data was present, log or square root transformation was conducted and the respective statistical analyses were performed with the transformed data. If this procedure did not improve the data heterogeneity (i.e., HbA1c, serum insulin concentration, HOMA-index and EQ-5D index), non-parametric tests were used for analysis, including the Friedman two-way analysis of variance by ranks, post hoc Dunn’s Bonferroni tests for group comparisons and Wilcoxon’s tests for within-group comparisons. Effect sizes were evaluated using partial eta-squared (ηp2) for the ANOVAs and Kendall’s coefficient of concordance (W) for the Friedman tests, respectively, and rated as small (0.01–0.05), medium (0.06–0.13) and large (≥0.14) for ηp2, and small (≤0.10), medium (≥0.30), and large (≥0.50) for W [75]. For all analyses, the significance level was set at $p \leq 0.05.$ Data are shown as means ± standard deviation (SD) and pre-/post-intervention changes of the outcome values are reported with $95\%$ confidence intervals ($95\%$ CI).
## 3.1. Study Flow
Fifty patients (25 each for the HIIT-HR and CON groups) were randomly selected from a larger cohort of the main trial [29] and agreed to additional blood draws during the CPET for the determination of lactate concentrations. Thirty-seven patients were additionally screened for eligibility to be included in the HIIT-LT group until 25 eligible patients were enrolled, resulting in a total sample of 75 patients (25 per group). Seventeen patients dropped out during the study (HIIT-HR = 5, HIIT-LT = 5 and CON = 7). The reasons for dropout are depictured in Figure 1. Consequently, the final analysis involved the data of 58 patients (HIIT-HR = 20, HIIT-LT = 20 and CON = 18). At the baseline, the three groups did not differ significantly in the primary outcome of VO2max and the other main outcomes of interest. Moreover, we did not detect any significant gender effects and therefore, the data of females and males were jointly evaluated in all analyses. Compliance with the LOW-HIIT protocols (the number of scheduled vs. completed exercise sessions) was noticeably high in both exercise groups with 96 ± $6\%$ in the HIIT-HR group and 94 ± $8\%$ in the HIIT-LT group.
## 3.2. Training Data and Adverse Events
The HRLT at the baseline examination corresponded to 94 ± $4\%$ of the HRmax in the HIIT-LT group. The average peak HR recorded during each interval bout over the 12 weeks corresponded to 93 ± $7\%$ of the HRmax in the HIIT-HR group and 96 ± $3\%$ of the HRmax in the HIIT-LT group, respectively, verifying that the target exercise intensity was successfully reached in both groups. The mean session HR (including the warm-up and cool-down phase) corresponded to 79 ± $6\%$ of the HRmax in the HIIT-HR group, and 82 ± $5\%$ of the HRmax in the HIIT-LT group, respectively. The average peak HR reached during the single intervals and the average session HR were not significantly different between both groups (Table 1). Furthermore, there were no significant differences between both groups in the average power output and energy expenditure per session, with average values of 99.7 ± 24.6 W, 550 ± 149 kilojoules (kJ) and 8.5 ± 2.3 kJ/FFM in the HIIT-HR group, and 105.8 ± 33.2 W, 549 ± 164 kJ and 8.5 ± 2.3 kJ/FFM in the HIIT-LT group, respectively. There were no adverse events observed that were related to the LOW-HIIT.
## 3.3. Hydration Status and Anthropometric Data
During both examinations, the USG values were within the normal ranges for all patients, without significant group differences. There were main effects of time for body weight ($p \leq 0.001$, ή2 = 0.54), BMI ($p \leq 0.001$, ή2 = 0.54), FM ($p \leq 0.001$, ή2 = 0.42), FM% ($p \leq 0.001$, ή2 = 0.29), FFM ($$p \leq 0.003$$, ή2 = 0.15), TBW ($p \leq 0.001$, ή2 = 0.21) and waist circumference ($p \leq 0.001$, ή2 = 0.56). Furthermore, there was a group-by-time interaction for waist circumference ($p \leq 0.001$, ή2 = 0.25) and trend toward an interaction effect for FM ($$p \leq 0.055$$, ή2 = 0.10) All groups significantly reduced their body weight (HIIT-HR: −3.9 kg, $95\%$ CI: −5.5 to −2.3 kg, $p \leq 0.001$; HIIT-LT: −5.6 kg, $95\%$ CI: −7.8 to −3.4 kg, $p \leq 0.001$; CON: −2.6 kg, $95\%$ CI: −4.2 to −1.0 kg, $$p \leq 0.003$$). The quantity of weight loss was not significantly different between the three groups ($$p \leq 0.064$$), but compared to the exercise groups, the CON did not significantly reduce FM and waist circumference. Compared to the CON, the decrease in waist circumference was larger in the HIIT-HR (−7 cm, $95\%$ CI: −9 to −1 cm, $$p \leq 0.010$$) and HIIT-LT (−8 cm, $95\%$ CI: −11 to −3 cm, $p \leq 0.001$) groups. Table 2 displays all group specific pre-/post-intervention anthropometric variables.
## 3.4. Nutrition Data
There was a main effect of time for energy ($$p \leq 0.024$$, ή2 = 0.09) and fat intake ($$p \leq 0.005$$, ή2 = 0.14). Post hoc tests indicated that the reduction in energy and fat intake per day only reached statistical significance in the CON (−410 kcal, $95\%$ CI: −747 to −72 kcal, $$p \leq 0.020$$, and −30 g, $95\%$ CI: −46 to −8 g, $$p \leq 0.007$$, respectively), however, there were no significant differences in daily calorie reduction between the groups. Pre- and post-intervention, there were no significant group differences in nutritional intake (Table 3).
## 3.5. Cardiopulmonary Exercise Testing Data
During both CPET examinations, all patients fulfilled at least two maximal exertion criteria [63]. In all three groups, there were no significant differences in pre- and post-intervention resting lactate concentrations (HIIT-HR: 1.2 ± 0.3 and 1.1 ± 0.2 mmol/L, HIIT-LT: 1.1 ± 0.2 and 1.0 ± 0.3 mmol/L, CON: 1.2 ± 0.3 and 1.2 ± 0.2 mmol/L), maximal lactate levels (HIIT-HR: 7.5 ± 1.7 and 7.7 ± 2.1 mmol/L, HIIT-LT: 7.9 ± 1.5 and 7.9 ± 2.6 mmol/L, CON: 7.9 ± 1.8 and 7.5 ± 1.8 mmol/L) RERmax (HIIT-HR: 1.03 ± 0.1 and 1.04 ± 0.1, HIIT-LT: 1.02 ± 0.1 and 1.03 ± 0.1, CON: 1.01 ± 0.1 and 1.01 ± 0.1) and HRmax (HIIT-HR: 158 ± 17 and 160 ± 18 b/min, HIIT-LT: 156 ± 21 and 158 ± 19 b/min, CON: 158 ± 21 and 157 ± 21 b/min), indicating that maximal exhaustion levels were similar during both examinations. In the HIIT-LT group, there was a high agreement between WLT and WVT2 ($p \leq 0.001$, $r = 0.78$).
A main effect of time and group-by-time interaction was found for relative ($p \leq 0.001$, ή2 = 0.50, and $p \leq 0.001$, ή2 = 0.39, respectively) and absolute VO2max ($p \leq 0.001$, ή2 = 0.32, and $p \leq 0.001$, ή2 = 0.42, respectively), relative ($p \leq 0.001$, ή2 = 0.67, and $p \leq 0.001$, ή2 = 0.48, respectively) and absolute Wmax ($p \leq 0.001$, ή2 = 0.66, and $p \leq 0.001$, ή2 = 0.62, respectively), WVT1 ($p \leq 0.001$, ή2 = 0.64, and $p \leq 0.001$, ή2 = 0.58, respectively) and WVT2 ($$p \leq 0.026$$, ή2 = 0.10, and $p \leq 0.001$, ή2 = 0.39, respectively). Additionally, there was a main effect of time for HRVT1 ($$p \leq 0.002$$, ή2 = 0.21) and HRVT2 ($p \leq 0.001$, ή2 = 0.26).
The HIIT-HR and HIIT-LT groups showed similar improvements in relative VO2max (3.6 mL/kg/min, $95\%$ CI: 2.5 to 4.7 mL/kg/min, $p \leq 0.001$, and 3.7 mL/kg/min, $95\%$ CI: 2.3 to 5.0 mL/kg/min, $p \leq 0.001$), absolute VO2max (301 mL/min, $95\%$ CI: 194 to 409 mL/min, $p \leq 0.001$, and 257 mL/min, $95\%$ CI: 154 to 360 mL/min, $p \leq 0.001$), relative Wmax (0.3 W/kg, $95\%$ CI: 0.2 to 0.4 W/kg, $p \leq 0.001$, and 0.3 W/kg, $95\%$ CI: 0.2 to 0.4 W/kg, $p \leq 0.001$), absolute Wmax (25 W, $95\%$ CI: 20 to 30 W, $p \leq 0.001$, and 26 W, $95\%$ CI: 20 to 31 W, $p \leq 0.001$), WVT1 (30 W, $95\%$ CI: 24 to 35 W, $p \leq 0.001$, and 30 W, $95\%$ CI: 22 to 38 W, $p \leq 0.001$) and WVT2 (17 W, $95\%$ CI: 5 to 30 W, $$p \leq 0.012$$, and 22 W, $95\%$ CI: 12 to 32 W, $p \leq 0.001$). In the HIIT-LT group, there was a significant increase in WLT (12 W, $95\%$ CI: 3 to 22 W, $$p \leq 0.012$$). None of these outcomes improved in the CON. By contrast, absolute VO2max (−214 mL/min, $95\%$ CI: −221 to −8 mL/min, $$p \leq 0.037$$) and WVT (−17 W, $95\%$ CI: −28 to −7 W, $$p \leq 0.003$$) decreased from pre- to post-intervention.
Compared to the CON, the HIIT-HR and HIIT-LT groups exhibited significantly greater increases in relative VO2max (4.0 mL/kg/min, $95\%$ CI: 2.0 to 5.9 mL/kg/min, $p \leq 0.001$, and 4.1 mL/kg/min, $95\%$ CI: 2.1 to 5.9 mL/kg/min, $p \leq 0.001$), absolute VO2max (415 mL/min, $95\%$ CI: 237 to 594 mL/min, $p \leq 0.001$, and 371 mL/min, $95\%$ CI: 196 to 547 mL/min, $p \leq 0.001$), relative Wmax (0.3 W/kg, $95\%$ CI: 0.2 to 0.4 W/kg, $p \leq 0.001$, and 0.3 W/kg, $95\%$ CI: 0.2 to 0.4 W/kg, $p \leq 0.001$), absolute Wmax (30 W, $95\%$ CI: 21 to 38 W, $p \leq 0.001$, and 30 W, $95\%$ CI: 21 to 39 W, $p \leq 0.001$), WVT1 (32 W, $95\%$ CI: 12 to 53 W, $p \leq 0.001$, and 40 W, $95\%$ CI: 19 to 60 W, $p \leq 0.001$) and WVT2 (35 W, $95\%$ CI: 16 to 54 W, $p \leq 0.001$, and 40 W, $95\%$ CI: 20 to 59 W, $p \leq 0.001$). Pre-/post-intervention CPET outcomes for each group are shown in Table 4.
## 3.6. Cardiometabolic Data
A group-by-time interaction was found for SBP ($p \leq 0.001$, ή2 = 0.29), DBP ($p \leq 0.001$, ή2 = 0.26), MAB ($p \leq 0.001$, ή2 = 0.33) and the MetS z-score ($p \leq 0.001$, ή2 = 0.30). Additionally, a main effect of time was observed for HRrest ($p \leq 0.001$, ή2 = 0.33), SBP ($p \leq 0.001$, ή2 = 0.43), DBP ($p \leq 0.001$, ή2 = 0.40), MAB ($p \leq 0.001$, ή2 = 0.48), HbA1c levels ($p \leq 0.001$, $W = 0.23$), serum insulin concentration ($p \leq 0.001$, $W = 0.25$), HOMA-index ($p \leq 0.001$, $W = 0.28$) and the MetS z-score ($p \leq 0.001$ ή2 = 0.61).
Post hoc tests showed that both in the HIIT-HR and HIIT-LT groups, there were significant reductions in HRrest (−6 b/min, $95\%$ CI: −9 to −2 b/min, $$p \leq 0.006$$, and −6 b/min, $95\%$ CI: −7 to −3 b/min, $p \leq 0.001$), SBP (−11 mmHg, $95\%$ CI: −15 to −7 mmHg, $p \leq 0.001$, and −13 mmHg, $95\%$ CI: −16 to −9 mmHg, $p \leq 0.001$), DBP (−8 mmHg, $95\%$ CI: −11 to −4 mmHg, $p \leq 0.001$, and −10 mmHg, $95\%$ CI: −13 to −7 mmHg, $p \leq 0.001$), MAB (−9 mmHg, $95\%$ CI: −12 to −6 mmHg, $p \leq 0.001$, and −11 mmHg, $95\%$ CI: −14 to −8 mmHg, $p \leq 0.001$), HbA1c levels (−$0.2\%$, $95\%$ CI: −0.4 to −$0.1\%$, $$p \leq 0.012$$, and −$0.3\%$, $95\%$ CI: −0.4 to −$0.2\%$, $p \leq 0.001$), serum insulin concentrations (−5 µU/mL, $95\%$ CI: −8 to −1 µU/mL, $$p \leq 0.007$$, and −3 µU/mL, $95\%$ CI: −8 to −2 µU/mL, $$p \leq 0.019$$), HOMA-index (−1.3 units, $95\%$ CI: −2.4 to −0.2 units, $$p \leq 0.005$$, and −1.0 units, $95\%$ CI: −2.1 to −0.2 units, $$p \leq 0.014$$) and the MetS z-score (−1.9 units, $95\%$ CI: −2.6 to −1.8 units, $p \leq 0.001$, and −2.5 units, $95\%$ CI: −3.0 to −2.0 units, $p \leq 0.001$). No significant changes occurred in the CON.
Compared to the CON, the HIIT-HR and HIIT-LT groups showed significantly greater pre-/post-intervention reductions in SBP (−12 mmHg, $95\%$ CI: −19 to −4 mmHg, $p \leq 0.001$, and −13 mmHg, $95\%$ CI: −20 to −5 mmHg, $p \leq 0.001$), DBP (−8 mmHg, $95\%$ CI: −13 to −2 mmHg, $$p \leq 0.007$$, and −10 mmHg, $95\%$ CI: −16 to −4 mmHg, $p \leq 0.001$), MAB (−9 mmHg, $95\%$ CI: −15 to −4 mmHg, $p \leq 0.001$, and −11 mmHg, $95\%$ CI: −17 to −6 mmHg, $p \leq 0.001$) and MetS z-score (−1.6 units, $95\%$ CI: −2.6 to −0.5 units, $p \leq 0.001$, and −2.2 units, $95\%$ CI: −3.2 to −1.1 units, $p \leq 0.001$). Pre-/post-intervention cardiometabolic variables for each group are shown in Table 5.
## 3.7. Self-Reported Quality of Life Data
A main effect of time was detected for EQ-VAS ($p \leq 0.001$, ή2 = 0.24) and the EQ-5D index ($$p \leq 0.031$$, $W = 0.08$). Both the HIIT-HR and HIIT-LT groups experienced a pre-/post-intervention increase in EQ-VAS (10 points, $95\%$ CI: 1 to 18 points, $$p \leq 0.029$$, and 11 points, $95\%$ CI: 5 to 17 points, $$p \leq 0.002$$), whereas no significant changes were recorded in the CON (Table 6).
## 4. Discussion
Exercise intensity is a crucial—if not the most pivotal—variable in exercise prescription [33]. Intensity prescriptions for (LOW-)HIIT programs are typically based on percentages of the HRmax, which, however, may be associated with several limitations in clinical populations. Given the rising popularity of LOW-HIIT in prevention programs and clinical exercise interventions, it is timely to investigate the viability of alternative approaches for exercise intensity prescriptions in individuals, where determination of the HRmax may not be feasible. To our knowledge, this investigation was the first to compare the effects of a LOW-HIIT intervention based on either the HRmax or the submaximal LT in obese patients with MetS. The major result was that the HIIT-HR and HIIT-LT produced similar improvements in key cardiometabolic outcomes and self-reported QoL after a period of 12 weeks.
The finding that the two LOW-HIIT protocols had similar beneficial effects on cardiometabolic health and QoL was in contrast to our hypothesis based on some previous research, reporting that threshold-based exercise intensity prescriptions are superior to relative percent concepts in improving various cardiometabolic outcomes [43,44,47]. When analyzing the training data (average HR and power output), however, it becomes evident that the physiological demands were comparable between both LOW-HIIT protocols. Furthermore, compliance with both protocols was similarly very high (HIIT-HR: 96 ± $6\%$, and HIIT-LT: 94 ± $8\%$) and thus, it is plausible that both protocols yielded similar benefits. In this context, it is noteworthy that 4 mmol/L LT data for obese MetS patients have rarely been described in the literature. We found that the HRLT corresponded to 94 ± $4\%$ of the HRmax in our patients, which is in accordance with the well-established 3-phase model introduced by Skinner et al. [ 76], illustrating that the HR at the 4 mmol/L LT typically exceeds $90\%$ of the HRmax.
When comparing both LOW-HIIT protocols, it is notable that all patients in our study were able and willing to reach maximal exertion during the CPET. Thus, in general, if patients are physically able and no symptoms occur during exercise that would require premature termination, we recommend that CPET should be performed until exhaustion in order to acquire maximum performance data and to use the established criteria to verify that maximum exertion has been reached [63]. However, it is an important practical result of this investigation that exercise intensity prescription for the LOW-HIIT protocols can also be feasibly generated using a submaximal exercise test until the LT is reached, which may constitute a viable approach if maximal CPET is contraindicated or patients are not able/motivated to exercise until exertion.
Both LOW-HIIT protocols induced improvements in several health-related outcomes that can be considered clinically meaningful. First, patients involved in the LOW-HIIT improved VO2max by ~3.7 mL/kg/min. The importance of CRF for health and longevity has been well-established in decades of research [13,14,77]. It has been reported, for example, that each VO2max increase by 1 mL/kg/min is associated with a $9\%$ risk decrease in overall mortality [78]. Recent large-scale research verified these findings, demonstrating that each 3.5 mL/kg/min improvement in CRF is related to a decreased risk of premature death due to cardiovascular disease and cancer each by $15\%$ [79]. Second, the reduction in MetS z-score indicates an improvement in overall MetS severity, which was mainly related to reductions in blood pressure (−12 mmHg SBP/−9 mmHg DBP, on average) and waist circumference (−8 cm, on average). Large prospective cohort studies have indicated a reduced risk of coronary heart disease by $22\%$ and stroke by $41\%$, respectively, per each −10 mmHg SBP/−5 mmHg DBP decrease [80] and an $8\%$ reduction in all-cause mortality per −5 cm decrease in waist circumference [81]. Additionally, both LOW-HIIT protocols had beneficial effects on glucose metabolism as indicated by significant reductions in the HbA1c levels, fasting insulin and HOMA-index. Improvements in these outcomes have been associated with improved cardiometabolic health [82] and a lower risk of colorectal cancer [83], for example. Third, self-reported QoL improved in response to both LOW-HIIT protocols. The mean pre-intervention EQ-VAS scores were markedly lower in our patient cohort than the values reported for the general population [71], which is in line with data from other researchers indicating a relationship between MetS and a diminished QoL [8]. The marked post-intervention improvement in EQ-VAS following LOW-HIIT underscores the well-established association between physical activity [84], CRF levels [85] and enhanced QoL.
Taken together, these findings highlight the pleiotropic effects of exercise on a broad range of important health markers and support the “exercise is medicine” message [86]. Although we clearly recommend that patients who are willing and capable of being more physically active should be encouraged to perform higher volumes of exercise in order maximize the health benefits, our observations provide further evidence [22,23,24,25,26] that even very small doses of targeted exercise can provide meaningful improvements in the physiological and psychological outcomes.
The three groups achieved an average weight loss of ~$3.5\%$ within the 12-week study period, which is in accordance with most lifestyle-intervention programs for obesity [87]. It is noteworthy that the relative weight loss amounts tended to be greater in the two exercise groups compared to the CON, but the total difference did not reach statistical significance (exercise groups vs. CON, $$p \leq 0.066$$). This finding is not surprising as the three groups did not significantly differ in the amount of caloric reduction, which is the key component to achieve a negative energy balance and to reduce body weight [88]. Although there is evidence that (LOW-)HIIT, compared to traditional continuous endurance training, may have different (more pronounced) effects on some physiological factors associated with weight loss, including higher excess post-exercise oxygen consumption [89], stronger post-exercise suppression of appetite perception [90] or greater changes in concentrations of distinctive gut hormones and leptin [91], our results suggest that the extremely low volume of exercise applied in the present study did not have a substantial impact on the daily overall energy balance. Thus, when it comes to pure weight loss, higher-volume exercise modalities with greater energy expenditure (e.g., longer-lasting endurance exercise or HIIT involving more and/or longer intervals) may be more effective compared to our very low-volume HIIT protocol. However, in agreement with previous reports, it is too short-sighted to define a successful obesity treatment solely in terms of pure weight loss because it is more important to improve the CRF and other cardiometabolic health outcomes than to strictly follow anthropometric measures to improve morbidity and longevity [13,14,77]. In this regard, we observed substantial differences between the patients allocated to the CON and those performing LOW-HIIT, with only the “exercisers” achieving significant improvements in cardiometabolic health and QoL, despite similar weight loss.
Finally, we note some potential limitations to this investigation. Firstly, we note that all patients received standard care nutritional counseling in addition to the LOW-HIIT, which may represent a confounding variable for the observed pre-/post intervention changes. However, we do not feel that the nutritional modification had any meaningful effect on the major research question of this study (HIIT-HR vs. HIIT-LT) because both groups received the same counseling and there were no significant differences in the nutritional intake between the HIIT-HR and HIIT-LT groups. Nevertheless, it cannot be completely ruled out that potential within- or between-group variations in nutrition might have affected the results to some extent.
Secondly, we are well aware that numerous LT as well as VT concepts exist [40,41,60,61,66,67,69] and one can argue why we used the fixed 4 mmol/L LT [64,65] to prescribe the exercise intensity to the HIIT-LT group. Specific reasons for selecting the 4 mmol/L LT are given in the methodology section, but we highlight that it was the major aim of this study to compare the effects of LOW-HIIT prescriptions based on maximal versus submaximal exercise parameters and not to investigate which threshold concept might be the best for obese MetS patients. Nevertheless, we do not rule out that another threshold concept/exercise prescription approach may have achieved even better results or might even have been superior to the HRmax-based prescription method. Future research may wish to explore this important question. Moreover, further research is necessary to investigate whether the findings obtained by this specific cohort of obese MetS patients may be transferred to other (clinical) populations. Lastly, it must be considered that all the examinations and the LOW-HIIT intervention were carried out in a well-controlled clinical environment. Thus, it remains to be elucidated to which degree our findings can be applied to non-clinical settings.
## 5. Conclusions
The HIIT-HR and HIIT-LT induced similar improvements in cardiometabolic health and QoL in obese MetS patients. Thus, the practical take-home message for clinicians and exercise physiologists who wish to implement LOW-HIIT in clinical populations, is that exercise intensity can feasibly and effectively be prescribed using a submaximal LT-based exercise test if patients are not willing or able to perform maximal CPET.
## References
1. Wu S.H., Liu W., Ho S.C.. **Metabolic syndrome and all-cause mortality: A meta-analysis of prospective cohort studies**. *Eur. J. Epidemiol.* (2010) **25** 375-384. DOI: 10.1007/s10654-010-9496-7
2. Saklayen M.G.. **The global epidemic of the metabolic syndrome**. *Curr. Hypertens. Rep.* (2018) **20** 12. DOI: 10.1007/s11906-018-0812-z
3. Noubiap J.J., Nansseu J.R., Lontchi-Yimagou E., Nkeck J.R., Nyaga U.F., Ngouo A.T., Tounouga D.N., Tianyi F.L., Foka A.J., Ndoadoumgue A.L.. **Geographic distribution of metabolic syndrome and its components in the general adult population: A meta-analysis of global data from 28 million individuals**. *Diabetes Res. Clin. Pract.* (2022) **188** 109924. DOI: 10.1016/j.diabres.2022.109924
4. Auriemma R.S., Pirchio R., Liccardi A., Scairati R., Del Vecchio G., Pivonello R., Colao A.. **Metabolic syndrome in the era of COVID-19 outbreak: Impact of lockdown on cardiometabolic health**. *J. Endocrinol. Investig.* (2021) **44** 2845-2847. DOI: 10.1007/s40618-021-01563-y
5. Xu W., Li Y., Yan Y., Zhang L., Zhang J., Yang C.. **Effects of coronavirus disease 2019 lockdown on metabolic syndrome and its components among Chinese employees: A retrospective cohort study**. *Front. Public Health* (2022) **10** 885013. DOI: 10.3389/fpubh.2022.885013
6. Mottillo S., Filion K.B., Genest J., Joseph L., Pilote L., Poirier P., Rinfret S., Schiffrin E.L., Eisenberg M.J.. **The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis**. *J. Am. Coll. Cardiol.* (2010) **56** 1113-1132. DOI: 10.1016/j.jacc.2010.05.034
7. Esposito K., Chiodini P., Colao A., Lenzi A., Giugliano D.. **Metabolic syndrome and risk of cancer: A systematic review and meta-analysis**. *Diabetes Care* (2012) **35** 2402-2411. DOI: 10.2337/dc12-0336
8. Saboya P.P., Bodanese L.C., Zimmermann P.R., Gustavo A.D., Assumpção C.M., Londero F.. **Metabolic syndrome and quality of life: A systematic review**. *Rev. Lat. Am. Enferm.* (2016) **24** e2848. DOI: 10.1590/1518-8345.1573.2848
9. De Lorenzo A., Estato V., Castro-Faria-Neto H.C., Tibirica E.. **Obesity-related inflammation and endothelial dysfunction inCOVID-19: Impact on disease severity**. *J. Inflamm. Res.* (2021) **14** 2267-2276. DOI: 10.2147/JIR.S282710
10. Mauvais-Jarvis F.. **Aging, male sex, obesity, and metabolic inflammation create the perfect storm for COVID-19**. *Diabetes* (2020) **69** 1857-1863. DOI: 10.2337/dbi19-0023
11. Yang M., Liu S., Zhang C.. **The related metabolic diseases and treatments of obesity**. *Healthcare* (2022) **10**. DOI: 10.3390/healthcare10091616
12. Liu D., Huang Y., Huang C., Yang S., Wei X., Zhang P., Guo D., Lin J., Xu B., Li C.. **Calorie restriction with or without time-restricted eating in weight loss**. *N. Engl. J. Med.* (2022) **386** 1495-1504. DOI: 10.1056/NEJMoa2114833
13. Barry V.W., Baruth M., Beets M.W., Durstine J.L., Liu J., Blair S.N.. **Fitness vs. fatness on all-cause mortality: A meta-analysis**. *Prog. Cardiovasc. Dis.* (2014) **56** 382-390. DOI: 10.1016/j.pcad.2013.09.002
14. Myers J., McAuley P., Lavie C.J., Despres J.P., Arena R., Kokkinos P.. **Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: Their independent and interwoven importance to health status**. *Prog. Cardiovasc. Dis.* (2015) **57** 306-314. DOI: 10.1016/j.pcad.2014.09.011
15. Guthold R., Stevens G.A., Riley L.M., Bull F.C.. **Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1·9 million participants**. *Lancet Glob. Health* (2018) **6** e1077-e1086. DOI: 10.1016/S2214-109X(18)30357-7
16. Tudor-Locke C., Brashear M.M., Johnson W.D., Katzmarzyk P.T.. **Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women**. *Int. J. Behav. Nutr. Phys. Act.* (2010) **7** 60. DOI: 10.1186/1479-5868-7-60
17. 17.
World Health Organization
Global Recommendations on Physical Activity for HealthWorld Health OrganizationGeneva, Switzerland2010. *Global Recommendations on Physical Activity for Health* (2010)
18. Cavallini M.F., Callaghan M.E., Premo C.B., Scott J.W., Dyck D.J.. **Lack of time is the consistent barrier to physical activity and exercise in 18 to 64 year-old males and females from both South Carolina and Southern Ontario**. *J. Phys. Act. Res.* (2020) **5** 100-106. DOI: 10.12691/jpar-5-2-6
19. Hoare E., Stavreski B., Jennings G.L., Kingwell B.A.. **Exploring motivation and barriers to physical activity among active and inactive Australian adults**. *Sports* (2017) **5**. DOI: 10.3390/sports5030047
20. Sheill G., Guinan E., Brady L., Hevey D., Hussey J.. **Exercise interventions for patients with advanced cancer: A systematic review of recruitment, attrition, and exercise adherence rates**. *Palliat. Support. Care* (2019) **17** 686-696. DOI: 10.1017/S1478951519000312
21. Murphy C.L., Sheane B.J., Cunnane G.. **Attitudes towards exercise in patients with chronic disease: The influence of comorbid factors on motivation and ability to exercise**. *Postgrad. Med. J.* (2011) **87** 96-100. DOI: 10.1136/pgmj.2010.105858
22. Gibala M.J., Little J.P.. **Physiological basis of brief vigorous exercise to improve health**. *J. Physiol.* (2020) **598** 61-69. DOI: 10.1113/JP276849
23. Gibala M.J., Gillen J.B., Percival M.E.. **Physiological and health-related adaptations to low-volume interval training: Influences of nutrition and sex**. *Sport. Med.* (2014) **44** S1273-S1277. DOI: 10.1007/s40279-014-0259-6
24. Gillen J.B., Gibala M.J.. **Is high-intensity interval training a time-efficient exercise strategy to improve health and fitness?**. *Appl. Physiol. Nutr. Metab.* (2014) **39** 409-412. DOI: 10.1139/apnm-2013-0187
25. Skelly L.E., Bailleul C., Gillen J.B.. **Physiological responses to low-volume interval training in women**. *Sport. Med. Open* (2021) **7** 99. DOI: 10.1186/s40798-021-00390-y
26. Sultana R.N., Sabag A., Keating S.E., Johnson N.A.. **The effect of low-volume high-intensity interval training on body composition and cardiorespiratory fitness: A systematic review and meta-analysis**. *Sport. Med.* (2019) **49** 1687-1721. DOI: 10.1007/s40279-019-01167-w
27. Reljic D., Dieterich W., Herrmann H.J., Neurath M.F., Zopf Y.. **“HIIT the Inflammation”: Comparative effects of low-volume interval training and resistance exercises on inflammatory indices in obese metabolic syndrome patients undergoing caloric restriction**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14101996
28. Reljic D., Frenk F., Herrmann H.J., Neurath M.F., Zopf Y.. **Low-volume high-intensity interval training improves cardiometabolic health, work ability and well-being in severely obese individuals: A randomized-controlled trial sub-study**. *J. Transl. Med.* (2020) **18** 419. DOI: 10.1186/s12967-020-02592-6
29. Reljic D., Frenk F., Herrmann H.J., Neurath M.F., Zopf Y.. **Effects of very low volume high intensity versus moderate intensity interval training in obese metabolic syndrome patients: A randomized controlled study**. *Sci. Rep.* (2021) **11** 2836. DOI: 10.1038/s41598-021-82372-4
30. Reljic D., Konturek P.C., Herrmann H.J., Siebler J., Neurath M.F., Zopf Y.. **Very low-volume interval training improves nonalcoholic fatty liver disease fibrosis score and cardiometabolic health in adults with obesity and metabolic syndrome**. *J. Physiol. Pharmacol.* (2021) **72** 927-938. DOI: 10.26402/jpp.2021.6.10
31. Ramos J.S., Dalleck L.C., Borrani F., Beetham K.S., Wallen M.P., Mallard A.R., Clark B., Gomersall S., Keating S.E., Fassett R.G.. **Low-volume high-intensity interval training is sufficient to ameliorate the severity of metabolic syndrome**. *Metab. Syndr. Relat. Disord.* (2017) **15** 319-328. DOI: 10.1089/met.2017.0042
32. Garber C.E., Blissmer B., Deschenes M.R., Franklin B.A., Lamonte M.J., Lee I.M., Nieman D.C., Swain D.P.. **American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise**. *Med. Sci. Sport. Exerc.* (2011) **43** 1334-1359. DOI: 10.1249/MSS.0b013e318213fefb
33. Shookster D., Lindsey B., Cortes N., Martin J.R.. **Accuracy of commonly used age-predicted maximal heart rate equations**. *Int. J. Exerc. Sci.* (2020) **13** 1242-1250. PMID: 33042384
34. Robergs R., Landwehr R.. **The surprising history of the “HRmax= 220-age” equation**. *J. Exer. Physiol. Online* (2002) **5** 1-10
35. Sarzynski M.A., Rankinen T., Earnest C.P., Leon A.S., Rao D.C., Skinner J., Bouchard C.. **Measured maximal heart rates compared to commonly used age-based prediction equations in the Heritage Family Study**. *Am. J. Hum. Biol.* (2013) **25** 695-701. DOI: 10.1002/ajhb.22431
36. Scheel P.J., Florido R., Hsu S., Murray B., Tichnell C., James C.A., Agafonova J., Tandri H., Judge D.P., Russell S.D.. **Safety and utility of cardiopulmonary exercise testing in arrhythmogenic right ventricular cardiomyopathy/dysplasia**. *J. Am. Heart. Assoc.* (2020) **9** e013695. DOI: 10.1161/JAHA.119.013695
37. Skalski J., Allison T.G., Miller T.D.. **The safety of cardiopulmonary exercise testing in a population with high-risk cardiovascular diseases**. *Circulation* (2012) **126** 2465-2472. DOI: 10.1161/CIRCULATIONAHA.112.110460
38. **ATS/ACCP Statement on cardiopulmonary exercise testing**. *Am. J. Respir. Crit. Care Med.* (2003) **167** 211-277. DOI: 10.1164/rccm.167.2.211
39. Binder R.K., Wonisch M., Corra U., Cohen-Solal A., Vanhees L., Saner H., Schmid J.P.. **Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing**. *Eur. J. Cardiovasc. Prev. Rehabil.* (2008) **15** 726-734. DOI: 10.1097/HJR.0b013e328304fed4
40. Amann M., Subudhi A.W., Foster C.. **Predictive validity of ventilatory and lactate thresholds for cycling time trial performance**. *Scand. J. Med. Sci. Sport.* (2006) **16** 27-34. DOI: 10.1111/j.1600-0838.2004.00424.x
41. Beneke R., Leithäuser R.M., Ochentel O.. **Blood lactate diagnostics in exercise testing and training**. *Int. J. Sport. Physiol. Perform.* (2011) **6** 8-24. DOI: 10.1123/ijspp.6.1.8
42. Billat L.V.. **Use of blood lactate measurements for prediction of exercise performance and for control of training. Recommendations for long-distance running**. *Sport. Med.* (1996) **22** 157-175. DOI: 10.2165/00007256-199622030-00003
43. Wolpern A.E., Burgos D.J., Janot J.M., Dalleck L.C.. **Is a threshold-based model a superior method to the relative percent concept for establishing individual exercise intensity? a randomized controlled trial**. *BMC Sport. Sci. Med. Rehabil.* (2015) **7**. DOI: 10.1186/s13102-015-0011-z
44. Weatherwax R.M., Ramos J.S., Harris N.K., Kilding A.E., Dalleck L.C.. **Changes in metabolic syndrome severity following individualized versus standardized exercise prescription: A feasibility study**. *Int. J. Environ. Res. Public Health* (2018) **15**. DOI: 10.3390/ijerph15112594
45. Asano R.Y., Sales M.M., Browne R.A., Moraes J.F., Coelho Júnior H.J., Moraes M.R., Simões H.G.. **Acute effects of physical exercise in type 2 diabetes: A review**. *World J. Diabetes* (2014) **5** 659-665. DOI: 10.4239/wjd.v5.i5.659
46. Irving B.A., Davis C.K., Brock D.W., Weltman J.Y., Swift D., Barrett E.J., Gaesser G.A., Weltman A.. **Effect of exercise training intensity on abdominal visceral fat and body composition**. *Med. Sci. Sport. Exerc.* (2008) **40** 1863-1872. DOI: 10.1249/MSS.0b013e3181801d40
47. Lehtonen E., Gagnon D., Eklund D., Kaseva K., Peltonen J.E.. **Hierarchical framework to improve individualised exercise prescription in adults: A critical review**. *BMJ Open Sport Exerc. Med.* (2022) **8** e001339. DOI: 10.1136/bmjsem-2022-001339
48. Reljic D., Wittmann F., Fischer J.E.. **Effects of low-volume high-intensity interval training in a community setting: A pilot study**. *Eur. J. Appl. Physiol.* (2018) **118** 1153-1167. DOI: 10.1007/s00421-018-3845-8
49. Saghaei M., Saghaei S.. **Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials**. *J. Biomed. Sci. Eng.* (2011) **4** 734-739. DOI: 10.4236/jbise.2011.411090
50. 50.
American College of Sports Medicine
ACSM’s Guidelines for Exercise Testing and Prescription8th ed.Lippincott Williams & WilkinsPhiladelphia, PA, USA20102627. *ACSM’s Guidelines for Exercise Testing and Prescription* (2010) 26-27
51. Alberti K.G., Zimmet P., Shaw J.. **Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation**. *Diabet. Med.* (2006) **23** 469-480. DOI: 10.1111/j.1464-5491.2006.01858.x
52. Zhu L., Spence C., Yang J.W., Ma G.X.. **The IDF definition is better suited for screening metabolic syndrome and estimating risks of diabetes in Asian American Adults: Evidence from NHANES 2011–2016**. *J. Clin. Med.* (2020) **9**. DOI: 10.3390/jcm9123871
53. Tholl U., Lüders S., Bramlage P., Dechend R., Eckert S., Mengden T., Nürnberger J., Sanner B., Anlauf M.. **The German Hypertension League (Deutsche Hochdruckliga) quality seal protocol for blood pressure-measuring devices: 15-year experience and results from 105 devices for home blood pressure control**. *Blood Press. Monit.* (2016) **21** 197-205. DOI: 10.1097/MBP.0000000000000186
54. Whelton P.K., Carey R.M., Aronow W.S., Casey D.E., Collins K.J., Dennison Himmelfarb C., DePalma S.M., Gidding S., Jamerson K.A., Jones D.W.. **2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines**. *Hypertension* (2018) **138** e426-e483. DOI: 10.1161/HYP.0000000000000066
55. DeMers D., Wachs D.. **Physiology, Mean Arterial Pressure**. *StatPearls [Internet]* (2022)
56. Matthews D.R., Hosker J.P., Rudenski A.S., Naylor B.A., Treacher D.F., Turner R.C.. **Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man**. *Diabetologia* (1985) **28** 412-419. DOI: 10.1007/BF00280883
57. Bosy-Westphal A., Jensen B., Braun W., Pourhassan M., Gallagher D., Müller M.J.. **Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices**. *Eur. J. Clin. Nutr.* (2017) **71** 1061-1067. DOI: 10.1038/ejcn.2017.27
58. Johnson J.L., Slentz C.A., Houmard J.A., Samsa G.P., Duscha B.D., Aiken L.B., McCartney J.S., Tanner C.J., Kraus W.E.. **Exercise training amount and intensity effects on metabolic syndrome (from Studies of a Targeted Risk Reduction Intervention through Defined Exercise)**. *Am. J. Cardiol.* (2007) **100** 1759-1766. DOI: 10.1016/j.amjcard.2007.07.027
59. Glaab T., Taube C.. **Practical guide to cardiopulmonary exercise testing in adults**. *Respir. Res.* (2022) **23** 9. DOI: 10.1186/s12931-021-01895-6
60. Mezzani A.. **Cardiopulmonary exercise testing: Basics of methodology and measurements**. *Ann. Am. Thorac. Soc.* (2017) **14** S3-S11. DOI: 10.1513/AnnalsATS.201612-997FR
61. Bentley D.J., Newell J., Bishop D.. **Incremental exercise test design and analysis: Implications for performance diagnostics in endurance athletes**. *Sport. Med.* (2007) **37** 575-586. DOI: 10.2165/00007256-200737070-00002
62. Borg G.A.. **Psychophysical bases of perceived exertion**. *Med. Sci. Sport. Exerc.* (1982) **14** 377-381. DOI: 10.1249/00005768-198205000-00012
63. Howley E.T., Bassett D.R., Welch H.G.. **Criteria for maximal oxygen uptake: Review and commentary**. *Med. Sci. Sport. Exerc.* (1995) **27** 1292-1301. DOI: 10.1249/00005768-199509000-00009
64. Mader A., Liesen H., Heck H., Philippi H., Rost R., Schürch P., Hollmann W.. **Zur Beurteilung der sportartspezifischen Ausdauerleistungsfähigkeit im Labor**. *Sportarzt Sportmed.* (1976) **27** 109-112
65. Heck H., Mader A., Hess G., Mücke S., Müller R., Hollmann W.. **Justification of the 4-mmol/l lactate threshold**. *Int. J. Sport. Med.* (1985) **6** 117-130. DOI: 10.1055/s-2008-1025824
66. Wackerhage H., Gehlert S., Schulz H., Weber S., Ring-Dimitriou S., Heine O.. **Lactate thresholds and the simulation of human energy metabolism: Contributions by the Cologne Sports Medicine Group in the 1970s and 1980s**. *Front. Physiol.* (2022) **13** 899670. DOI: 10.3389/fphys.2022.899670
67. Faude O., Kindermann W., Meyer T.. **Lactate threshold concepts: How valid are they?**. *Sport. Med.* (2009) **39** 469-490. DOI: 10.2165/00007256-200939060-00003
68. Messonnier L.A., Emhoff C.A., Fattor J.A., Horning M.A., Carlson T.J., Brooks G.A.. **Lactate kinetics at the lactate threshold in trained and untrained men**. *J. Appl. Physiol.* (2013) **114** 1593-1602. DOI: 10.1152/japplphysiol.00043.2013
69. Heuberger J.A., Gal P., Stuurman F.E., de Muinck Keizer W.A., Mejia Miranda Y., Cohen A.F.. **Repeatability and predictive value of lactate threshold concepts in endurance sports**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0206846
70. Torres G., Crowther N.J., Rogers G.. **Reproducibility and levels of blood lactate transition thresholds in persons with metabolic syndrome**. *Metab. Syndr. Relat. Disord.* (2013) **11** 121-127. DOI: 10.1089/met.2012.0092
71. Grochtdreis T., Dams J., König H.H., Konnopka A.. **Health-related quality of life measured with the EQ-5D-5L: Estimation of normative index values based on a representative German population sample and value set**. *Eur. J. Health Econ.* (2019) **20** 933-944. DOI: 10.1007/s10198-019-01054-1
72. Harris J.A., Benedict F.G.. **A biometric study of basal metabolism in man**. *Proc. Natl. Acad. Sci. USA* (1918) **4** 370-373. DOI: 10.1073/pnas.4.12.370
73. Carels R.A., Young K.M., Coit C., Clayton A.M., Spencer A., Hobbs M.. **Can following the caloric restriction recommendations from the Dietary Guidelines for Americans help individuals lose weight?**. *Eat. Behav.* (2008) **9** 328-335. DOI: 10.1016/j.eatbeh.2007.12.003
74. Al-Nimr R.I.. **Optimal protein intake during weight loss interventions in older adults with obesity**. *J. Nutr. Gerontol. Geriatr.* (2019) **38** 50-68. DOI: 10.1080/21551197.2018.1544533
75. Cohen J.. *Statistical Power Analysis for the Behavioral Sciences* (1988)
76. Skinner J.S., McLellan T.M.. **The transition from aerobic to anaerobic metabolism**. *Res. Q. Exerc. Sport.* (1980) **51** 234-248. DOI: 10.1080/02701367.1980.10609285
77. Gaesser G.A., Tucker W.J., Jarrett C.L., Angadi S.S.. **Fitness versus fatness: Which influences health and mortality risk the most?**. *Curr. Sport. Med. Rep.* (2015) **14** 327-332. DOI: 10.1249/JSR.0000000000000170
78. Kavanagh T., Mertens D.J., Hamm L.F., Beyene J., Kennedy J., Corey P., Shephard R.J.. **Prediction of long-term prognosis in 12 169 men referred for cardiac rehabilitation**. *Circulation* (2002) **106** 666-671. DOI: 10.1161/01.CIR.0000024413.15949.ED
79. Vainshelboim B., Myers J., Matthews C.E.. **Non-exercise estimated cardiorespiratory fitness and mortality from all-causes, cardiovascular disease, and cancer in the NIH-AARP diet and health study**. *Eur. J. Prev. Cardiol.* (2022) **29** 599-607. DOI: 10.1093/eurjpc/zwaa131
80. Law M.R., Morris J.K., Wald N.J.. **Use of blood pressure lowering drugs in the prevention of cardiovascular disease: Meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies**. *BMJ* (2009) **338** b1665. DOI: 10.1136/bmj.b1665
81. Cerhan J.R., Moore S.C., Jacobs E.J., Kitahara C.M., Rosenberg P.S., Adami H.O., Ebbert J.O., English D.R., Gapstur S.M., Giles G.G.. **A pooled analysis of waist circumference and mortality in 650,000 adults**. *Mayo Clin. Proc.* (2014) **89** 335-345. DOI: 10.1016/j.mayocp.2013.11.011
82. González-González J.G., Violante-Cumpa J.R., Zambrano-Lucio M., Burciaga-Jimenez E., Castillo-Morales P.L., Garcia-Campa M., Solis R.C., González-Colmenero A.D., Rodríguez-Gutiérrez R.. **HOMA-IR as a predictor of health outcomes in patients with metabolic risk factors: A systematic review and meta-analysis**. *High Blood Press. Cardiovasc. Prev.* (2022) **29** 547-564. DOI: 10.1007/s40292-022-00542-5
83. Xu J., Ye Y., Wu H., Duerksen-Hughes P., Zhang H., Li P., Huang J., Yang J., Wu Y., Xia D.. **Association between markers of glucose metabolism and risk of colorectal cancer**. *BMJ Open* (2016) **6** e011430. DOI: 10.1136/bmjopen-2016-011430
84. Marquez D.X., Aguiñaga S., Vásquez P.M., Conroy D.E., Erickson K.I., Hillman C., Stillman C.M., Ballard R.M., Sheppard B.B., Petruzzello S.J.. **A systematic review of physical activity and quality of life and well-being**. *Transl. Behav. Med.* (2020) **10** 1098-1109. DOI: 10.1093/tbm/ibz198
85. Sloan R.A., Sawada S.S., Martin C.K., Church T., Blair S.N.. **Associations between cardiorespiratory fitness and health-related quality of life**. *Health Qual. Life Outcomes* (2009) **7** 47. DOI: 10.1186/1477-7525-7-47
86. Pedersen B.K., Saltin B.. **Exercise as medicine—Evidence for prescribing exercise as therapy in 26 different chronic diseases**. *Scand. J. Med. Sci. Sport.* (2015) **25** 1-72. DOI: 10.1111/sms.12581
87. 87.
National Institute for Health and Care Excellence (NICE)
Weight Management: Lifestyle Services for Overweight or Obese AdultsPublic Health Guideline PH53NICELondon, UK2014Available online: www.nice.org.uk/guidance/ph53(accessed on 7 December 2022). *Weight Management: Lifestyle Services for Overweight or Obese Adults* (2014)
88. Catenacci V.A., Wyatt H.R.. **The role of physical activity in producing and maintaining weight loss**. *Nat. Clin. Pract. Endocrinol. Metab.* (2007) **3** 518-529. DOI: 10.1038/ncpendmet0554
89. Panissa V.L., Fukuda D.H., Staibano V., Marques M., Franchini E.. **Magnitude and duration of excess of post-exercise oxygen consumption between high-intensity interval and moderate-intensity continuous exercise: A systematic review**. *Obes. Rev.* (2021) **22** e13099. DOI: 10.1111/obr.13099
90. Hu M., Nie J., Lei O.K., Shi Q., Kong Z.. **Acute effect of high-intensity interval training versus moderate-intensity continuous training on appetite perception: A systematic review and meta-analysis**. *Appetite* (2022) **182** 106427. DOI: 10.1016/j.appet.2022.106427
91. Larsen P., Marino F., Melehan K., Guelfi K.J., Duffield R., Skein M.. **High-intensity interval exercise induces greater acute changes in sleep, appetite-related hormones, and free-living energy intake than does moderate-intensity continuous exercise**. *Appl. Physiol. Nutr. Metab.* (2019) **44** 557-566. DOI: 10.1139/apnm-2018-0503
|
---
title: 'Healthcare Resource Utilization in Patients with Newly Diagnosed Atrial Fibrillation:
A Global Analysis from the GARFIELD-AF Registry'
authors:
- Lorenzo G. Mantovani
- Paolo Cozzolino
- Pietro Ferrara
- Saverio Virdone
- A. John Camm
- Freek W. A. Verheugt
- Jean-Pierre Bassand
- Alexander G. G. Turpie
- Werner Hacke
- Gloria Kayani
- Samuel Z. Goldhaber
- Shinya Goto
- Karen S. Pieper
- Bernard J. Gersh
- Keith A. A. Fox
- Sylvia Haas
- Martin van Eickels
- Ajay K. Kakkar
journal: Healthcare
year: 2023
pmcid: PMC10000823
doi: 10.3390/healthcare11050638
license: CC BY 4.0
---
# Healthcare Resource Utilization in Patients with Newly Diagnosed Atrial Fibrillation: A Global Analysis from the GARFIELD-AF Registry
## Abstract
The management of atrial fibrillation (AF), the most common sustained arrhythmia, impacts healthcare resource utilization (HCRU). This study aims to estimate global resource use in AF patients, using the GARFIELD-AF registry. A prospective cohort study was conducted to characterize HCRU in AF patients enrolled in sequential cohorts from 2012 to 2016 in 35 countries. Components of HCRU studied were hospital admissions, outpatient care visits, and diagnostic and interventional procedures occurring during follow-up. AF-related HCRU was reported as the percentage of patients demonstrating at least one event and was quantified as rate-per-patient-per-year (PPPY) over time. A total of 49,574 patients was analyzed, having an overall median follow-up of 719 days. Almost all patients ($99.5\%$) had at least one outpatient care visit, while hospital admissions were the second most frequent medical contact, with similar proportions in North America ($37.5\%$) and Europe ($37.2\%$), and slightly higher in the other GARFIELD-AF countries ($42.0\%$; namely Australia, Egypt, and South Africa). Asia and Latin America showed lower percentages of hospitalizations, outpatient care visits, and diagnostic and interventional procedures. Analyses of GARFIELD-AF highlighted the vast AF-related HCRU, underlying significant geographical differences in the type, quantity, and frequency of AF-related HCRU. These differences were likely attributable to health service availability and differing models of care.
## 1. Introduction
Atrial fibrillation (AF) is the most common arrhythmia and, with its progressively increasing prevalence, impacts public health and healthcare resource utilization (HCRU) [1,2]. AF affects approximately 37.5-million adults worldwide with about 400 new cases per 1-million inhabitants diagnosed annually [3]. AF patients are at increased risk for stroke and suffer an increase in morbidity and mortality [4]. AF’s association with hypercholesterolemia, diabetes mellitus, arterial hypertension, chronic kidney disease (CKD), dementia, obesity, and sleep apnea may confer a negative prognosis [4,5].
AF’s association with healthcare resource utilization (HCRU) presents a large economic burden [6]. AF is estimated to account for more than $1\%$ of total healthcare expenditures in high-income countries, mostly attributable to hospitalization [7]. Other resource use and cost contributors include medical visits, emergency room (ER) admissions, and diagnostic and interventional procedures often required by AF patients (e.g., electrocardiography, laboratory tests, cardioversion, catheter ablation, etc.) [ 5,6,7,8].
Several studies have evaluated the multiple aspects of AF, including its HCRU burden, studied according to specific contexts, settings, or treatment options [9,10,11,12,13]. The objective of this study was to characterize the global HCRU in AF patients within the Global Anticoagulant Registry in the FIELD-AF (GARFIELD-AF). The GARFIELD-AF registry defines a non-interventional, observational study that characterized a global population of non-valvular AF patients. This multicenter global registry documented patients’ and sub-populations’ baseline characteristics, treatment strategies, and outcome measures by including five prospective cohorts of adult subjects who were newly diagnosed with non-valvular AF (diagnosed within the previous six weeks before enrolment) and having at least one additional risk factor for stroke. GARFIELD-AF also included a validation cohort of retrospective patients diagnosed with non-valvular AF between 6 and 24 months prior to enrolment [14,15].
## 2.1. Study Design and Data Source
A prospective cohort design was used to characterize resource utilization associated with the care of AF patients. The study investigated the GARFIELD–AF registry, an observational worldwide registry that prospectively and consecutively enrolled sequential cohorts of 52,167 newly diagnosed AF patients at risk of stroke from December 2009 to August 2016 in 35 countries. Eligible patients were aged 18 years or older, enrolled consecutively into five cohorts (representing seven years of enrollment, from 2010 to 2016) including ~10,000 participants each; the additional retrospective cohort (GARFIELD–AF Cohort 1) was excluded. Participants with a follow-up period of less than three months were excluded from the analysis. Data were extracted from the final study database lock (June 2019) in 2020. The GARFIELD–AF study design has been reported elsewhere [14,15]. Baseline patient characteristics—including demographic information, clinical conditions, risk stratification, and antithrombotic treatment—were collected at inclusion in the registry [14,15]. Risk stratification was documented through CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years [doubled], diabetes, stroke [doubled], vascular disease, age 65–74 years, and sex category [female]). Follow-up data on treatments and outcomes were collected at four monthly intervals up to 24 months. GARFIELD–AF data were captured using an electronic case report form (eCRF) designed by Dendrite Clinical Systems Ltd. (Henley-on-Thames, UK). Oversight of operations and data management were performed by the coordinating center (Thrombosis Research Institute, London, UK). The study is registered at ClinicalTrials.gov (unique identifier: NCT01090362). Patients were selected from multiple healthcare settings and were registered by the identifying clinician registered using the eCRF. Data were collected from five clinical sources associated with the patient (i.e., hospital, emergency department, anticoagulation clinic, stroke unit, and office-based settings such as general or family practitioners, cardiologists, and internists) through a review of patient notes and clinical records [14]. Data on HCRU and changes in medication treatment were stored in a dedicated follow-up and events dataset.
## 2.2. Outcomes Measures and Definitions
HCRU in AF patients was evaluated focusing on medical contacts and is reported as the proportion and frequency of at least one event (besides the recruitment visit, which was excluded from the analysis). Events included in the analysis of HCRU studied were those linked to AF and its sequalae and collected during follow-up visits as per study protocol and according to standardized outcome definitions [14]. Studied HCRU items include hospital admissions, outpatient hospital attendance, ER admissions, family doctor visits, stroke unit admissions and office-based specialist visits, and diagnostic and interventional procedures occurring during the follow-up period. General practitioner visits, office-based specialist visits, and hospital-based outpatient visits were grouped as “outpatient care visits,” to adequately compare information from different countries and settings. Diagnostic and interventional procedures covered all those derived from follow-up events, including those specific to AF (such as electrical cardioversion and ablation), methods for pulmonary embolism diagnosis (e.g., computed tomography scan, magnetic resonance imaging scan, and invasive angiography) and interventions required for cardiovascular diseases (including percutaneous coronary intervention [PCI] bare metal stent, PCI drug eluting stent, PCI balloon angioplasty, coronary artery bypass graft, valve replacement, pacemaker, and carotid stent). Data on medication use were not included in this study, as this has been evaluated in previous analyses of the GARFIELD-AF registry [16,17,18].
For the purpose of this analysis, patients were divided into two groups according to the enrolment cohorts, which allowed to account for possible differences in HCRU over the whole study period. Group A included participants recruited into GARFIELD–AF Cohorts 2 and 3 from 2010 to 2013; Group B included those in Cohorts 4 to 6 from 2013 to 2016. In particular, we split patients into two 3-year timeslots since, by Cohort 3, the non-Vitamin K antagonist oral anticoagulants (NOAC) were approved in most of the countries included in the GARFIELD-AF registry. In addition, there has been an increased use in newly diagnosed patients with AF receiving guideline-recommended treatment [18].
The 35 countries within the registry were grouped by geographical region, according to the classification provided by the GARFIELD–AF dataset used: Asia (China, India, Japan, Korea, Singapore, Thailand, Turkey, and United Arab Emirates), Europe (Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, The Netherlands, Norway, Poland, Russia, Spain, Sweden, Switzerland, Ukraine, and United Kingdom), Latin America (Argentina, Brazil, Chile, and Mexico), North America (Canada and United States), and other GARFIELD–AF countries (Australia, Egypt, and South Africa, henceforth defined as “others”).
## 2.3. Statistical Analysis
Continuous variables were described with mean and median as central tendency measures, while standard deviation (SD) and interquartile range (IQR) were described as dispersion measures. Categorical variables have been presented using frequency and percentage. The Student’s t test was used to assess differences between continuous variables, and the Chi square (χ2) or Fisher’s exact tests were used when needed to assess differences between categories.
AF-related HCRU was reported as the percentage of patients having at least one event included in the analyses and quantified as rate-per-patient-per-year (PPPY). Region-specific HCRU rates were subsequently compared using a Poisson regression model, which was adjusted for possible known confounders and modifiers collected in the registry, such as sex, age at enrolment, type of AF (i.e., (i) paroxysmal: AF that lasts less than 7 days and resolves spontaneously or with intervention; (ii) persistent: AF episode that continues for more than 7 days, irrespective of whether the episode was terminated by cardioversion or if it self-terminated; (iii) permanent: when AF is accepted by the patient (and physician) and a rate-control strategy is needed; or (iv) new onset—unclassified), AF therapy (i.e., antiplatelet [AP], alone or in combination with Vitamin K antagonists [VKA] or NOAC), comorbidities, prior transient ischemic attack, prior bleeding, CHA2DS2-VASc score, country income level (i.e., high, upper-middle, or lower-middle), and healthcare system payer (i.e., single payer, universal public insurance, public-private insurance, or private insurance). Results are expressed as incidence rate ratios (IRR) with $95\%$ confidence intervals ($95\%$ CI). All p-values were two-sided, with values <0.05 considered statistically significant. Analyses were performed using STATA statistical software version 13.1 [19].
## 3.1. Baseline Sample Characteristics
This study involved a total of 49,574 patients, with an overall median follow-up period of 719 days (IQR, 597–730). The cohort was mainly constituted of subjects enrolled in Europe ($56.5\%$) and Asia ($28.4\%$), with a median age of 71 years (IQR, 63–78). More than half of the participants were men ($55.7\%$). Prior bleeding was reported in $2.5\%$; transient ischemic attack in $4.4\%$; diabetes in $22.4\%$. A complete overview of patient demographics and clinical characteristics, according to region, is listed in Table 1.
Differences according to the cohorts of enrollment are presented in Table 2. The two cohort groups differed in almost all the baseline clinical characteristics.
## 3.2. Healthcare Resource Utilization
The vast majority of patients ($99.5\%$) had at least one outpatient care visit, excluding the enrollment medical contact. Hospitalization was the second-most frequent medical contact, with almost one-third ($30.4\%$) of patients having at least one hospital visit. Higher proportions of patients with more than one hospitalization were observed in North America ($37.5\%$), Europe ($37.2\%$), and others ($42.0\%$). Of these, stroke unit admissions accounted for around $1\%$ in all groups. Higher numbers of procedures and ER admissions were registered in North America with, respectively, $25.1\%$ and $31.0\%$ patients. A lower proportion of patients with at least one ER admission was seen in Asia ($8.1\%$), and a lower proportion of ER procedures was recorded in Latin America ($7.5\%$). Table 3 reports the number of GARFIELD–AF participants with at least one HCRU event.
The cohort groups’ PPPYs results aggregated by region are presented in Figure 1 and Figure 2. Outpatient care visits were the most frequent event in both groups (i.e., participants enrolled between 2010 and 2013 and those enrolled from 2013 to 2016). Large variations in the type of other medical contacts were observed across regions and between the two cohort groups.
In Group A (GARFIELD–AF Cohorts 2 and 3), patients with higher PPPY rates for procedures and hospitalizations were in Europe and others, while lesser values for both events were seen in Asia. In the latter, the lowest PPPY rate for ER visits was also registered. Patients in Europe showed lower PPPY rates for outpatient visits (Figure 1).
Overall, AF-related HCRU trends in Group B (GARFIELD–AF Cohorts 4 to 6) mirrored those in Group A, with narrower regional differences as compared with the previous (Figure 2). Compared with Europe, patients in North America showed higher HRCU rates for all medical contacts, and those in Latin America and Asia showed lower ones.
## 4. Discussion
In this real-world observational study, we combined global data from the GARFIELD–AF registry on the estimated HCRU in AF patients and compared it among regions and over time. Our findings highlighted the extensive resources utilized in almost 50,000 subjects from 35 countries worldwide. Important disparities still exist in their utilization among patients in the five regions after controlling for various confounders, such as patients’ characteristics and clinical status, as well as societal aspects (for instance, country income level and healthcare system payer).
All HCRU components showed an overlapping pattern across the five regions in the two study groups, but frequencies changed across cohorts. In particular, the analyses highlighted narrower regional differences in the second period along with the differences shown between the two groups as compared with the first one. Overall, these changes may indicate an increasingly progressive concordance with evidence-based guidelines for patients newly diagnosed with AF across countries, mirroring trends seen in previous GARFIELD–AF research. It appears that clinical practice and treatment of AF patients has become more uniform over time, likely due to a wider use of NOACs and specific AF procedures, such as electrical cardioversion and ablation [20,21].
As regards to the regional distribution of AF-related HCRU, a primary reason for these differences may be the availability of services and the differing models of healthcare and AF-care organization, beyond differences in healthcare system and payer [22]. In certain settings, gate-keeping systems—such as an initial visit to a general practitioner for access to specialist care or the presence of transitional care facilities—influenced the patient’s use of services. A previous analysis conducted in Latin American countries included in the GARFIELD–AF registry has suggested inadequate management of AF patients, with therapy underuse attributable to physician choice, difficulties in accessing healthcare, adverse economic conditions, and lower educational levels [23]. Additionally, access to primary and cardiology care in rural communities may be a recurring challenge for older and disabled AF adults, resulting in gaps in access to health services [24].
Another factor influencing HCRU variations across regions is inconsistent patient demographics, particularly the population age structure. These differing age structures may persist also after appropriate confounder correction [25]. Thus, greater numbers and frequencies of medical contacts may be at least partly attributed to the larger proportion of elderly people in some countries. Similarly, AF epidemiological metrics should be considered in the interpretation of our results. Although global rates are relatively stable, higher and more premature mortality due to AF was shown in low- and middle-income countries [4,23,26]. In contrast, a lower risk of death in Asia and Europe compared with other regions is a common observation, likely linked to the highly protective healthcare system and easier access to services in these regions [27]. Living in North America or Latin America was instead associated with a higher risk of early death [27]. A bias toward lower reported medical contacts may exist in countries where such services lack or are underused, resulting in a suboptimal level of care.
When analyzing the type of healthcare contacts, it is worth noting that hospitalizations account for higher HCRU rates. Drivers for urgent and elective hospitalization in AF patients have been extensively described in the literature, and include cardiovascular and non-traditional risk factors, as well as considerable rates of readmission, particularly in comorbid, higher CHA2DS2VASc score, and post-ablated AF patients [28,29,30]. Overall, inpatient care is the main determinant of healthcare costs associated with AF. Thus, further research is needed to develop specific effective transitional and integrated care interventions [6,7,29].
In summary, although marked differences in resource use for AF patient care were observed worldwide, using the expansive GARFIELD–AF registry, our findings suggest that AF substantially contributes to resource consumption with a subsequent important impact on healthcare expenditure worldwide [2,29,31].
The management of AF is complex, and convergence towards guideline-directed care is crucial to maximize patient’s benefit from tailored treatment options. Yet, implanting integrated AF care models has been proven to reduce disease and resource burden of AF [29]. In this sense, our findings may serve as actionable indicators of novel value-based organizational approaches to support changes in the management of AF.
This paper has a number of strengths and weaknesses. The design features of GARFIELD–AF registry include the random selection of sites and the enrolment of patients without exclusion according to comorbidities or treatment that ensures, respectively, the representativeness of the national care settings and population aimed to study, thus providing reliable estimates of research outcomes. Despite these strengths, this research should be interpreted in the context of its limitations. The study did not consider other possible unmeasured confounders, which may influence HCRU in AF patients. However, we included those mainly associated with the outcomes, and the use of robust statistical analysis allowed us to balance factors potentially correlated to such confounders. The reported burden of resource consuming was quantified excluding medication use, which was previously characterized in other GARFIELD–AF studies [16,17,18]. The differences in healthcare systems and organization across the countries included in the GARFIELD–AF registry may reflect variability in types, amounts, and patterns of HCRU events.
## 5. Conclusions
Within the GARFIELD–AF registry, a vast amount of HCRU was documented in AF patients from 35 countries worldwide. Important geographical differences exist in the type, quantity, and frequency of HCRU in patients with AF. Changes in AF care and variable adherence to evidence-based guidelines determined different patterns of HCRU, with a trend toward convergence of clinical practices over time.
## References
1. Roth G.A., Mensah G.A., Johnson C.O., Addolorato G., Ammirati E., Baddour L.M., Barengo N.C., Beaton A.Z., Benjamin E.Z., Benzinger C.P.. **Global Burden of Cardiovascular Diseases and Risk Factors, 1990 to 2019: Update from the Global Burden of Disease 2019 Study**. *JACC J. Am. Coll. Cardiol.* (2020) **76** 2982-3021. DOI: 10.1016/j.jacc.2020.11.010
2. Patel N.J., Atti V., Mitrani R.D., Viles-Gonzalez J.F., Goldberger J.J.. **Global rising trends of atrial fibrillation: A major public health concern**. *Heart* (2018) **104** 1989-1990. DOI: 10.1136/heartjnl-2018-313350
3. Lippi G., Sanchis-Gomar F., Cervellin G.. **Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge**. *Int. J. Stroke* (2021) **16** 217-221. DOI: 10.1177/1747493019897870
4. Li H., Song X., Liang Y., Bai X., Liu-Huo W.S., Tang C., Chen W., Zhao L.. **Global, regional, and national burden of disease study of atrial fibrillation/flutter, 1990-2019: Results from a global burden of disease study, 2019**. *BMC Public Health* (2022) **22**. DOI: 10.1186/s12889-022-14403-2
5. Borschel C.S., Schnabel R.B.. **The imminent epidemic of atrial fibrillation and its concomitant diseases—Myocardial infarction and heart failure—A cause for concern**. *Int. J. Cardiol.* (2019) **287** 162-173. DOI: 10.1016/j.ijcard.2018.11.123
6. Mukherjee K., Kamal K.M.. **Impact of atrial fibrillation on inpatient cost for ischemic stroke in the USA**. *Int. J. Stroke* (2019) **14** 159-166. DOI: 10.1177/1747493018765491
7. Ball J., Carrington M.J., McMurray J.J., Stewart S.. **Atrial fibrillation: Profile and burden of an evolving epidemic in the 21st century**. *Int. J. Cardiol.* (2013) **167** 1807-1824. DOI: 10.1016/j.ijcard.2012.12.093
8. Burdett P., Lip G.Y.H.. **Atrial Fibrillation in the United Kingdom: Predicting Costs of an Emerging Epidemic Recognising and Forecasting the Cost Drivers of Atrial Fibrillation-related costs**. *Eur. Heart J. Qual. Care Clin. Outcomes* (2020) **8** 187-194. DOI: 10.1093/ehjqcco/qcaa093
9. Mittal S.V., Wu B., Song J., Milentijevic D., Ashton V., Mahajan D.. **Healthcare resource utilization and costs among nonvalvular atrial fibrillation patients initiating rivaroxaban or warfarin in skilled nursing facilities: A retrospective cohort study**. *Curr. Med. Res. Opin.* (2020) **36** 529-536. DOI: 10.1080/03007995.2019.1706464
10. Sobhy M.A., Khoury M., Almahmeed W.A., Sah J., Di Fusco M., Mardekian J., Kherraf S.A., Lopes R.D., Hersi A.. **The atrial FibriLlatiOn real World management registry in the Middle East and Africa: Design and rationale**. *J. Cardiovasc. Med.* (2020) **21** 704-710. DOI: 10.2459/JCM.0000000000001007
11. Patel N.J., Deshmukh A., Pant S., Singh V., Singh V., Patel N., Arora S., Shah N., Chothani A., Savani G.T.. **Contemporary trends of hospitalization for atrial fibrillation in the United States, 2000 through 2010: Implications for healthcare planning**. *Circulation* (2014) **129** 2371-2379. DOI: 10.1161/CIRCULATIONAHA.114.008201
12. Le Heuzey J.V., Bassand J.P., Berneau J.B., Cozzolino P., D’Angiolella L., Doucet B., Mantovani L.G., Mertelet M., Mouallem J., Muller J.J.. **Stroke prevention, 1-year clinical outcomes and healthcare resource utilization in patients with atrial fibrillation in France: Data from the GARFIELD-AF registry**. *Arch. Cardiovasc. Dis.* (2018) **111** 749-757. DOI: 10.1016/j.acvd.2018.03.012
13. Reynolds M.R., Essebag V., Zimetbaum P., Cohen D.J.. **Healthcare resource utilization and costs associated with recurrent episodes of atrial fibrillation: The FRACTAL registry**. *J. Cardiovasc. Electrophysiol.* (2007) **18** 628-633. DOI: 10.1111/j.1540-8167.2007.00819.x
14. Kakkar A.K., Mueller I., Bassand J.P., Fitzmaurice D.A., Goldhaber S.Z., Goto S., Haas S., Hacke W., Lip G.Y., Mantovani L.G.. **International longitudinal registry of patients with atrial fibrillation at risk of stroke: Global Anticoagulant Registry in the FIELD (GARFIELD)**. *Am. Heart J.* (2012) **163** 13-19.e1. DOI: 10.1016/j.ahj.2011.09.011
15. Kakkar A.K., Mueller I., Bassand J.P., Fitzmaurice D.A., Goldhaber S.Z., Goto S., Haas S., Hacke W., Lip G.Y.H., Mantovani L.G.. **Risk profiles and antithrombotic treatment of patients newly diagnosed with atrial fibrillation at risk of stroke: Perspectives from the international, observational, prospective GARFIELD registry**. *PLoS ONE* (2013) **8**. DOI: 10.1371/journal.pone.0063479
16. Ambrosio G., Camm A.J., Bassand J.P., Corbalan R., Kayani G., Carlucio E., Mantivani L.G., Virdone S., Kakkar A.K.. **Characteristics, treatment, and outcomes of newly diagnosed atrial fibrillation patients with heart failure: GARFIELD-AF**. *ESC Heart Fail.* (2021) **8** 1139-1149. DOI: 10.1002/ehf2.13156
17. Camm A.J., Cools F., Virdone S., Bassand J.-P., Fitzmaurice D.A., Fox K.A.A., Goldhaber S.Z., Goto S., Haas S., Mantovani L.G.. **Mortality in Patients with Atrial Fibrillation Receiving Nonrecommended Doses of Direct Oral Anticoagulants**. *J. Am. Coll. Cardiol.* (2020) **76** 1425-1436. DOI: 10.1016/j.jacc.2020.07.045
18. Camm A.J., Accetta G., Ambrosio G., Atar D., Bassand J.-P., Berge E., Cools F., A Fitzmaurice D., Goldhaber S.Z., Goto S.. **Evolving antithrombotic treatment patterns for patients with newly diagnosed atrial fibrillation**. *Heart* (2017) **103** 307-314. DOI: 10.1136/heartjnl-2016-309832
19. 19.
StataCorp
Stata Statistical Software: Release 13StataCorp LLCCollege Station, TX, USA2013. *Stata Statistical Software: Release 13* (2013)
20. Apenteng P.N., Gao H., Hobbs R., Fitzmaurice D.A.. **Temporal trends in antithrombotic treatment of real-world UK patients with newly diagnosed atrial fibrillation: Findings from the GARFIELD-AF registry**. *BMJ Open* (2018) **8** e018905. DOI: 10.1136/bmjopen-2017-018905
21. Fox K.A.A., Accetta G., Pieper K.S., Bassand J.-P., Camm A.J., Fitzmaurice D.A., Kayani G., Kakkar A.K.. **Why are outcomes different for registry patients enrolled prospectively and retrospectively? Insights from the global anticoagulant registry in the FIELD-Atrial Fibrillation (GARFIELD-AF)**. *Eur. Heart J. Qual. Care Clin. Outcomes* (2018) **4** 27-35. PMID: 28950344
22. Klarenbach S.W., Jacobs P.. **International Comparison of Health Resource Utilization in Subjects with Diabetes. An analysis of Canadian and American national health surveys**. *Diabetes Care* (2003) **26** 1116-1122. DOI: 10.2337/diacare.26.4.1116
23. Jerjes-Sanchez C., Corbalan R., Barretto A.C.P., Luciardi H.L., Allu J., Illingworth L., Pieper K.S., Kayani G.. **Stroke prevention in patients from Latin American countries with non-valvular atrial fibrillation: Insights from the GARFIELD-AF registry**. *Clin. Cardiol.* (2019) **42** 553-560. DOI: 10.1002/clc.23176
24. Rush K.L., Burton L., Van Der Merwe F., Hatt L., Galloway C.. **Atrial fibrillation care in rural communities: A mixed methods study of physician and patient perspectives**. *BMC Fam. Pract.* (2019) **20**. DOI: 10.1186/s12875-019-1029-1
25. Frohlich N., Carriere K.C., Potvin L., Black C.. **Assessing socioeconomic effects on different sized populations: To weight or not to weight?**. *J. Epidemiol. Community Health* (2001) **55** 913-920. DOI: 10.1136/jech.55.12.913
26. Santos I.S., Goulart A.C., Olmos R.D., Thomas G.N., Lip G.Y.H., A Lotufo P., Benseñor I.M., Arasalingam A., Brocklehurst P., Cheng K.K.. **Atrial fibrillation in low- and middle-income countries: A narrative review**. *Eur. Heart J.* (2020) **22** O61-O77. DOI: 10.1093/eurheartj/suaa181
27. Bassand J.P., Virdone S., Goldhaber S.Z., Camm A.J., Fitzmaurice D.A., Fox K.A., Goto S., Haas S., Hacke W., Kayani G.. **Early Risks of Death, Stroke/Systemic Embolism, and Major Bleeding in Patients with Newly Diagnosed Atrial Fibrillation**. *Circulation* (2019) **139** 787-798. DOI: 10.1161/CIRCULATIONAHA.118.035012
28. Steinberg B.A., Kim S., Fonarow G.C., Thomas L., Ansell J., Kowey P.R., Mahaffey K.W., Gersh B.J., Hylek E., Naccarelli G.. **Drivers of hospitalization for patients with atrial fibrillation: Results from the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF)**. *Am. Heart J.* (2014) **167** 735-742.e2. DOI: 10.1016/j.ahj.2014.02.003
29. Bhat A., Khanna S., Chen H.H.L., Gan G.C.H., MacIntyre C.R., Tan T.C.. **Drivers of hospitalization in atrial fibrillation: A contemporary review**. *Heart Rhythm* (2020) **17** 1991-1999. DOI: 10.1016/j.hrthm.2020.06.015
30. Bumpus S., Krallman R., McMahon C., Gupta A., Montgomery D., Kline-Rogers E., Vaishnava P.. **Insights into hospital readmission patterns of atrial fibrillation patients**. *Eur. J. Cardiovasc. Nurs.* (2020) **19** 545-550. DOI: 10.1177/1474515120911607
31. Tripathi B., Atti V., Kumar V., Naraparaju V., Sharma P., Arora S., Wojtaszek E., Gopalan R., Siontis K.C., Gersh B.J.. **Outcomes and Resource Utilization Associated with Readmissions After Atrial Fibrillation Hospitalizations**. *J. Am. Heart Assoc.* (2019) **8** e013026. DOI: 10.1161/JAHA.119.013026
|
---
title: A Single-Center, Randomized Controlled Trial to Test the Efficacy of Nurse-Led
Motivational Interviewing for Enhancing Self-Care in Adults with Heart Failure
authors:
- Federica Dellafiore
- Greta Ghizzardi
- Ercole Vellone
- Arianna Magon
- Gianluca Conte
- Irene Baroni
- Giada De Angeli
- Ida Vangone
- Sara Russo
- Alessandro Stievano
- Cristina Arrigoni
- Rosario Caruso
journal: Healthcare
year: 2023
pmcid: PMC10000833
doi: 10.3390/healthcare11050773
license: CC BY 4.0
---
# A Single-Center, Randomized Controlled Trial to Test the Efficacy of Nurse-Led Motivational Interviewing for Enhancing Self-Care in Adults with Heart Failure
## Abstract
Background: The role of nurse-led motivational interviewing (MI) in improving self-care among patients with heart failure (HF) is promising, even if it still requires further empirical evidence to determine its efficacy. For this reason, this study tested its efficacy in enhancing self-care maintenance (primary endpoint), self-care management, and self-care confidence after three months from enrollment in adults with HF compared to usual care, and assessed changes in self-care over follow-up times (3, 6, 9, and 12 months). Methods: A single-center, randomized, controlled, parallel-group, superiority study with two experimental arms and a control group was performed. Allocation was in a 1:1:1 ratio between intervention groups and control. Results: MI was effective in improving self-care maintenance after three months when it was performed only for patients (arm 1) and for the patients–caregivers dyad (arm 2) (respectively, Cohen’s $d = 0.92$, p-value < 0.001; Cohen’s $d = 0.68$, p-value < 0.001). These effects were stable over the one-year follow-up. No effects were observed concerning self-care management, while MI moderately influenced self-care confidence. Conclusions: This study supported the adoption of nurse-led MI in the clinical management of adults with HF.
## 1. Introduction
Heart failure (HF) is a major public health concern worldwide, affecting approximately 1–$2\%$ of the global adult population [1]. HF is a clinical syndrome caused by several potential underlying etiologies and characterized by key symptoms such as dyspnea, ankle swelling, exhaustion, and clinical signs (e.g., peripheral edema) [2]. HF is associated with poorer quality of life, increased hospitalization rates, more health-related costs, and decreased overall survival in patients [1,3,4]. It also places health-related challenges on the well-being of informal caregivers, because it is associated with a reduced quality of life and health-related issues [5].
Patients with HF need to adhere to the recommended medication regimen and pay special attention to dietary sodium and liquids restrictions, exercise regimen, body condition monitoring, behaviors and mood control, accurate symptom detection, therapy impact evaluation, and other self-care behaviors [2,6]. These demands are often mismatched from the required self-care practices, as the self-care behaviors of adults with HF were extensively described as mainly inadequate [7,8,9,10,11,12].
Self-care is the decision-making process that includes behaviors that help maintain heart failure stability (self-care maintenance), allow patients to perceive symptoms (self-care monitoring), and manage signs and symptoms (self-care management) [13,14]. Self-care maintenance includes exercising (e.g., brisk walking), avoiding getting sick, medical adherence, and dietary and liquids adherence. Self-care monitoring is based on promptly recognizing the cardinal HF symptoms and signs (e.g., gaining weight, dyspnea, peripheral edema). Self-care management reflects patients’ knowledge and health literacy in decision-making when symptoms and/or signs occur. Overall, self-care behaviors are positively influenced by the patient’s perception of adequately performing demanding self-care behaviors (self-care confidence) [13,14].
Among the strategies to sustain adequate self-care in patients with HF, motivational interviewing (MI) showed promising results [15,16,17,18,19]. By exploring and resolving ambivalence, MI, a goal-directed and patient-centered counseling technique, assists individuals in improving their health-related behaviors [16,20,21,22]. The essential components of MI include showing empathy, creating discrepancies in the perceptions derived from interpreting the gap between expected behaviors and unhealthy performed ones, refraining from disagreements, promoting self-efficacy, and sustaining a shared strategy [23]. Individual psychosocial behavioral interventions utilizing MI showed improved medication adherence and high levels of participant satisfaction in several chronic conditions [16,24]. Recent studies show that nurse-led MI is safe and effective in pursuing behavioral changes in patients with chronic conditions because nurses are healthcare professionals who work closely with the patient’s needs, beliefs, and behaviors, and are able to detect misconceptions regarding clinical aspects [24,25].
A recent meta-analysis of nine experimental studies shows that MI has moderate effects on enhancing self-care confidence and self-care management and large effects on improving self-care maintenance [26]. Despite this important synthesis of evidence, the authors stated that more empirical and experimental research is still required to corroborate the efficacy of MI on self-care in patients with HF because of the current heterogeneity in the several included populations and the poor adoption of clinical trials measuring self-care with theory-grounded self-report scales [26]. In other words, more randomized controlled trials are required to close the gap of evidence that currently undermines the generalizability and transferability of the efficacy of MI in managing HF [27]. For this reason, this randomized clinical trial (RCT) aimed (a) to test the efficacy of nurse-led MI in enhancing self-care maintenance (primary endpoint), self-care management, and self-care confidence after three months from enrollment in adults with HF compared to usual care, and (b) assess changes in self-care over follow-up times (3, 6, 9, and 12 months).
## 2.1. Design
This was a single-center, randomized, controlled, parallel-group, superiority study with two experimental arms and a control group. Allocation was based on a 1:1:1 ratio between intervention groups and control. The ClinicalTrial.gov identifier is NCT05595655. This study was approved by the Ethical Committee of San Raffaele Hospital (approval #74/INT).
## 2.2. Study Setting
This study enrolled ambulatory patients in the Heart Failure Clinic of the IRCCS Policlinico San Donato in northern Italy. The focus of care ranges from prenatal diagnosis to rehabilitation, from newborns to the very elderly; the medical–nursing staff is specialized in several areas of cardiology, heart surgery, vascular surgery, and anesthesia with a high focus on clinical research [28]. IRCCS Policlinico San *Donato is* a reference center for cardiovascular diseases [29].
## 2.3. Participants
Participants were patients with HF who did not practice adequate self-care and their caregivers. Patients met the requirements for participation if they met the following criteria: (a) had a diagnosis of HF classified as New York Heart Association (NYHA) class II–IV; (b) had evidence of inadequate self-care determined by a score of 0, 1, or 2 on at least two items of the self-care maintenance or self-care management scales of the Self-Care of HF Index v.6.2 (SCHFI v.6.2) [30]; (c) were willing to sign the informed consent to be enrolled; and (d) with age ≥ 18 years. Patients who had a myocardial infarction during the previous three months and/or had severe cognitive impairment with a six-item Screener score between 0 and 4 [31] and/or residing in a nursing home where self-care was not required or had an informal caregiver who did not wish to be involved in the study were all excluded from the study. Informal caregivers were eligible to be enrolled if the patients confirmed them as the principal caregivers. Both were not eligible to be enrolled if either the patient or the caregiver refused to participate in the trial in the baseline period; however, if one participant left the study after enrollment, the other one was allowed to continue. Eligible dyads were enrolled after having received a clinician invitation letter stating the aim of the study and the procedure.
## 2.4. Experimental Arms
A trained nurse with experience in educating patients with HF delivered MI. Four registered nurses were trained to participate in a 32 h training course on MI and 8 h refresh training on evidence-based care regarding HF. The registered nurses were females; two of them had a Master of Science in nursing, one was a doctoral student (PhD student) in nursing science, and one had a bachelor’s degree. The nurses’ average age was 28.75 years (standard deviation, SD = 5.12; range: 24–36). They had 5.75 years of work experience in cardiology (SD = 4.35; ranges, 2–12). The intervention included face-to-face nurse-led MI interventions that lasted around 30 min. The first MI had to be performed within 2 months from enrolment and followed by four other MI interventions at 3, 6, 9, and 12 months performed by the same interventionist. To strengthen the intervention and to sustain adherence to the protocol, the nurse who performed the MI contacted the patients via telephone three times during the first two months after MI. This scheduled approach for delivering MI 5 times during the study has never been tested in previous studies [16].
In arm 1, the MI was delivered only to patients; in arm 2, MI was delivered simultaneously to the dyad patient and caregiver. Participants enrolled in the experimental arms (arms 1 and 2) received MI interventions as an add-on approach to the standard of care.
## 2.5. Standard of Care and Control Group
Standard of care included clinical visits in the outpatient settings every 6 to 12 months, depending on the severity of the patients’ HF conditions and their specific clinical pathways. Education in the standard of care was based on discussions with patients about relevant materials geared toward HF self-care. Patients in the control group received standard of care only.
## 2.6. Procedures
A research assistant (outcome assessor) screened the patients using the SCHFI v.6.2 [30] and the six-item Screener [31] following the study protocol after patients and caregivers gave their consent. After the eligibility screening, when a patient was eligible, the protocol-required questionnaires were administrated to both patients and caregivers. They received questionnaires individually at baseline and at each follow-up, and they were not permitted to work together to complete the questionnaires. At 3, 6, 9, and 12 months after enrollment, follow-up data were collected via telephone. The outcome assessor was kept blind regarding the research arms at both the baseline and all follow-up points. Interventionists and participants were not blind to the study arm.
## 2.7. Randomization
A web-based system generated the randomization sequence, assigning participants in a 1:1:1 ratio to either the intervention or control group using a simple randomized process. Allocation sequences were accomplished using computer-generated algorithms that were made available after the trial. The interventionists were not informed of the allocation sequence. The randomization process started after the site employees (study nurses) entered the patients’ information into the database (RedCap). Each randomization number was generated and sent by a study nurse to the interventionist (a trained nurse who performed the MI), who was not the professional who had to assess the outcomes. Each participant’s enrollment and follow-ups were always communicated to the trial coordinator.
## 2.8. Measurements
The measurements for patients were socio-demographic and clinical characteristics. Socio-demographics were sex (male, female); age (years); marital status (single, married, divorced, widower); education (high schools or higher, lower than high schools); employment (active worker, retired); income (more than necessary to live, the necessary to live, and not the necessary to live). Clinical characteristics were NYHA class (II, II, IV functional class), Charlson comorbidity index (CCI, score) [32], ejection fraction (HFpEF = preserved ejection fraction; HFmrEF = midrange ejection fraction; HFrEF = reduced ejection fraction), time with HF (months), BMI (kg/m2), Montreal cognitive assessment (MoCA) (score) [33]. The outcomes of this study were the self-care maintenance scores measured using SCHFI v.6.2 [30].
## Outcomes
The SCHFI v.6.2 was used to assess the score of self-care maintenance at baseline, after 3 months (primary endpoint), and over the follow-up times. The SCHFI v.6.2 also allowed researchers to measure secondary outcomes: self-care management and self-care confidence at baseline and over the follow-up times. Each score has a range of 0 to 100. Higher scores indicate better self-care. Only if a patient had previously reported experiencing HF symptoms, such as dyspnea, did they have to fill out the self-care management scale. A score of less than 70 on each domain denoted adequate self-care.
## 2.9. Sample Size
The pooled mean of self-care maintenance described using the SCHFI v.6.2 in two previous descriptive studies performed in northern Italy was 53.55, with a pooled standard deviation of 18.98 [34]. Previous studies showed that MI could improve the mean of self-care scores in patients with HF by increasing the mean scores with a delta (Δ) ranging from 6 to 15 (pooled mean Δ = 10.95) [26]. Therefore, 49 patients per arm were required to reject the two-tailed null hypothesis of equal mean scores between the study arms with a power of $80\%$. A sensitivity analysis considering slights variations in the Δ and accounting for 20–$25\%$ of attrition as per similar research [16] showed that a total of 180 ± 6 participants was necessary to preserve enough power ($80\%$) to detect significant mean differences between the experimental arms and the control group (60 ± 2 participants per arm).
## 2.10. Treatment Fidelity
The trial coordinator evaluated treatment fidelity by randomly applying an evaluation of the performed MI in arms 1 and 2 using the Motivational Interviewing Treatment Integrity (MITI) Scale [35]. The MI interventions were all audio-recorded, and the MITI was used to randomly evaluate 4 MI interventions per arm at each time point. The scores ranged between 2 and 5, and the median of the assessments in both arms was 3, indicating an ideal technical quality score.
## 2.11. Timeline
Enrollment required approximately 36 months (from May 2017 to May 2020; the study ended with the last follow-up in May 2021) to avoid overwhelming the activities of the involved staff in the study (i.e., four interventionists, a trial coordinator, two outcome assessors, a study nurse, a data manager, the principal investigator, and the co-investigators). The study was conducted at a cardiovascular hub center that remained operational during the COVID-19 pandemic waves. As a result, the researchers were able to conclude the study during the pandemic by leveraging the center’s ongoing interactions with heart failure patients. Figure 1 shows the patient flow.
## 2.12. Statistical Analysis
All data were analyzed by using an intention-to-treat approach. Categorical variables were described in terms of absolute and relative frequencies. Interval and continuous variables were evaluated for normality using the Shapiro–Wilk test, and data with a normal distribution are presented using the mean and standard deviation (SD). The median and interquartile ranges (IQR) were used to summarize non-normally distributed data. Baseline characteristics were compared between arms to determine if they were equal. Missing scores in the outcomes were $12\%$, $10\%$, and $11\%$ (respectively, the extent of the missingness in arms 1, 2, and 3) in each arm, which were imputed by employing multiple imputations based on random effects models after having assessed that the missing mechanisms (missing in relation to time and study arm) and patterns (monotone missingness based on sensitivity analysis) supported the missing at random (MAR) assumptions.
The delta (Δ) of the self-care scores was calculated at each follow-up period by subtracting the baseline self-care score (T0) to determine the changes in self-care scores during follow-up times (T1, T2, T3, and T4). As the primary endpoint was a significant improvement in arms 1 and 2 of self-care maintenance scores over the control group, a two-sample t-test was employed to compare the delta of self-care score in arms 1 and 2 versus the control arm 3, under the assumptions of the central limit theorem [36]. A similar approach was performed for each follow-up time and the secondary outcomes (self-care management and self-care confidence). Precisely, the t-test effect size estimates were computed using d statistics for independent t-tests (Cohen’s d), where d values lower than 0.5 indicated small effects, between 0.5 and 0.8 moderate effects, and greater than 0.8 large effects [37]. In addition to this approach, data on the primary and secondary outcomes at each follow-up time were summarized in adequate (scores equal to or greater than 70) or inadequate (scores lower than 70) and compared (arm 1 vs. arm 3; arm 2 vs. arm 3) using chi-square test or Fisher’s exact test when appropriate.
Mixed models for repeated measures were used to analyze changes across time (from baseline to T4) in primary and secondary outcomes, following the strategy of a previous study [16]. As a dependent variable, these models included the outcome scores available from T0 to T4 for each patient in the study arm. By having included a random intercept in the models, the inter-dependence between self-care maintenance, management, and confidence on the same subject was addressed. The randomization arm (nominal variable) was included in the models as an independent variable, along with the baseline characteristics (i.e., age, sex, income, NYHA, CCI score, MoCA, time since diagnosis, ejection fraction, and self-care confidence). Furthermore, the slopes derived from the models were compared between arms 1 and 2 versus the slopes of arm 3 for each outcome.
The significance level was set at 0.05 in all tests, and analytics were performed using Stata Statistical Software: Release 17 (StataCorp. 2021; College Station, TX, USA: StataCorp LLC).
## 3.1. Participants’ Characteristics
Patients’ baseline characteristics, stratified and compared by arm, are shown in Table 1. No differences are found in relation to the baseline characteristics. The majority of patients were females (r in arms 1, 2, and 3: $51.1\%$, $55.0\%$, and $52.5\%$, respectively) as well as the majority of caregivers (in arms 1, 2, and 3: $73.4\%$, $69.5\%$, and $74.0\%$, respectively). In arms 1, 2, and 3, patients reported mean ages of 68.39 (SD = 12.14), 69.44 (SD = 6.71), and 71.08 (SD = 12.95), respectively. In arms 1, 2, and 3, caregivers reported mean ages of 56.28 (SD = 9.12), 59.44 (SD = 11.10), and 58.17 (SD = 9.08), respectively. In arms 1, 2, and 3, most of caregivers were married: $57.1\%$, $63.2\%$, and $61.50\%$, respectively; for patients, $54.1\%$, $45.0\%$, and $37.7\%$, respectively. Most patients and caregivers reported an educational status lower than high schools: for patients in arms 1, 2, and 3, $72.1\%$, $75.0\%$, and $73.8\%$, respectively; for caregivers, $59.1\%$, $63.7\%$, and $62.9\%$, respectively.
Specifically, regarding patients, most of them answered that they have the necessary income to live. The median (IQR) time with HF was approximately 4 years in the three arms. The median (IQR) BMI indicated values within normal scores. Overall, most patients were in NYHA II class, with two comorbidities, an HF with preserved ejection fraction (HFprEF) and inadequate self-care maintenance and management scores.
## 3.2. Self-Care Maintenance (Primary Endpoint), Management, and Confidence at the First Follow-Up (T1, 3 Months)
The increase in the self-care maintenance scores (primary endpoint) from baseline to T1 (3 months after enrolment) is higher in arms 1 and 2 compared to arm 3 (Figure 2).
In arms 1, 2, and 3, the mean Δ indicating an increase in the self-care maintenance score is 12.84 (SD = 11.50), 10.81 (SD = 13.05), and 2.78 (SD = 10.33), respectively, indicating a large effect size in the Δ between arm 1 and arm 3 (Cohen’s $d = 0.92$, p-value < 0.001), and moderate effect size in the Δ between arm 2 and arm 3 (Cohen’s $d = 068$, p-value < 0.001).
Regarding self-care management scores, no differences are found between arm 1 and arm 2 versus arm 3 (see Table 2). Conversely, regarding self-care confidence scores, only the increased scores observed in arm 2 are significantly higher than those in arm 3, with a moderate effect size (Cohen’s $d = 058$, p-value = 0.002).
The comparisons of the dichotomized scores into adequate (scores ≥ 70) and inadequate (scores < 70) do not show significant differences for each outcome (see Table 2).
## 3.3. Changes in Self-Care Maintenance, Management, and Confidence over Follow-Up Times
The description of self-care maintenance, self-care management, and self-care confidence scores over time are reported in Figure 2 and Table 2. In relation to self-care maintenance, we generally find stability since 1 year (T4) of the effects detected at T1 (after 3 months). No differences are found in relation to self-care management scores over time. Conversely, regarding self-care confidence, at T1 and T2, moderate–small improvements are observed in arm 2 compared to arm 3; in arm 1, self-care confidence shows small–moderate improvements at T3 and T4 (see Table 2).
The trends over time (from baseline to T4) derived from the mixed models in self-care maintenance, self-care management, and self-care confidence scale scores are shown in Figure 3. Regarding the self-care maintenance slopes, arm 1 and arm 2 versus arm 3 show significant differences (p-values = 0.038; p-values = 0.047, respectively). No differences are found concerning self-care management scores (p-values = 0.398; p-values = 0.447, respectively). Regarding self-care confidence, only the comparison between trends of arm 2 and 3 show significant differences (p-values = 0.031). These trends are confirmed when the mixed models are adjusted for age, sex, income, NYHA, CCI score, MoCA, time since diagnosis, ejection fraction, and baseline self-care confidence.
## 4. Discussion
This study demonstrated that nurse-led MI performed using a scheduled approach (every three months over one year) was effective in improving self-care maintenance with stable effects over the follow-up times. The scheduled approach used to deliver MI in this study is a significant innovation, since no previous randomized controlled trials have utilized a similar approach [6,16,26,38,39,40,41]. This approach allows for a more structured and consistent delivery of motivational interviewing to participants, which may enhance its effectiveness. In this study, nurse-led MI also improved self-care confidence, with some differences when the intervention was performed only for patients (arm 1) or for the dyads of patients and caregivers (arm 2).
Overall, the results derived from this RCT corroborate previous evidence [6,16,26,38,39,40,41], adding additional insights regarding five main aspects: (a) the nurse-led MI performed with scheduled recurrences over time likely produces stable effects in improving self-care maintenance over time; (b) the characteristics of HF (e.g., NYHA class or ejection fraction) seem to play a non-significant role on influencing the efficacy of MI in improving self-care maintenance over time; (c) the effects of MI performed only for patients seemed to be more stable over the effects showed by performing MI to the dyads in a different way from the effects shown in a previous study [16]; (d) the role of MI in improving self-care management remains unclear; (e) self-care confidence seems positively influenced by MI.
The efficacy of nurse-led MI on self-care maintenance has important clinical implications because it means that aspects such as treatment adherence, which are highly problematic among patients with HF, might be susceptible to significant improvements when trained nurses employ MI in clinical practice. It is not surprising to find that the nurse-led MI effectively leads patients toward behavioral change [42,43,44]. In this regard, the key features of MI, such as adopting open-ended questions, affirmation of patients’ strengths, adopting reflective listening, and summarizing key points of the discussion, have the potential to be effective in patients with several clinical conditions, from individuals with HFprEF to patients with HFrEF.
The more stable effects on improving self-care maintenance shown in arm 1 over arm 2 may be explained by the nature of the training performed by the interventionists, which was mainly focused on the elements of MI per se and a brief refresh about evidence-based care for patients with HF rather than focusing on providing the skills to manage the complexity of the dyadic relationships during the MI. In other words, it is reasonable that interventionists found it easier to perform the MI only for patients rather than simultaneously managing the dyad as required in arm 2. In this regard, we have to acknowledge that a previous multicentric RCT found that the effects of MI performed for the patient–caregiver dyad were larger than the MI performed only for patients [16]. From a theoretical perspective, if we consider the contribution of caregivers to the self-care practices of patients with HF [45,46,47], the MI performed for the dyad should be the best option. However, the evidence from this study points out that delivering MI to the dyad should be based on different training from the one designed only to provide skills for delivering MI-based interventions because the complexity of the dyadic relationship should be included in educating the interventionists.
Among self-care behaviors, self-care management practices seem to be less susceptible to changes than self-care maintenance. This aspect is theoretically explainable by the role of several aspects that determine self-care management, such as disease-specific knowledge and, broadly speaking, health literacy [48,49,50]. In fact, self-care management reflects different individual-level characteristics into actions, from values, beliefs, knowledge, and so on, to behaviors that reflect a decision-making process triggered by the detection of signs and/or symptoms [51,52,53]. Considering these aspects, it is reasonable to think that self-care management requires complex and multiple interventions to be modified (e.g., psychosocial interventions combined with knowledge-based education and MI). Therefore, complex and multiple interventions should aim to affect the main determinants of self-care management rather than self-care management per se.
Self-care confidence is also susceptible to improvement after MI interventions. Considering that self-care confidence is one of the strongest predictors of self-care behaviors [51,54,55], this result might have interesting clinical implications because improving self-care confidence may trigger virtuous circles to improve several other health-related outcomes. The differences emerging between the two experimental arms of this study (i.e., arm 1 shows effects after six months, while arm 2 shows brief-term effects) require more investigations with future studies and might reflect the complexity of managing MI in a dyadic setting.
This study has several limitations. First, the single-center design limits the generalizability of the results. Second, the self-report scale used to assess primary and secondary outcomes (SCHFI v6.2) was the best option when the protocol of this RCT was written; however, it is currently outdated because the new SCHFI v7.2 is psychometrically more robust and allows researchers to assess self-care monitoring. Third, patient attrition over the trial was considerably large ($19.3\%$ at T4); this aspect requires further mitigation strategies in future studies and a more robust approach to ensure patient adherence to the protocol. Four, the poor focus of the educational course for educating the interventionists regarding managing the complexity of the dyadic relationships between patients and caregivers might be considered a source of bias, especially in interpreting the effects of arm 2. Finally, it is important to interpret the stability of the effects observed in relation to self-care maintenance with caution, given the repeated MI in the experimental procedure. While the results of this study suggest that the repeated MI approach may produce stable effects over a one-year follow-up period, it is important to consider that individual patients may respond differently to repeated interventions. Therefore, the generalizability of the findings to all patients with heart failure should be approached with caution.
## 5. Conclusions
Nurse-led MI shows efficacy in improving self-care maintenance in patients with HF over a one-year follow-up. This RCT confirms previous evidence and supports the adoption of nurse-led MI in the clinical management of HF. Future research should corroborate this evidence in specific subgroups to enhance the external validity of this intervention and should explore the effects of nurse-led MI on clinical outcomes.
## References
1. Tsao C.W., Aday A.W., Almarzooq Z.I., Alonso A., Beaton A.Z., Bittencourt M.S., Boehme A.K., Buxton A.E., Carson A.P., Commodore-Mensah Y.. **Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association**. *Circulation* (2022) **145** e153-e639. DOI: 10.1161/CIR.0000000000001052
2. McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M., Burri H., Butler J., Čelutkienė J., Chioncel O.. **2021 ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure**. *Eur. Heart J.* (2021) **42** 3599-3726. DOI: 10.1093/eurheartj/ehab368
3. Patel H.A., Hayden K.A., Raffin Bouchal S., King-Shier K.. **Self-Care Practices of Patients with Heart Failure Using Wearable Electronic Devices: A Systematic Review**. *J. Cardiovasc. Nurs.* (2022). DOI: 10.1097/JCN.0000000000000957
4. Santos G.C., Liljeroos M., Dwyer A.A., Jaques C., Girard J., Strömberg A., Hullin R., Schäfer-Keller P.. **Symptom Perception in Heart Failure—Interventions and Outcomes: A Scoping Review**. *Int. J. Nurs. Stud.* (2021) **116** 103524. DOI: 10.1016/j.ijnurstu.2020.103524
5. Kim E.Y., Oh S., Son Y.-J.. **Caring Experiences of Family Caregivers of Patients with Heart Failure: A Meta-Ethnographic Review of the Past 10 Years**. *Eur. J. Cardiovasc. Nurs.* (2020) **19** 473-485. DOI: 10.1177/1474515120915040
6. Sokalski T., Hayden K.A., Raffin Bouchal S., Singh P., King-Shier K.. **Motivational Interviewing and Self-Care Practices in Adult Patients with Heart Failure: A Systematic Review and Narrative Synthesis**. *J. Cardiovasc. Nurs.* (2020) **35** 107-115. DOI: 10.1097/JCN.0000000000000627
7. Dellafiore F., Conte G., Baroni I., Magon A., Pittella F., Casole L., Caruso R.. **Gender Differences in Heart Failure Self-Care Behaviors: Do We Know Enough?**. *Minerva Med.* (2018) **109** 401-403. DOI: 10.23736/S0026-4806.18.05579-9
8. Trenta A.M., Ausili D., Caruso R., Arrigoni C., Moro M., Nania T., Vellone E., Dellafiore F.. **Living with Heart Failure during the COVID-19 Pandemic: An Interpretative Phenomenological Analysis**. *Clin. Nurs. Res.* (2021) **30** 1071-1078. DOI: 10.1177/10547738211016614
9. Westland H., Page S.D., van Rijn M., Aryal S., Freedland K.E., Lee C., Strömberg A., Vellone E., Wiebe D.J., Jaarsma T.. **Self-Care Management of Bothersome Symptoms as Recommended by Clinicians for Patients with a Chronic Condition: A Delphi Study**. *Heart Lung* (2022) **56** 40-49. DOI: 10.1016/j.hrtlng.2022.06.001
10. Yang Y.-F., Hoo J.-X., Tan J.-Y., Lim L.-L.. **Multicomponent Integrated Care for Patients with Chronic Heart Failure: Systematic Review and Meta-Analysis**. *ESC Heart Fail.* (2022). DOI: 10.1002/ehf2.14207
11. Huang Z., Liu T., Chair S.Y.. **Effectiveness of Nurse-Led Self-Care Interventions on Self-Care Behaviors, Self-Efficacy, Depression and Illness Perceptions in People with Heart Failure: A Systematic Review and Meta-Analysis**. *Int. J. Nurs. Stud.* (2022) **132** 104255. DOI: 10.1016/j.ijnurstu.2022.104255
12. Liu S., Li J., Wan D.-Y., Li R., Qu Z., Hu Y., Liu J.. **Effectiveness of EHealth Self-Management Interventions in Patients With Heart Failure: Systematic Review and Meta-Analysis**. *J. Med. Internet Res.* (2022) **24** e38697. DOI: 10.2196/38697
13. Riegel B., Jaarsma T., Strömberg A.. **A Middle-Range Theory of Self-Care of Chronic Illness**. *Adv. Nurs. Sci.* (2012) **35** 194-204. DOI: 10.1097/ANS.0b013e318261b1ba
14. Riegel B., Dickson V.V., Faulkner K.M.. **The Situation-Specific Theory of Heart Failure Self-Care: Revised and Updated**. *J. Cardiovasc. Nurs.* (2016) **31** 226-235. DOI: 10.1097/JCN.0000000000000244
15. Rebora P., Spedale V., Occhino G., Luciani M., Alvaro R., Vellone E., Riegel B., Ausili D.. **Effectiveness of Motivational Interviewing on Anxiety, Depression, Sleep Quality and Quality of Life in Heart Failure Patients: Secondary Analysis of the MOTIVATE-HF Randomized Controlled Trial**. *Qual. Life Res.* (2021) **30** 1939-1949. DOI: 10.1007/s11136-021-02788-3
16. Vellone E., Rebora P., Ausili D., Zeffiro V., Pucciarelli G., Caggianelli G., Masci S., Alvaro R., Riegel B.. **Motivational Interviewing to Improve Self-care in Heart Failure Patients (MOTIVATE-HF): A Randomized Controlled Trial**. *ESC Heart Fail.* (2020) **7** 1309-1318. DOI: 10.1002/ehf2.12733
17. Chew H.S.J., Cheng H.Y., Chair S.Y.. **The Suitability of Motivational Interviewing versus Cognitive Behavioural Interventions on Improving Self-Care in Patients with Heart Failure: A Literature Review and Discussion Paper**. *Appl. Nurs. Res.* (2019) **45** 17-22. DOI: 10.1016/j.apnr.2018.11.006
18. Kent B., Cull E., Phillips N.M.. **A Systematic Review of the Effectiveness of Current Interventions to Assist Adults with Heart Failure to Comply with Therapy and Enhance Self-Care Behaviours**. *JBI Libr. Syst. Rev.* (2011) **9** 2572-2626. DOI: 10.11124/jbisrir-2011-81
19. Poudel N., Kavookjian J., Scalese M.J.. **Motivational Interviewing as a Strategy to Impact Outcomes in Heart Failure Patients: A Systematic Review**. *Patient* (2020) **13** 43-55. DOI: 10.1007/s40271-019-00387-6
20. Barrett S., Begg S., O’Halloran P., Kingsley M.. **Integrated Motivational Interviewing and Cognitive Behaviour Therapy for Lifestyle Mediators of Overweight and Obesity in Community-Dwelling Adults: A Systematic Review and Meta-Analyses**. *BMC Public Health* (2018) **18**. DOI: 10.1186/s12889-018-6062-9
21. Judice Jones N., Richard A.. **Implementing Evidence-Based Motivational Interviewing Strategies in the Care of Patients with Heart Failure**. *Crit. Care Nurs. Clin. N. Am.* (2022) **34** 191-204. DOI: 10.1016/j.cnc.2022.02.011
22. Wu J., Yu Y., Xu H.. **Influence of Targeted Motivational Interviewing on Self-Care Level and Prognosis during Nursing Care of Chronic Heart Failure**. *Am. J. Transl. Res.* (2021) **13** 6576-6583. PMID: 34306399
23. Miller W.R., Rollnick S.. **Ten Things That Motivational Interviewing Is Not**. *Behav. Cogn. Psychother.* (2009) **37** 129-140. DOI: 10.1017/S1352465809005128
24. McKenzie K., Chang Y.-P.. **The Effect of Nurse-Led Motivational Interviewing on Medication Adherence in Patients With Bipolar Disorder: The Effect of Nurse-Led Motivational Interviewing on Medication Adherence in Patients With Bipolar Disorder**. *Perspect. Psychiatr. Care* (2015) **51** 36-44. DOI: 10.1111/ppc.12060
25. Ehrlich O., Brandoff D., Gorman D.P., Berry D.L.. **Nurse-Led Motivational Interviewing for Setting Functional Cancer Pain Goals**. *Pain Manag. Nurs.* (2021) **22** 716-723. DOI: 10.1016/j.pmn.2021.03.003
26. Ghizzardi G., Arrigoni C., Dellafiore F., Vellone E., Caruso R.. **Efficacy of Motivational Interviewing on Enhancing Self-Care Behaviors among Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis of Randomized Controlled Trials**. *Heart Fail. Rev.* (2022) **27** 1029-1041. DOI: 10.1007/s10741-021-10110-z
27. Granholm A., Alhazzani W., Møller M.H.. **Use of the GRADE Approach in Systematic Reviews and Guidelines**. *Br. J. Anaesth.* (2019) **123** 554-559. DOI: 10.1016/j.bja.2019.08.015
28. Mollica G., Caruso R., Conte G., Ambrogi F., Boveri S.. **Analysing Researchers’ Engagement in Research Hospitals: A Pilot Study in IRCCS-Italian Research Hospitals**. *Healthcare* (2022) **10**. DOI: 10.3390/healthcare10122458
29. Frigiola A., Moussaidi N., Giamberti A., Pomé G., Isgrò G., Youssef T., Reali M., Varrica A., Nuri H.A., Cirri S.. **International Cooperation in Healthcare: Model of IRCCS Policlinico San Donato and Bambini Cardiopatici Nel Mondo Association for Congenital Heart Diseases**. *Eur. Heart J. Suppl.* (2016) **18** E72-E78. DOI: 10.1093/eurheartj/suw023
30. Vellone E., Riegel B., Cocchieri A., Barbaranelli C., D’Agostino F., Antonetti G., Glaser D., Alvaro R.. **Psychometric Testing of the Self-care of Heart Failure Index Version 6.2**. *Res. Nurs. Health* (2013) **36** 500-511. DOI: 10.1002/nur.21554
31. Callahan C.M., Unverzagt F.W., Hui S.L., Perkins A.J., Hendrie H.C.. **Six-Item Screener to Identify Cognitive Impairment among Potential Subjects for Clinical Research**. *Med. Care* (2002) **40** 771-781. DOI: 10.1097/00005650-200209000-00007
32. Charlson M.E., Pompei P., Ales K.L., MacKenzie C.R.. **A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation**. *J. Chronic Dis.* (1987) **40** 373-383. DOI: 10.1016/0021-9681(87)90171-8
33. Nasreddine Z.S., Phillips N.A., Bédirian V., Charbonneau S., Whitehead V., Collin I., Cummings J.L., Chertkow H.. **The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool for Mild Cognitive Impairment: MOCA: A Brief Screening Tool for Mci**. *J. Am. Geriatr. Soc.* (2005) **53** 695-699. DOI: 10.1111/j.1532-5415.2005.53221.x
34. Dellafiore F., Arrigoni C., Palpella F., Diazzi A., Orrico M., Magon A., Pittella F., Caruso R.. **Effects of Mutuality, Anxiety, and Depression on Quality of Life of Patients with Heart Failure: A Cross-Sectional Study**. *Create. Nurs.* (2021) **27** 181-189. DOI: 10.1891/CRNR-D-20-00025
35. Moyers T.B., Martin T., Manuel J.K., Hendrickson S.M.L., Miller W.R.. **Assessing Competence in the Use of Motivational Interviewing**. *J. Subst. Abus. Treat.* (2005) **28** 19-26. DOI: 10.1016/j.jsat.2004.11.001
36. Kwak S.G., Kim J.H.. **Central Limit Theorem: The Cornerstone of Modern Statistics**. *Korean J. Anesth.* (2017) **70** 144. DOI: 10.4097/kjae.2017.70.2.144
37. Kraft M.A.. **Interpreting Effect Sizes of Education Interventions**. *Educ. Res.* (2020) **49** 241-253. DOI: 10.3102/0013189X20912798
38. Flores P.V.P., Rocha P.A., Figueiredo L.. **da S.; Guimarães, T.M.L.; Velasco, N.S.; Cavalcanti, A.C.D. Effect of motivational interviewing on self-care of people with heart failure: A randomized clinical trial**. *Rev. Esc. Enferm. USP* (2020) **54** e03634. DOI: 10.1590/s1980-220x2019013703634
39. Paranjpe R., Vadhariya A., Choi J., Essien E.J., Esse T.W., Gallardo E., Serna O., Abughosh S.. **Evaluating Trajectories of Statin Adherence after a Motivational Interviewing Intervention**. *J. Am. Pharm. Assoc.* (2020) **60** 892-898. DOI: 10.1016/j.japh.2020.06.011
40. Masterson Creber R., Patey M., Lee C.S., Kuan A., Jurgens C., Riegel B.. **Motivational Interviewing to Improve Self-Care for Patients with Chronic Heart Failure: MITI-HF Randomized Controlled Trial**. *Patient Educ. Couns.* (2016) **99** 256-264. DOI: 10.1016/j.pec.2015.08.031
41. Celano C.M., Freedman M.E., Harnedy L.E., Park E.R., Januzzi J.L., Healy B.C., Huffman J.C.. **Feasibility and Preliminary Efficacy of a Positive Psychology-Based Intervention to Promote Health Behaviors in Heart Failure: The REACH for Health Study**. *J. Psychosom. Res.* (2020) **139** 110285. DOI: 10.1016/j.jpsychores.2020.110285
42. Beckwith V.Z., Beckwith J.. **Motivational Interviewing: A Communication Tool to Promote Positive Behavior Change and Optimal Health Outcomes**. *NASN Sch. Nurse* (2020) **35** 344-351. DOI: 10.1177/1942602X20915715
43. Tooley E.M., Kolahi A.. **Motivating Behavioral Change**. *Med. Clin. N. Am.* (2022) **106** 627-639. DOI: 10.1016/j.mcna.2022.01.006
44. Budhwani H., Naar S.. **Training Providers in Motivational Interviewing to Promote Behavior Change**. *Pediatr. Clin. N. Am.* (2022) **69** 779-794. DOI: 10.1016/j.pcl.2022.04.008
45. Wilson A.M.M.M., de Almeida G.S.M., Santos B.. **de C.F. dos; Nakahara-Melo, M.; Conceição, A.P. da; Cruz, D. de A.L.M. da Fatores Associados à Contribuição Dos Cuidadores Para o Autocuidado Na Insuficiência Cardíaca**. *Rev. Lat. Am. Enferm.* (2022) **30** e3632. DOI: 10.1590/1518-8345.5838.3632
46. Vellone E., D’Agostino F., Buck H.G., Fida R., Spatola C.F., Petruzzo A., Alvaro R., Riegel B.. **The Key Role of Caregiver Confidence in the Caregiver’s Contribution to Self-Care in Adults with Heart Failure**. *Eur. J. Cardiovasc. Nurs.* (2015) **14** 372-381. DOI: 10.1177/1474515114547649
47. Vellone E., Biagioli V., Durante A., Buck H.G., Iovino P., Tomietto M., Colaceci S., Alvaro R., Petruzzo A.. **The Influence of Caregiver Preparedness on Caregiver Contributions to Self-Care in Heart Failure and the Mediating Role of Caregiver Confidence**. *J. Cardiovasc. Nurs.* (2020) **35** 243-252. DOI: 10.1097/JCN.0000000000000632
48. Cajita M.I., Cajita T.R., Han H.-R.. **Health Literacy and Heart Failure: A Systematic Review**. *J. Cardiovasc. Nurs.* (2016) **31** 121-130. DOI: 10.1097/JCN.0000000000000229
49. Liu X.B., Ayatollahi Y., Yamashita T., Jaradat M., Shen J.J., Kim S.J., Lee Y.-J., Hwang J., Yeom H., Upadhyay S.. **Health Literacy and Mortality in Patients with Heart Failure: A Systematic Review and Meta-Analysis**. *Res. Gerontol. Nurs.* (2019) **12** 99-108. DOI: 10.3928/19404921-20181018-01
50. Fabbri M., Murad M.H., Wennberg A.M., Turcano P., Erwin P.J., Alahdab F., Berti A., Manemann S.M., Yost K.J., Finney Rutten L.J.. **Health Literacy and Outcomes Among Patients with Heart Failure: A Systematic Review and Meta-Analysis**. *JACC Heart Fail.* (2020) **8** 451-460. DOI: 10.1016/j.jchf.2019.11.007
51. Riegel B., Dickson V.V., Vellone E.. **The Situation-Specific Theory of Heart Failure Self-Care: An Update on the Problem, Person, and Environmental Factors Influencing Heart Failure Self-Care**. *J. Cardiovasc. Nurs.* (2022) **37** 515-529. DOI: 10.1097/JCN.0000000000000919
52. Jaarsma T., Hill L., Bayes-Genis A., La Rocca H.-P.B., Castiello T., Čelutkienė J., Marques-Sule E., Plymen C.M., Piper S.E., Riegel B.. **Self-Care of Heart Failure Patients: Practical Management Recommendations from the Heart Failure Association of the European Society of Cardiology**. *Eur. J. Heart Fail.* (2021) **23** 157-174. DOI: 10.1002/ejhf.2008
53. Riccardi M., Sammartino A.M., Piepoli M., Adamo M., Pagnesi M., Rosano G., Metra M., von Haehling S., Tomasoni D.. **Heart Failure: An Update from the Last Years and a Look at the near Future**. *ESC Heart Fail.* (2022) **9** 3667-3693. DOI: 10.1002/ehf2.14257
54. Vellone E., Fida R., D’Agostino F., Mottola A., Juarez-Vela R., Alvaro R., Riegel B.. **Self-Care Confidence May Be the Key: A Cross-Sectional Study on the Association between Cognition and Self-Care Behaviors in Adults with Heart Failure**. *Int. J. Nurs. Stud.* (2015) **52** 1705-1713. DOI: 10.1016/j.ijnurstu.2015.06.013
55. Caruso R., Rebora P., Dellafiore F., Fabrizi D., Riegel B., Ausili D., Di Mauro S.. **Clinical and Socio-Demographic Determinants of Inadequate Self-Care in Adults with Type 1 Diabetes Mellitus: The Leading Role of Self-Care Confidence**. *Acta Diabetol.* (2019) **56** 151-161. DOI: 10.1007/s00592-018-1259-z
|
---
title: Exploration of Sex and Age-Based Associations in Clinical Characteristics,
Predictors of Severity, and Duration of Stay among COVID-19 Patients at the University
Hospital of Saudi Arabia
authors:
- Rasha Assad Assiri
- Asmatanzeem Bepari
- Waseemoddin Patel
- Syed Arif Hussain
- Shaik Kalimulla Niazi
- Asma Alshangiti
- Safia Ali Alshangiti
- Mary Anne Wong Cordero
- Shazima Sheereen
journal: Healthcare
year: 2023
pmcid: PMC10000835
doi: 10.3390/healthcare11050751
license: CC BY 4.0
---
# Exploration of Sex and Age-Based Associations in Clinical Characteristics, Predictors of Severity, and Duration of Stay among COVID-19 Patients at the University Hospital of Saudi Arabia
## Abstract
COVID-19 infection has a spectrum of variable clinical severity between populations because of their characteristic demographic features, co-morbidities, and immune system reactions. This pandemic tested the healthcare system’s preparedness, which depends on predictors of severity and factors related to the duration of hospital stays. Therefore, we carried out a single-center, retrospective cohort study in a tertiary academic hospital to investigate these clinical features and predictors of severe disease and study the different factors that affect hospital stay. We utilized medical records from March 2020 to July 2021, which included 443 confirmed (positive RT-PCR) cases. The data were explained using descriptive statistics and analyzed via multivariate models. Among the patients, $65.4\%$ were female and $34.5\%$ were male, with a mean age of 45.7 years (SD ± 17.2). We presented seven age groups with ranges of 10 years and noticed that patients aged 30–39 years old comprised $23.02\%$ of the records, while patients aged 70 and above comprised $10\%$. Nearly $47\%$ were diagnosed as having mild, $25\%$ as moderate, $18\%$ as asymptomatic, and $11\%$ as having a severe case of COVID-19 disease. Diabetes was the most common co-morbidity factor in $27.6\%$ of patients, followed by hypertension ($26.4\%$). Our population’s predictors of severity included pneumonia, identified on a chest X-ray, and co-morbid conditions such as cardiovascular disease, stroke, ICU stay, and mechanical ventilation. The median length of hospital stay was six days. It was significantly longer in patients with a severe disease and who were administered systemic intravenous steroids. An empirical assessment of various clinical parameters could assist in effectively measuring the disease progression and follow-up with patients.
## 1. Introduction
Coronavirus disease 2019 (COVID-19), defined by the World Health Organization (WHO), is a highly transmittable acute respiratory disease caused by a novel strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (nCoV-2019 or SARS-CoV-2), which belongs to the Coronaviridae family and caused the recent viral outbreak [1]. Its first case was identified in Wuhan, China, in December 2019, and within a short period of time, it had crossed most borders [2]. It spread to more than 200 countries, resulting in the first-ever pandemic caused by a coronavirus. As a result, WHO announced the outbreak of COVID-19 to be a “public health emergency of international consideration” on January 30 2020 [3]. In Saudi Arabia, the largest country in the Arabian Peninsula, the first confirmed case of COVID-19 was documented on March 2 2020 [4]. Saudi Arabia has a well-established healthcare system with 497 hospitals (287 are MOH hospitals), 99,617 total physicians, and 22.6 beds per 10,000 people. Among the MOH hospitals, 65 are accredited by the Central Board of Accreditation for Healthcare Institutions (CBAHI), established by the Ministry of Health [5]. In addition, several private and public hospitals have international accreditations, such as the Joint Commission International (JCI), which is accredited by the International Society for Quality in Health Care (ISQua), which serves as a safety collaborating center designated by the World Health Organization [6]. Nearly 75 Saudi hospitals are JCI-accredited [7].
Saudi authorities prepared public and private institutions to deal with the pandemic with strategic preparedness and a COVID-19 response plan, in line with the WHO operational planning guidelines to support the country. The authorities launched a governance system constituted of responsible committees to continuously monitor national and international updates, trace contacts, screen the population, increase awareness, and take appropriate actions to arrest the spread of this disease. Restrictions on social and religious gatherings, travel, and businesses were set ahead of the first 100 confirmed COVID-19 cases [8]. Mass screening programs were carried out in three stages. The first stage focused on screening individuals in highly populated districts through field tests in 807 locations; the second stage was facilitated through the Mawid app self-assessment tool, which classifies users as low- or high-risk. The low-risk group was the targeted population and was screened in designated primary care centers. The third stage was screening suspected COVID-19 cases with no symptoms at specialized drive-through testing centers, so-called takkad centers. The Hajj pilgrimage for 2020 was scaled down to confine participants, and no cases of COVID-19 were detected among pilgrims. As of 30 March 2020, the Saudi health authority announced that COVID-19 treatment is free for all citizens and residents. The country preserved all primary health services and immunization programs and supported all COVID-19 drugs and vaccine proposals [9]. According to the WHO, as of November 1, 2022, globally, over 600 million confirmed cases of COVID-19, including more than six million deaths, were reported; Saudi Arabia accounted for less than a million cases and around nine thousand deaths [10].
Usually, coronavirus causes a mild flu-like infection. However, like the severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which caused severe outbreaks worldwide in the last decade, COVID-19 causes a spectrum of clinical features ranging from asymptomatic cases and mild upper respiratory infections to life-threatening pneumonia [11]. COVID-19 usually presents as a fever, cough, fatigue, and shortness of breath. Less frequent manifestations include a sore throat, chest pain, headache, diarrhea, and loss of taste or smell. Severe pneumonia can cause acute respiratory distress syndrome (ARDS), demanding respiratory support and extended care in intensive care, which can lead to life-threatening complications, multiple organ dysfunction syndrome, and death [11,12,13]. Despite the reports showing that SARS-CoV-2 centrally manifested as a respiratory infection, new data demonstrated that it must be considered a systemic disease infecting multiple systems, such as the gastrointestinal, immune, hematopoietic, cardiovascular, and respiratory systems [14].
Contemporary studies have documented that the clinical characteristics of the disease are variable between populations because of their distinctive demographic characteristics and co-morbidities, specifically correlating with the severe form of the disease compared to the milder form [15,16]. However, few studies have concentrated on discrepancies between sex and age clusters for clinical characteristics and severity among COVID-19 patients for an extended period in the early infection waves in Saudi Arabia. Therefore, recognizing these variations is often helpful in identifying the progression to a severe form of the disease and possibly optimizing COVID-19 case management [11,17,18,19]. In addition, this awareness helps in formulating preventive measures, recognizing the effect of COVID-19 on hospital capacity, and improving patient bed capacity and health systems through risk stratification.
King Abdullah bin Abdulaziz University Hospital (KAAUH), located in the capital city of Saudi Arabia, Riyadh—the region with the highest population—and one of the mainstream hospitals of this region, is a COVID-19 testing and vaccination center. It is on the Princess Nourah bint Abdulrahman University (PNU) campus, which has 20 colleges and 121 academic programs. It is a 400-bed, designated academic MOH hospital accredited by CBAHI and JCI; it is, therefore, one of the few hospitals that have national and international accreditations reflecting the commitment to the maintenance of high-quality health services and a compelling center for research documentation. Nearly 450 COVID-19 cases from March 2020 to July 2021 were treated at this hospital. During this period, the country faced its highest peak of cases, and most citizens and residents were not vaccinated. Therefore, we aimed to conduct a retrospective cohort study, investigating the differences in clinical variables and analyzing predictors of severity and factors that influence the duration of hospital stay among COVID-19-admitted cases. As far as our knowledge, to date, very few studies have targeted a more extended period for recruiting admitted COVID-19 patients (one and a half years) that focused on diverse age groups and sex-based differences for clinical characteristics, co-morbidities, severity, and duration of stay in Saudi Arabia.
## 1.1.1. Aim of the Study
The study’s goal was to evaluate the severity of diverse symptoms and signs that developed in patients with COVID-19 disease and analyze variations in different clinical factors with age clusters and sex in confirmed COVID-19 patients admitted to a university hospital in Saudi Arabia retrospectively.
## 2.1. Study Design and Setting
This study was conducted as per the guidelines from STROBE (strengthening the reporting of observational studies in epidemiology) [20]. This was a single-center, non-interventional, non-exhaustive, retrospective cohort study, conducted at King Abdullah bin Abdulaziz University Hospital (KAAUH), Riyadh, Riyadh Province, Saudi Arabia. KAAUH is a referral hospital with nearly 400 beds and one of the designated COVID-19 hospitals and COVID-19 vaccination centers in Saudi Arabia (KSA).
## 2.2. Population
We included all patients with confirmed COVID-19 infection who were admitted to King Abdullah bin Abdulaziz University Hospital (KAAUH) between March 2020 and July 2021. Confirmed case of COVID-19 was defined as positive for SARS-CoV-2 virus through real-time reverse transcriptase–polymerase chain reaction (RT-PCR) assay on nasopharyngeal swab specimens. On admission, RT-PCR nasopharyngeal swabs were sent to all patients with clinical suspicion of COVID-19. The result was reported as either positive or negative and was available within 24 h. Therefore, we included 443 cases with positive RT-PCR results; cases with negative RT-PCR and incomplete clinical data were excluded.
## 2.3. Institutional Ethical Approval
The study was approved by Institutional Ethical and Review Board (PNU IRB and KAAUH IRBN), Riyadh, Saudi Arabia (PNU IRB number 21-0352, dated 16 September 2021, KAAUH IRB number RO2021-P-019, dated 3 October 2021).
## 2.4. Data Collection
A convenient sampling method for recruiting records from COVID-19 patients was used. Health Information Management (HIM) office provided Medical Record Numbers (MRN) of these confirmed COVID-19 patients admitted to Internal Medicine, Pulmonology, and Critical Care departments. Electronic medical record data were obtained using institutional software (TrakCare) and entered into the data collection form for detailed review. Data collected included: demographic characteristics (age, sex); co-morbidities, such as diabetes, hypertension, obesity, asthma, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cardiovascular disease, thyroid disease, obesity, and immunosuppression; relevant clinical features (fever, cough, breathlessness, chest pain, loss of taste or smell, headache, myalgia, loose stools, respiratory rate, oxygen saturation (SpO2), chest X-ray); severity; and outcome (discharge or death), as displayed in Table 1.
## 2.5. Data Management and Analysis Plan
Data were analyzed using the JMP SAS statistical software. They were described through descriptive statistics, such as frequency, percentages, measures of central tendency, and proportions, and were analyzed via inferential statistical tests, such as chi-square test to assess categorical variables and multivariate models. Odds ratios (ORs) and their corresponding $95\%$ CIs were calculated, and logistic regression analysis was applied to adjust for confounders of the association between different variables and case fatality of COVID-19. The values were compared at 0.05 level of significance to test the results of the study for the corresponding degrees of freedom.
## 3.1. Age and Sex
A total of 443 patients were included in the analysis; $65.4\%$ of them were female and $34.5\%$ were male, with a mean age of 45.7 years (SD ± 17.2). Of these, we presented seven age groups with ranges of 10 years and noticed that patients aged 30–39 years comprised $23.02\%$ of the total, followed by patients aged 18–29 years ($19.86\%$), 40–49 years ($18.96\%$), 60–69 years ($14.67\%$), and 50–59 years ($12.87\%$), as depicted in Figure 1. Nearly 7.5 % of patients were in the range of 70–79 years and $3.2\%$ were 80 and above. This lower number of patients aged 70 and above reflects the country’s expected life expectancy at birth of 75 years.
## 3.2. Clinical Characteristics of COVID-19 among Study Participants
Table 2 shows the frequency distribution of a variety of symptoms seen in our study participants on admission. The most common clinical manifestation was a fever ($55.76\%$), followed by a cough ($53.27\%$), dyspnea or shortness of breath ($37.92\%$), headache ($22.35\%$), diarrhea ($20.32\%$), and myalgia ($19.41\%$). A further analysis was conducted among age clusters and sex-based differences.
## 3.3. A. Different Age Groups
Table 3 displays the spectrum of clinical characteristics among the seven age clusters of the cohort. Fever, cough, and dyspnea were found to have a higher incidence in the age groups 60–69, 50–59, and 40–49 years and were statistically significant. Moreover, nearly $65\%$ of “70–79 years” and “80 and above” patients reported these symptoms. Ageusia and abdominal pain rates were found to be comparatively higher in younger age groups, and were statistically significant; however, they remained low (around $2\%$), as seen in Table 3.
## 3.4. B. Sex-Based Differences
A higher percentage of males complained of a fever ($$p \leq 0.0103$$), a cough, dyspnea, ageusia, and arthralgia, while headache, myalgia, abdominal pain, and anosmia were seen comparatively more in females, but were not statistically significant except for fever, as displayed in Table 3.
## 3.5. Co-Morbidities in the Study Participants
In our study, $46.28\%$ of patients were obese. Diabetes was the most common co-morbidity found in $27.6\%$ of patients, followed by hypertension ($26.4\%$) and asthma or other respiratory disorders ($14\%$), as depicted in Figure 2.
## 3.6. A. Different Age Groups
Of the patients that were obese, nearly $10\%$ belonged to the 30–39 years group, followed by the 40–49 years group, which comprised $9\%$ of them. Hypertension ($8.8\%$), diabetes ($7.67\%$), and cardiovascular diseases, such as congestive heart failure and hypothyroidism ($$p \leq 0.0481$$ *), were higher in incidence among the age group 60–69 years and were statistically significant ($p \leq 0.0001$ ****). It was also seen that diabetes and HTN were notably more common in ages 70–79 years ($4.74\%$, $5.42\%$), 50–59 years ($5.65\%$, $4.5\%$), and 40–49 years ($4.3\%$, $4.5\%$). While asthma and other respiratory disorders were comparatively higher in the younger age group of 30–39 years, as seen in Figure 3 and Table 4. A nominal logistic fit analysis was used to adjust for covariates of age and sex.
## 3.7. B. Sex-Based Differences
A higher percentage of males suffered HTN ($$p \leq 0.0119$$) and cardiovascular disease ($$p \leq 0.0467$$ *), while obesity (0.0467 *), hypothyroidism (0.0448 *), and a vitamin D deficiency (0.0235 *) had comparatively higher rates in females and were statistically significant, as displayed in Table 4.
## 3.8. The Severity of COVID-19
Nearly $47\%$ of patients were diagnosed as having mild, $25\%$ as moderate, $18\%$ as asymptomatic, and $11\%$ as having severe COVID-19 disease, as depicted in Figure 4. Pneumonia on chest X-rays was seen in $48\%$ of patients, while $18.08\%$ required oxygen support on arrival, $14.22\%$ were transferred to the ICU, and $4.07\%$ needed intubation. There was a statistically significant difference between different age groups and sexes, as seen in Table 5. A higher percentage of elderly patients needed oxygen support on arrival, were admitted to the ICU, and required intubation.
For further analysis, the four severity groups were narrowed down into two groups, ‘mild disease’ with asymptomatic–mild disease patients together, and ‘severe disease’ comprising moderate–severe disease. Table 6A–C represent the association found among various clinical parameters: age, sex, and interventions opted by the severity of the disease.
## 3.9. Predictors of Severity and Duration of Hospital Stay (DoHS)
A multivariate logistic regression analysis indicated that the patient’s respiratory need for O2 on arrival, the presence of pneumonia on chest X-ray, admission to the ICU, and existing cardiovascular disease (MI, CHF, IHD) and stroke were significant predictors for the severity (Table 7).
Figure 5 displays the leverage plots of independent predictors with the duration of stay in the hospital. Table 8 shows the duration of hospital stay (DoHS) in days for the patients, with a maximum of 118, an average of 9.72 ± 14.2 (SD), and a median of 6 days. A comparison of the factors showed that patients with severe symptoms were associated with a more prolonged DoHS than those with mild-to-moderate symptoms (mean 32 and 5.5 days, estimate = 3.29, $p \leq 0.0018$, respectively). The results also indicated that patients admitted or transferred to the ICU were associated with a significantly longer DoHS (mean 29.26 and 6.4 days, estimate = 6.41, $p \leq 0.0001$) than patients in the general isolation ward. Similarly, patients on mechanical ventilation, CVD, or stroke, were associated with a longer DoHS than those without these disorders (estimate = 5.65, $$p \leq 0.0011$$; 2.19, $$p \leq 0.04$$; and 5.6 days, $$p \leq 0.02$$, respectively). We also discovered that patients who received systemic intravenous steroids had a longer DoHS (estimate = 1.75, $$p \leq 0.02$$).
## 3.10. The Outcome of COVID-19 Patients with Severity Level, Age, and Sex
Nearly $97.5\%$ of the patients were cured and discharged, while $2.5\%$ succumbed to the disease. As displayed in Table 9 and Figure 6, this $2.5\%$ comprised elderly males diagnosed with severe disease on admission.
## 4. Discussion
In our study, we explained the different clinical factors, co-morbidities, and severity of 443 documented patients with COVID-19 at KAAUH, a JCIA- and CBAHI-accredited MOH hospital in Riyadh, Saudi Arabia, along with predictors of severity and characteristics associated with more extended hospital stays. Hospital accreditation generates a positive impact on most patient safety indicators and thereby is one of the driving forces towards improving quality healthcare in KSA. The Riyadh region is the most COVID-19-infected province of Saudi Arabia, according to the MOH-launched “COVID-19 Statistics E-Platform” [21]. One of the limitations of the previous studies is the main focus on midlife adults and older adults aged 60 and above. However, further subgroup analysis of elderly patients is present in very few studies; thereby, exploring the age-related clinical features of COVID-19 across all age groups is a matter of interest for study. Another typical limitation in earlier research works was the insufficient or missed information about the healthcare accreditation standards of the hospitals studied. In our study, the early midlife age range of 30–39 years comprised one-fourth of our patients, followed by younger patients who comprised one-fifth ($19.86\%$), while the remaining one-fourth were 40–49 years ($18.96\%$) and 50–59 years ($12.87\%$), as depicted in Figure 1. The previous reports showed similar results: the middle-aged “40 to 60 years” were the most commonly infected group [22,23,24]. While only one-tenth of patients were in the ranges of 70–79 years and 80 and above. However, individuals of all age groups can be infected by the virus [23,25]. Nearly two-thirds of the patients were female ($65.4\%$) and the remaining one-third ($35\%$) were male, with a mean age of 45.7 years (SD, ±17.2). However, a single-arm meta-analysis indicated that men represent a significantly higher percentage of COVID-19 patients at $60\%$ ($95\%$ CI [0.54, 0.65]) [26], and other previous studies have also reported a significantly higher number of men having the infection compared to women [22,27]. In addition, previous studies have recorded more men than women infected with other coronavirus infections, such as SARS-CoV [28] and MERS-CoV [29]. The disproportionate number of female hospitalizations in the present study can be due to the greater input of female patients from the affiliated women’s university (PNU).
Our findings indicated that a fever ($55.76\%$), a cough ($53.27\%$), and dyspnea (shortness of breath, $37.92\%$) were the most frequent symptoms in COVID-19 patients, which is in accordance with multiple earlier studies [30,31,32,33]. These symptoms were more commonly found in older patients of 60–69 years old, 70–79 years old, or 80 years old and above, which were statistically significant compared to younger groups (Figure 2 and Table 3). Nearly one-fourth of patients complained of headache ($22.35\%$), diarrhea ($20.32\%$), or myalgia ($19.41\%$). It was noted that ageusia and abdominal pain were statistically higher in younger age groups than in older patients; however, their incidence was low (around $2\%$). Research evidence indicates that the disorder’s complete clinical characteristics are nonspecific and unclear, as the associated manifestations range from mild to severe, and resulting in death in several patients [10,34]. There were no differences between the sexes for most symptoms among the COVID-19 patients, except for the fever, which was higher in males than females ($$p \leq 0.0103$$, Table 3). Previous studies have consistently noted poorer outcomes in men in terms of morbidity and mortality, confirming the male sex as an independent risk aspect for COVID-19 [35,36,37]. The more robust innate and adaptive immune response in females can be attributed to numerous factors, though primarily to estrogen being immune strengthening as opposed to testosterone being immune suppressing [38].
It was found that diabetes was the most common co-morbidity ($27.6\%$) seen in the admitted COVID-19 patients of our study, followed by hypertension ($26.4\%$), which is comparable to the prevalence in the general population. Our findings revealed that, among different age clusters, many 60–69-years-old patients infected by SARS-CoV-2 had chronic underlying diseases, including hypertension ($8.8\%$), diabetes ($7.67\%$), and cardiovascular diseases such as congestive heart failure ($p \leq 0.0001$ ****) and hypothyroidism ($$p \leq 0.0481$$ *), which were statistically noteworthy, as depicted in Table 4 and Figure 3. It was also seen that diabetes and HTN were notably more common in ages “70–79” years ($4.74\%$, $5.42\%$), 50–59 years ($5.65\%$, $4.5\%$), and 40–49 years ($4.3\%$, $4.5\%$). This agrees with an earlier report in which COVID-19 patients were documented to have co-morbid disorders [22,39]. In addition, research data show that patients aged 60 years old and above are at a higher risk than children, who are less likely to have an infection or show mild to asymptomatic infections [40]. In addition, our study revealed that 60–$70\%$ of patients in the “70–79 years” and “80 and above” ranges were suffering from diabetes or hypertension, and nearly $55\%$ of each group were obese. The augmented risk of acquiring severe COVID-19 complications in older people with co-morbid conditions such as diabetes or hypertension has been well documented in the literature [22]. Previous studies from the United States, Italy, and China have noted that the diabetic population is at a more prominent risk for disease complications and infection susceptibility [41]. The most extensive study of COVID-19 cases (72,314 cases) from China showed a higher incidence of mortality among patients with diabetes and COVID-19 ($2.3\%$ without diabetes vs. $7.3\%$ with the disease) [42]. In contrast, the occurrence of asthma and other respiratory disorders was comparatively higher in the younger age group of 30–39 years (Figure 3 and Table 4). Therefore, identifying host risk characteristics associated with severe COVID-19 infections may allow the design of specific approaches to prevent and treat the disease [39].
There were differences among the sexes in co-morbidities among our cohort of admitted COVID-19-positive patients. It was seen that a higher percentage of males suffered HTN ($$p \leq 0.0119$$ *) and cardiovascular disease ($$p \leq 0.0467$$ *), while obesity (0.0467 *), hypothyroidism (0.0448 *), and vitamin D deficiencies (0.0235 *) were comparatively higher in females and were statistically significant, as displayed in Table 4. The lower vitamin D level has been invariably associated with an augmented risk of upper respiratory tract infections and pneumonia, secondary to weak immune systems and raised inflammatory cytokines [43]. Research studies also link vitamin D depletion to poor COVID-19 prognosis and mortality [44,45].
We found that $47\%$ of the patients were diagnosed as having mild, $25\%$ as moderate, $18\%$ as asymptomatic, and $11\%$ as having severe COVID-19, as depicted in Figure 5. Nearly $15\%$ of the patients were transferred to the ICU, which is sensitive to understanding the severity of the disease, wherein $50\%$ of them ($7\%$ of the total, $p \leq 0.0001$ ****) were elderly patients, followed by the 40–49 years age group ($3.4\%$ of the total), which was found to be statistically significant compared to the younger age group, as displayed in Table 5. Additionally, more males ($18.95\%$, $$p \leq 0.0417$$ *) were admitted to the ICU. Similar findings were reported in a systematic review and meta-analysis comprising seven studies (1813 COVID-19 cases with a more significant proportion of male patients), demonstrating that patients admitted to the ICU were older (mean age = 62.4 years) compared with non-ICU patients (mean age = 46 years). Additionally, they found that 1591 primarily older male patients with co-morbid diseases admitted to ICUs had moderate to severe ARDS [46,47]. In our study, pneumonia discovered on chest X-rays was seen in $48\%$ of patients, $18.08\%$ required oxygen support on arrival, and $4.07\%$ required intubation. In our study, oxygen on arrival was required more in the age group of 60–69 years, followed by 40–49 years, 50–59 years, and 70–79 years ($p \leq 0.0001$ ****), and was managed with systemic steroids ($11.29\%$, $p \leq 0.0001$ ****). Our study revealed that more patients aged 70–79 years and 60–69 years required intubation, which was statistically significant compared to younger patients ($$p \leq 0.002$$). Therefore, the exploration of age-related effects on COVID-19 severity helps us progress strategies required for developing the healthcare system in COVID-19-treating hospitals. In addition, more males ($18.95\%$, $$p \leq 0.0417$$ *) were admitted to ICU, and there was a statistical difference between the sexes. A higher percentage of males were given oxygen support on arrival ($$p \leq 0.0081$$ *) and treated with systemic steroids ($$p \leq 0.0294$$ *), while no difference in sexes was found for intubation. Similar findings were reported by Chen et al., who proposed that nCoV-2019 is more likely to infect elderly adult men with chronic co-morbidities due to these patients’ more vulnerable immune functions [22].
Furthermore, the analysis of two groups of mild and severe COVID-19 disease showed that a fever, a cough, and dyspnea were more common in severe disease cases. At the same time, diarrhea and ageusia were more common in mild disease cases (Table 6A–C). We used a multiple logistic regression and found that pneumonia discovered by chest X-rays and co-morbid conditions, such as cardiovascular disease, stroke, an ICU stay, and mechanical ventilation, were predictors of severity depicting statistically significant differences (Table 7). Guan et al. documented that patients with severe COVID-19 disease were older than those with non-severe disease, and any co-morbidity was more expected among patients with severe disease than those with non-severe COVID-19 disease [48]. Another study documented similar findings, wherein it was seen that older age and hypertension were independently associated with severe disease at admission [39].
The median period of hospital stay in our study was six days. A similar result was reported by Alwafi et al. ( range: 0–55 days) [49]. Alghamdi et al. presented that the DoHS in Saudi Arabia among COVID-19 patients ranged from 4 to 15.6 days [50]. We found that it was significantly longer in patients with severe disease who needed oxygen support or mechanical ventilation, as expected, and also that a longer stay occurred in patients with CVD or stroke who were administered systemic intravenous steroids, as depicted in Figure 6 and Table 8. Wang et al. reported similar results, showing that clinical severity was firmly correlated to the period of stay ($p \leq 0.01$) and that a longer DoHS was associated with patients with admission to a provincial hospital, 45 years of age or older, and severe illness [51].
Our analysis’ mortality rate for COVID-19 was $2.5\%$, proximate to the $3.4\%$ reported in the literature [30]. Previous studies found that the prevailing fatality rate of the disease was in the range of $3\%$ to $14\%$ [52]. In severe cases, the virus causes alveolar damage, leading to advanced respiratory failure, and causing death [34,53]. In our study, the mortality rate was relatively high among the elderly ($$p \leq 0.0006$$ *) (Figure 6, those admitted to the ICU ($p \leq 0.0001$ *), and patients with pneumonia on chest X-ray ($p \leq 0.0001$ ****), which resembles previous findings. Research evidence demonstrates that the aged and those with underlying chronic disorders produce severe and lethal respiratory failure because of alveolar injury from the virus [22,23,27]. In our study, the age groups of 70–79 years and 80 and above were significantly associated with in-hospital mortality. In addition, our study revealed that nearly $55.67\%$ of patients aged “70–79 years” and the majority ($78.5\%$) of patients aged “80 and above” were suffering from severe disease at the time of admission, and nearly $15\%$ of each group succumbed to the disease. This was compared to another study conducted by Abolfoutoh et al., wherein mortality was high among patients aged 70 and above [54]. A meta-analysis, including studies from different countries—fifty from China, three from the USA, and one each from Germany, Iran, Italy, Singapore, South Korea, and the UK—revealed that patients aged 70 years and above have a higher infection threat, severity, and mortality risk compared with patients younger than 70 years [55].
Moreover, ICU patients were more likely to receive prolonged treatment and mechanical ventilation. No independent mortality association was observed in the present study with any co-morbidities [55]. However, established risk factors such as CVD, lung infiltrates, and stroke were noted to be substantial risk factors for severe COVID-19 in our study. It nonetheless suggests that the increased risk for worse outcomes is the accumulative effect of clustering with cardiometabolic multimorbidity or chronic diseases, which increases complications [56,57]. Most of these characteristics have been connected to advancing acute respiratory distress syndrome (ARDS), secondary to SARS-CoV and MERS-CoV [58]. All these risk factors, together with DM and HTN, lead to worsening pre-existing chronic inflammation, progressing to cytokine storm and prompt impairment of the endothelial function if left untreated [59]. Additionally, previous studies indicated that HTN, renal failure, and CVD raised the death risk in COVID-19 cases [34,60,61,62]. Furthermore, ACE inhibitors as antihypertensive are linked between hypertension and COVID-19 severity as ACE2 serves a role in SARS infections [63]. Table 10 summarizes the important highlights of the current study in comparison to the available literature.
In comparing the death rate of COVID-19 to that of other coronaviruses, the available data demonstrate that COVID-19 infection resulted in a lower mortality rate than that documented for SARS ($9.60\%$) and MERS ($34.4\%$) [60]. Thus, comprehensive analyses of the pathogenic and virulence mechanisms of SARS-CoV, SARS-CoV-2, and MERS-CoV are required to demonstrate these deviations.
We acknowledge some limitations. Our findings are confined to the accuracy of medical record-keeping, given its retrospective design. In addition, the single-centric nature of the investigation limits the generalization of results to the general population as the study sample is limited to 1 hospital among 497 hospitals (of which 65 are accredited by the Ministry of Health). Therefore, the study sample is not fully representative of COVID-19 patients admitted to hospitals in Saudi Arabia during the study period, which directs future research toward multicentric studies and huge sample sizes. Despite these limitations, the results of the present study are strong and add significance to the literature on COVID-19 patients within the Arab region, as it is a study performed in a hospital with international accreditation in the capital city of KSA, in a region with the highest COVID-19 infection rate, is longer than one and a half years in duration, thoroughly describes hospitalized patients during earlier waves, and differentiates clinical characteristics based on sex, seven different age clusters from “18–29 years” to “80 and above”, severity and its predictors, duration of hospital stay, and outcomes.
## 5. Conclusions
In summary, this study, conducted in a JCI- and CBAHI-accredited MOH hospital in Riyadh, Saudi Arabia, discovered that COVID-19 patients were most likely to present with a mild fever, cough, and shortness of breath on admission. There was an increased disease severity rate in elderly male patients aged “70 and above” who were transferred to the ICU, showed pneumonia on a chest X-ray, required intubation, and had a higher incidence of various co-morbidities, such as hypertension and cardiovascular diseases. In contrast, younger female patients suffered from vitamin D deficiency, obesity, and hypothyroidism. Moreover, the older age groups of 70–79 years and 80 years and above were significantly associated with in-hospital mortality. The hospital’s preparedness and quality of care were reflected in the lower mortality rate and the average length of hospital stay. More extensive epidemiologic studies and randomized trials covering multiple institutions are needed to determine a more accurate in-hospital severity and mortality rate in the country. Therefore, age and sex must be considered when estimating the clinical findings, severity, and mortality of COVID-19. This may improve the management of potentially severe COVID-19 patients by ensuring appropriate resource allocations and putting forward preventative measures.
## References
1. Lu R., Zhao X., Li J., Niu P., Yang B., Wu H., Wang W., Song H., Huang B., Zhu N.. **Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding**. *Lancet* (2020.0) **395** 565-574. DOI: 10.1016/S0140-6736(20)30251-8
2. Bai Y., Yao L., Wei T., Tian F., Jin D.Y., Chen L., Wang M.. **Presumed Asymptomatic Carrier Transmission of COVID-19**. *JAMA* (2020.0) **323** 1406-1407. DOI: 10.1001/jama.2020.2565
3. Alhazzani W., Alshahrani M., Alshamsi F., Aljuhani O., Eljaaly K., Hashim S., Alqahtani R., Alsaleh D., Al Duhailib Z., Algethamy H.. **The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section**. *J. Infect. Public Health* (2022.0) **15** 142-151. DOI: 10.1016/j.jiph.2021.10.005
4. Alsofayan Y.M., Althunayyan S.M., Khan A.A., Hakawi A.M., Assiri A.M.. **Clinical characteristics of COVID-19 in Saudi Arabia: A national retrospective study**. *J. Infect. Public Health* (2020.0) **13** 920-925. DOI: 10.1016/j.jiph.2020.05.026
5. 5.
The Government of Saudi Arabia, Riyadh
Key Health Indicators 2021MOH PublicationsRiyadh, Saudi Arabia2021. *Key Health Indicators 2021* (2021.0)
6. Qureshi A.Z., Ullah S., Ullah R.. **The trend of hospital accreditation in the Kingdom of Saudi Arabia**. *Saudi Med. J.* (2012.0) **33** 1350-1351. PMID: 23232687
7. **Joint Commission International: Chicago, IL, USA**. (2023.0)
8. 8.
The Government of Saudi Arabia, Riyadh
The Kingdom of Saudi Arabia’s Experience in Health Preparedness and Response to COVID-19 PandemicMOH PublicationsRiyadh, Saudi Arabia2020. *The Kingdom of Saudi Arabia’s Experience in Health Preparedness and Response to COVID-19 Pandemic* (2020.0)
9. Khan A., Alsofayan Y., Alahmari A., Alowais J., Algwizani A., Alserehi H., Assiri A., Jokhdar H.. **COVID-19 in Saudi Arabia: The national health response**. *East. Mediterr. Health J.* (2021.0) **27** 1114-1124. DOI: 10.26719/emhj.21.048
10. 10.
WHO
WHO COVID-19 DashboardWorld Health OrganizationGeneva, Switzerland2020Available online: https://covid19.who.int/(accessed on 15 February 2023). *WHO COVID-19 Dashboard* (2020.0)
11. Ibrahim M.E., Al-Aklobi O.S., Abomughaid M.M., Al-Ghamdi M.A.. **Epidemiological, clinical, and laboratory findings for patients of different age groups with confirmed coronavirus disease 2019 (COVID-19) in a hospital in Saudi Arabia**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0250955
12. Chen R., Chen J., Meng Q.-T.. **Chest computed tomography images of early coronavirus disease (COVID-19)**. *Can. J. Anaesth. J. Can. D’anesthésie* (2020.0) **67** 754-755. DOI: 10.1007/s12630-020-01625-4
13. Zhou M., Zhang X., Qu J.. **Coronavirus disease 2019 (COVID-19): A clinical update**. *Front. Med.* (2020.0) **14** 126-135. DOI: 10.1007/s11684-020-0767-8
14. Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X.. **Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study**. *Lancet* (2020.0) **395** 1054-1062. DOI: 10.1016/S0140-6736(20)30566-3
15. Aljondi R., Alghamdi S., Tajaldeen A., Abdelaziz I., Bushara L., Alghamdi H.A., Alhinishi H., Alharbi B., Alshehri R., Aljehani A.. **Chest Radiological Findings and Clinical Characteristics of Laboratory-Confirmed COVID-19 Patients from Saudi Arabia**. *Med. Sci. Monit. Int. Med. J. Exp. Clin. Res.* (2021.0) **27** e932441. DOI: 10.12659/MSM.932441
16. Zeng H., Ma Y., Zhou Z., Liu W., Huang P., Jiang M., Liu Q., Chen P., Luo H., Chen Y.. **Spectrum and Clinical Characteristics of Symptomatic and Asymptomatic Coronavirus Disease 2019 (COVID-19) With and Without Pneumonia**. *Front. Med.* (2021.0) **8** 645651. DOI: 10.3389/fmed.2021.645651
17. Bairwa M., Kumar R., Beniwal K., Kalita D., Bahurupi Y.. **Hematological profile and biochemical markers of COVID-19 non-survivors: A retrospective analysis**. *Clin. Epidemiol. Glob. Health* (2021.0) **11** 100770. DOI: 10.1016/j.cegh.2021.100770
18. Ghaith M.M., Albanghali M.A., Aldairi A.F., Iqbal M.S., Almaimani R.A., AlQuthami K., Alqasmi M.H., Almaimani W., El-Readi M.Z., Alghamdi A.. **Potential Predictors of Poor Prognosis among Severe COVID-19 Patients: A Single-Center Study**. *Can. J. Infect. Dis. Med. Microbiol. J. Can. Des. Mal. Infect. Microbiol. Med.* (2021.0) **2021** 6656092. DOI: 10.1155/2021/6656092
19. Kantri A., Ziati J., Khalis M., Haoudar A., El Aidaoui K., Daoudi Y., Chikhaoui I., El Yamani K., Mouhaoui M., El Bakkouri J.. **Hematological and biochemical abnormalities associated with severe forms of COVID-19: A retrospective single-center study from Morocco**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0246295
20. von Elm E., Altman D.G., Egger M., Pocock S.J., Gøtzsche P.C., Vandenbroucke J.P.. **The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies**. *J. Clin. Epidemiol.* (2008.0) **61** 344-349. DOI: 10.1016/j.jclinepi.2007.11.008
21. **COVID-19 Statistics E-Platform**
22. Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y.. **Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study**. *Lancet* (2020.0) **395** 507-513. DOI: 10.1016/S0140-6736(20)30211-7
23. Feng Y., Ling Y., Bai T., Xie Y., Huang J., Li J., Xiong W., Yang D., Chen R., Lu F.. **COVID-19 with Different Severities: A Multicenter Study of Clinical Features**. *Am. J. Respir. Crit. Care Med.* (2020.0) **201** 1380-1388. DOI: 10.1164/rccm.202002-0445OC
24. Li Q., Guan X., Wu P., Wang X., Zhou L., Tong Y., Ren R., Leung K.S.M., Lau E.H.Y., Wong J.Y.. **Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia**. *N. Engl. J. Med.* (2020.0) **382** 1199-1207. DOI: 10.1056/NEJMoa2001316
25. Gao X., Yang D., Yuan Z., Zhang Y., Li H., Gao P., Liu X., Zhao W., Xiao T., Guan Y.. **Improving the early diagnosis of suspected patients with COVID-19: A retrospective study of 106 patients**. *J. Infect. Dev. Ctries.* (2020.0) **14** 547-553. DOI: 10.3855/jidc.12992
26. Li L.Q., Huang T., Wang Y.Q., Wang Z.P., Liang Y., Huang T.B., Zhang H.Y., Sun W., Wang Y.. **COVID-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis**. *J. Med. Virol.* (2020.0) **92** 577-583. DOI: 10.1002/jmv.25757
27. Xia W., Shao J., Guo Y., Peng X., Li Z., Hu D.. **Clinical and CT features in pediatric patients with COVID-19 infection: Different points from adults**. *Pediatr. Pulmonol.* (2020.0) **55** 1169-1174. DOI: 10.1002/ppul.24718
28. Channappanavar R., Fett C., Mack M., Ten Eyck P.P., Meyerholz D.K., Perlman S.. **Sex-Based Differences in Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Infection**. *J. Immunol.* (2017.0) **198** 4046-4053. DOI: 10.4049/jimmunol.1601896
29. Badawi A., Ryoo S.G.. **Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): A systematic review and meta-analysis**. *Int. J. Infect. Dis. IJID Off. Publ. Int. Soc. Infect. Dis.* (2016.0) **49** 129-133. DOI: 10.1016/j.ijid.2016.06.015
30. Guo Y.R., Cao Q.D., Hong Z.S., Tan Y.Y., Chen S.D., Jin H.J., Tan K.S., Wang D.Y., Yan Y.. **The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak—An update on the status**. *Mil. Med. Res.* (2020.0) **7** 11. DOI: 10.1186/s40779-020-00240-0
31. Jin Y.-H., Cai L., Cheng Z.-S., Cheng H., Deng T., Fan Y.-P., Fang C., Huang D., Huang L.-Q., Huang Q.. **A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)**. *Mil. Med. Res.* (2020.0) **7** 4. DOI: 10.1186/s40779-020-0233-6
32. Lake M.A.. **What we know so far: COVID-19 current clinical knowledge and research**. *Clin. Med.* (2020.0) **20** 124-127. DOI: 10.7861/clinmed.2019-coron
33. Liu K., Chen Y., Lin R., Han K.. **Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients**. *J. Infect.* (2020.0) **80** e14-e18. DOI: 10.1016/j.jinf.2020.03.005
34. Adhikari S.P., Meng S., Wu Y.J., Mao Y.P., Ye R.X., Wang Q.Z., Sun C., Sylvia S., Rozelle S., Raat H.. **Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: A scoping review**. *Infect. Dis. Poverty* (2020.0) **9** 29. DOI: 10.1186/s40249-020-00646-x
35. Galbadage T., Peterson B.M., Awada J., Buck A.S., Ramirez D.A., Wilson J., Gunasekera R.S.. **Systematic Review and Meta-Analysis of Sex-Specific COVID-19 Clinical Outcomes**. *Front. Med.* (2020.0) **7** 348. DOI: 10.3389/fmed.2020.00348
36. Jin J.M., Bai P., He W., Wu F., Liu X.F., Han D.M., Liu S., Yang J.K.. **Gender Differences in Patients With COVID-19: Focus on Severity and Mortality**. *Front. Public Health* (2020.0) **8** 152. DOI: 10.3389/fpubh.2020.00152
37. Strope J.D., Chau C.H., Figg W.D.. **Are sex discordant outcomes in COVID-19 related to sex hormones?**. *Semin. Oncol.* (2020.0) **47** 335-340. DOI: 10.1053/j.seminoncol.2020.06.002
38. Ortona E., Pierdominici M., Rider V.. **Editorial: Sex Hormones and Gender Differences in Immune Responses**. *Front. Immunol.* (2019.0) **10** 1076. DOI: 10.3389/fimmu.2019.01076
39. Shi Y., Yu X., Zhao H., Wang H., Zhao R., Sheng J.. **Host susceptibility to severe COVID-19 and establishment of a host risk score: Findings of 487 cases outside Wuhan**. *Crit. Care* (2020.0) **24** 108. DOI: 10.1186/s13054-020-2833-7
40. Velavan T.P., Meyer C.G.. **The COVID-19 epidemic**. *Trop. Med. Int. Health TMIH* (2020.0) **25** 278-280. DOI: 10.1111/tmi.13383
41. Luzi L., Radaelli M.G.. **Influenza and obesity: Its odd relationship and the lessons for COVID-19 pandemic**. *Acta Diabetol.* (2020.0) **57** 759-764. DOI: 10.1007/s00592-020-01522-8
42. Wu Z., McGoogan J.M.. **Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention**. *JAMA* (2020.0) **323** 1239-1242. DOI: 10.1001/jama.2020.2648
43. Weir E.K., Thenappan T., Bhargava M., Chen Y.. **Does vitamin D deficiency increase the severity of COVID-19?**. *Clin. Med.* (2020.0) **20** e107-e108. DOI: 10.7861/clinmed.2020-0301
44. Munshi R., Hussein M.H., Toraih E.A., Elshazli R.M., Jardak C., Sultana N., Youssef M.R., Omar M., Attia A.S., Fawzy M.S.. **Vitamin D insufficiency as a potential culprit in critical COVID-19 patients**. *J. Med. Virol.* (2021.0) **93** 733-740. DOI: 10.1002/jmv.26360
45. Singh S.K., Jain R., Singh S.. **Vitamin D deficiency in patients with diabetes and COVID-19 infection**. *Diabetes Metab. Syndr.* (2020.0) **14** 1033-1035. DOI: 10.1016/j.dsx.2020.06.071
46. Carfì A., Bernabei R., Landi F.. **Persistent Symptoms in Patients After Acute COVID-19**. *JAMA* (2020.0) **324** 603-605. DOI: 10.1001/jama.2020.12603
47. Jain V., Yuan J.M.. **Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: A systematic review and meta-analysis**. *Int. J. Public Health* (2020.0) **65** 533-546. DOI: 10.1007/s00038-020-01390-7
48. Guan W.-J., Ni Z.-Y., Hu Y., Liang W.-H., Ou C.-Q., He J.-X., Liu L., Shan H., Lei C.-L., Hui D.S.C.. **Clinical Characteristics of Coronavirus Disease 2019 in China**. *N. Engl. J. Med.* (2020.0) **382** 1708-1720. DOI: 10.1056/NEJMoa2002032
49. Alwafi H., Naser A.Y., Qanash S., Brinji A.S., Ghazawi M.A., Alotaibi B., Alghamdi A., Alrhmani A., Fatehaldin R., Alelyani A.. **Predictors of Length of Hospital Stay, Mortality, and Outcomes Among Hospitalised COVID-19 Patients in Saudi Arabia: A Cross-Sectional Study**. *J. Multidiscip. Healthc.* (2021.0) **14** 839-852. DOI: 10.2147/JMDH.S304788
50. Alghamdi S.. **Clinical characteristics and treatment outcomes of severe (ICU) COVID-19 patients in Saudi Arabia: A single centre study**. *Saudi Pharm. J. SPJ Off. Publ. Saudi Pharm. Soc.* (2021.0) **29** 1096-1101. DOI: 10.1016/j.jsps.2021.08.008
51. Wang Z., Liu Y., Wei L., Ji J.S., Liu Y., Liu R., Zha Y., Chang X., Zhang L., Liu Q.. **What are the risk factors of hospital length of stay in the novel coronavirus pneumonia (COVID-19) patients? A survival analysis in southwest China**. *PLoS ONE* (2022.0) **17**. DOI: 10.1371/journal.pone.0261216
52. Ryu S., Chun B.C.. **An interim review of the epidemiological characteristics of 2019 novel coronavirus**. *Epidemiol. Health* (2020.0) **42** e2020006. DOI: 10.4178/epih.e2020006
53. Alboaneen D., Pranggono B., Alshammari D., Alqahtani N., Alyaffer R.. **Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17124568
54. Abolfotouh M.A., Musattat A., Alanazi M., Alghnam S., Bosaeed M.. **Clinical characteristics and outcome of COVID-19 illness and predictors of in-hospital mortality in Saudi Arabia**. *BMC Infect. Dis.* (2022.0) **22**. DOI: 10.1186/s12879-022-07945-8
55. Pijls B.G., Jolani S., Atherley A., Derckx R.T., Dijkstra J.I.R., Franssen G.H.L., Hendriks S., Richters A., Venemans-Jellema A., Zalpuri S.. **Demographic risk factors for COVID-19 infection, severity, ICU admission and death: A meta-analysis of 59 studies**. *BMJ Open* (2021.0) **11** e044640. DOI: 10.1136/bmjopen-2020-044640
56. Apicella M., Campopiano M.C., Mantuano M., Mazoni L., Coppelli A., Del Prato S.. **COVID-19 in people with diabetes: Understanding the reasons for worse outcomes**. *Lancet Diabetes Endocrinol.* (2020.0) **8** 782-792. DOI: 10.1016/S2213-8587(20)30238-2
57. Li D., Wang D., Dai X., Ni Y., Xu X.. **Change of serum uric acid and progression of cardiometabolic multimorbidity among middle aged and older adults: A prospective cohort study**. *Front. Public Health* (2022.0) **10** 1012223. DOI: 10.3389/fpubh.2022.1012223
58. Liu J., Xie W., Wang Y., Xiong Y., Chen S., Han J., Wu Q.. **A comparative overview of COVID-19, MERS and SARS: Review article**. *Int. J. Surg.* (2020.0) **81** 1-8. DOI: 10.1016/j.ijsu.2020.07.032
59. Longo M., Caruso P., Maiorino M.I., Bellastella G., Giugliano D., Esposito K.. **Treating type 2 diabetes in COVID-19 patients: The potential benefits of injective therapies**. *Cardiovasc. Diabetol.* (2020.0) **19** 115. DOI: 10.1186/s12933-020-01090-9
60. Deng S.Q., Peng H.J.. **Characteristics of and Public Health Responses to the Coronavirus Disease 2019 Outbreak in China**. *J. Clin. Med.* (2020.0) **9**. DOI: 10.3390/jcm9020575
61. Pan F., Yang L., Li Y., Liang B., Li L., Ye T., Li L., Liu D., Gui S., Hu Y.. **Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): A case-control study**. *Int. J. Med. Sci.* (2020.0) **17** 1281-1292. DOI: 10.7150/ijms.46614
62. Yan Y., Yang Y., Wang F., Ren H., Zhang S., Shi X., Yu X., Dong K.. **Clinical characteristics and outcomes of patients with severe COVID-19 with diabetes**. *BMJ Open Diabetes Res. Care* (2020.0) **8** e001343. DOI: 10.1136/bmjdrc-2020-001343
63. Ferrando C., Mellado-Artigas R., Gea A., Arruti E., Aldecoa C., Bordell A., Adalia R., Zattera L., Ramasco F., Monedero P.. **Patient characteristics, clinical course and factors associated to ICU mortality in critically ill patients infected with SARS-CoV-2 in Spain: A prospective, cohort, multicentre study**. *Rev. Esp. Anestesiol. Reanim.* (2020.0) **67** 425-437. DOI: 10.1016/j.redar.2020.07.003
|
---
title: 'The Role of the COVID-19 Crisis in Shaping Urban Planning for Improved Public
Health: A Triangulated Study'
authors:
- Koudoua Ferhati
- Saliha Chouguiat Belmallem
- Adriana Burlea-Schiopoiu
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10000844
doi: 10.3390/ijerph20053804
license: CC BY 4.0
---
# The Role of the COVID-19 Crisis in Shaping Urban Planning for Improved Public Health: A Triangulated Study
## Abstract
This paper aims to assess the impact of the COVID-19 pandemic on the link between urban planning practices and public health. A triangulated study was conducted to gain a comprehensive understanding of the topic. The first phase consisted of semi-structured interviews with health and urban planning experts, which were analyzed with the aid of Artificial Intelligence tools. The second phase involved an on-site investigation in the city of Algiers, including a survey, site visits, and a thorough analysis of the master plan for land use and urban planning. The findings emphasize the critical importance of a comprehensive health-centric approach to city design, improved governance and management practices, community involvement, and political commitment to prioritize health in urban planning. Furthermore, the results proved a strong correlation between prioritizing public health in urban planning practices and residents’ satisfaction with the city’s response to the COVID-19 pandemic. In conclusion, it is necessary to consider public health as a priority in urban planning practices and as a need for all stakeholders to work towards a healthier and more equitable urban environment.
## 1. Introduction
The increasing rate of urbanization has raised concerns about urban areas’ environmental and health conditions. The challenges emphasize the need for safe distancing and precautionary measures in cities during and after the COVID-19 pandemic. A review of the existing literature and empirical evidence helps in understanding the impact of the COVID-19 pandemic on the relationship between public health and urban planning practices and guides the consideration of human health in city planning after the pandemic [1].
More information is needed to connect urban planning and public health in light of the consequences of COVID-19 in order to ensure adequate environmental conditions and create healthy cities, which has been an ongoing effort for nearly 30 years based on past research and practices, including sanitary surveys, park planning, and urban environment investigations [2,3].
A straightforward guide that outlines the factors impacting public health is necessary for policymakers and urban planners to understand the connection between shared urban planning and public health aspects [4]. The consideration of environmental and health factors as determinants of public health by all stakeholders has become crucial [5], especially in light of the COVID-19 pandemic. However, there need to be more explicit references in the literature and in the policy that addresses this topic. The impact of the pandemic on city health and well-being has been studied, and it has been found that changes in health and well-being were negative, especially in crowded neighborhoods [1]. The results show that urban planning practices need to be revised in the current circumstances, leading to changes in city management rules and urban planning, particularly in Algeria.
The pandemic has accelerated the shift towards remote work and online learning, significantly increasing sedentary behavior and screen time, which can negatively affect mental and physical health. Additionally, with the outbreak of COVID-19, cities worldwide have faced significant disruptions to daily life as governments implemented measures to slow the spread of the virus. These measures included the closure of schools, offices, and businesses, the cancellation of cultural events such as concerts and tradeshows, and bans on gatherings [6,7], which showed the importance of access to green and outdoor spaces for physical activity and mental well-being after passing this challenging era.
Three years after the COVID-19 pandemic, it is important to study the long-term impact of urban planning practices on public health. The pandemic has changed our understanding of what makes a city healthy; the focus is on access to healthcare facilities, clean air, and water, and adequate public spaces. Understanding how the pandemic has altered our perception of a healthy city is essential. Conducting a study on the impact of post-COVID-19 urban planning practices on public health can provide valuable insights into how urban design can promote healthy behaviors and reduce health disparities.
This paper raises the challenge of comprehending the effect of the pandemic on urban planning behavior and the relationship between urban planning practices and public health to offer clear guidance to engineers and policymakers. This study is innovative in its examination of the context of northeastern Algeria and its focus on identifying opportunities for improvement in urban spaces through the evaluation of green spaces, sewage treatment technologies, and the role of urban planners in promoting healthy cities. It bridges the gap between macro health-oriented strategies and actual managerial practices by examining the practical implementation of policies to enhance public health.
## 2. Theoretical Framework and Hypotheses Development
To understand how urban public health and urban planning might be affected by COVID-19 consequences, we need to have a clear understanding of the main elements in direct contact with the individual’s health, namely their daily activities, macro agendas affecting their lifestyle, and also the rudiments that shape the environment surrounding these individuals. Every urban element, idea, or practice has associated health issues [6].
According to Lazuardi et al. [ 8], there is a close relationship between health and urban planning; they highlight the role of public health indicators in cities in improving citizens’ psychological conditions. Giacoman and colleagues [9] found that urban planning practices such as increasing access to green spaces and promoting active transportation can positively affect public health outcomes, such as reducing rates of obesity and improving mental health. Braubach et al. [ 10] and EL helou [11] suggest that certain urban design elements, such as mixed land use and pedestrian-friendly streets, can improve public health outcomes, such as reducing rates of obesity and promoting physical activity.
Cain and colleagues [12] support their study that urban design practices that encourage walking and biking, such as providing safe and accessible pedestrian and bike infrastructure, can increase physical activity levels and reduce rates of obesity and other chronic diseases. In addition, Rowe and colleagues [13] found that some aspects of the built environment, such as access to green spaces and social cohesion, can positively affect mental health outcomes, such as reducing rates of depression and anxiety.
Many researchers [14,15,16] agree that there are different levels of influence, explained as follows: First, at the sanitary level, the rapid and dispersed urban growth in 19th-century cities due to fast industrialization practices and lack of epidemiological considerations in urban planning led to unhealthy living conditions and the importance of considering the impact of architectural spaces on public health [17]; the environment of the urban periphery can greatly impact the state of public health [18], which does not consider the illnesses’ special requirements.
The characteristics of the urban space are linked to negative health outcomes, such as health issues, disability, mental illness, and even mortality [19]. In addition, evidence shows that air and noise pollution from traffic, poor sanitary systems, and residential exposure to high traffic are shown to have negative health effects, including asthma, lung development, allergies, sleep disturbance, children’s cognitive development, and increased risk of hypertension and coronary heart disease [20,21,22,23].
Based on the above considerations, it is hypothesized that: The second level of influence on public health in urban environments is related to governance and the decision-making processes in urban planning. The design and planning of neighborhoods and cities can significantly impact residents’ mental and physical well-being. Jutraz and Kukec [24] suggest that neighborhoods can impact individual health by shaping behaviors and limiting resources, with proper facilities and resources such as parks and recreational centers promoting healthy behaviors and improving public health outcomes.
The availability of green spaces can also impact public health. For example, industrialization and technological advancements in the 19th and 20th centuries negatively impacted green space availability, which led to a decline in urban green spaces and negatively affected water quality and quantity [16].
Furthermore, modern planning practices have often ignored the importance of greenery in cities [25], but recent studies suggest that access to green spaces can positively impact mental and physical health [26,27].
Based on these considerations, it is hypothesized that: Policy implications in urban planning could be observed and evaluated in programs and initiatives provided through the last decade [28,29]. The Algerian government has shown a commitment to sustainable development over the years. Moreover, the government commitment was solidified in the aftermath of the United Nations Conference on Environment and Development in 1992, which emphasized the importance of sustainable policy orientations that prioritize the well-being of citizens and their harmonious relationship with nature [30,31]. The high council for the Environment and Sustainable Development was established in 1994 to oversee environmental protection and sustainable development efforts [32]. However, this council faced criticism for its lack of efficiency [33]. In 2002, the National Plan of Action for the Environment and Sustainable Development was created to improve the quality of the environment and urban life quality. The decree n°03-10 of 2003 established the fundamental principles and regulations of environmental management and encouraged public participation in environmental protection efforts [34]. In 2006, the Town Orientation Law was approved by the parliament to establish specific provisions for a sustainable development policy, and an international competition was launched to create a sustainable urban master plan for Algiers. The winning project, “Making *Algiers a* Green Metropolis of the Mediterranean”, will be implemented in 2035.
In the regulation context, in response to the critical environmental issues resulting from the intensive development strategy since the post-independence period, the Algerian government has developed a National Environmental Strategy. This strategy involves the creation of effective policies for the protection, enhancement, and regulation of environmental-related usage at various scales and in different management contexts, including land management, waste management, natural resource management, and energy control through the implementation of various laws and regulations, such as the 1983 Law on Environmental Protection, the 1987 Law on Territorial Planning, the 1990 Law on Land orientation, the 2001 Law on Sustainable Territorial Development, the 2002 Law on Coastal Protection and Valorization, the 2003 Law on Environmental Protection in the Context of Sustainable Development, and the 2004 Law on Major Risk Prevention, as well as laws on energy control and the promotion of renewable energy [35]. Finally, we hypothesized that: Based on the previous theoretical framework study [36,37], discussing the different elements and issues related to urban planning that might affect public health and the development of the three research hypotheses, we propose in Figure 1 the following study model:
## 3. Materials and Methods
A triangulated study that employs a mixed methodology approach was conducted in two phases. In the first phase, qualitative methods, including semi-structured interviews, were used to gain an in-depth understanding of the relationship between urban planning and public health after COVID-19, as viewed by health and urban planning professionals. The second phase involved a combination of quantitative and qualitative methods, including a survey, site visits, and document analysis to supplement the findings of the first phase. The triangulated study is a research strategy that employs multiple data sources, methods, theories, and/or researchers to address a research question, enhance the validity and credibility of the findings, and reduce the presence of any research biases in this paper.
## 3.1. Planning and Carrying out the Interview
There were three main phases for conducting the interview: the planning, the actual carrying out, and the analysis of the empirical material [38].
In the interview planning process, we had to make sure that the method was suitable to the study’s objectives and that it could answer the research question. According to the participatory design processes [39] and the evaluation and action research, the choice of research methods is always linked to the scope and objective of the study [40]. Since the primary study objective is to understand and have an impact on relations based on professional experience, local practices, or human constructions of the meaning of public and urban planning practices, the qualitative interview should be considered [41].
After ensuring that an in-depth interview is a suitable method for the research, it is time to ask the important questions: who, where, and what should be the topics to be included in the interview? *It is* essential to pick suitable informants when the research methodology is based on personal records [42]. The choice of participants for the semi-structured interviews was based on the criteria of expertise and relevance to the research topic. Managers, doctors, and engineers from urban planning and health administration were selected as participants because they have professional experience and knowledge in the areas of urban planning, public health, and governance and are therefore well-suited to provide valuable insight and perspectives on the northeastern part of Algeria: 09 wilayas, as shown in Figure 2.
The nine wilayas studied in the research showcase a diverse socio-economic profile, reflecting Algeria’s cultural and geographic richness. Algiers serves as the political and economic center, while Tizi *Ouzou is* a significant city in the Kabylie region known for its coastal forests. Constantine is the capital of eastern Algeria, and a commercial hub such as Annaba boasts large steel, food, and port industries. Despite having well-developed healthcare systems, these metropoles faced significant health challenges during the pandemic, including overcrowding and stress on healthcare systems. Guelma, Setif, Skikda, and Jijel are mixed-activity wilayas with a smaller population featuring agriculture and industry. Finally, El *Taref is* a smaller coastal city with a lower population size, agricultural economy, and cultural heritage. Before the pandemic, these cities faced complex public health issues such as air and water pollution and insufficient healthcare facilities and staff.
The sampling phase for the interviews was guided by the Hagaman and Wutich method [43], which recommended around 20 participants to identify new themes and saturate categories; ultimately, 12 interviews were conducted due to the non-availability of some participants. We designed the semi-structured interviews based on the themes from the theoretical framework: sanitary system, spaces management, and policy implementation-related questions (25 questions), enabling openly valid and reliable answers (Appendix A). The interviews were done in person and via an online platform (Google meet and zoom) between 7 September 2022 and 19 December 2022, depending on the ability of the participants and authors. The study conducted in-depth qualitative interviews with 12 participants, with varying lengths between 36 and 48 min; anonymity was ensured and consent was obtained before discussing public health, urban managerial planning strategies, and potential impacts.
The thematic analysis of the categorized questions was conducted using Generative Pre-trained Transformer (GBT) to identify patterns and trends in the interview data, which generated three main themes:Sanitary system and epidemiology. Healthy city, management, and green spaces implementation. Health integration in urban planning policy.
The themes were analyzed to extract qualitative data.
## 3.2. On-Site Investigations
The methodology involved a mixed-method approach. We used a combination of a survey, site visits, and an Urban Development Master Plan (PDAU) analysis.
A questionnaire (Appendix B) was designed using a 5-point Likert scale based on the theoretical framework and the semi-structured interview perspectives to conduct the survey. In addition to the survey, we conducted a site visit to investigate the main declared initiatives and to observe ongoing projects and initiatives. Then we analyzed Algiers city PDAU horizon 2035 to understand the urban planning practices from all perspectives.
The respondents were the citizens of Algiers, the capital and largest city of Algeria, which had undergone significant changes in its urban conditions before and during the COVID-19 pandemic. Pre-pandemic, Algiers was characterized by a rapidly growing population, an estimated 4.5 million residents in 2020, and a bustling commercial center. However, like many large cities, Algiers faced challenges related to the lack of appropriate accessible public spaces, sewage and water pollution problems, air pollution due to of the high industrial concentration and the considerable number of car users in the center of the city, and overcrowding in certain areas. During the pandemic, the city saw a significant decrease in population mobility and economic activity, with measures such as lockdowns and social distancing regulations implemented to slow the spread of the virus.
A random sampling method was used to select 200 participants from the population, and we identified 112 valid and complete questionnaire responses. The sample was chosen to be representative of the population in terms of demographic characteristics such as age, gender, occupation, and education level.
The survey was administered in person to the selected participants. We made sure to explain the purpose of the survey and ensure that the participants understood the questions before they began answering.
A categorical data analysis was conducted to examine the relationship between variables. The analysis included the use of cross-tabulations, the chi-squared test of independence, and Kendall’s Tau B correlation analysis.
The cross-tabulation, also known as contingency tables, was used to determine the variables’ frequency distribution and test the relationship between two categorical variables. The chi-squared test of independence was used to determine whether the frequency distribution of one categorical variable is independent of the frequency distribution of another categorical variable. Finally, Kendall’s Tau B correlation analysis was used to determine the association between two ordinal categorical variables. This method allowed us to examine the relationship between different variables in each hypothesis to provide insight into their association and dependence.
In order to validate the findings from the survey and to investigate the main declared initiatives and ongoing projects in Algiers that reflect the COVID-19 effect on urban behaviors in the city, we conducted a series of site visits. To summarize the findings of our on-site visits, we employed a statistical approach by calculating the mean completion percentages for each initiative. This was achieved by summing the completion percentages and dividing them by the number of initiatives, as detailed in Table 4. Our estimation of the progress of each initiative was based on a combination of our judgment and observed documents, where we observed the ongoing activities and their progress, and then conducted informal interviews with government officials and members of civil society to gather additional information about the status of initiatives and projects’ execution; we also analyzed available data and documents when needed. These sources of information were used to estimate the completion percentages for each initiative and record them in our findings.
In this step, we analyzed the main urban planning tool for Algiers: PDAU horizon 2035 [44]. We examined and categorized the content from the presentation and components, pressures, opportunities, risks, and emerging problems, and synthesized the key points in a SWOT matrix used for identifying and analyzing an initiative’s strengths, weaknesses, opportunities, and threats.
## 4.1. The Semi-Structured Interview
For the purpose of analysis, a code was assigned to each of the twelve interviewees as follows: I#1, I#2, I#3, I#4, I#5, I#6, I#7, I#8, I#9, I#10, I#11, and I#12. In Figure 3, the variables of age and experience are presented on the Y axis (in years), with interviewees I#1 to I#12 shown on the X axis. The age range varied between 32 to 61 years, with a mean age of 45.83, and most of the interviewees were in the age range between 37 and 52. The experience variable ranged from 7 to 38, with a mean of 18.58. Similarly, when we look at the frequency of the experience variable, most interviewees had between 11 and 24 years of experience, with only one interviewee at each level of experience. Males (9 males and 3 females) were predominant in the gender distribution among the interviewees because of the specificity of the field of activity. This data provides a general overview of the demographic characteristics of the interviewees and can be used to understand the background and qualifications of the interviewees.
In this phase, 12 interview answers were analyzed to investigate the perceptions of urban planners, managers, and public health professionals on the impact of managerial urban planning practices on public health. The results were grouped into three main themes.
## 4.1.1. Sanitary System: Sewage, Toxification, Trash, and Epidemiology Theme
The results from the analysis of the interviews from the first theme are presented and interpreted in Table 1.
## 4.1.2. Healthy City, Management, and Governance Theme
The results from the analysis of the interviews from the theme management, governance, and leadership skills for the identification of the healthy city concept are presented and interpreted in Table 2.
## 4.1.3. Health Integration in Urban Planning Policy Theme
The results from the analysis of the interviews from the third theme, integrating health concepts in the urban planning process, are presented and interpreted in Table 3.
## 4.2. On-Site Investigation
The demographic analysis of the sample of 112 survey respondents provides insight into the characteristics of the population surveyed. The gender distribution of the respondents is notably skewed, with $67\%$ identifying as male and $33\%$ identifying as female. The respondents’ ages range from 36–55 years old, and the preponderant are those between 36–45 years old ($28.6\%$).
## 4.2.1. The Survey
Regarding occupation, the sample comprises $47.3\%$ of respondents working in the public sector, $15.2\%$ are from the private sector, $16.1\%$ are retired, $6.3\%$ are self-employed, and $15.2\%$ are students.
Regarding educational attainment, the sample is relatively well-educated, with $55.4\%$ of respondents holding a university degree, $13.4\%$ having post-graduate education, $20.5\%$ having a high school education, and $10.7\%$ having less than a high school education.
Categorical data analysis: Before performing categorical data analysis in SPSS, we ensured that the data met specific requirements. Firstly, the data should be in a format appropriate for the tests. This typically means that the data should be in a categorical format; in our case, it is ordinal. Additionally, it is important to check for missing data and ensure no missing values in the analyzed variables.
The test results on satisfaction with the city’s response to COVID-19 with the city’s public health system accessible to all residents. The case processing summary shows no missing data, with all 112 respondents included in the analysis.
The distribution of responses for both variables, with the chi-square tests indicating a statistically significant association between the two variables ($p \leq 0.001$) through Pearson Chi-Square, Likelihood Ratio, and Linear-by-Linear Association.
The symmetric measures of Kendall’s tau-b and Spearman Correlation were used to examine the ordinal association between the two variables and both measures yielded high values of 0.698 and 0.772, respectively, which are statistically significant ($p \leq 0.001$) and indicate a strong ordinal association between the two variables. The data suggest a statistically significant and robust association between city residents’ satisfaction with the city’s response to COVID-19 and their perceptions of the city’s public health system’s accessibility and preparation for future epidemics.
The test results addressed satisfaction with the city’s response to COVID-19 and with the city’s public health system’s preparation for future epidemics. The crosstab table shows the count of responses for satisfaction with the city’s response to COVID-19 and the city’s public health system preparation for future epidemics. The chi-square tests indicate a significant association between these two variables (with a p-value less than 0.001). The symmetric measures (Kendall’s tau-b, Spearman Correlation, and Pearson’s R) also show a strong positive correlation between the two variables, with values of 0.741; 0.816; and 0.789, respectively. These results suggest that as the city’s public health system prepares for future epidemics, satisfaction with the city’s response to COVID-19 also increases.
For the second research hypothesis, the results of the tests addressed satisfaction with the city’s efforts to promote a healthy lifestyle with the city’s management level for green spaces. The first set of results is a crosstabulation and chi-square analysis examining the relationship between satisfaction with the city’s response to the COVID-19 pandemic and the city’s public health system’s preparation for future epidemics. The chi-square tests indicate a statistically significant association between the two variables ($p \leq 0.001$), with the largest expected count being 8 and the smallest expected count being 1.13. Additionally, the symmetric measures (Kendall’s tau-b, Spearman correlation, and Pearson’s R) all indicate a strong correlation between the two variables, with Kendall’s tau-b, spearman correlation, and Pearson’s R coefficient of 0.741; 0.816; and 0.789, respectively.
The second set of results is a crosstabulation and chi-square analysis examining the relationship between satisfaction with the city’s efforts to promote a healthy lifestyle, the city’s management level for green spaces, and the importance of green space accessibility. The chi-square tests indicate that there is a statistically significant association between the two variables ($p \leq 0.001$), with the largest expected count being 47 and the smallest expected count being 0.64. Additionally, the symmetric measures (Kendall’s tau-b, Spearman correlation, and Pearson’s R) all indicate a strong correlation between the two variables, with Kendall’s tau-b, spearman correlation, and Pearson’s R coefficient of 0.836; 0.882; and 0.916, respectively.
The crosstab shows the counts of responses for the two variables “satisfaction with the city’s efforts to promote a healthy lifestyle” and “the importance of green spaces accessibility” on a 5-point Likert scale. The chi-square tests indicate a significant association between the two variables, as the p-values are less than 0.001 for all three tests (Pearson Chi-Square, Likelihood Ratio, Linear-by-Linear Association). The symmetric measures section provides information on the strength and direction of the association between the two variables, with all three measures (Kendall’s tau-b, Spearman Correlation, Pearson’s R) showing a strong positive association (values ranging from 0.699 to 0.804) and significant p-values (less than 0.001). The N of valid cases is 112. Overall, the results suggest that there is a significant positive association between satisfaction with the city’s efforts to promote a healthy lifestyle and the importance of green space accessibility.
Results for statistical analysis of the third hypothesis show the frequency of responses for the two variables being studied: “satisfaction with health and safety measures implemented in the city after COVID-19” and “the city’s urban planning policies level promoting health and well-being”. The chi-square tests determine if there is a significant association between the two variables. The Pearson Chi-Square, Likelihood Ratio, and Linear-by-Linear Association tests were significant, with p-values less than 0.001.
The symmetric measures are used to quantify the strength of the association between the two variables. The ordinal-by-ordinal measures, Kendall’s tau-b, and Spearman Correlation indicate a strong association between the two variables, with values of 0.811 and 0.862, respectively. The interval-by-interval measure, Pearson’s R, also indicates a strong association with a value of 0.844.
The results of the following two variables show a significant association between satisfaction with the health and safety measures implemented in the city after COVID-19 and the effectiveness of management addressing public health concerns related to COVID-19. Moreover, the p-values are <0.001 for all three chi-square tests (Pearson Chi-Square, Likelihood Ratio, Linear-by-Linear Association) and all three symmetric measures (Kendall’s tau-b, Spearman Correlation, and Pearson’s R). The chi-square tests and symmetric measures provide measures of the strength and direction of the association between the two variables. For example, Kendall’s tau-b, Spearman Correlation, and Pearson’s R coefficient are 0.889; 0.912; and 0.908, respectively, all of which are close to 1 and indicate a strong positive association between the two variables.
## 4.2.2. On-Site Visit
The data concerning the public health situation in the city of Algiers after COVID-19 were collected from the interviews and revealed ongoing efforts in various domains, including sanitary systems and epidemiology, water quality, toxic exposure risk, waste management, efficient land utilization, and green space administration. To validate these findings, a series of site visits were conducted, including the examination of VRD (Roads and various networks) plans, observations of construction sites, systematic analysis of potable water, assessments of pipeline conditions during precipitation, interactions with corporations and citizens, and inspections of public parks. The key initiatives spotted are presented in Figure 4.
The findings indicate that the efforts to prevent sewage mishaps and maintain the sewage network are underway and currently stand at $40\%$ completion, which included operational measures to clean sewer systems, address malfunctioning systems, and repair existing breakdowns. The quality of water was found to comply with established standards. Upgrading outdated pipelines and the maintenance of access points is ongoing at $60\%$ completion. The measures implemented include cleaning and maintaining 696,751 gutters and stormwater channels, essential for proper water drainage, and removing over 400,000 tons of mud and waste. The mitigation of toxic exposure risk is underway at $30\%$. The collection and disposal of waste are being handled by a private company and the government and are currently at $40\%$ completion. The incorporation of new technologies in waste management is underway at $30\%$.
Additionally, the transformation of the Oued S’mar public landfill into a public garden has been fully completed. Furthermore, the renovation and creation of green spaces across all districts of Algiers are in progress and stand at $20\%$ completion, although we found gaps in the availability and accessibility of green spaces in different parts of the city. Finally, the Bainam forest tree plantation initiative is underway at $90\%$ completion after its severe degradation over the last ten years due to several reasons such as fires and natural causes.
When discussing with citizens during our investigation, we also observed the launch of awareness campaigns in partnership with members of civil society, which focus on the role of citizens in preserving the environment and enhancing public health conditions. As a result, on average, $58\%$ of the areas inspected have been found to validate the initial findings. The detailed steps of the investigation are presented in Table 4.
## 4.2.3. Document Analysis: PDAU Horizon 2035
A rigorous diagnostic method, accompanied by continuous communication with local authorities and sectoral institutions, was ensured to deeply understand the territory and its specific realities for making this document analysis that englobes all the parts of the PDAU for the identification of the conditions, options, and principles established by the new territorial management tool that should guide and structure the development of the territory of the wilaya of Algiers for the next 12 years. We synthesized the main strengths, opportunities, weaknesses, and threats from the analysis in a SWOT matrix as shown in Figure 5 below.
Since independence, Algiers has faced a demographic explosion which resulted in infrastructure construction without regard for coherence, sustainability, or public health considerations. To address this issue, the Algerian government has decided to develop a strategic plan for the city (in 2016) to become a flagship city, safe for its residents, and competitive for economic agents, with good sustainable urban governance.
Therefore, the PDAU analysis is structured as follows:Presentation and Components: The master plan of the PDAU in Algiers has six pillars that form the foundation for the future of Algiers, intending to become a reference in the Mediterranean and the world:Economic Development, Competitiveness, and Employment. Opening the city to the World and Internationalization. Territorial Cohesion, Social Cohesion, and Housing. Environment, Protection, and Enhancement. Territorial Model. Governance.
These pillars are materialized through 82 key projects corresponding to concrete intervention proposals and provide substance to the territorial model proposed in the master plan. The key projects include the Port of Algiers, the Logistics Corridor of Ezzouar and Bab Ezzouar, the Hussein Dey/Mohammedia Seashore, the Central Station of Algiers, the Complementary Network of Industrial and Service Activities, the Faculty of Medicine, the Faculty of Law, the Douera Stadium, the participation of the private sector in residential production, the improvement of conditions for businesses and the banking sector, the revitalization and commercial upgrading of Algiers, the revitalization of urban core areas and agricultural activity, El Harrach Park and El Hamiz Park, the regional energy network of Algiers, the regional communication systems, e-wilaya strategy, the management of public space, and several ports and universities.
The report analyzes the current pressures faced by Algeria. The economy relies heavily on tertiary activities and is struggling with a $20\%$ unemployment rate and a growing informal economy. The country is also facing an economic crisis and a slow population growth rate, with $70\%$ of its population under the age of 40, and an increasing demand for housing, equipment, and infrastructure. Housing conditions are precarious, with informal housing being reconstructed, and there is a new migration phenomenon due to factors such as climate change, economic challenges, and geopolitical issues.
Urbanization is a significant challenge, with a $94\%$ urbanization rate and a shortage of urbanized land leading to food insecurity. The country also faces water stress, with only 160 L per capita per day, and energy security risks, with an average of 3150 kWh per household per year. Finally, climate change poses significant risks, including a temperature increase of +2° by 2030, a sea level rise of +16 cm, a decrease in rainfall of −10–$15\%$, increased drought, and erosion.
Algeria is facing urbanization issues, excessive consumption of natural resources, degradation of the environment, and transportation problems. The Algerian economy faces structural weaknesses such as a lack of diversification and integration into the global economy, dependence on international markets (hydrocarbons and agriculture), low human resources qualifications, technological innovation lag, and a lack of infrastructure to support economic activities. The challenges of sustainable development, based on the three pillars of efficient economic development, social equity, and environmental sustainability, are constantly being postponed due to various pressures.
However, with its excellent geographical location, Alger enjoys a rich cultural and natural heritage. The country and its capital are increasingly attracting foreign direct investment, and with its rich natural resources (energy and minerals) and economic stability, Algeria has a favorable industrial potential supported by public investment in infrastructure to support economic activity. The youth of its population provide Algeria with a promising future and a good foundation for economic dynamism, provided it is trained, educated, and qualified. Therefore, it is important to prioritize sustainable development through the new economic, environmental, and social models stated in various national, regional, and sectoral plans to ensure a sustainable future. The various communities and sectors need to use all the tools developed at the highest level to address the existing problems in resource protection, environmental protection, and territorial planning. Agriculture is a valuable economic resource that should be preserved and developed sustainably, considering the climate and soil richness. Technological innovations should be introduced in agricultural production and processing to improve the profitability of the sector and the utilization of its resources. The export potential of agricultural products opens up prospects for this sector, especially for specific products with natural comparative advantages and growing demand. Urbanization operations should maintain strong agricultural dynamics, mainly when they spread to fertile areas of the territory. Although the equipment networks are generally well organized in terms of quantity and territory, they still need to be reorganized to better respond to social needs and the aspirations of the population and allow the emergence of a polycentric urban system. The road and highway network, well established in much of the wilaya of Alger territory, is overgrowing due to a vast construction and expansion program.
The public transportation network is a crucial aspect of the mobility sector, including different modes, their new paths, redirections, and requirements that have been highlighted in the PDAU as a crucial tool for territorial management and provides a vision for the city’s mobility plans involving stakeholders: first highways and expressways—equipped with characteristics to ensure optimal mobility and safety; main arteries—which form the leading urban network, complementing the first level. On these arteries, the traffic is mostly passing, and the first function is to serve the main generators and development poles of the region; secondary arteries have a similar function to the previous level but with less importance from a geographical point of view; and collector streets that locally important roads at the commune level. In complement to the road network, the mobility system envisages a three-level parking subsystem: park-and-ride lots, street parking (paid and non-paid), and off-street parking—accompanying parking lots. Finally, the mobility system considers the soft mode networks, i.e., the pedestrian and bike path networks.
The plan considered managing earthquakes and reducing seismic risk in the region, focusing primarily on the seismic vulnerability of buildings. Measures to improve seismic resistance in new construction, renovation of existing buildings to increase their resistance, restrictions on construction in high-risk areas, and preparation for earthquakes by securing unstable equipment, are all part of the plan. Additional studies may be conducted for high-risk seismic areas to further understand the risks and determine appropriate technical solutions, as mentioned in the document. The plan also includes measures to improve infrastructure, such as increasing open spaces and improving road connectivity. In addition, the plan provides guidelines for reducing the risk of landslides and falling rocks. The laws related to urban planning and development in Algeria, such as Law No. 90.29 and Law No. 04-05, also address these issues and identify high-risk areas and implement measures to mitigate the risks.
From the observed urban planning practices expressed in the document, it is evident that hard work has been dedicated to the document’s execution, which included many positive points in fields of land use, sustainable resources alternatives implementation, and mega projects planning, but some points present a weakness in the plan’s strategies. First, we are continuing with the existing urban development plans (POS—land use plan), while we should be transitioning to more comprehensive urban development projects (Projets Urbains de Développement). Second, we are limiting our focus to the wilaya, while the metropolitan area is already a functional entity and the Mediterranean network is becoming more important. Third, we are neglecting external pressures such as the climate, geopolitics, and resources, which significantly impact development. Fourth, we are comparing ourselves to other countries, while we should first focus on internal benchmarks between the different communes in Algiers. Finally, while the PDAU covers various aspects of seismic risk mitigation, it does not include any considerations for pandemics or other similar health crises. In today’s world, where pandemics like COVID-19 have profoundly impacted communities, it is important to include public health considerations in any risk mitigation plan. Failure results from a limited response to such crises and leaves communities vulnerable.
## 5. Discussion
The first theme of healthy cities and sanitary systems encompasses the issues of sewage, toxic materials, waste management, and epidemiology, which are of crucial importance in urban planning practices. The opinions and insights shared by the interviewees shed light on the current state of the sewage systems, industrial zones, and the associated risks to public health. The consensus among the interviewees is that there is a need for collaboration between the conservation and public health sectors to address these issues and to maintain public health and cleanliness in cities. While some interviewees believe that the risk of exposure to toxic materials and chemical mixtures in urban environments is relatively low, others consider it a significant cause of concern. In addition, the rapid development of industrial zones in some areas of Algeria presents difficulties for control agencies monitoring toxic waste data, thus emphasizing the importance of a well-functioning waste management system to ensure public health.
In addition, the interviewees emphasize the significance of preventive strategies in urban planning, particularly in light of the ongoing pandemic. They advocate for a shift in traditional planning approaches towards a more holistic, health-centric approach to city design to ensure public health and safety in urban areas. The importance of staying informed about new waste management technologies and their impact on public health is also noted.
In conclusion, the insights shared by the interviewees provide evidence for the critical role of the sanitary system in promoting public health in urban areas. Furthermore, their views highlight the need for a concerted effort between various stakeholders and the government to address these pressing issues.
Examining the interviewees’ viewpoints about the impact of urban planning practices on public health reveals several salient themes. Firstly, there is a perception of a disconnect between overarching health-oriented strategies and their implementation at the city level, requiring improved governance and management practices to bridge this gap. Secondly, the interviewees recognize the importance of collaboration between various sectors to achieve the goals of healthy cities, highlighting the need for clear plans, budgets, and a continuous approach.
Additionally, the qualifications and training of healthy urban planners emerged as a point of contention among the interviewees, with some perceiving a need for sustainability and green city expertise. In contrast, others acknowledged the vital role of qualified managers and urbanists in achieving healthy city objectives. The interviewees also emphasized the crucial role of community involvement in realizing healthy city goals, highlighting successful examples in Algeria, where cooperation and collaboration between the government and citizens have been instrumental in advancing the healthy city concept.
In conclusion, the collective views of the interviewees demonstrate the significance of urban planning practices that prioritize residents’ mental and physical well-being in achieving improved public health outcomes. In addition, the need for competent governance, effective managerial decisions, and community involvement is emphasized, further underlining the importance of prioritizing health in urban planning.
The participants highlighted the importance of taking a comprehensive approach to improving the health and well-being of urban communities. Moreover, they acknowledge the current limitations in urban planning, and the need for increased efforts to address the identified gaps in urban health. Also, they emphasized the need for a shared vision among stakeholders, policy decision-makers, and partners to achieve a healthier and more equitable urban environment. As a result, they advocate for establishing healthy urban planning as a norm and the need for evidence-based leadership and political commitment to bring about change in urban planning practices.
Furthermore, they discuss the significance of identifying urban health problems and the need for appropriate actions to address them. Finally, they recognize the importance of innovative technologies and nature-based solutions in promoting public health, and suggest that programs and initiatives aimed at sustainable development will positively impact public health.
In conclusion, the participants provide valuable insights into the need for a holistic and proactive approach to urban planning that prioritizes public health. Furthermore, the emphasis on the role of nature-based solutions, innovative technologies, and sustainable development highlights the potential for positive change in the urban environment.
The findings provide a snapshot of the socioeconomic profile of the respondents. The gender distribution shows a slight skew towards males, while the age distribution highlights a significant presence of individuals aged between 36 and 55. The occupation distribution highlights a majority of respondents working in the public sector, with a notable presence of students and self-employed. Furthermore, the educational attainment of the sample reveals a relatively high level of education among respondents, with the majority holding a university degree.
The results from the first hypothesis-related variables provide strong evidence that implementing urban planning practices that promote improved sanitary systems has a significant positive impact on public health outcomes, as measured by residents’ satisfaction with the city’s response to the COVID-19 pandemic. Through various statistical tests, we have determined a statistically significant and strong association between residents’ perceptions of the city’s public health system’s accessibility and preparation for future epidemics and their satisfaction with the city’s response to the COVID-19 pandemic.
This highlights the importance of effective public health measures in fostering positive perceptions of government response during times of crisis. The city’s public health system’s preparation for future epidemics is critical in ensuring that residents feel satisfied with the city’s response to the COVID-19 pandemic. Our results suggest that the city’s public health system preparation after COVID-19 for future epidemics positively impacts the public health outcome.
The results from the second hypothesis-related variables, including chi-square tests and symmetric measures, indicate a statistically significant and strong association between satisfaction with the city’s efforts to promote a healthy lifestyle, the city’s management level for green spaces, and the importance of green space accessibility. These findings suggest that the city’s efforts to promote a healthy lifestyle, such as through accessible green spaces, significantly impact residents’ perceptions of their overall well-being. This highlights the crucial role that urban planning practices can play in fostering positive public health outcomes, particularly in the context of the ongoing COVID-19 pandemic and its impact on mental and physical health, which provides strong empirical evidence supporting the research, which posits that the implementation of urban planning practices that prioritize residents’ mental and physical well-being post-COVID-19 has a significant positive impact on public health outcomes.
The results from the third hypothesis-related variables provide evidence of a significant positive association between policy initiatives and programs that support sustainable development and improve the urban environment and their impact on public health outcomes. The use of chi-square tests and symmetric measures such as Kendall’s tau-b, Spearman correlation, and Pearson’s R all indicate a strong positive correlation between the studied variables. Specifically, the analysis shows a strong association between satisfaction with health and safety measures implemented in the city after COVID-19 and the city’s urban planning policies’ level of promoting health and well-being, as well as between satisfaction with health and safety measures implemented in the city after COVID-19 and the effectiveness of management in addressing public health concerns related to COVID-19. These findings suggest that cities prioritizing sustainable development and improving the urban environment through policy initiatives and programs positively impact public health outcomes, particularly during the COVID-19 pandemic.
The site visits investigation aimed to examine the public health situation in Algiers and the urban planning practices after the COVID-19 pandemic, as declared in the semi-structured interviews’ responses from two perspectives:Sanitary System and Epidemiology: Results revealed ongoing efforts in various domains of sanitary systems and epidemiology from many levels, such as the efforts to prevent sewage mishaps and maintain the sewage network, including operational measures to clean sewer systems, address malfunctioning systems, repairing existing breakdowns, enhancing the quality of potable water, upgrading the outdated pipelines, and maintaining its access points. The mitigation of toxic exposure risk is also ongoing in the city’s industrial zone. Waste collection and disposal are being handled by a private company and the government incorporating new technologies and sorting strategies, which supports that there is a positive effect of the pandemic on strengthening the link between urban planning and public health in Algiers. Healthy City, Management, and Green Spaces Implementation: The results suggest that efforts to improve and maintain green spaces are increasing. Nevertheless, disparities in the availability and accessibility of green spaces were identified across the city. The Bainam forest tree plantation initiative, which had suffered severe degradation in the past decade due to fires and natural conditions, is progressing well and is nearly complete. The launch of awareness campaigns in partnership with civil society, aimed at raising awareness about the role of citizens in preserving the environment and improving public health, was noted during the investigation. Furthermore, the transformation of the Oued S’mar public landfill into a public garden has been completed. These findings highlight the positive impact of the COVID-19 pandemic on the relationship between city management and public health outcomes.
The COVID-19 pandemic has shaped new behaviors in urban planning to promote public health. The study’s findings indicate ongoing efforts in various domains to improve public health conditions and address the challenges posed by the pandemic, even if it is still at the modest progress rate of $58\%$ of the required degree, which sheds light on the gap between the macro health-oriented strategies and actual managerial practices and the need to change its strategies.
The document analysis of the PDAU (Master Plan of Algiers) highlighted the various challenges and opportunities faced by the city in its efforts to achieve sustainable urban planning practices. The city is grappling with issues such as urbanization, excessive consumption of natural resources, degradation of the environment, transportation problems, and economic structural weaknesses, such as a lack of diversification and infrastructure. Despite these challenges, the city has several advantages: its favorable geographical location, rich cultural and natural heritage, rich natural resources, and a young, dynamic population. The Master Plan of Algiers seeks to address these challenges through six pillars, including economic development, territorial cohesion, environment, territorial model, and governance, and through 82 key projects to improve the city’s infrastructure, housing, and public health. The SWOT matrix helped us to identify the strengths, weaknesses, opportunities, and threats of the urban planning document, which gave us a clear understanding of the actual current urban planning practices and allowed us to locate public health considerations in the city’s Physical Development and Urbanism plan (PDAU).
The city must prioritize sustainable models, including economic, environmental, and social models, and preserve and develop its agricultural sector. The urbanization of Algiers must be carefully planned so as not to compromise its agricultural dynamics; additionally, it should aim to respond to the needs and aspirations of its population.
According to the investigation done after the classification of the interview’s findings, we found that while the relevant laws, regulations, and plans related to sustainable development and environmental protection, such as the National Plan of Action for the Environment and Sustainable Development and the Town Orientation Law, were in place, their implementation and enforcement on-site were not always consistent. On the other hand, a new policy announced by the Algerian Ministry of Environment and Renewable Energy at the end of December 2022 has initiated, in conjunction with the healthcare sector, the creation of a report on the implementation of the Arab Strategy for Health and Environment 2017–2030.
In urban planning practices, it was observed that the importance of waste management was considered; however, a need for implementation and enforcement was identified. Moreover, we found that the formation and qualification of healthy city urban planners faced some difficulties as some things could have been improved in terms of sustainability and green city knowledge among the local urban administration.
The investigation on the implementation of the significant decisions, initiatives, and policies for enhancing public health after COVID-19 in Algeria revealed that current urban planning practices require improvement to align better with initiatives and policies aimed at promoting sustainable development and environmental protection.
On-site visits revealed positive progress in certain areas, such as transforming a public landfill into a public garden. Still, only $58\%$ of the defined initiatives and decisions were applicable. The study also highlighted the recent policy announced by the Ministry of Environment and Renewable Energy, which aims to create a report on the implementation of the Arab Strategy for Health and Environment and a new regulation for the management, protection, and development of green spaces.
In order to summarize and clarify the findings from all the discussion parts, we gathered in the table below all the study phases that contributed to either supporting or rejecting the research hypotheses (Table 5).
## 6. Conclusions
The relationship between urban planning practices and public health is crucial in shaping individuals’ well-being in urban spaces. The findings of this study, obtained through semi-structured interviews and a survey, emphasized the role of the COVID-19 pandemic in the shaping of a comprehensive, health-centric approach to city design, improved governance and management practices, community involvement, and political commitment to prioritize health in urban planning. The results showed a strong correlation between implementing urban planning practices prioritizing public health and residents’ satisfaction with the city’s response to the COVID-19 pandemic. The need for a proactive, preventive approach to addressing public health and environmental issues was highlighted rather than it being a reactive one. It is imperative that policymakers invest in advanced and effective sewage treatment technologies, upgrade existing sewage infrastructure, incorporate health considerations into urban planning policies, and promote sustainable development and eco-friendly practices. In addition, the availability and accessibility of green spaces and the implementation of healthy-urban planning concepts in universities should also be prioritized.
The findings emphasize the critical importance of sanitary systems, healthy city management and governance, and health integration in urban planning policy. In addition, the need for stakeholders to work together towards a healthier and more equitable urban environment was also emphasized. These insights highlight the significance of considering public health as a priority in urban planning practices and the need for a comprehensive long-term approach that considers the needs of both individuals and communities. In conclusion, urban planning practices are vital in promoting public health and ensuring the well-being of individuals living in urban spaces. Therefore, all stakeholders, including policymakers, city planners, and the general public, must work towards achieving a healthier and more equitable urban environment.
## References
1. Afrin S., Chowdhury F.J., Rahman M.. **COVID-19 Pandemic: Rethinking Strategies for Resilient Urban Design, Perceptions, and Planning**. *Front. Sustain. Cities* (2021) **3**. DOI: 10.3389/frsc.2021.668263
2. Tonne C., Adair L., Adlakha D., Anguelovski I., Belesova K., Berger M., Brelsford C., Dadvand P., Dimitrova A., Giles-Corti B.. **Defining pathways to healthy sustainable urban development**. *Environ. Int.* (2020) **146** 106236. DOI: 10.1016/j.envint.2020.106236
3. Rydin Y., Bleahu A., Davies M., Dávila J.D., Friel S., De Grandis G., Groce N., Hallal P.C., Hamilton I., Howden-Chapman P.. **Shaping cities for health: Complexity and the planning of urban environments in the 21st century**. *Lancet* (2012) **379** 2079-2108. DOI: 10.1016/S0140-6736(12)60435-8
4. Carmichael L., Townshend T.G., Fischer T.B., Lock K., Petrokofsky C., Sheppard A., Sweeting D., Ogilvie F.. **Urban planning as an enabler of urban health: Challenges and good practice in England following the 2012 planning and public health reforms**. *Land Use Policy* (2019) **84** 154-162. DOI: 10.1016/j.landusepol.2019.02.043
5. Barton H., Grant M.. **A health map for the local human habitat**. *Perspect. Public Health* (2006) **126** 252-253. DOI: 10.1177/1466424006070466
6. Yasin R., Jauhar J., Rahim N.F.A., Namoco S.I.O., Bataineh M.S.E.. **COVID-19 and religious tourism: An overview of impacts and implications**. *Int. J. Relig. Tour. Pilgr.* (2020) **8** 155-162. DOI: 10.21427/f4j9-cf82
7. Marinescu I.E., Popirlan C.-I.. **Assessment of GSM HF-Radiation Impact Levels within the Residential Area of Craiova City**. *Procedia Environ. Sci.* (2016) **32** 177-183. DOI: 10.1016/j.proenv.2016.03.022
8. Lazuardi S.N., Sulistyantara B., Pratiwi P.I.. **The Role of Home Gardens in Developing Cities for Improving Workers’ Psychological Conditions**. *J. Contemp. Urban Aff.* (2022) **6** 233-248. DOI: 10.25034/ijcua.2022.v6n2-9
9. Giacoman C., Herrera M., Arancibia P.A.. **Household food insecurity before and during the COVID-19 pandemic in Chile**. *Public Health* (2021) **198** 332-339. DOI: 10.1016/j.puhe.2021.07.032
10. Braubach M., Egorov A., Mudu P., Wolf T., Thompson C.W., Martuzzi M.. **Effects of Urban Green Space on Environmental Health, Equity and Resilience**. *Theory Pract. Urban Sustain. Transit.* (2017) 187-205. DOI: 10.1007/978-3-319-56091-5_11
11. EL Helou M.A.. **Shaping the City that Decreases Overweight and Obesity through Healthy Built Environment**. *J. Contemp. Urban Aff.* (2019) **3** 16-27. DOI: 10.25034/ijcua.2018.4697
12. Dixon B.E., Caine V.A., Halverson P.K.. **Deficient Response to COVID-19 Makes the Case for Evolving the Public Health System**. *Am. J. Prev. Med.* (2020) **59** 887-891. DOI: 10.1016/j.amepre.2020.07.024
13. Ashton P.M., Owen S.V., Kaindama L., Rowe W.P.M., Lane C.R., Larkin L., Nair S., Jenkins C., de Pinna E.M., Feasey N.A.. **Public health surveillance in the UK revolutionises our understanding of the invasive**. *Genome Med.* (2017) **9** 92. DOI: 10.1186/s13073-017-0480-7
14. Karmilah M., Puspitasari A.Y.. **The Impact of MCK+ Prangkuti Luhur towards the Improvement of Community Life Quality in Bustaman Village**. *J. Contemp. Urban Aff.* (2020) **4** 59-66. DOI: 10.25034/ijcua.2020.v4n2-6
15. Sima V., Gheorghe I.G., Subić J., Nancu D.. **Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review**. *Sustainability* (2020) **12**. DOI: 10.3390/su12104035
16. Burlea-Schiopoiu A., Ferhati K.. **The Managerial Implications of the Key Performance Indicators in Healthcare Sector: A Cluster Analysis**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9010019
17. Rosa-Jimenez C., Jaime-Segura C.. **Living Space Needs of Small Housing in the Post-Pandemic Era: Malaga as a case study**. *J. Contemp. Urban Aff.* (2021) **6** 51-58. DOI: 10.25034/ijcua.2022.v6n1-5
18. Kondo M.C., Fluehr J.M., McKeon T., Branas C.C.. **Urban Green Space and Its Impact on Human Health**. *Int. J. Environ. Res. Public Health* (2018) **15**. DOI: 10.3390/ijerph15030445
19. Alegría M., NeMoyer A., Bagué I.F., Wang Y., Alvarez K.. **Social Determinants of Mental Health: Where We Are and Where We Need to Go**. *Curr. Psychiatry Rep.* (2018) **20** 95. DOI: 10.1007/s11920-018-0969-9
20. Brunner P.H.. *Handbook of Material Flow Analysis* (2020)
21. Whitehouse A.. **Air pollution and me: What do we know and what should I do?**. *Physiol. News* (2021). DOI: 10.36866/122.30
22. Rojas-Quiroz J., Marmolejo-Duarte C.. **Determining Equality of Infection Rates: A Spatial Analysis of Factors Associated with the Spread of COVID-19 in Barcelona, Spain**. *J. Urban Plan. Dev.* (2022) **148** 05022026. DOI: 10.1061/(ASCE)UP.1943-5444.0000848
23. Münzel T., Sørensen M., Daiber A.. **Transportation noise pollution and cardiovascular disease**. *Nat. Rev. Cardiol.* (2021) **18** 619-636. DOI: 10.1038/s41569-021-00532-5
24. Jutraz A., Kukec A.. **New methods in teaching architecture and medicine students while designing quality living environment**. *Edulearn Proc.* (2016) **1** 7513-7521. DOI: 10.21125/edulearn.2016.0640
25. Colding J., Gren A., Barthel S.. **The Incremental Demise of Urban Green Spaces**. *Land* (2020) **9**. DOI: 10.3390/land9050162
26. Pratiwi P.I., Sulistyantara B., Sisriany S., Lazuardi S.N.. **The Psychological Effects of Park Therapy Components on Campus Landscape Preferences**. *J. Contemp. Urban Aff.* (2022) **6** 143-155. DOI: 10.25034/ijcua.2022.v6n2-3
27. Burlea-Schiopoiu A., Baldo M.D., Idowu S.O.. **The Spirit of Adventure: A Driver of Attractiveness of the Hospitality Industry for Young People during a Pandemic Crisis**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph19041913
28. Neirotti P., De Marco A., Cagliano A.C., Mangano G., Scorrano F.. **Current trends in Smart City initiatives: Some stylised facts**. *Cities* (2014) **38** 25-36. DOI: 10.1016/j.cities.2013.12.010
29. Chabbi-Chemrouk N.. **How Much Faith Should We Have in the ‘Algiers: Sustainable City’ Project? The Conversation**. (2019)
30. Leicht U., Heiss A., Won Jung Byun J.. *Issues and Trends in Education for Sustainable Development* (2018)
31. **United Nations Conference on Environment and Development: Rio Declaration on Environment and Development**. *Int. Leg. Mater.* (1992) **31** 874-880. DOI: 10.1017/S0020782900014765
32. Gherbi M.. **Problematic of Environment Protection in Algerian Cities**. *Energy Procedia* (2012) **18** 265-275. DOI: 10.1016/j.egypro.2012.05.038
33. Jerome A.. **Infrastructure, Economic Growth and Poverty Reduction in Africa**. *J. Infrastruct. Dev.* (2011) **3** 127-151. DOI: 10.1177/097493061100300203
34. **Law No. 03–10 Relating to the Protection of the Environment within the Framework of Sustainable Development**. (2003)
35. Boudjadja R., Boudemagh S.S.. **Efficacité Environnementale de La Politique et Du Droit Urbains Dans un Projet de Reconquête D’une Friche Urbaine à Constantine**. *Noteb. Politics Law* (2020) **160**. DOI: 10.35156/0492-012-003-012
36. Burlea-Schiopoiu A., Idowu S.O., Vertigans S., Idowu S.O., Vertigans S., Burlea-Schiopoiu A.. **Corporate Social Responsibility in Times of Crisis: A Summary**. *CSR, Sustainability, Ethics & Governance, Corporate Social Responsibility in Times of Crisis* (2017) 154-196. DOI: 10.1007/978-3-319-52839-7_14
37. Sall M.C.A., Burlea-Schiopoiu A.. **An Analysis of the Effects of Public Investment on Labor Demand through the Channel of Economic Growth with a Focus on Socio-Professional Categories and Gender**. *J. Risk Financial Manag.* (2021) **14**. DOI: 10.3390/jrfm14120580
38. DeJonckheere M., Vaughn L.M.. **Semistructured interviewing in primary care research: A balance of relationship and rigour**. *Fam. Med. Community Health* (2019) **7** e000057. DOI: 10.1136/fmch-2018-000057
39. Bogatinoska B., Lansu A., Hugé J., Dekker S.C.. **Participatory Design of Nature-Based Solutions: Usability of Tools for Water Professionals**. *Sustainability* (2022) **14**. DOI: 10.3390/su14095562
40. Mohajan H.K.. **qualitative research methodology in social sciences and related subjects**. *J. Econ. Dev. Environ. People* (2018) **7** 23-48. DOI: 10.26458/jedep.v7i1.571
41. Green J., Thorogood N.. *Qualitative Methods for Health Research (Introducing Qualitative Methods Series)* (2018)
42. De Massis A., Kotlar J.. **The case study method in family business research: Guidelines for qualitative scholarship**. *J. Fam. Bus. Strat.* (2014) **5** 15-29. DOI: 10.1016/j.jfbs.2014.01.007
43. Wutich A., Beresford M., SturtzSreetharan C., Brewis A., Trainer S., Hardin J.. **Metatheme Analysis: A Qualitative Method for Cross-Cultural Research**. *Int. J. Qual. Methods* (2021) **20**. DOI: 10.1177/16094069211019907
44. **Le Plan Directeur d’Aménagement Urbain d’Alger, Horizon 2035. Assemblée Populaire de Wilaya d’Alger**. (2016)
|
---
title: Patterns and Trends in Pharmacological Treatment for Outpatients with Postherpetic
Neuralgia in Six Major Areas of China, 2015–2019
authors:
- Gang Han
- Yun Han
- Lingyan Yu
- Yuhua Zhao
- Zhenwei Yu
journal: Healthcare
year: 2023
pmcid: PMC10000853
doi: 10.3390/healthcare11050764
license: CC BY 4.0
---
# Patterns and Trends in Pharmacological Treatment for Outpatients with Postherpetic Neuralgia in Six Major Areas of China, 2015–2019
## Abstract
The aim of this study was to assess the patterns and trends of pharmacological treatment for outpatients with postherpetic neuralgia (PHN) in China in the period 2015–2019. Prescription data for outpatients with PHN were extracted from the database of the Hospital Prescription Analysis Program of China according to the inclusion criteria. The trends in yearly prescriptions and corresponding costs were analyzed and stratified by drug class and specific drugs. A total of 19,196 prescriptions from 49 hospitals in 6 major regions of China were included for analysis. The yearly prescriptions increased from 2534 in 2015 to 5676 in 2019 ($$p \leq 0.027$$), and the corresponding expenditures increased from CNY 898,618 in 2015 to CNY 2,466,238 in 2019 ($$p \leq 0.027$$). Gabapentin and pregabalin are the most commonly used drugs for PHN, and more than $30\%$ of these two drugs were combined with mecobalamin. Opioids were the second most frequently prescribed drug class, and oxycodone accounted for the largest share of the cost. Topical drugs and TCAs are rarely used. The frequent use of pregabalin and gabapentin was in accordance with current guidelines; however, the use of oxycodone raised concerns about rationality and economic burden. The results of this study may benefit the allocation of medical resources and management for PHN in China and other countries.
## 1. Introduction
Postherpetic neuralgia (PHN) is a chronic complication of herpes zoster (HZ) caused by damage to peripheral nerve tissue during the onset of herpes zoster, which is defined as obvious pain 3 months after herpes zoster [1]. PHN is often described as burning pain, tingling or itching, and its pain score is always ≥4 on a 10-point visual analog scale [2,3]. The burning and allodynia pain of PHN in the thoracolumbar region are more intensive, while the tingling and numbness of PHN in the face are more intense [4]. Approximately $20\%$ of herpes zoster patients develop PHN [5]. In the United States, the total incidence rate of PHN is 57.5 cases per 100,000 person-years [6]. A study on herpes zoster and its complications in China reported that the incidence rate of PHN was 0.48 per 1000 person-years [7]. The pain caused by PHN is often unbearable, which impairs the work of many employees. This is equivalent to an annual indirect loss of CNY 28,025 (USD 4221), which not only leads to the loss of personal wages but also has a wider economic impact on society as a whole through productivity loss [8,9].
There are many treatment options for PHN, mainly including the most widely used pharmacological therapy and nonpharmacological methods such as nerve blocks, neuromodulation and nerve stimulation [10,11]. In the field of pharmacological treatment, some anticonvulsants, antidepressants, opioids and local therapeutic drugs have been proven to be able to relieve pain. Among the drug recommendations in many countries, gabapentin, pregabalin and TCAs are often used as first-line drugs, while topical drugs and opioids are also often suggested in treatment [1,10,12]. At present, we have known that many drugs, such as gabapentin and pregabalin, are widely used in clinical treatment [13,14,15]. However, side effects of PHN drugs are common, and some drugs, especially opioids, have a potential risk of addiction [16]. Currently, little is known about the status of PHN drug application in China. Considering the increasing prevalence of herps zoster in China [17], we designed this cross-sectional study to analyze the patterns and trends of PHN drug use, as well as its costs.
## 2.1. Study Design and Ethics
This study was a retrospective prescription-based cross-sectional study, and informed consent was waived as part of the approval. Ethical approval was obtained from the Ethics Committee of Run Run Shaw Hospital, College of Medicine, Zhejiang University (Reference Number KEYAN20210924-33).
## 2.2. Data Source and Prescription Inclusion
Prescription data were derived from the widely used database of the Hospital Prescription Analysis Cooperative Project of China for pharmaco-epidemic studies [17,18,19,20,21]. The database was initiated in 2003, and the following items of prescription were included in the database: prescription code, date of prescription issued, sex and age of patient, department of physician, hospital code, drug generic name, strength, price and cost of drug, diagnosis. Prescriptions for patients with a diagnosis of PHN were extracted, and those meeting the following criteria were included for analysis: [1] prescriptions written from 2015 to 2019; [2] prescriptions from hospitals situated in 6 major regions of China (Beijing, Shanghai, Hangzhou, Guangzhou, Chengdu and Tianjin) and participated in the program continuously; [3] prescriptions for adult outpatients (age > 18 years) diagnosed with PHN. Prescriptions with missing values were excluded from the analysis.
## 2.3. Analysis
The prescriptions for patients with PHN were represented by the number of corresponding prescriptions per year. The annual cost was the sum of the total cost of PHN patients’ prescriptions. It should be noted that inflation factor or discount rate was not considered either. The trends in annual prescriptions and expenditures were analyzed, and further stratified and illustrated by drug class and specific drugs.
The PHN-treated drugs were classified to analyzed as follows: [1] anticonvulsants, including gabapentin, pregabalin, carbamazepine, oxcarbazepine, lamotrigine, valproic acid, topiramate and other analogs; [2] antidepressants, including tricyclic antidepressants (TCAs) and other serotonin (5-HT) and norepinephrine (NE) reuptake inhibitors; [3] opioids, compound preparations containing opioids that are also classified as opioids; and [4] topical drugs, including capsaicin, lidocaine, flurbiprofen and diclofenac [11,13,14,22,23,24].
The trends in prescription numbers and costs for overall and individual drugs were assessed using the Mann–Kendall test. The trends in percentages were assessed using the log-linear test. The Wilcoxon signed rank test was used for the difference between the male and female prescription percentages. The average proportion and standard deviation of the combined use of gabapentin and pregabalin in five years were calculated. The trend package in R (version 4.2.1) software was used for statistical analysis. The statistical significance was set as $p \leq 0.05.$
## 3.1. Demographic Characteristics of Patients and Overall Trends
A total of 19,196 prescriptions were included in this study. Detailed demographic characterizations of patients with PHN prescriptions are shown in Table 1. The percentages of prescriptions for females were slightly higher ($$p \leq 0.043$$), and the proportion did not significantly change during the study period ($$p \leq 0.198$$). The yearly prescriptions and expenditures are shown in Figure 1A. The yearly prescriptions increased from 2534 in 2015 to 5676 in 2019 ($$p \leq 0.027$$), and the corresponding expenditures increased from CNY 898,618 in 2015 to CNY 2,466,238 in 2019 ($$p \leq 0.027$$).
## 3.2. Trends in Prescriptions and Cost of Drug Class and Specific Drug
The yearly total prescriptions for four major classes of PHN drugs—anticonvulsants, antidepressants, opioids and topical drugs—increased during the study period (Figure 1B), and detailed prescription numbers are listed in Table 2. Anticonvulsants were the most frequently prescribed drug class, followed by opioids. Antidepressants and topical drugs were rarely used.
Table 3 shows the costs and percentage of specific drugs. There was a certain difference between the trend in expenditure and the trend in prescriptions. The total costs of opioids were always higher than those of anticonvulsants, which had most prescriptions (Figure 1C).
Gabapentin and pregabalin were the most frequently used drugs. Prescriptions of pregabalin increased rapidly ($$p \leq 0.002$$), with the largest increase of $379\%$ in 2018. Regarding second-line opioid drugs, the proportion of oxycodone prescriptions was large and continuously increasing ($$p \leq 0.031$$). For antidepressants, the number of prescriptions of traditional TCA amitriptyline was greater than the others. The topical drugs were mainly lidocaine and capsaicin.
In anticonvulsants, the costs of pregabalin also increased. The average proportion of oxycodone costs per year was approximately $21.3\%$ of the total costs. This was the drug with the largest proportion of the annual amount, and the proportion was stable ($$p \leq 0.220$$). Among antidepressants, the total costs of duloxetine and venlafaxine were higher, while the total costs of amitriptyline were lower.
## 3.3. Trends in Combination of Drugs
Gabapentin and pregabalin, as the first-line choice drugs, were combined with other drugs (Figure 1D). Mecobalamin was the drug most commonly used in combinations. On average, $36.7\%$ of gabapentin prescriptions jointly used mecobalamin, as well as $30.0\%$ of pregabalin prescriptions.
## 4. Discussion
This is the first study to analyze the patterns and trends in pharmacological treatment for outpatients with PHN in China. The yearly prescriptions and costs of PHN drugs have been increasing. Two anticonvulsant drugs—gabapentin and pregabalin—were the most commonly used drugs, which was in line with current practice guidelines. At the same time, we also found that oxycodone in opioids was used in large quantities and costs in a large proportion, which might be an unreasonable use in the treatment of PHN. The percentages of antidepressants and topical drugs were relatively low, in both prescriptions and corresponding costs. Regarding the combination of gabapentin and pregabalin, we unexpectedly found that mecobalamin was used more frequently.
The prescriptions for patients with PHN increased during the study period. In China, $7.26\%$ of herpes zoster patients have PHN, and the incidence rate in women is slightly higher than that of men ($7.45\%$ vs. $7.03\%$) [7], which is consistent with the results of our study. In addition, the number of people diagnosed with herpes zoster continued to increase from 2015 to 2019 [17]. Therefore, the increase in the number of people diagnosed with PHN might be related to the progressive rise of prescriptions for PHN.
At present, the treatment of PHN is based on symptom control, and many studies have proven that antiviral drugs for herpes zoster have no significant effect on PHN or its prevention. Therefore, the treatment of PHN usually follows the principle of neuralgia treatment [25,26]. In the Chinese guidelines, the first-line drugs include pregabalin and gabapentin, TCAs (such as amitriptyline, etc.) and $5\%$ lidocaine patches, and the second-line drugs include opioids [27]. The first-line treatment of PHN in the United States includes TCAs, gabapentin and pregabalin, and a topical lidocaine $5\%$ patch. Opioids and capsaicin patches are recommended as second-line or third-line therapeutic drugs [14]. The French guidelines for neuralgia regard TCAs and other serotonin-norepinephrine reuptake inhibitors (duloxetine and venlafaxine, etc.), and gabapentin as first-line drugs for the treatment of neuralgia, with pregabalin, weak opioid tramadol and capsaicin patches recommended as second-line drugs, and other powerful opioids as third-line drugs [13]. According to the Canadian Pain Society consent statement, gabapentin, TCAs and serotonin-norepinephrine reuptake inhibitors are first-line drugs for the treatment of neuropathic pain. Opioids are recommended as second-line drugs, while cannabinoids are newly recommended as second-line drugs [15]. *In* general, gabapentin, pregabalin and TCAs are often used as first-line drugs, while opioids are not the first choice for PHN. Thus, the use of most frequently prescribed anticonvulsant, mainly gabapentin and pregabalin, was in accordance with current guidelines and evidences. Regional differences in drug use were not significant in this study.
Gabapentin and pregabalin, which are used most, are voltage-gated cation channel regulators [28,29]. Daily doses of 1800 mg to 3600 mg of gabapentin can provide patients with effective pain relief levels [30]. *The* general dosage of pregabalin for the treatment of PHN is between 75 mg and 600 mg per day, which is taken two to three times per day [31]. In our study, the treatment time of gabapentin single prescription is generally about 16 days, and the single oral dose is between 100 mg and 1500 mg, two to four times a day. The treatment time of pregabalin in a single prescription varies greatly, and the single oral dose is between 75 mg and 300 mg, one to three times a day. It is similar to the recommended dosage. The use of pregabalin had a significant increase during the study period, and its prescriptions exceeded gabapentin in 2018. Gabapentin and pregabalin are recognized as drugs with good relief effects on PHN [32]. The increase in the use of pregabalin may be due to the following reasons. The first is the difference in the results of drug action. Omar et al. ’s study on the difference between pregabalin and gabapentin initially showed that pregabalin was better at alleviating pain, while gabapentin had better effects on anxiety, insomnia and fatigue symptoms [33]. Previous studies have confirmed that pregabalin is highly effective and safe for patients with PHN in China [34]. Additionally, the widespread use of gabapentin and pregabalin calls special focus to the effective management of its use, as these drugs also have side effects. An overdose of gabapentin and pregabalin will produce euphoric effects and can lead to delirium [35]. Compared with pregabalin, the abuse of gabapentin is a growing trend. A British survey found that the proportion of lifetime gabapentin abuse was $1.1\%$, compared with $0.5\%$ in pregabalin [36,37]. Another reason for this may be related to the expiration of the patent. According to the database of the China Pharmaceutical Industry Information Center, the patent protection date of pregabalin expired in 2018. Although the brand of pregabalin used by patients has not changed in the past five years, the expiration of the patent has increased the attention it has received in wider society, especially for PHN patients, medical institutions and related pharmaceutical companies. More prescribers realize that the role of pregabalin in PHN may be better than that of gabapentin, so they are more willing to prescribe pregabalin. Other anticonvulsant drugs, such as oxcarbazepine, have proven to have no better therapeutic effect than both gabapentin and pregabalin on neuralgia, and their use is rare [38].
Opioids are widely used in pain control, and oxycodone is the drug with the largest number of prescriptions in the current study. Oxycodone is a semisynthetic μ- and κ-opioid receptor agonist with a wide range of applications [39]. Some studies have also shown that the use of oxycodone is not completely beneficial to the treatment of PHN [29,40]. A study by Gaskell et al. on oxycodone in the treatment of neuralgia suggested that there was no reported result within the scope of their study that can prove that oxycodone has substantial benefit results, such as the overall impression of clinical changes in the treatment of neuralgia [41]. Thus, although opioids were recommended as second-line treatment for PHN, oxycodone was not recommended, or only a very weak recommendation. However, oxycodone has the advantages of long duration of action and no histamine release or ceiling effect compared with other opioids, so it is still used frequently [39]. Compared with the status of other countries, the study by Gudin et al. found that $21.6\%$ of PHN patients received opioids as initial treatment for PHN in the United States, while among the other first-line treatment methods of PHN, gabapentin was $15.1\%$, pregabalin was $3.3\%$ and TCAs were $2.5\%$, which proved that excessive use of opioids was common [42]. Opioids are prone to cause peripheral nerve injury, which leads to increased noxious hypersensitivity, various adverse reactions and drug interactions [43,44,45]. It can also be seen from the conclusion that the cost of oxycodone accounts for a large proportion of overall expenditure and its spending has been sustained at a high level over the five years period of the study. Therefore, the widespread use of oxycodone has raised concerns about rationality and the economic burden on patients. For this phenomenon, the relevant departments should maintain a high degree of vigilance and remind prescribers to reduce or limit the use of related addictive drugs if necessary. Prescribers should evaluate the pain degree of patients before using drugs, and relevant departments can set different indicators of analgesic use for different pain levels, so as to re-evaluate whether opioid analgesics should be used to manage the PHN.
The use of antidepressants is far lower than that of anticonvulsants and opioids, which reflects physician behavior and patient preference. Antidepressants have a certain relieving effect on PHN [46]. However, TCAs such as amitriptyline cannot achieve satisfactory effects for all people in the treatment of PHN pain [47,48]. Another reason for the infrequent use of TCAs is the adverse effects of TCAs. They may cause nausea, headache, constipation and other negative effects that patients are unwilling to bear [49,50]. At present, there are also experiments proving that the combination of amitriptyline and other analgesic drugs, such as pregabalin, may have a better effect [47,51]. Topical drug use did not change much in our study range, although many clinical trials have confirmed that local drug use has a certain therapeutic effect on PHN and has fewer adverse effects [52,53,54].
In the combination of drugs, we found that the frequency of mecobalamin, which does not belong to main treatment drugs, was high. Mecobalamin is a vitamin medicine and is the activated form of vitamin B12. A few studies shows that it not only has a good therapeutic effect on PHN, but can also relieve peripheral polynomialism, entrapment neuropathy and glossopharyngeal neuropathy [55,56]. A study showed that in four trials including 383 participants, the scores of the pain numerical scale in the vitamin B12 group decreased faster, compared with the placebo group. Vitamin B12 can improve the quality of life of patients with PHN and significantly reduce the number of patients using analgesics [57]. The combined use of mecobalamin seems to be justified and reasonable, but more relevant studies are also needed in the future to confirm the safety of its use and its impact on patients.
There are also several limitations to our study. First, the severity of PHN and clinical outcome were not measured and matched with the prescription. If the patient’s pain degree is included in future studies, hierarchical statistics on the drugs used could be better gathered. Second, the rationale of drug use was not assessed, due to the large number of prescriptions. Other comorbidities may cause some deflection among the statistical results. Although all patients with prescriptions are diagnosed with PHN, they also contain many drugs that might not be used to treat PHN. Finally, sampling bias may exist: although prescriptions were from many hospitals located in representative areas of China, primary care or non-hospital-based outpatient prescriptions are not included in our study. Therefore, in future research plans, the study of the correlation between the pain degree of patients and the corresponding drug selection should be included, and include more patient disease information, making it possible to better analyze the rationality of drug use.
## 5. Conclusions
The status and trends of pharmacological treatment for outpatients with PHN in China during a five-year period were analyzed in this study, and the yearly prescriptions and corresponding costs were both found to have increased. Gabapentin and pregabalin were the most frequently used drugs for PHN, which is in accordance with current practice guidelines. Among them, the use and cost of pregabalin showed a significant increasing trend. Oxycodone, as an opioid drug with a strong analgesic effect, had the third most yearly prescriptions but took the largest share of cost, which raised concerns about the rationality of its use and economic burden for PHN patients. The discovery of mecobalamin as the most commonly used drug may be due to its beneficial effect on peripheral nerves, but more research is still needed to study its mechanism of action on PHN in future. The percentages of antidepressants and dermal drugs were relatively low, which reflected physicians’ behavior and patients’ preferences in China. The results of this study indicate that the relevant departments and prescribers should attach great importance to the use of drugs in the treatment of PHN, especially with regard to the use of addictive drugs. Our study may benefit the allocation of medical resources and management for PHN in China, as well as other countries.
## References
1. Sampathkumar P., Drage L.A., Martin D.P.. **Herpes Zoster (Shingles) and Postherpetic Neuralgia**. *Mayo Clin. Proc.* (2009) **84** 274-280. DOI: 10.4065/84.3.274
2. Finnerup N.B., Kuner R., Jensen T.S.. **Neuropathic Pain: From Mechanisms to Treatment**. *Physiol. Rev.* (2021) **101** 259-301. DOI: 10.1152/physrev.00045.2019
3. Bulilete O., Leiva A., Rullan M., Roca A., Llobera J.. **Efficacy of gabapentin for the prevention of postherpetic neuralgia in patients with acute herpes zoster: A double blind, randomized controlled trial**. *PLoS ONE* (2019) **14**. DOI: 10.1371/journal.pone.0217335
4. Rehm S., Grobetakopf M., Kabelitz M., Keller T., Freynhagen R., Tolle T.R., Baron R.. **Sensory symptom profiles differ between trigeminal and thoracolumbar postherpetic neuralgia**. *Pain Rep.* (2018) **3** e636. DOI: 10.1097/PR9.0000000000000636
5. Saguil A., Kane S., Mercado M., Lauters R.. **Herpes Zoster and Postherpetic Neuralgia: Prevention and Management**. *Am. Fam. Physician* (2017) **96** 656-663. PMID: 29431387
6. Thompson R.R., Kong C.L., Porco T.C., Kim E., Ebert C.D., Acharya N.R.. **Herpes Zoster and Postherpetic Neuralgia: Changing Incidence Rates From 1994 to 2018 in the United States**. *Clin. Infect. Dis.* (2021) **73** e3210-e3217. DOI: 10.1093/cid/ciaa1185
7. Sun X., Wei Z., Lin H., Jit M., Li Z., Fu C.. **Incidence and disease burden of herpes zoster in the population aged >/= 50 years in China: Data from an integrated health care network**. *J. Infect.* (2021) **82** 253-260. DOI: 10.1016/j.jinf.2020.12.013
8. Yang F., Yu S., Fan B., Liu Y., Chen Y.X., Kudel I., Concialdi K., DiBonaventura M., Hopps M., Hlavacek P.. **The Epidemiology of Herpes Zoster and Postherpetic Neuralgia in China: Results from a Cross-Sectional Study**. *Pain. Ther.* (2019) **8** 249-259. DOI: 10.1007/s40122-019-0127-z
9. Yu S.Y., Fan B.F., Yang F., DiBonaventura M., Chen Y.X., Li R.Y., King-Concialdi K., Kudel I., Hlavacek P., Hopps M.. **Patient and economic burdens of postherpetic neuralgia in China**. *Clin. Outcomes Res.* (2019) **11** 539-550. DOI: 10.2147/CEOR.S203920
10. Shrestha M., Chen A.. **Modalities in managing postherpetic neuralgia**. *Korean J. Pain* (2018) **31** 235-243. DOI: 10.3344/kjp.2018.31.4.235
11. Dosenovic S., Jelicic K.A., Miljanovic M., Biocic M., Boric K., Cavar M., Markovina N., Vucic K., Puljak L.. **Interventions for Neuropathic Pain: An Overview of Systematic Reviews**. *Anesth. Analg.* (2017) **125** 643-652. DOI: 10.1213/ANE.0000000000001998
12. Le P., Rothberg M.. **Herpes zoster infection**. *BMJ* (2019) **364** k5095. DOI: 10.1136/bmj.k5095
13. Moisset X., Bouhassira D., Avez C.J., Alchaar H., Conradi S., Delmotte M.H., Lanteri-Minet M., Lefaucheur J.P., Mick G., Piano V.. **Pharmacological and non-pharmacological treatments for neuropathic pain: Systematic review and French recommendations**. *Rev. Neurol. (Paris)* (2020) **176** 325-352. DOI: 10.1016/j.neurol.2020.01.361
14. Argoff C.E.. **Review of current guidelines on the care of postherpetic neuralgia**. *Postgrad Med.* (2011) **123** 134-142. DOI: 10.3810/pgm.2011.09.2469
15. Mu A., Weinberg E., Moulin D.E., Clarke H.. **Pharmacologic management of chronic neuropathic pain: Review of the Canadian Pain Society consensus statement**. *Can. Fam. Physician* (2017) **63** 844-852. PMID: 29138154
16. Leppert W., Malec-Milewska M., Zajaczkowska R., Wordliczek J.. **Transdermal and Topical Drug Administration in the Treatment of Pain**. *Molecules* (2018) **23**. DOI: 10.3390/molecules23030681
17. Yu Z., Zhao Y., Jin J., Zhu J., Yu L., Han G.. **Antiviral treatment in outpatients with herps zoster in six major areas of China, 2010–2019**. *Front. Public Health* (2022) **10** 942377. DOI: 10.3389/fpubh.2022.942377
18. Yu Z., Zhu J., Jin J., Yu L., Han G.. **Trends in Outpatient Prescribing Patterns for Ocular Topical Anti-Infectives in Six Major Areas of China, 2013–2019**. *Antibiotics (Basel)* (2021) **10**. DOI: 10.3390/antibiotics10080916
19. Yu L., Zhu W., Zhu X., Lu Y., Yu Z., Dai H.. **Anti-seizure Medication Prescription in Adult Outpatients With Epilepsy in China, 2013–2018**. *Front. Neurol.* (2021) **12** 649589. DOI: 10.3389/fneur.2021.649589
20. Yu Z., Wu X., Zhu J., Jin J., Zhao Y., Yu L.. **Trends in Topical Prescriptional Therapy for Old Patients With Dry Eye Disease in Six Major Areas of China: 2013-2019**. *Front. Pharm.* (2021) **12** 690640. DOI: 10.3389/fphar.2021.690640
21. Wang Z., Wu X., Yu Z., Yu L.. **Utilization of Drugs for Attention-Deficit Hyperactivity Disorder Among Young Patients in China, 2010–2019**. *Front. Psychiatry* (2021) **12** 802489. DOI: 10.3389/fpsyt.2021.802489
22. Liu X., Wei L., Zeng Q., Lin K., Zhang J.. **The Treatment of Topical Drugs for Postherpetic Neuralgia: A Network Meta-Analysis**. *Pain. Physician* (2020) **23** 541-551. PMID: 33185370
23. Johnson R.W., Rice A.S.. **Clinical practice. Postherpetic neuralgia**. *N. Engl. J. Med.* (2014) **371** 1526-1533. DOI: 10.1056/NEJMcp1403062
24. Bianchi L., Piergiovanni C., Marietti R., Renzini M., Gori F., Hansel K., Tramontana M., Stingeni L.. **Effectiveness and safety of lidocaine patch 5% to treat herpes zoster acute neuralgia and to prevent postherpetic neuralgia**. *Derm. Ther.* (2021) **34** e14590. DOI: 10.1111/dth.14590
25. Kowalsky D.S., Wolfson A.B.. **Antiviral Medications for the Prevention of Postherpetic Neuralgia After Herpes Zoster Infection**. *Acad. Emerg. Med.* (2019) **26** 684-685. DOI: 10.1111/acem.13662
26. Song D., He A., Xu R., Xiu X., Wei Y.. **Efficacy of Pain Relief in Different Postherpetic Neuralgia Therapies: A Network Meta-Analysis**. *Pain. Physician* (2018) **21** 19-32. PMID: 29357328
27. Shengyuan Y., You W., Qi W., Ke M., Shuangjia W., Zhenhe L., Yanqing L., Xiaoli L., Hui L., Ruozhuo L.. **Consensus of Chinese experts on the diagnosis and treatment of postherpetic neuralgia**. *Chin. J. Pain Med.* (2016) **22** 161-167
28. Alles S., Smith P.A.. **Peripheral Voltage-Gated Cation Channels in Neuropathic Pain and Their Potential as Therapeutic Targets**. *Front. Pain Res. (Lausanne)* (2021) **2** 750583. DOI: 10.3389/fpain.2021.750583
29. Alles S., Smith P.A.. **Etiology and Pharmacology of Neuropathic Pain**. *Pharm. Rev.* (2018) **70** 315-347. DOI: 10.1124/pr.117.014399
30. Wiffen P.J., Derry S., Bell R.F., Rice A.S., Tolle T.R., Phillips T., Moore R.A.. **Gabapentin for chronic neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2017) **6** D7938. DOI: 10.1002/14651858.CD007938.pub4
31. Derry S., Bell R.F., Straube S., Wiffen P.J., Aldington D., Moore R.A.. **Pregabalin for neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2019) **1** D7076. DOI: 10.1002/14651858.CD007076.pub3
32. Moore A., Derry S., Wiffen P.. **Gabapentin for Chronic Neuropathic Pain**. *JAMA* (2018) **319** 818-819. DOI: 10.1001/jama.2017.21547
33. Gammoh O., Al-Smadi A., Shawagfeh M.Q., Abadi T., Kasawneh J., Malkawi S., Zein S.. **The Clinical Difference between Gabapentin and Pregabalin: Data from a Pilot Comparative Trial**. *Rev. Recent Clin. Trials* (2021) **16** 279-287. DOI: 10.2174/1574887116666210201110507
34. Liu Q., Chen H., Xi L., Hong Z., He L., Fu Y., Fang H., Shang N., Yan P., Fan D.. **A Randomized, Double-blind, Placebo-controlled Trial to Evaluate the Efficacy and Safety of Pregabalin for Postherpetic Neuralgia in a Population of Chinese Patients**. *Pain Pr.* (2017) **17** 62-69. DOI: 10.1111/papr.12413
35. Evoy K.E., Covvey J.R., Peckham A.M., Ochs L., Hultgren K.E.. **Reports of gabapentin and pregabalin abuse, misuse, dependence, or overdose: An analysis of the Food and Drug Administration Adverse Events Reporting System (FAERS)**. *Res. Social Adm. Pharm.* (2019) **15** 953-958. DOI: 10.1016/j.sapharm.2018.06.018
36. Evoy K.E., Morrison M.D., Saklad S.R.. **Abuse and Misuse of Pregabalin and Gabapentin**. *Drugs* (2017) **77** 403-426. DOI: 10.1007/s40265-017-0700-x
37. Evoy K.E., Sadrameli S., Contreras J., Covvey J.R., Peckham A.M., Morrison M.D.. **Abuse and Misuse of Pregabalin and Gabapentin: A Systematic Review Update**. *Drugs* (2021) **81** 125-156. DOI: 10.1007/s40265-020-01432-7
38. Zhou M., Chen N., He L., Yang M., Zhu C., Wu F.. **Oxcarbazepine for neuropathic pain**. *Cochrane Database Syst. Rev.* (2017) **12** D7963. DOI: 10.1002/14651858.CD007963.pub3
39. Umukoro N.N., Aruldhas B.W., Rossos R., Pawale D., Renschler J.S., Sadhasivam S.. **Pharmacogenomics of oxycodone: A narrative literature review**. *Pharmacogenomics* (2021) **22** 275-290. DOI: 10.2217/pgs-2020-0143
40. Mucke M., Phillips T., Radbruch L., Petzke F., Hauser W.. **Cannabis-based medicines for chronic neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2018) **3** D12182
41. Gaskell H., Derry S., Stannard C., Moore R.A.. **Oxycodone for neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2016) **7** D10692. DOI: 10.1002/14651858.CD010692.pub3
42. Gudin J., Fudin J., Wang E., Haylon T., Patel K., Goss T.F.. **Treatment Patterns and Medication Use in Patients with Postherpetic Neuralgia**. *J. Manag. Care Spec. Pharm.* (2019) **25** 1387-1396. DOI: 10.18553/jmcp.2019.19093
43. Green-Fulgham S.M., Ball J.B., Kwilasz A.J., Fabisiak T., Maier S.F., Watkins L.R., Grace P.M.. **Oxycodone, fentanyl, and morphine amplify established neuropathic pain in male rats**. *Pain* (2019) **160** 2634-2640. DOI: 10.1097/j.pain.0000000000001652
44. Duehmke R.M., Derry S., Wiffen P.J., Bell R.F., Aldington D., Moore R.A.. **Tramadol for neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2017) **6** D3726. DOI: 10.1002/14651858.CD003726.pub4
45. Miotto K., Cho A.K., Khalil M.A., Blanco K., Sasaki J.D., Rawson R.. **Trends in Tramadol: Pharmacology, Metabolism, and Misuse**. *Anesth. Analg.* (2017) **124** 44-51. DOI: 10.1213/ANE.0000000000001683
46. Kolacz M., Kosson D., Puchalska-Kowalczyk E., Mikaszewska-Sokolewicz M., Lisowska B., Malec-Milewska M.. **Analysis of Antidepressant, Benzodiazepine Anxiolytic, and Hypnotic Use When Treating Depression, Anxiety, and Aggression in Pain Clinic Patients Treated for Neuropathic Pain**. *Life (Basel)* (2022) **12**. DOI: 10.3390/life12030433
47. Tesfaye S., Sloan G., Petrie J., White D., Bradburn M., Julious S., Rajbhandari S., Sharma S., Rayman G., Gouni R.. **Comparison of amitriptyline supplemented with pregabalin, pregabalin supplemented with amitriptyline, and duloxetine supplemented with pregabalin for the treatment of diabetic peripheral neuropathic pain (OPTION-DM): A multicentre, double-blind, randomised crossover trial**. *Lancet* (2022) **400** 680-690. PMID: 36007534
48. Moore R.A., Derry S., Aldington D., Cole P., Wiffen P.J.. **Amitriptyline for neuropathic pain and fibromyalgia in adults**. *Cochrane Database Syst. Rev.* (2012) **12** D8242
49. Powers S.W., Coffey C.S., Chamberlin L.A., Ecklund D.J., Klingner E.A., Yankey J.W., Korbee L.L., Porter L.L., Hershey A.D.. **Trial of Amitriptyline, Topiramate, and Placebo for Pediatric Migraine**. *N. Engl. J. Med.* (2017) **376** 115-124. DOI: 10.1056/NEJMoa1610384
50. Riediger C., Schuster T., Barlinn K., Maier S., Weitz J., Siepmann T.. **Adverse Effects of Antidepressants for Chronic Pain: A Systematic Review and Meta-analysis**. *Front. Neurol.* (2017) **8** 307. DOI: 10.3389/fneur.2017.00307
51. Rodrigues R.F., Kawano T., Placido R.V., Costa L.H., Podesta M., Santos R.S., Galdino G., Barros C.M., Boralli V.B.. **Investigation of the Combination of Pregabalin with Duloxetine or Amitriptyline on the Pharmacokinetics and Antiallodynic Effect During Neuropathic Pain in Rats**. *Pain Physician* (2021) **24** E511-E520. PMID: 34213877
52. Voute M., Morel V., Pickering G.. **Topical Lidocaine for Chronic Pain Treatment**. *Drug Des. Devel. Ther.* (2021) **15** 4091-4103. DOI: 10.2147/DDDT.S328228
53. Derry S., Rice A.S., Cole P., Tan T., Moore R.A.. **Topical capsaicin (high concentration) for chronic neuropathic pain in adults**. *Cochrane Database Syst. Rev.* (2017) **1** D7393
54. Derry S., Wiffen P.J., Kalso E.A., Bell R.F., Aldington D., Phillips T., Gaskell H., Moore R.A.. **Topical analgesics for acute and chronic pain in adults—An overview of Cochrane Reviews**. *Cochrane Database Syst. Rev.* (2017) **5** D8609. DOI: 10.1002/14651858.CD008609.pub2
55. Julian T., Syeed R., Glascow N., Angelopoulou E., Zis P.. **B12 as a Treatment for Peripheral Neuropathic Pain: A Systematic Review**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12082221
56. Buesing S., Costa M., Schilling J.M., Moeller-Bertram T.. **Vitamin B12 as a Treatment for Pain**. *Pain Physician* (2019) **22** E45-E52. DOI: 10.36076/ppj/2019.22.E45
57. Wang J.Y., Wu Y.H., Liu S.J., Lin Y.S., Lu P.H.. **Vitamin B12 for herpetic neuralgia: A meta-analysis of randomised controlled trials**. *Complement Med.* (2018) **41** 277-282. DOI: 10.1016/j.ctim.2018.10.014
|
---
title: Association of Vitamin D Deficiency and Insufficiency with Pathology in Hospitalized
Patients
authors:
- Sandica Bucurica
- Ioana Prodan
- Mihaela Pavalean
- Corina Taubner
- Ana Bucurica
- Calin Socol
- Roxana Calin
- Florentina Ionita-Radu
- Mariana Jinga
journal: Diagnostics
year: 2023
pmcid: PMC10000859
doi: 10.3390/diagnostics13050998
license: CC BY 4.0
---
# Association of Vitamin D Deficiency and Insufficiency with Pathology in Hospitalized Patients
## Abstract
Vitamin D deficiency is one of the most common medical conditions, with approximately one billion people having low vitamin D levels. Vitamin D is associated with a pleiotropic effect (immunomodulatory, anti-inflammatory and antiviral), which can be essential for a better immune response. The aim of this research was to evaluate the prevalence of vitamin D deficiency/insufficiency in hospitalized patients focusing on demographic parameters as well as assessing the possibility of its associations with different comorbidities. Of 11,182 Romanian patients evaluated in the study over 2 years, $28.83\%$ had vitamin D deficiency, $32.11\%$ insufficiency and $39.05\%$ had optimal vitamin D levels. The vitamin D deficiency was associated with cardiovascular disorders, malignancies, dysmetabolic disorders and SARS-CoV2 infection, older age and the male sex. Vitamin D deficiency was prevalent and showed pathology association, while insufficiency of vitamin D (20–30 ng/mL) had lower statistical relevance and represents a grey zone in vitamin D status. Guidelines and recommendations are necessary for homogeneity of the monitoring and management of inadequately vitamin D status in the risk categories.
## 1. Introduction
Vitamin D deficiency is one of the most common medical condition, with approximately one billion people having low vitamin D levels [1]. Vitamin D is a fat soluble vitamin synthetized from precursors as 7-dehydrocholesterol into vitamin D3 (cholecalciferol) which represents the main source and from plant ergosterol into vitamin D2, through a multistep process (25-hydroxylation, 1α-hydroxylation, and 24-hydroxylation) to the biologic active form 1,25-dihydroxvitamin D3 [1,25(OH)2D3], respective 1,25(OH)2D2 [2,3]. The serum concentration of 25(OH)D, as an intermediate metabolite is the serum biomarker of vitamin D status, as it represents the main storage form and it is reliable to be measured, because the 1,25(OH)2 D form has around 4 h half-life, while 25(OH)D reaches 3 weeks [4].
Vitamin D production is secondary to conversion of the provitamin D3 into the skin by sunlight, Ultraviolet B rays (UVB) [1].
The levels of serum 25 hydroxyvitamin D (25(OH) vitamin D) are influenced by uptake, season, latitude, gender, ethnicity, environmental factors, sunscreen, skin pigmentation, age, intestinal absorption and use of supplements [1].
Vitamin D is essential for calcium homeostasis by increasing the absorption of the dietary calcium, reducing the calcium excretion and mobilizing calcium from the storages. There are ubiquitously distributed receptors for vitamin D in the human body with a series of roles, many still unfound. Vitamin D can be synthesized into the skin; however, its production can be decreased by a series of factors: clothing, sunscreens and reduced sunlight. The deficit of vitamin D is not only associated with bone pathology, but it is also related to cardiovascular diseases, diabetes, cancer, autoimmune disorders and infectious disorders [5].
Vitamin D has a pleiotropic effect (immune modulatory, anti-inflammatory and antiviral), which can be essential for a better immune response [5,6].
The conventional and well known pathway of vitamin D action involves the steroid vitamin D receptor (VDR) mediation and calcium absorption modulation with major implication in bone metabolism, but there are various non-conventional systemic effects of vitamin D [2].
In cancer, vitamin D and VDR regulation are involved in apoptosis, invasion, inhibition of inflammatory cytokines and regulation of microRNA, but there are equivocal results regarding relation of causality because the of the lack of standardization of 25(OH)D testing method and timing (before and after the diagnosis) [2,7,8,9,10].
Regarding the cardiovascular system and vitamin D relationship, it is stated that VDR are found in endothelial cells, in smooth muscle tissue and lack of vitamin D promotes atherogenesis [11]. In addition, VDR is expressed in pancreatic cells and mediates transcription in stellate cells, suggesting the mechanism of involvement in metabolic diseases such as diabetes mellitus [12,13].
There are still gaps in establishing thresholds for adequate levels of 25(OH)D and in the most recent statements and consensus, it is mentioned that the present data are not sufficient to define certain vitamin D status thresholds, because of the lack of standardization, but at least 20 ng/mL 25(OH)D should be achieved, treatment is recommended for values below, and may be considered for serum 25(OH)D < 30 ng/mL.
Moreover, the recommendations for dose supplementation or treatment of vitamin D inadequacy vary according to age, risk category, geographical situation and regional authority or medical society, creating a large heterogeneity in management of vitamin D deficit and leaving place for debates, but there is consistency in the agreement that general population screening for vitamin D deficiency is not recommended [14,15,16].
In 2019, Romanian Ministry of Health released a recommendation for vitamin D status assessment in children, pregnant women and adults. According to this, there are several risk categories of adult population that are exposed to vitamin D deficiency and measurement of 25(OH)D is recommended for adults with: chronic diseases, low ultraviolet light exposure, diseases of liver, renal diseases, maldigestion and malabsorption, cardio-vascular diseases, metabolic and nutritional impairment, cancer, allergies, autoimmune diseases, endocrinopathies and musculoskeletal diseases [17,18]. Moreover, the pandemic era started in March 2020 and serum 25(OH)D were assessed in the context of COVID-19 risk.
According to the Romanian Ministry of Health recommendation for adults, a value of 25(OH)D between 10–20 ng/mL (25–50 nmol/L) is considered deficient, while <10 ng/mL (<25 nmol/L) represents a severe deficiency.
Although a level >20 ng/mL (>50 nmol/L) may be considered sufficient, American Endocrine Society Guidelines suggest that a level >30 ng/mL is associated with lower risk of osteomalacia. A level between 20–30 ng/mL is considered insufficiency, while values of 25(OH)D > 250 nmol/L (100 ng/mL), are considered toxic [1,17]. Moreover, there are no recommendations of vitamin D status screening in overall healthy populations [18].
For inpatients, previous studies showed that vitamin D deficiency was associated with higher probability of exacerbation of the disease and need for hospitalization [19], with longer hospitalization period and worst outcome of hospital admission, and higher mortality rates in intensive care units [20,21,22]. In addition, residents of hospices and hospitalized patients presented the highest risk for deficient vitamin D compared with outpatients, and longer hospitalization of those with lower 25(OH)D [23], and overall hospitalized people tend to have higher rates of deficiency of 25(OH)D [24].
A particular attention was paid to COVID-19 and vitamin D beginning with the first wave of pandemic, since that multiple observational studies showed that low serum levels of 25(OH)D were associated with severe disease, but not showing a causal relationship. One of the most recent umbrella review of multiple meta-analyses over COVID-19 and vitamin D status reported a significant risk for acquiring the infection, for severity of progression and for high incidence of the mortality in cases with low serum of 25(OH)D, and it was suggested a potential benefit of vitamin D administrated after the diagnosis [25].
Although the etiopathogenetic mechanisms of rickets/osteomalacia is directly related to vitamin D deficiency, the literature is inconsistent regarding the cause and effect relationship between lower 25(OH)D and occurrence or progression of a specific disease or if lack of vitamin D could be a mark of deteriorated health condition.
The aim of the study was to evaluate the prevalence of vitamin D deficiency/insufficiency in hospitalised patients, considering demographic factors and to establish an association with different comorbidities.
## 2. Materials and Methods
Individuals who had their serum 25 hydroxyvitamin D (25(OH)D) measured at the Central Military Emergency University Hospital, Bucharest, Romania (latitude 44 N) over a 24-month period, starting 1 June 2020, until 31 May 2022, were included in this retrospective cross-sectional study.
Inclusion criteria: Patients >18 years old, admitted in Central Military Emergency University Hospital, Bucharest, Romania whom 25(OH)D level were measured in our laboratory and had a discharge diagnostic coded according to International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM).
Exclusion criteria: patients under 18 years old, outpatients or patients that presented to the emergency room, but were not admitted to the hospital, patients with multiple admissions, pregnant women, patients with unspecific diagnosis (according to ICD-10-CM).
Gender, age, chronic diseases and 25(OH)D serum levels of the patients were evaluated. Adults were divided into five groups based on age (<20, 20–39, 40–59, 60–79, ≥80) (Figure 1).
The seasons were split into spring (March–May), summer (June–August), autumn (September–November) and winter (December–February).
In the last decade, the number of vitamin-D-related articles increased, with more concern for different afflictions. The goal was to focus on the largest, most relevant, and most recent studies.
Pathologies included were considered according to the first discharge diagnostics and were categorised according to International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Additionally, there were used, as distinct codes, the new agreed codes for positive status of COVID-19 (since 1 March 2020); respective U07.1 and coronavirus infection B97.2, according to National Institute of Health Services Management (Institutul National de Management al Serviciilor de Sanatate). Additional to the primary discharge diagnostic, the secondary diagnostics which have been taken into consideration were grouped in five categories: cardiovascular disorders, dysmetabolic status (including obesity, diabetes and dyslipidemic disorders), endocrine disorders (excluding diabetes and dyslipidemic disorders), malignancy and COVID-19.
Pathologies/comorbidities with non-specific codes (Z00–Z99), (V01–X59) or special purposes codes (U00–U85) were excluded, except for U07.1 used for COVID-19, which was taken into account. The codes for pregnancy, childbirth and the puerperium (O00–O99), certain conditions originating in the perinatal period (P00–P96) and congenital malformations, deformations and chromosomal abnormalities (Q00–Q99) and multiple admissions were also excluded. In addition, we excluded patients under 18 years of age, outpatients or patients that presented to the emergency room, but were not admitted to the hospital.
Serum 25(OH)D was measured using chemiluminescence microparticle immunoassay (CMIA) for quantitative determination of 25-hydroxyvitamin D in human serum and plasma, with a measuring interval between 3.5 to 154.2 ng/mL (8.8 to 385.5 nmol/L) analyser. The same method of measuring was used for all the patients.
Serum vitamin D levels have been categorized as deficiency (25-OH vitamin D <20 ng/mL), insufficiency (20 to 30 ng/mL) and vitamin D optimal values ≥30 ng/mL [1].
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Committee of Ethics from Central Emergency University Military Hospital no $\frac{572}{13}$/$\frac{01}{2023.}$ Informed consent was obtained for using and analysing data in scientific purposes from all subjects involved.
We focused on the largest studies and most recent ones published for our discussion chapter. We excluded the articles published in languages other than English.
## Statistical Analysis
Descriptive analyses were used for general characteristics and quantitative data were analysed using mean, median and standard deviation. Numbers and percentages were used for categorical values. Cross tabs and a chi-square test were used to examine the associations between categorical variables, including the adjusted residuals for the variation for the larger sample size. The test was considered valid when the test statistics in chi-squared distributed under the null hypothesis, specifically Pearson’s chi-squared test, Relative risk, odd ratio (OR) and $95\%$ confidence intervals were calculated for association between categorical variables.
A p-value < 0.05 was considered significant. Adjusted residuals were considered with positive association >2 and negative association <−2 and for OR > 1 was considered statistically significant for the outcome occurring and <1 with lower odds of outcome. We tested for normal distribution the continuous variables in the whole group (11,182 subjects) using the Kolmogorov–Smirnov test for skewness and kurtosis and all continuous variables are expressed as mean ± standard deviation, median (min, max). The diagnostic accuracy was assessed with area under the curve (AUC) analysis of receiver operating characteristic (ROC) curves. Statistical analyses were performed using IBM SPSS® version 26.
## 3.1. Overall Demographic Variations
Data were collected from 14,090 patients who had their serum vitamin D level determined between 1 June 2020 and 31 May 2022 at the Central Military Hospital, Bucharest and were included in the analysis, according to inclusion criteria, 11,182 ($65.9\%$ women, $$n = 7366$$ and $34.1\%$ males, $$n = 3186$$) with mean age 55.22 and median of 55 (18–102) (Table 1), but the age was not normally distributed, being skewed to the left ($p \leq 0.01$, skewness = −0.175, kurtosis = −0.603).
The prevalence of vitamin D insufficiency (20–30 ng/mL) was overall $32.1\%$ ($$n = 3591$$)—from which $34.5\%$ were males ($$n = 1239$$) and $65.5\%$ were females ($$n = 2352$$) ($$p \leq 0.5634$$), while the overall vitamin D deficiency (<20 ng/mL) was $28.8\%$ ($$n = 3224$$), $39.2\%$ ($$n = 1265$$) males ($p \leq 0.01$; OR = 1.369; $95\%$Cl [1.257 to 1.490]) and $60.7\%$ ($$n = 1959$$) females ($p \leq 0.0001$).
Overall, median levels of 25(OH)D were lower in the male group; 25.1 ng/mL < 27.2 ng/mL for females (Table 1).
Optimal vitamin D, ≥30 ng/mL, has been found in $39.05\%$ ($$n = 4367$$) of the patients; $30.04\%$ ($$n = 1312$$) males and $69.95\%$ ($$n = 3055$$) females included in the study ($p \leq 0.0001$) (Table 2 and Table S1, Figure 2).
The level of 25(OH)D was not normally distributed, being skewed to the right. The median level of 25(OH)D was 26.45 ng/mL (with range 3.5–154.2), with coefficient of skewness 1.384, coefficient of kurtosis of 6.283 with the Kolmogorov–Smirnov test for normality ($p \leq 0.01$).
## 3.2. Group Age Variations
In terms of age group, patients under 20 years of age were the fewest in this study, $0.82\%$ ($$n = 92$$), $27.17\%$ ($$n = 25$$) having insufficiency, $41.30\%$ ($$n = 38$$) with deficiency and $31.52\%$ ($$n = 29$$) with optimal levels of 25(OH)D.
The most prevalent age groups were 40–59 and 60–79 years of age. A total of $40.4\%$ of patients ($$n = 4517$$) were in the 40–59 years of age group, $22.76\%$ ($$n = 1028$$) of them presenting deficiency, $33.96\%$ ($$n = 1534$$) insufficiency and $43.28\%$ ($$n = 1955$$) with sufficient vitamin D.
For the age group 60–79 years, representing $36.6\%$ ($$n = 4094$$) of the patients, $38.15\%$ ($$n = 1562$$) had optimal levels of vitamin D, $29.46\%$ ($$n = 1206$$) had insufficiency and $32.39\%$ ($$n = 1326$$) had vitamin D deficiency. For the patients >80 years old ($5.7\%$ of all patients, $$n = 641$$), $26.05\%$ ($$n = 167$$) had optimal values of vitamin D, $50.55\%$ ($$n = 324$$) had vitamin D deficiency and $23.4\%$ ($$n = 150$$) had insufficiency. Median 25(OH)D was lower in the extremities of age groups (Table 1 and Table 2, Figure 3).
## 3.3. Seasonal Variation
Based on the time period when 25(OH)D levels were tested, of all 11,182 patients, $20.8\%$ had their 25(OH)D levels tested in winter, $31.1\%$ of patients in spring, $25.0\%$ in summer and $23.1\%$ in the fall. The median level of the 25(OH)D for spring was 24.3 (3.5–154.2), for summer was 27.5 (3.5–154.2), for autumn was 28.7 (3.5–115.6) and for the winter season was 25.5 (3.5–154.2) (Table 1).
The vitamin D deficiency has been predominant in spring and winter. The vitamin D deficiency was $24.4\%$ ($$n = 787$$) in winter, $16.6\%$ ($$n = 534$$) in autumn, $20.6\%$ ($$n = 665$$) in summer and $38.4\%$ ($$n = 1238$$) in spring. Vitamin D insufficiency was predominant in the spring season ($30.8\%$, $$n = 1105$$), summer ($27.3\%$, $$n = 982$$) and autumn ($24.2\%$, $$n = 868$$). Among patients with optimal 25(OH)D values, $26.1\%$ ($$n = 1139$$) had 25(OH)D levels determined in the spring season, $26.3\%$ ($$n = 1147$$) in the summer, $27.0\%$ ($$n = 1180$$) autumn and $20.6\%$ ($$n = 901$$) in winter.
A total of $24.5\%$ of the study group ($$n = 2737$$) was evaluated in 2020, $42.2\%$ ($$n = 1155$$) having normal 25(OH)D values, $22.8\%$ ($$n = 625$$) having vitamin D deficiency and $34.9\%$ ($$n = 957$$) having insufficiency. A total of $50.1\%$ ($$n = 5606$$) of patients was evaluated in 2021, among whom $39.44\%$ ($$n = 2211$$) with optimal values of 25(OH)D, $31.50\%$ ($$n = 1766$$) with vitamin D deficiency and $29.05\%$ ($$n = 1629$$) with insufficiency. For 2022, there was $25.4\%$ ($$n = 2839$$) of the patients; $35.25\%$ ($$n = 1001$$) with optimal values of 25(OH)D, $34.16\%$ ($$n = 970$$) having vitamin D deficiency and $30.57\%$ ($$n = 868$$) insufficiency.
## 3.4. Pathologies Variations
Different pathologies were evaluated and an association with vitamin D status has been determined for some afflictions.
Vitamin D status (deficiency, insufficiency and optimal) in each pathology categorised as first discharge diagnostic was determined and represented in Table 3.
Regarding the association with one pathology, respecting the first discharge diagnostic, we found a significant association between vitamin D deficiency and infectious disease ($p \leq 0.01$, OR = 3.762, $95\%$ confidence interval [CI] 2.584–5.475), malignant neoplasm ($p \leq 0.01$, OR = 2.673, $95\%$ CI [1.859–3.845]), mental and behavioural disorders ($$p \leq 0.17$$, OR = 1.259, $95\%$ CI [1.042–1.521]), other forms of heart disease and pulmonary heart diseases ($p \leq 0.001$, OR = 1.714, $95\%$ CI [1.481–1.985]), diseases of the respiratory system ($p \leq 0.001$, OR = 1.26, $95\%$ CI [1.124–1.413]), urinary and renal diseases ($p \leq 0.001$, OR = 2.358, $95\%$ CI [1.801–3.088]) (Supplementary Tables S2 and S3).
Because most of the patients had multiple pathologies, the 25(OH)D level has been evaluated also depending on the secondary diagnostic pathologies grouped in five categories: malignancies, cardiovascular disorders, dysmetabolic status, endocrine disorders and COVID-19. It has been shown that $41.9\%$ of all included patients presented cardiovascular disorders; from them, a high percentage, $52.1\%$, had vitamin D deficiency ($p \leq 0.0001$) at a significantly higher rate compared to patients with cardiovascular disorders and levels >20 ng/mL of serum 25(OH)D.
The dysmetabolic status has been found in $38.4\%$ of the patients and those also presented an increased percent of vitamin D deficiency ($44.3\%$) compared to insufficient and optimal ($38.6\%$, and, respectively, $34\%$). Although malignancy was found in only $3.8\%$ ($$n = 420$$) of all patients studied, we found a high prevalence of vitamin D deficiency ($45.7\%$; $$n = 190$$; $p \leq 0.0001$) among them. In the group of patients with endocrine disorders, deficiency and insufficiency had a prevalence equal to that of normal 25(OH)D levels (one third of patients falling into each category).
However, when we took into consideration the secondary diagnostics of the patients, we found an association of vitamin D deficiency with cardiovascular diseases ($p \leq 0.0001$; OR = 1.785; $95\%$ Cl [1.257–1.490]), metabolic disorders ($p \leq 0.01$; OR = 1.409; $95\%$ Cl [1.296–1.531]), COVID-19 ($p \leq 0.0001$; OR = 1.381; $95\%$ Cl [1.244–1.534]) and with malignancy ($p \leq 0.0001$; OR = 2.11; $95\%$ Cl [1.702–2.626]). The vitamin D deficiency is not associated with endocrine disorders ($$p \leq 0.7851$$). Moreover, the vitamin D insufficiency has been associated with cardiovascular diseases ($p \leq 0.0001$; OR = 0.848; $95\%$ Cl [0.780–0.921]) and malignancy ($p \leq 0.0001$; OR = 0.727; $95\%$ Cl [0.577–0.916]). There has not been found an association between vitamin D insufficiency and COVID-19 infection ($$p \leq 0.1938$$) and with the endocrine comorbidities ($$p \leq 0.9583$$) (Table 4, Table 5, Tables S2 and S3).
The results from the ROC curve analysis between vitamin D levels and renal impairment showed that the AUC was 0.621 ($95\%$ CI [0.579–0.662]; $p \leq 0.001$), showing acceptable accuracy of the diagnostic. The ROC curve analysis between 25(OH)D levels and infectious diseases showed that the AUC was 0.700, showing acceptable ability for discrimination of infective patients ($95\%$ CI [0.643–0.757]; $p \leq 0.001$). The results from the ROC curve analysis between 25(OH)D levels and renal impairment showed that the AUC was 0.621 ($95\%$ CI [0.579–0.662]; $p \leq 0.001$), showing acceptable accuracy of the diagnostic. The results from the ROC curve analysis between 25(OH)D levels and malignancy showed that the AUC was 0.632 ($95\%$ CI [0.578–0.687]; $p \leq 0.001$), revealing a moderate diagnostic accuracy.
## 4.1. Overall Deficiencies and Insufficiencies and Demographic Variations
Vitamin D deficiency and insufficiency were observed in $60.9\%$ ($$n = 6816$$) of 11,282 patients evaluated in the study, $28.8\%$ having a deficit in vitamin D ($$n = 3224$$) and $32.1\%$ having vitamin D insufficiency ($$n = 3592$$). A study conducted in Turkey (latitude 38 N) on 22,044 hospitalised patients that examined the deficiency and insufficiency of vitamin D has shown that $89.4\%$ had low levels of 25(OH)D ($78.6\%$ females, $21.4\%$ males) [26]. The prevalence of vitamin D deficiency is estimated at $5.9\%$ in US [27] and $13\%$ in Europe [28], while the prevalence of vitamin D insufficiency has been reported as $24\%$ in US, and $40\%$ in Europe [27,28,29,30]. It is clear that vitamin D deficiency represents a global health issue.
A Romanian study that evaluated the level of the 25(OH)D with a male to female ratio 1:2.9 emphasized the need for supplementation of vitamin D especially for older age, females and winter season [31]. In one of the most recent studies published in Romania, it was shown that females have lower levels of serum 25(OH)D compared with males [32].
In our study, we found that males have 1.36 times higher odds of having vitamin D deficiency and 1.02 times chances of insufficiency, while females presented a lower odds for both categories, although the females predominated in the sample (twice more numerous). We emphasise the fact that one third of all males presented <20 ng 25(OH)D serum level, while only $26\%$ of all females presented deficient levels.
A previous study from Romania, performed on samples from 7544 patients, reported an overall mean of 25(OH)D of 27.20 ± 16.76, (34.45 ± 21.56 for males and 28.42 ± 14.45 for females), with an overall deficiency of $26\%$. There are limitations because there were also children included and the patients were referred from private practice or hospital, with no data about their diagnosis [31]. In contrast, in our study, males had lower values comparative to females. Similar to our results, there is another study from the Romanian population that reported lower mean values in males comparative with females for the 19–30 age group >70 years old, and one prospective study found male inpatients more deficient than females [33,34].
We should consider that in the last few years, vitamin D supplementation was used intensively, especially in the pandemic era and we have no data regarding vitamin D supplementation in our study population.
Moreover, French studies on healthy volunteers showed a dynamic of vitamin D status in population over the years, and showed a decreasing tendency of vitamin D deficiency prevalence from $57.7\%$ to $34.6\%$ in a 2 decades period, but with suboptimal level still prevalent, so updated data are necessary to have an accurate overview and to compare [35].
For Europe, there has been reported a prevalence of $53\%$ of suboptimal vitamin D (severe deficiency <12 ng/mL prevalence of $13\%$ and $40\%$ between 12 ng/mL and 29 ng/mL) [16], but there are scarce data from the Eastern *European area* and individual studies showed a poorer vitamin D status (mean 25(OH)levels <20 ng/mL) compared with western and northern areas of the European continent [14].
## 4.2. Group Age Variations
In the age group distribution, it was revealed there was a predominance of the deficiency/insufficiency in middle-aged groups and, also, in the elderly, >80 years old, where half of the patients had vitamin D deficiency, similarly to the literature data.
The highest prevalence of vitamin D deficiency has been found in children [36], childbearing women [37] and elderly people (>75 years old) [38]. There has been a comparison made between China and the US, revealing different predictors for low levels of 25(OH)D. China individuals affected are of older age, females, people with high income, while for US, the most affected are males, lower income, no physical activity, overweight and obese [39]. Ethnic minorities and individuals with altered health are found in both countries [39].
Moreover, an increased incidence of vitamin D deficiency for older adults related to lower capacity for cutaneous synthetizations and less sunlight exposure has been postulated [40]. Schöttker has shown that there is a decrease of 3 nmol/L serum 25(OH)D for every 10 years of age in a large cohort ($$n = 9940$$) [41].
Romanian data from more than 8000 patients showed a high deficiency in elderly patients [32]. Our data are concordant with the literature data sustaining, once again, that people <20 or >60 years old (especially > 80) are at risk of having vitamin D deficiency rather than insufficiency (which is more encountered in 40–60 years age group), but another study from Romania on patients from private practice and hospital referral with indication for 25(OH)D serum measurement showed a variation of vitamin D deficiency slightly higher for different age groups [31].
In our study, we found a relatively similar prevalence of deficiency compared with the literature, slightly lower for the 20–39 age group, the 40–59 age group and the 60–79 age group, with mean levels of serum 25(OH)D, at the lower limit, especially in the 40–59 age group and 60–79 group and lower for the 20–39 age group and >80 years compared with previous published data [31,33].
We hypothesise that this could be the result of increasing awareness of vitamin deficiency correction and possible supplementation due to coronavirus pandemic.
## 4.3. Seasonal Variation
There was a slight predominance of the winter and spring season for vitamin D deficiency and for vitamin D insufficiency; it has been linked to spring and summer seasons. The sun exposure and seasonal dependency of 25(OH)D levels is well known and our study shows that in spring and winter seasons there is a higher risk of having vitamin D deficiency, while the summer season is related to the insufficiency. The months which are associated with higher rates vitamin D deficiency are from January until May, with no effect in June and with no relation to the others months.
Most studies showed a higher prevalence of the vitamin D deficit in autumn–winter seasons [28,42,43,44]. The median levels of 25(OH)D are high in summer and autumn seasons and are low in winter and spring. This aspect is correlated with the sun exposure. The most recent data from Romania revealed that the seasonal variability is descending from September to March (highest to lowest level of serum 25(OH)D), accordingly to the seasonal pattern in Romania, which has four seasons [32].
A Brazilian study assessed the vitamin D deficiency/insufficiency depending on seasons, showing higher prevalence of the deficiency at the end of autumn and winter ($21.7\%$ and $8.7\%$) than at the end of spring and summer ($1.5\%$ and $0\%$) for male individuals who have performed outdoor activities wearing shorts and t-shirts twice a week for 6 h. It has been shown there is a seasonal variation of the levels of 25(OH)D in obese patients following bariatric surgery. In 1071 evaluated patients, the highest levels have been assessed during summer (33.52 ± 12.70 ng/mL) and lowest levels during spring (29.72 ± 12.34 ng/mL and winter (29.90 ± 14.80 ng/mL) [42]. Moreover, the prevalence of the insufficiency, <30 ng/mL, was higher in the study group in autumn versus summer ($88.4\%$ vs $43.8\%$) [43].
A Mexican longitudinal study published in 2017 evaluated the season variation of vitamin D, showing lower levels during winter, increasing during spring with maximum levels during summer and autumn. The vitamin D deficiency was prevalent in winter, $60.0\%$, while, during summer, the deficiency of 25 (OH)D was $40.0\%$. The vitamin D insufficiency was $87.5\%$ in the winter–autumn period and $91.3\%$ during spring–summer [44]. A study from 2016, evaluating 55,844 European individuals, showed that $17.7\%$ had low levels of 25(OH)D, <30 nmol/L in the October–March period and $8.3\%$ during the April–November period. Moreover, the incidence of levels of 25(OH)D < 50 nmol/L, according to different definition, was $40.4\%$ [28].
We found similar seasonal variation with results from another Romanian study, with the lowest serum 25(OH)D in the spring and the highest in the fall season, consistent with global seasonal variation of 25(OH)D levels [32].
## 4.4. Pathologies Variations
Infectious disease are under the influence of vitamin D activity as an independent immunomodulation factor that contributes to immune defence through pleiotropic action [45]. Overall, the vitamin D deficiency is considered as a predisposing factor for acquiring an infection and in our study, we found that patients with infectious diseases were predisposed 3.7 times more to have lower than 20 ng/mL serum 25(OH)D.
The relationship between COVID-19 infection and vitamin D has been studied intensively in the last period [46,47]. 25(OH)D regulates the renin angiotensin system and ACE2 expression [46]. It was postulated low levels of 25(OH)D influenced susceptibility, severity and mortality of COVID-19 infection [48,49,50]. Vitamin D supplementation in COVID-19 was evaluated in some cohorts, revealing anti-inflammatory, antiviral, apoptotic and autophagic activity through antimicrobial peptides [47,48,49,50,51]. Vitamin D deficiency was associated, in our study, with SARS-CoV2 infection, but with no association with insufficiency of the 25(OH)D. The patients with infection had 1.38 times likelihood to have lower 25(OH)D levels (respective deficiency), while those with insufficiency had no statistical relevance.
The cardiovascular diseases included in our study were associated mainly with vitamin D deficiency and slightly with insufficient vitamin D, and presented 1.78 times more odds to have 25(OH)D < 20 ng/mL. The role of vitamin D was assessed in coronary artery disease, heart failure and atrial fibrillation and was linked with short-term and long-term prognosis [52,53,54,55]. The influence of vitamin D for cardiovascular disease (CVD) was evaluated in a large study (VITAL) [51] that assessed the role of vitamin D and omega 3 fatty acids as preventive factors for neoplasms and cardiac and vascular pathology [51] with no significant improvement for overall heart diseases mortality and major cardiac events. Vitamin D also showed anticoagulant activity, as it can regulate the expression of procoagulant and antifibrinolytic factors [56].
In this research, vitamin D deficiency was statistically significant associated with metabolic impairment. In our study, we found that obesity relates to vitamin D status, showing a 1.2 times increased risk to have vitamin D insufficiency, but when we addressed dysmetabolic status, we discovered that there was a 1.4 times higher probability to have vitamin D deficiency. Obesity seems to be one of the risk factors for low vitamin D levels [57] and there are more possible explanations: lower variation of vitamin D level through sunlight exposure and seasonal influence in the obese, comparative with normal weight people, impairment of 25-hydroxylation in the liver associated to NAFLD and obesity, larger volume of distribution (with consecutive dilution) and a lessened gene expression for cytochrome P450 in this category of patients [58].
For dyslipidaemia or diabetes mellitus (included in the general frame of metabolic syndrome), it was shown there was an association between lower 25(OH)D levels and poorer glycaemic control in diabetic patients and higher level of cholesterol (LDL fraction) in dyslipidemic patients [59]. Regarding other endocrine disorders, our study showed no particular pattern of vitamin D status, except the fact that patients with thyroid diseases tend to have rather optimal or >20 ng serum 25(OH)D levels, but no higher percentage of deficiency. This could be explained by the awareness and active screening of this patients leading to supplementation (data not available about any supplements of this patients).
The malignancies showed significant association with vitamin D deficiency, patients with malignancies are 2.1 times more likely to have vitamin D deficiency than the patients with no malignant diseases and also had a decreased probability to have sufficient or optimal 25(OH)D levels. In the previous VITAL study, vitamin D was associated with decreased death rates from cancer, but without any significant impact on cancer invasiveness character [51].
Vitamin D has been associated with a series of cancers (prostate, myeloma, colorectal, breast cancer), mediated through the vitamin D receptor (VDR) [60]. Vitamin D has revealed an essential role in the aetiology and management of cancer [60]. Moreover, higher levels of 25(OH)D are present in patients with early stage cancer than in those with advanced/metastatic disease [61,62]. It has been shown that vitamin D may protect against death from cancer [51,62]. The cut-off of vitamin D ≥40 ng/mL has been linked with a low risk for malignancy and all-cause mortality, as a consequences of the pleiotropic effects of the vitamin D [51,63]. A study that evaluated the risk for vitamin D deficiency in women with breast cancer established that $66\%$ of the women had deficit of 25(OH)D at baseline [64].
Although the literature assessed an association between vitamin D and various gastrointestinal diseases, our study showed a feeble relationship between deficient and insufficient 25(OH)D serum values and specific digestive pathology.
The published data indicated an association of vitamin D status with inflammatory bowel disease (IBD) [65,66], non-alcoholic fatty liver disease(NAFLD) [67] and irritable bowel syndrome (IBS) [68,69], but our study showed no specific relevance. Because vitamin D and calcium supplementation in many afflictions was shown to be linked to decreasing of the inflammatory status [70,71], further studies are necessary.
It is stated that mental and behavioural disorders covering a large spectrum are influenced by vitamin D through multiple factors: intrinsic (widespread of 25 (OH) vitamin D receptor in amygdala and neurons, the neuronal calcium regulation, cognitive functions) and extrinsic, such as sun exposure, deficient diet and some medication [72]. In our study, we found an association between vitamin D deficiency and the presence of mental diseases with OR= 1.259.
Regarding the widely studied disorders of musculoskeletal system and vitamin D interrelation, it is well known that lack of vitamin D is involved in the appearance of different degrees of low bone density and sarcopenia [73]. In our study, we found no significant associations between lower levels of vitamin D and diseases of the musculoskeletal system and connective tissue, and the most probable explanation is that those patients are in treatment with vitamin D supplements (we had no access to this data).
Renal impairment could influence the vitamin D level because the process of hydroxylation takes places in the kidneys and the action of 1-alfa hydroxylase is decreased, resulting lower level of active vitamin D [74].
In our research, patients with urinary and renal diseases had 2.3 times more odds to associate vitamin D deficiency, so we can take into consideration to measure the serum level and correct the eventual perturbances or prophylactic supplementation in those patients.
It has also been shown that low levels of 25(OH)D are associated with the development and progression of chronic kidney disease (CKD) and high mortality [75,76,77]. Moreover, vitamin D is essential for protecting kidney function through reducing 24 h urine protein and inflammatory status (CRP, TNF- α, IL-6) in patients with diabetic nephropathy, but without effects on eGFR [75].
The reports from prospective studies on admitted patients described high prevalence of 25(OH) suboptimal in more than >$50\%$ of inpatient and an important association with longer hospitalisation, malnourishment, poor quality of life and mortality, with greater rates of deficiency compared with general population, but the relationship of causality was not established [34,78].
In the context of increased awareness of vitamin D supplementation, the use of vitamin D over the counter in high doses revealed the possibility for complications. The literature has showed promising data on this topic showing possible alterations in case of high levels of 25(OH)D described as hypervitaminosis, >250 nmol/L (100 ng/mL) and intoxication, for levels over >375 nmol/L (150 ng/mL), which lead to hypercalciuria and hypercalcemia. The risk for hypervitaminosis from endogenous causes, such as granulomatous disorders or lymphomas has been assessed [30,79,80,81,82]. This study included five patients with vitamin D levels ≥150 ng/mL, but as the main subject of our research was not hypervitaminosis D, serum calcium levels were not evaluated, nor the possible symptomatology of the patients.
To our knowledge, this study is the first and the largest from Romania that reports vitamin D status association with pathology categories.
In this research, the main objective was to assess vitamin D status in a population with a mean of 8–10 h of sunlight (48 N to 43 N) and to establish a correlation with different pathologies. Still our findings must be interpreted with caution because of certain limitations. One of the limitations is the fact that we could not substantiate the relationship of cause and effect and to determine if deficiency of vitamin D firstly contributed to poorer health status or, inversely, the presence of the actual disease led to lower levels of 25(OH)D. We cannot address causality, because 25(OH)D was measured at the time of or after diagnosis, but we can hypothesize that disease-related factors such as reduced outdoor activity due to illness are associated with lower exposure to sunlight, decreased mobility and faulty nutrition and may contribute to 25(OH)D deficiency in hospitalized people. Another limitation of our study is the fact that is difficult to extrapolate our population sample with overall Romanian population and we are unable to compare with general data from healthy people due to no screening recommendations of vitamin D status in the normal population and the existing data are inhomogeneous, although our hospital is a regional one with addressability from all Romanian counties.
The strength of this study is the large contingent of patients, with heterogenous and wide range of pathologies that permitted to analyse multiple subgroups and parameters with high statistical power. Our data are congruent with the literature concerning vitamin D status monitoring in high risk categories, such as hospitalized patients, as it was shown that 25(OH)D deficiency is associated with morbidity in various medical condition subgroups.
## 5. Conclusions
There is an association of suboptimal vitamin D status and pathology, sex and age, especially with cardiovascular disease, malignancy, metabolic diseases, SARS-CoV2 infection, male sex and older age for hospitalised patients. The insufficient 25(OH)D (20–30 ng/mL) stands as the grey zone of vitamin D status, and the monitoring and vitamin D supplementation should be considered for risk categories, and national or international consensus and guidelines are mandatory.
## References
1. Holick M.F., Binkley N.C., Bischoff-Ferrari H.A., Gordon C.M., Hanley D.A., Heaney R.P., Murad M.H., Weaver C.M.. **Evaluation, Treatment, and Prevention of Vitamin D Deficiency: An Endocrine Society Clinical Practice Guideline**. *Med. J. Clin. Endocrinol. Metab.* (2011.0) **96** 1911-1930. DOI: 10.1210/jc.2011-0385
2. Christakos S., Dhawan P., Verstuyf A., Verlinden L., Carmeliet G.. **Vitamin D: Metabolism, Molecular Mechanism of Action, and Pleiotropic Effects**. *Physiol. Rev.* (2016.0) **96** 365-408. DOI: 10.1152/physrev.00014.2015
3. Bikle D.D.. **Vitamin D Metabolism, Mechanism of Action, and Clinical Applications**. *Chem. Biol.* (2014.0) **21** 319-329. DOI: 10.1016/j.chembiol.2013.12.016
4. Lahoz R., Sánchez J.P., Górriz S., Calmarza P.. **Comparative study of two immunoassays used for the determination of serum vitamin D**. *Pract. Lab. Med.* (2021.0) **26** e00242. DOI: 10.1016/j.plabm.2021.e00242
5. Janoušek J., Pilařová V., Macáková K., Nomura A., Veiga-Matos J., da Silva D.D., Remião F., Saso L., Malá-Ládová K., Malý J.. **Vitamin D: Sources, physiological role, biokinetics, deficiency, therapeutic use, toxicity, and overview of analytical methods for detection of vitamin D and its metabolites**. *Crit. Rev. Clin. Lab. Sci.* (2022.0) **59** 517-554. DOI: 10.1080/10408363.2022.2070595
6. Nimavat N., Singh S., Singh P., Singh S.K., Sinha N.. **Vitamin D deficiency and COVID-19: A case-control study at a tertiary care hospital in India**. *Ann. Med. Surg.* (2021.0) **68** 102661. DOI: 10.1016/j.amsu.2021.102661
7. Zierold C., Nehring J.A., Deluca H.F.. **Nuclear receptor 4A2 and C/EBPβ regulate the parathyroid hormone-mediated transcriptional regulation of the 25-hydroxyvitamin D3-1α-hydroxylase**. *Arch. Biochem. Biophys.* (2007.0) **460** 233-239. DOI: 10.1016/j.abb.2006.11.028
8. Tomaschitz A., Pilz S., Ritz E., Grammer T., Drechsler C., Boehm B.O., März W.. **Independent association between 1,25-dihydroxyvitamin D, 25-hydroxyvitamin D and the renin–angiotensin system: The Ludwigshafen Risk and Cardiovascular Health (LURIC) study**. *Clin. Chim. Acta* (2010.0) **411** 1354-1360. DOI: 10.1016/j.cca.2010.05.037
9. Kovalenko P.L., Zhang Z., Cui M., Clinton S.K., Fleet J.C.. **1,25 dihydroxyvitamin D-mediated orchestration of anticancer, transcript-level effects in the immortalized, non-transformed prostate epithelial cell line, RWPE1**. *BMC Genom.* (2010.0) **11**. DOI: 10.1186/1471-2164-11-26
10. Gaksch M., Jorde R., Grimnes G., Joakimsen R., Schirmer H., Wilsgaard T., Mathiesen E.B., Njølstad I., Løchen M.-L., März W.. **Vitamin D and mortality: Individual participant data meta-analysis of standardized 25-hydroxyvitamin D in 26916 individuals from a European consortium**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0170791
11. Aggarwal R., Akhthar T., Jain S.K.. **Coronary artery disease and its association with Vitamin D deficiency**. *J. Midlife Health* (2016.0) **7** 56-60. DOI: 10.4103/0976-7800.185334
12. Lips P., Eekhoff M., van Schoor N., Oosterwerff M., de Jongh R., Krul-Poel Y., Simsek S.. **Vitamin D and type 2 diabetes**. *J. Steroid Biochem. Mol. Biol.* (2017.0) **173** 280-285. DOI: 10.1016/j.jsbmb.2016.11.021
13. Udomsinprasert W., Jittikoon J.. **Vitamin D and liver fibrosis: Molecular mechanisms and clinical studies**. *Biomed. Pharmacother.* (2019.0) **109** 1351-1360. DOI: 10.1016/j.biopha.2018.10.140
14. Pludowski P., Takacs I., Boyanov M., Belaya Z., Diaconu C.C., Mokhort T., Zherdova N., Rasa I., Payer J., Pilz S.. **Clinical Practice in the Prevention, Diagnosis and Treatment of Vitamin D Deficiency: A Central and Eastern European Expert Consensus Statement**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14071483
15. Lips P., Cashman K.D., Lamberg-Allardt C., Bischoff-Ferrari H.A., Obermayer-Pietsch B., Bianchi M.L., Stepan J., El-Hajj Fuleihan G., Bouillon R.. **Current vitamin D status in European and Middle East countries and strategies to prevent vitamin D deficiency: A position statement of the European Calcified Tissue Society**. *Eur. J. Endocrinol.* (2019.0) **180** P23-P54. DOI: 10.1530/EJE-18-0736
16. Giustina A., Bouillon R., Binkley N., Sempos C., Adler R.A., Bollerslev J., Dawson-Hughes B., Ebeling P.R., Feldman D., Heijboer A.. **Controversies in Vitamin D: A Statement From the Third International Conference**. *JBMR Plus* (2020.0) **4** e10417. DOI: 10.1002/jbm4.10417
17. **Anexa2-Ghid-Pentru-Evaluarea-Statusului-Vitaminei-D-La-Adulti-1-1.pdf**
18. **ANEXA1vit-D-La-Nou-Nascut-Copil-Gravida.pdf**
19. Malinovschi A., Masoero M., Bellocchia M., Ciuffreda A., Solidoro P., Mattei A., Mercante L., Heffler E., Rolla G., Bucca C.. **Severe vitamin D deficiency is associated with frequent exacerbations and hospitalization in COPD patients**. *Respir. Res.* (2014.0) **15** 131. DOI: 10.1186/s12931-014-0131-0
20. Mc Williams C., Golestany K., Sharma R., Nejati G., Cyrus-Murden A., Kripichnikov D.. **Correlation of admitted nursing home residents’ hospital length of stay and vitamin D levels**. *J. Community Hosp. Intern. Med. Perspect.* (2011.0) **1** 6313. DOI: 10.3402/jchimp.v1i3.6313
21. Botros R.M., Besibes M.M.A., Bahaaeldin A.M., Elyazed S.A.. **Vitamin D Status in Hospitalized Chronically Ill Patients**. *J. Am. Coll. Nutr.* (2018.0) **37** 578-582. DOI: 10.1080/07315724.2018.1446194
22. Venkatram S., Chilimuri S., Adrish M., Salako A., Patel M., Diaz-Fuentes G.. **Vitamin D deficiency is associated with mortality in the medical intensive care unit**. *Crit. Care* (2011.0) **15** R292. DOI: 10.1186/cc10585
23. Griffin T.P., Wall D., Blake L., Griffin D.G., Robinson S.M., Bell M., Mulkerrin E.C., O’Shea P.M.. **Vitamin D Status of Adults in the Community, in Outpatient Clinics, in Hospital, and in Nursing Homes in the West of Ireland**. *J. Gerontol. Ser. A* (2020.0) **75** 2418-2425. DOI: 10.1093/gerona/glaa010
24. Moore N.L., Kiebzak G.M.. **Suboptimal vitamin D status is a highly prevalent but treatable condition in both hospitalized patients and the general population**. *J. Am. Acad. Nurse Pract.* (2007.0) **19** 642-651. DOI: 10.1111/j.1745-7599.2007.00277.x
25. Petrelli F., Oldani S., Borgonovo K., Cabiddu M., Dognini G., Ghilardi M., Parati M.C., Petro’ D., Dottorini L., Rea C.. **Vitamin D3 and COVID-19 Outcomes: An Umbrella Review of Systematic Reviews and Meta-Analyses**. *Antioxidants* (2023.0) **12**. DOI: 10.3390/antiox12020247
26. Okan S., Okan F., Demir O.. **Relation of Vitamin D Status with Season, Living Place, Age Gender and Chronic Disease**. *Erciyes Med. J.* (2020.0) **42** 78-83. DOI: 10.14744/etd.2019.45577
27. Schleicher R.L., Sternberg M.R., Looker A.C., Yetley E.A., Lacher D.A., Sempos C.T., Taylor C.L., Durazo-Arvizu R.A., Maw K.L., Chaudhary-Webb M.. **National Estimates of Serum Total 25-Hydroxyvitamin D and Metabolite Concentrations Measured by Liquid Chromatography–Tandem Mass Spectrometry in the US Population during 2007–2010**. *J. Nutr.* (2016.0) **146** 1051-1061. DOI: 10.3945/jn.115.227728
28. Cashman K.D., Dowling K.G., Škrabáková Z., Gonzalez-Gross M., Valtueña J., De Henauw S., Moreno L., Damsgaard C.T., Michaelsen K.F., Mølgaard C.. **Vitamin D deficiency in Europe: Pandemic?**. *Am. J. Clin. Nutr.* (2016.0) **103** 1033-1044. DOI: 10.3945/ajcn.115.120873
29. Cashman K.D.. **Vitamin D Deficiency: Defining, Prevalence, Causes, and Strategies of Addressing**. *Calcif. Tissue Int.* (2020.0) **106** 14-29. DOI: 10.1007/s00223-019-00559-4
30. Amrein K., Scherkl M., Hoffmann M., Neuwersch-Sommeregger S., Köstenberger M., Berisha A.T., Martucci G., Pilz S., Malle O.. **Vitamin D deficiency 2.0: An update on the current status worldwide**. *Eur. J. Clin. Nutr.* (2020.0) **74** 1498-1513. DOI: 10.1038/s41430-020-0558-y
31. Chirita-Emandi A., Socolov D., Haivas C., Calapiș A., Gheorghiu C., Puiu M.. **Vitamin D Status: A Different Story in the Very Young versus the Very Old Romanian Patients**. *PLoS ONE* (2015.0) **10**. DOI: 10.1371/journal.pone.0128010
32. Niculescu D.A., Capatina C.A.M., Dusceac R., Caragheorgheopol A., Ghemigian A., Poiana C.. **Seasonal variation of serum vitamin D levels in Romania**. *Arch. Osteoporos.* (2017.0) **12** 113. DOI: 10.1007/s11657-017-0407-3
33. Ene M.C., Terþiu O., Vrâncianu O., Chifiriuc M.C.. **Vitamin D Status in Adult and Pediatric Romanian Population**
34. Merker M., Amsler A., Pereira R., Bolliger R., Tribolet P., Braun N., Hoess C., Pavlicek V., Bilz S., Sigrist S.. **Vitamin D deficiency is highly prevalent in malnourished inpatients and associated with higher mortality**. *Medicine* (2019.0) **98** e18113. DOI: 10.1097/MD.0000000000018113
35. Souberbielle J.-C., Massart C., Brailly-Tabard S., Cavalier E., Chanson P.. **Prevalence and determinants of vitamin D deficiency in healthy French adults: The VARIETE study**. *Endocrine* (2016.0) **53** 543-550. DOI: 10.1007/s12020-016-0960-3
36. Khadilkar A., Kajale N., Oza C., Oke R., Gondhalekar K., Patwardhan V., Khadilkar V., Mughal Z., Padidela R.. **Vitamin D status and determinants in Indian children and adolescents: A multicentre study**. *Sci. Rep.* (2022.0) **12** 16790. DOI: 10.1038/s41598-022-21279-0
37. da Silveira E.A., Moura L.d.A.N.e., Castro M.C.R., Kac G., Hadler M.C.C.M., Noll P.R.E.S., Noll M., Rezende A.T.d.O., Delpino F.M., de Oliveira C.. **Prevalence of Vitamin D and Calcium Deficiency and Insufficiency in Women of Childbearing Age and Associated Risk Factors: A Systematic Review and Meta-Analysis**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14204351
38. Kweder H., Eidi H.. **Vitamin D deficiency in elderly: Risk factors and drugs impact on vitamin D status**. *Avicenna J. Med.* (2018.0) **8** 139-146. DOI: 10.4103/ajm.AJM_20_18
39. Wei J., Zhu A., Ji J.S.. **A Comparison Study of Vitamin D Deficiency among Older Adults in China and the United States**. *Sci. Rep.* (2019.0) **9** 19713. DOI: 10.1038/s41598-019-56297-y
40. Holick M.F.. **High Prevalence of Vitamin D Inadequacy and Implications for Health**. *Mayo Clin. Proc.* (2006.0) **81** 353-373. DOI: 10.4065/81.3.353
41. Schöttker B., Hagen L., Zhang Y., Gào X., Holleczek B., Gao X., Brenner H.. **Serum 25-Hydroxyvitamin D Levels as an Aging Marker: Strong Associations With Age and All-Cause Mortality Independent From Telomere Length, Epigenetic Age Acceleration, and 8-Isoprostane Levels**. *J. Gerontol. Ser. A* (2018.0) **74** 121-128. DOI: 10.1093/gerona/gly253
42. Hays H., Flores L.E., Kothari V., Bilek L., Geske J., Skinner A.. **Vitamin D Status and Seasonal Variation: A Retrospective Single Institution Database Study of Patients Pursuing Metabolic/Bariatric Surgery**. *Clin. Nutr. Open Sci.* (2021.0) **41** 1-9. DOI: 10.1016/j.nutos.2021.11.002
43. Fontanive T.O., Dick N.R.M., Valente M.C.S., Laranjeira V.D.S., Antunes M.V., Corrêa M.D.P., Alves R.D.C.M., Linden R., Furlanetto T.W.. **Seasonal variation of vitamin D among healthy adult men in a subtropical region**. *Rev. Assoc. Med. Bras.* (2020.0) **66** 1431-1436. DOI: 10.1590/1806-9282.66.10.1431
44. Elizondo-Montemayor L., Castillo E.C., Rodríguez-López C., Villarreal-Calderón J.R., Gómez-Carmona M., Tenorio-Martínez S., Nieblas B., García-Rivas G.. **Seasonal Variation in Vitamin D in Association with Age, Inflammatory Cytokines, Anthropometric Parameters, and Lifestyle Factors in Older Adults**. *Mediat. Inflamm.* (2017.0) **2017** 1-14. DOI: 10.1155/2017/5719461
45. Miragliotta G., Miragliotta L.. **Vitamin D and Infectious Diseases**. *Endocr. Metab. Immune Disord.—Drug Targets* (2014.0) **14** 267-271. DOI: 10.2174/1871530314666141027102627
46. Varikasuvu S.R., Thangappazham B., Vykunta A., Duggina P., Manne M., Raj H., Aloori S.. **COVID-19 and vitamin D (Co-VIVID study): A systematic review and meta-analysis of randomized controlled trials**. *Expert Rev. Anti Infect. Ther.* (2022.0) **20** 907-913. DOI: 10.1080/14787210.2022.2035217
47. Pinzariu A.C., Sova I.A., Maranduca M.A., Filip N., Drochioi I.C., Vamesu C.G., Clim A., Hurjui L.L., Moscalu M., Soroceanu R.P.. **Vitamin D Deficiency in Both Oral and Systemic Manifestations in SARS-CoV-2 Infection: Updated Review**. *Medicina* (2022.0) **59**. DOI: 10.3390/medicina59010068
48. Gois P.H.F., Ferreira D., Olenski S., Seguro A.C.. **Vitamin D and Infectious Diseases: Simple Bystander or Contributing Factor?**. *Nutrients* (2017.0) **9**. DOI: 10.3390/nu9070651
49. AlSafar H., Grant W.B., Hijazi R., Uddin M., Alkaabi N., Tay G., Mahboub B., Al Anouti F.. **COVID-19 Disease Severity and Death in Relation to Vitamin D Status among SARS-CoV-2-Positive UAE Residents**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13051714
50. Mahdavi A.M.. **A brief review of interplay between vitamin D and angiotensin-converting enzyme 2: Implications for a potential treatment for COVID-19**. *Rev. Med. Virol.* (2020.0) **30** e2119. DOI: 10.1002/rmv.2119
51. Manson J.E., Bassuk S.S., Buring J.E.. **Principal results of the VITamin D and OmegA-3 TriaL (VITAL) and updated meta-analyses of relevant vitamin D trials**. *J. Steroid Biochem. Mol. Biol.* (2019.0) **198** 105522. DOI: 10.1016/j.jsbmb.2019.105522
52. Zittermann A., Trummer C., Theiler-Schwetz V., Lerchbaum E., März W., Pilz S.. **Vitamin D and Cardiovascular Disease: An Updated Narrative Review**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22062896
53. Mensah G.A., Wei G.S., Sorlie P.D., Fine L.J., Rosenberg Y., Kaufmann P.G., Mussolino M.E., Hsu L., Addou E., Engelgau M.M.. **Decline in Cardiovascular Mortality: Possible Causes and Implications**. *Circ. Res.* (2017.0) **120** 366-380. DOI: 10.1161/CIRCRESAHA.116.309115
54. Zittermann A.. **Vitamin D Status, Supplementation and Cardiovascular Disease**. *Anticancer Res.* (2018.0) **38** 1179-1186. DOI: 10.21873/anticanres.12338
55. Cosentino N., Campodonico J., Milazzo V., De Metrio M., Brambilla M., Camera M., Marenzi G.. **Vitamin D and Cardiovascular Disease: Current Evidence and Future Perspectives**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13103603
56. Wang H., Chen W., Li D., Yin X., Zhang X., Olsen N., Zheng S.G.. **Vitamin D and Chronic Diseases**. *Aging Dis.* (2017.0) **8** 346-353. DOI: 10.14336/AD.2016.1021
57. Lips P.. **Vitamin D physiology**. *Prog. Biophys. Mol. Biol.* (2006.0) **92** 4-8. DOI: 10.1016/j.pbiomolbio.2006.02.016
58. Vranić L., Mikolašević I., Milić S.. **Vitamin D Deficiency: Consequence or Cause of Obesity?**. *Medicina* (2019.0) **55**. DOI: 10.3390/medicina55090541
59. Alkhatatbeh M.J., Abdul-Razzak K.K., Khasawneh L.Q., Saadeh N.A.. **High Prevalence of Vitamin D Deficiency and Correlation of Serum Vitamin D with Cardiovascular Risk in Patients with Metabolic Syndrome**. *Metab. Syndr. Relat. Disord.* (2017.0) **15** 213-219. DOI: 10.1089/met.2017.0003
60. Gupta D., Vashi P.G., Trukova K., Lis C.G., Lammersfeld C.A.. **Prevalence of serum vitamin D deficiency and insufficiency in cancer: Review of the epidemiological literature**. *Exp. Ther. Med.* (2011.0) **2** 181-193. DOI: 10.3892/etm.2011.205
61. Feldman D., Krishnan A.V., Swami S., Giovannucci E., Feldman B.J.. **The role of vitamin D in reducing cancer risk and progression**. *Nat. Rev. Cancer* (2014.0) **14** 342-357. DOI: 10.1038/nrc3691
62. Manson J.E., Cook N.R., Lee I.M., Christen W., Bassuk S.S., Mora S., Gibson H., Gordon D., Copeland T., D’Agostino D.. **Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease**. *N. Engl. J. Med.* (2019.0) **380** 33-44. DOI: 10.1056/NEJMoa1809944
63. Caprio M., Infante M., Calanchini M., Mammi C., Fabbri A.. **Vitamin D: Not just the bone. Evidence for beneficial pleiotropic extraskeletal effects**. *Eat. Weight. Disord.-Stud. Anorex. Bulim. Obes.* (2017.0) **22** 27-41. DOI: 10.1007/s40519-016-0312-6
64. Finstad W., Murphy K., Markey G., Connor D., Murphy C.. **Prevalence of vitamin D3 deficiency among women with early breast cancer receiving chemotherapy in an oncology dayward**. *Ann. Oncol.* (2019.0) **30** v95. DOI: 10.1093/annonc/mdz240.106
65. Wu Z., Liu D., Deng F.. **The Role of Vitamin D in Immune System and Inflammatory Bowel Disease**. *J. Inflamm. Res.* (2022.0) **15** 3167-3185. DOI: 10.2147/JIR.S363840
66. Li X.-X., Liu Y., Luo J., Huang Z.-D., Zhang C., Fu Y.. **Vitamin D deficiency associated with Crohn’s disease and ulcerative colitis: A meta-analysis of 55 observational studies**. *J. Transl. Med.* (2019.0) **17** 323. DOI: 10.1186/s12967-019-2070-5
67. Adenote A., Dumic I., Madrid C., Barusya C., Nordstrom C.W., Prada L.R.. **NAFLD and Infection, a Nuanced Relationship**. *Can. J. Gastroenterol. Hepatol.* (2021.0) **2021** 1-10. DOI: 10.1155/2021/5556354
68. Tazzyman S., Richards N., Trueman A.R., Evans A.L., Grant V.A., Garaiova I., Plummer S.F., Williams E., Corfe B.M.. **Vitamin D associates with improved quality of life in participants with irritable bowel syndrome: Outcomes from a pilot trial**. *BMJ Open Gastroenterol.* (2015.0) **2** e000052. DOI: 10.1136/bmjgast-2015-000052
69. Huang H., Lu L., Chen Y., Zeng Y., Xu C.. **The efficacy of vitamin D supplementation for irritable bowel syndrome: A systematic review with meta-analysis**. *Nutr. J.* (2022.0) **21** 24. DOI: 10.1186/s12937-022-00777-x
70. Hopkins M.H., Owen J., Ahearn T., Fedirko V., Flanders W.D., Jones D.P., Bostick R.M.. **Effects of Supplemental Vitamin D and Calcium on Biomarkers of Inflammation in Colorectal Adenoma Patients: A Randomized, Controlled Clinical Trial**. *Cancer Prev. Res.* (2011.0) **4** 1645-1654. DOI: 10.1158/1940-6207.CAPR-11-0105
71. Agrawal D., Yin K.. **Vitamin D and inflammatory diseases**. *J. Inflamm. Res.* (2014.0) **7** 69-87. DOI: 10.2147/jir.s63898
72. Cuomo A., Maina G., Bolognesi S., Rosso G., Crescenzi B.B., Zanobini F., Goracci A., Facchi E., Favaretto E., Baldini I.. **Prevalence and Correlates of Vitamin D Deficiency in a Sample of 290 Inpatients With Mental Illness**. *Front. Psychiatry* (2019.0) **10** 167. DOI: 10.3389/fpsyt.2019.00167
73. Wintermeyer E., Ihle C., Ehnert S., Stöckle U., Ochs G., de Zwart P., Flesch I., Bahrs C., Nussler A.K.. **Crucial Role of Vitamin D in the Musculoskeletal System**. *Nutrients* (2016.0) **8**. DOI: 10.3390/nu8060319
74. Melamed M.L., Thadhani R.I.. **Vitamin D Therapy in Chronic Kidney Disease and End Stage Renal Disease**. *Clin. J. Am. Soc. Nephrol.* (2012.0) **7** 358-365. DOI: 10.2215/CJN.04040411
75. Wang Y., Yang S., Zhou Q., Zhang H., Yi B.. **Effects of Vitamin D Supplementation on Renal Function, Inflammation and Glycemic Control in Patients with Diabetic Nephropathy: A Systematic Review and Meta-Analysis**. *Kidney Blood Press. Res.* (2019.0) **44** 72-87. DOI: 10.1159/000498838
76. Obi Y., Hamano T., Isaka Y.. **Prevalence and Prognostic Implications of Vitamin D Deficiency in Chronic Kidney Disease**. *Dis. Markers* (2015.0) **2015** 868961. DOI: 10.1155/2015/868961
77. Kantas T., Capriles C.A.A., Babor S., Tamdin T., Al-Rihani H., Thalla A., Abdelmawla A.A., Muthanna F.M.S., Tousif S.. **Relationship between Chronic Kidney Disease Staging and Vitamin D Deficiency: A Retrospective Study**. *Cureus* (2022.0). DOI: 10.7759/cureus.r55
78. Graedel L., Merker M., Felder S., Kutz A., Haubitz S., Faessler L., Kaeslin M., Huber A., Mueller B., Schuetz P.. **Vitamin D Deficiency Strongly Predicts Adverse Medical Outcome Across Different Medical Inpatient Populations: Results from a Prospective Study**. *Medicine* (2016.0) **95** e3533. DOI: 10.1097/MD.0000000000003533
79. Alkundi A., Momoh R., Musa A., Nwafor N.. **Vitamin D intoxication and severe hypercalcaemia complicating nutritional supplements misuse**. *BMJ Case Rep.* (2022.0) **15** e250553. DOI: 10.1136/bcr-2022-250553
80. Dudenkov D.V., Yawn B.P., Oberhelman S.S., Fischer P.R., Singh R.J., Cha S.S., Maxson J.A., Quigg S.M., Thacher T.. **Changing Incidence of Serum 25-Hydroxyvitamin D Values above 50 ng/mL: A 10-Year Population-Based Study**. *Mayo Clin. Proc.* (2015.0) **90** 577-586. DOI: 10.1016/j.mayocp.2015.02.012
81. Holick M.F.. **Vitamin D Is Not as Toxic as Was Once Thought: A Historical and an Up-to-Date Perspective**. *Mayo Clin. Proc.* (2015.0) **90** 561-564. DOI: 10.1016/j.mayocp.2015.03.015
82. Tebben P.J., Singh R.J., Kumar R.. **Vitamin D-Mediated Hypercalcemia: Mechanisms, Diagnosis, and Treatment**. *Endocr. Rev.* (2016.0) **37** 521-547. DOI: 10.1210/er.2016-1070
|
---
title: Caloric and Lipid Profiles during Pregnancy in a Socio-Culturally Diverse Society
authors:
- Elisabet Fernández-Gómez
- Miriam Mohatar-Barba
- María López-Olivares
- Trinidad Luque-Vara
- María Angustias Sánchez-Ojeda
- Adelina Martín-Salvador
- Carmen Enrique-Mirón
journal: Foods
year: 2023
pmcid: PMC10000863
doi: 10.3390/foods12051111
license: CC BY 4.0
---
# Caloric and Lipid Profiles during Pregnancy in a Socio-Culturally Diverse Society
## Abstract
This research analyzes the determining factors in diet quality among the Spanish pregnant population with the aim of promoting healthier eating habits and preventing the development of non-communicable diseases. It is a diagnostic, non-experimental, cross-sectional, and observational study, with correlational descriptive methodology, and 306 participants. The information was collected using the 24 h dietary recall. Various sociodemographic factors that influence diet quality were analyzed. It was found that pregnant women consume too much protein and fat, score high in SFA consumption, and do not achieve the CH recommendations, consuming twice as much sugar. Carbohydrate intake is inversely related to income (β = −0.144, $p \leq 0.005$). Likewise, protein intake is linked to marital status (β = −0.114, $p \leq 0.005$) and religion (β = 0.110, $p \leq 0.005$). Finally, lipid intake appears conditional upon age (β = 0.109, $p \leq 0.005$). As regards the lipid profile, a positive association is only observed with age and MFA consumption (β = 0.161, $p \leq 0.01$). On the other hand, simple sugars are positively related to education (β = 0.106, $p \leq 0.005$). The results of this research show that the diet quality of pregnant women does not meet the nutritional recommendations established for the Spanish population.
## 1. Introduction
At present, diet is considered one of the factors of greatest influence on well-being, health, and life quality, thus showing a direct action on the morbidity and mortality of a determined population. Hence, a healthy diet is one of the most important aspects for improving health [1].
Noncommunicable diseases (NCD) are the main cause of death and disability in women worldwide, including women at a reproductive age [2]. The Sustainable Development Agency (SDA) includes specific targets on maternal health and NCD, such as a reduction in the global maternal mortality rate to 70 deaths for every 100,000 live births, and a reduction by one-third in premature mortality due to NCD [3].
NCD, including cardiovascular diseases (CVD), cancer, chronic respiratory diseases, and neurodegenerative diseases, are the main cause of morbidity and mortality in the world. Among the main risk factors, we can highlight those aspects relating to lifestyle, such as an unbalanced diet, obesity, physical inactivity, emotional state, and quality of life as well as smoking and alcohol consumption [4,5,6]. Nevertheless, excess weight and obesity continue to rise each year due to multiple factors, including poor eating habits [7,8,9], characterized by high levels of processed meats and fats, saturated fats, refined grains, salt and sugars, and a lack of fresh foods, fruit, and vegetables [10].
At the end of the 19th century, the Spanish diet gradually covered nutrient and energy requirements, being more costly in minors, adult women, and pregnant women. At the end of the 20th century and the beginning of the 21st, as has occurred in other countries, energy intake has increased in an excessive and unbalanced manner causing deficiencies in the main micronutrients [11].
The gestation period is a critical time for establishing the risks of chronic diseases in offspring [12]. Nutrition plays a key role during this period of development and, since it is a determining factor of risk throughout life, it is a modifiable risk factor. Although the World Health Organization (OMS) provides guidelines for prenatal care [13], there is a lack of comprehensive guidelines detailing the nutritional requirements of women during reproduction, from preconception through pregnancy and breastfeeding [14].
Pregnancy is a vulnerable stage where the evidence on maternal nutrition shows the importance it exercises on the mother’s health and on healthy fetal growth and development [15]. An inadequate nutritional intake during this stage of life may have negative consequences for health in the short and long term, both for the mother [16] and for the child [17] such as premature births or miscarriages [18], hypertensive disorders [19], obesity or diabetes in childhood [20,21,22], alterations in fetal growth [23], and susceptibility to allergies and bacterial infections [24] among others. Nevertheless, a healthy diet before and during pregnancy is associated with a lower risk of all these diseases [25,26,27,28].
Some studies indicate that Spanish women do not meet the dietary recommendations of scientific societies [29,30]. This failure is related to pregnant women’s socioeconomic level, culture, age, and tobacco and alcohol consumption [31,32,33], among different factors. Therefore, culture is a factor that influences dietary habits. The fact is that the frequency of daily food intake or the quantity or the type of food consumed will depend more on culture than the availability of the food in itself [34,35].
The city of Melilla, where this study is performed, is a border city with Morocco, which contributes to the city’s multiculturality. This proximity means that many Moroccans cross the border to seek healthcare [36]. Melilla is, therefore, an optimum scenario for studying the social and cultural differences in relation to eating habits during pregnancy.
Hence, a general objective of this study is to analyze dietary quality in pregnant women in the multicultural city of Melilla, as well as the factors that may influence it with the aim of promoting healthy eating habits at this stage.
## 2.1. Study Design and Participants
It is a diagnostic, non-experimental, cross-sectional, and observational study, with correlational descriptive research methodology.
The sample was selected by convenience probability sampling from the populational data on pregnant women collected in the public health system of the city of Melilla in the last 18 years. The sample is formed of 306 pregnant women, with an average age of 29.92 (5.51) years old, with the minimum age being 18 and a maximum age of 43; specifically, 196 (64.1) were born in the city of Melilla. The characteristics of the sample such as residence, place of birth, number of children, marital status, education, and income are shown in Table 1.
## 2.2. Instruments and Procedure
The 24 h dietary recall by Rodríguez et al. [ 37] was used to determine diet quality. It is a questionnaire completed by the participants recording the number of grams of food ingested during the previous day (breakfast, lunch, afternoon snack, and food intake between meals) after providing them with the appropriate explanation to estimate said amounts in order to increase result reliability [38]. Tables were used with images of representations of food and drinks with various sizes and grams to facilitate data collection.
This questionnaire not only obtains in detail the quality and quantity of the food and drink (grams), but also details the culinary process and places emphasis on the quantity and quality of bread, oil, and sugar.
The participants completed the questionnaires in-person after written informed consent was provided. These data were gathered between March and December 2021.
## 2.3. Statistical Analysis
The data obtained were analyzed with the statistical program SPSS in its version 26.0 (International Business Machines Corporation (IBM), Armonk, NY, USA).
The basic statistics were used, according to the nature of the variables, for the descriptive analysis. Thus, for the quantitative variables the measures of central tendency (mean, median, mode), dispersion (typical deviation), and position (distribution limits) measurements were used whilst absolute and relative frequencies (percentages) were used for the qualitative variables.
Non-parametric tests were used for the interferential analysis according to the values presented by the Kolmogorov–Smirnov test. The chi-squared test was used for the comparison of proportions and $p \leq 0.05$ was considered a value of statistical significance. Likewise, the Mann–Whitney U test and the Kruskal–Wallis test were used to relate diet quality with sociocultural factors depending on the number of categories of independent variables.
Three multiple regression models were performed with the independent variables dichotomized with the aim of verifying the degree of determination that the sociodemographic variables may have on the caloric, lipid, and simple sugar consumption profiles. An Ordinary Least Squares (OLC) analysis was performed to compare the dependent variables (caloric profile, lipid profile, and simple sugar consumption) with the rest of the study variables; standardized and non-standardized regression coefficients were also obtained (β).
The data provided by the 24 h dietary recall in the form of food were transformed into energy intake, consumption of macronutrients (carbohydrates, lipids, and proteins), micronutrients (vitamins and minerals), and plant fiber using the IENVA dietary calculator (https://calcdieta.ienva.org/tu_menu.php, accessed on 3 January 2023) with the advice of a nutritionist. The grams of daily consumption of each immediate principle were compiled, as well as the sugars and types of fatty acids, it was later multiplied by the kcal that each one of them provides, and to calculate the percentage of the total caloric value (TCV), it was multiplied by 100 and divided by the total kcal ingested.
## 2.4. Ethical Considerations
This research was governed by the ethical principles of the Declaration of Helsinki.
The participants were informed of the study characteristics, as well as its objectives, and agreed to take part voluntarily. Formal consent was requested by signing the informed consent.
## 3. Results
With respect to the intake of macro and micronutrients, the nutritional requirements varied for pregnant women depending on whether they were in the first or second half of pregnancy. For this reason, the data referring to nutrient intake are shown distributed in line with this nutritional parameter. Table 2 shows the median and interquartile range values related to the intake of energy, macronutrients, fiber, and cholesterol.
The caloric and lipid profiles are found in Table 3. With respect to the caloric profile for the total sample, proteins provide $16.29\%$ of the energy consumed, $45.46\%$ corresponds to carbohydrates ($18.6\%$ in the form of simple sugar), and the remaining $38.36\%$ is provided by lipids. In terms of the lipid profile, the energy intake of saturated fats (SFA) for the total sample is $11.53\%$, monounsaturated fats (MFA) provide $18.26\%$, and polyunsaturated fats (PFA) $4.93\%$. There are significant differences between the two gestation periods for lipids ($$p \leq 0.006$$) in the case of the caloric profile and for MFA ($$p \leq 0.010$$) and PFA ($$p \leq 0.018$$).
Table 4 shows how the caloric profile is distributed within the limits established according to the nutritional targets established for the Spanish population [39]. In this regard, it is observed how more than half of the participants in both periods have a higher protein consumption (for $54.5\%$ and $57.8\%$, in the first half of pregnancy and in the second, respectively, the proteins make up a TCV contribution greater than $15\%$). Likewise, for $71\%$ and $65.8\%$, the fats were greater than $35\%$ of the VCT. With respect to carbohydrates, only $16.6\%$ and $21.1\%$ of the pregnant women corresponding to the first and second gestation period, respectively, follow the recommendations (between 50 and $55\%$). In relation to the aggregate sugars, it should be highlighted that $87.6\%$ and $91.9\%$ of pregnant women belonging to the first or second gestation period consume more than $10\%$ of the recommended sugars.
As regards the lipid profile, over half of the participants, belonging to both gestation groups, tend to consume more than $10\%$ of SFA (Table 5).
Table 6 sets down the consumption of micronutrients (minerals and vitamins) in both gestation periods. It should be stressed that the mineral intake does not meet the recommendations with respect to calcium, iron, zinc, magnesium, and potassium. On the other hand, as regards the vitamins, the intake is not met for FA and vitamins A, D, and E.
The results of the regression models performed considering the caloric profile as dependent variables and the sociodemographic variables as independent variables are shown in Table 7. Carbohydrate intake is inversely related to income (β = −0.144, $p \leq 0.005$), so with lower incomes there would be greater carbohydrate consumption. Likewise, protein intake is linked to marital status (β = −0.114, $p \leq 0.005$) and religion (β = 0.110, $p \leq 0.005$) since pregnant women with a partner and those who are Muslim consume the least amount of protein. Finally, lipid intake appears conditional upon age (β = 0.109, $p \leq 0.005$) with the oldest women consuming the largest amount of this macronutrient.
Table 8 shows the results of the regression performed on the lipid profile as dependent variables, and the sociodemographic variables as independent variables. As regards the lipid profile, a positive association is only observed with age and MFA consumption (β = 0.161, $p \leq 0.01$); i.e., the youngest pregnant women consumed the least MFA. On the other hand, simple sugars are inversely related to education (β = 0.106, $p \leq 0.005$). The women with the lowest education are those who consume the highest amount of simple sugars.
## 4. Discussion
An imbalanced caloric and lipid profile is observed in this research. Over half of the participants show an intake exceeding the protein recommendations, which also occurs in other studies [41,42]. Carbohydrate intake was below the recommendations and the total fat intake was exceeded according to the dietary references, coinciding with other studies [29,43,44,45]. Nevertheless, although these studies did not differentiate between the types of fats consumed in the diet, it is known that ϖ-3 fatty acids during pregnancy improve infant cognitive development [46] and prevent allergic diseases [47]. However, the fact that the total fat intake exceeds the recommendations may contribute to an unhealthy increase in maternal weight. This is associated with a higher risk of preeclampsia, gestational Diabetes Mellitus, macrosomia, congenital anomalies, and newborns with low birth weight and maternal mortality [48].
The group studied ingests an average of 1891.35 ± 529.18 kcal/day, practically coinciding with Izquierdo Guerrero [40], which introduced an average of 1984.75 ± 579.84 kcal/day. The average energy intake in pregnant women during the first gestational period is 1767 Kcal/day and of 1898 Kcal/day in the second. Therefore, it can be observed that pregnant women do not reach the energy recommendations [39].
Around $90\%$ of pregnant women eat more sugar than they should. The high consumption of commercial juices, pastries, sweets, and ice cream is responsible for this; our data are similar to the ANIBES study [49]. A higher consumption of sugars and fatty acids in pregnancy is associated with high adiposity in the offspring [50,51,52].
With respect to sugars, the WHO [53] recommends decreasing consumption below $10\%$ of the total energy intake, since it causes an increase in weight and tooth decay. A consumption below $5\%$ would give rise to health benefits.
Furthermore, the participants’ lipid profile has a high energy intake through SFA, as occurs in other studies with similar groups [41,54,55]. MFA and PFA, however, do not reach the recommended consumption percentage. Likewise, the study by Ortega Anta et al. [ 56] highlights the low PFA consumption, recommending the increase in consumption of fish and/or food enriched with PFA to achieve health benefits.
According to the FEN (Spanish Nutritional Foundation) [57] women of fertile age should take care of their diet, not excluding any essential nutrients so that when they become pregnant, they do not undergo additional nutritional risks. AGP-3 should be provided daily in the diet through fish or nuts, among other foods containing contain them.
An intake of 22–25 g of fiber per day is recommended in women. Unfortunately, in Europe, these recommendations are not reached and very few countries offer guidance on the sources of food that contain fiber to achieve a suitable intake [58]. There are countries, such as the Scandinavian countries, which recommend a higher intake of wholegrains, approximately 75 g per day [59]. Fiber consumption in this study is considerably less than the recommendations, 12–15 g per day, far from the reference data. Furthermore, Carbajal-Azcona [60] adds the intake of around 35 g/day of fiber and moderating sugar intake in his general nutritional advice for pregnant women.
A meta-analysis of the dietary data obtained in several developed countries informed that pregnant women have difficulties in following the national dietary guidelines for macro and micronutrients [61]. The same occurs in the research presented here: they do not meet the intake recommendations of the minerals, calcium, iron, magnesium, zinc, and K and vitamins A, D, and E, and FA, coinciding with Kocyłowski et al. [ 62], and with Rodríguez-Bernal et al. [ 29] where an insufficient intake of FA, iron, and vitamin E was shown. Likewise, the results shown coincide with other studies where a lack of folates, vitamin D, calcium, iron, iodine, zinc, and vitamins of the group [63,64] is verified.
Adequate iron intake during pregnancy may reduce the risk of anemia, newborns with low weight, and premature births [65,66]. It is also important to maintain an adequate calcium intake since it helps reduce the risk of preeclampsia [67].
The Expect I study, based on calcium intake in the diet and the use of supplements in pregnant women in the Netherlands, concluded that $42\%$ of pregnant women had an inadequate calcium intake and that supplements are frequently used, but the majority do not contain sufficient quantities to remedy this inadequate intake [68]. Likewise, calcium consumption in this study is fairly insufficient, lacking around 400–500 mg daily.
FIGO highlights the importance of a healthy and varied diet, with supplements or fortified foods when necessary, it promotes the adoption of healthy eating habits before pregnancy, and it recognizes and provides adequate intervention for micronutrient deficiencies [69].
Various studies performed with Spanish pregnant women have demonstrated that the diet followed by pregnant women is not totally adequate. Thus, Izquierdo-Guerrero [41], in his research with pregnant women from Madrid, finds a high protein and fat consumption, especially saturated fat, and a deficiency in micronutrients, which do not meet the recommended intakes (IR). On the other hand, Jardí et al. [ 32] determined that the consumption of red and processed meat and cakes and pastries exceeded recommendations whilst the consumption of healthy food decreased from the first trimester until after the postpartum period.
Two studies performed by Ruiz [70] and Izquierdo-Guerrero [41] asserted that older women have a greater consumption of sugars, lipids, and MFA; similar data to that obtained in our study. Likewise, Izquierdo-Guerrero [41] did not find significant associations with income and CH consumption, unlike our study, which found that women with the lowest income consumed greater CH. On the other hand, according to Mohatar-Barba [71], Muslims usually eat less protein, which is similar to our results. The scarcity of studies that link sociocultural factors and diet quality in pregnant women is noteworthy. For this reason, we can consider that this is a novel study, as it is one of the first that links these variables.
Among the study’s limitations we should highlight its cross-sectional design since it does not allow cause–effect relationships to be established among the variables. This study considers the eating habits of pregnant women, but it does not collect information about diet quality before conception and during the postpartum period, something that is considered important for correct monitoring. It should also be mentioned that the only factor not included in this study is tobacco consumption, which is usually considered when assessing lifestyles. As an incidental non-probability sampling is performed, it does not cover a significant representation of all the cultures found in Melilla. The sample quality can be improved by increasing the number of participants and choosing representatives from the different cities of Spain. On the other hand, the incorporation of nutrition education content into the educational system should be addressed, with the aim of fighting against inadequate eating habits that may cause long-term health problems. Finally, it should be mentioned that there was difficulty in finding studies on pregnant women that link diet quality with other sociodemographic factors.
With a view to the future, it is recommended that the first nutritional interventions are implemented in the preconception period, since this influences the state of the mother’s health, in addition to having an influence on the results of the pregnancy [30,72,73]. Performing an educational nutritional intervention as a pilot project should also be considered to be able to assess its subsequent implementation and improve the proposal’s design. Finally, another important aspect to consider is performing a stratified probability sampling. This would achieve a more representative sampling of Melilla’s population.
## 5. Conclusions
The results obtained in this study reveal that, in general, the caloric and lipid profiles of pregnant women in the city of Melilla do not meet the healthy recommendations established for the Spanish population. They consume too many proteins and fats, score high on SFA, and do not achieve CH recommendations as their diet contains twice the amount of sugar recommended. Likewise, they do not meet the recommendations for the intake of calcium, iron, magnesium, zinc, potassium, FA, and vitamins A, D, and E.
Furthermore, there are certain factors that may influence said intakes, such as religion, age, income, and marital status. Nevertheless, residency showed no association.
Regarding religion and marital status, Muslims and pregnant women with partners show a lower protein consumption. On the other hand, women with lower income consumed greater CH, and those with the lowest level of education consumed more simple sugars. Finally, older women consumed more lipids, specifically MFA.
## References
1. Alzahrani S.H., Saeedi A.A., Baamer M.K., Shalabi A.F., Alzahrani A.M.. **Eating Habits Among Medical Students at King Abdulaziz University, Jeddah, Saudi Arabia**. *Int. J. Gen. Med.* (2020.0) **13** 77-88. DOI: 10.2147/IJGM.S246296
2. **Global Health Estimates 2019: Deaths by Cause, Age, Sex, by Country and by Re-gion, 2000–2019**. (2020.0)
3. **Objetivos de desarrollos sostenible/ ODS**
4. Bennett J.M., Reeves G., Billman G.E., Sturmberg J.P.. **Inflammation-Nature’s Way to Efficiently Respond to All Types of Challenges: Implications for Understanding and Managing "the Epidemic" of Chronic Diseases**. *Front. Med.* (2018.0) **27** 316. DOI: 10.3389/fmed.2018.00316
5. Calder P.C., Bosco N., Bourdet-Sicard R., Capuron L., Delzenne N., Doré J., Franceschi C., Lehtinen M.J., Recker T., Salvioli S.. **Health relevance of the modification of low grade inflammation in ageing (inflammageing) and the role of nutrition**. *Aging Res. Rev.* (2017.0) **40** 95-119. DOI: 10.1016/j.arr.2017.09.001
6. Hotamisligil G.S.. **Inflammation, metaflammation and immunometabolic disorders**. *Nature* (2017.0) **542** 177-185. DOI: 10.1038/nature21363
7. Roth G.A., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A.. **Mortalidad global, regional y nacional específica por edad y sexo por 282 causas de muerte en 195 países y territorios, 1980–2017: Un análisis sistemático para el Estudio de carga global de enfermedad 2017**. *Lancet* (2018.0) **392** 1736-1788. DOI: 10.1016/S0140-6736(18)32203-7
8. Royo-Bordonada M.Á., Rodríguez-Artalejo F., Bes-Rastrollo M., Fernández-Escobar C., González C.A., Rivas F., Martínez-González M.Á., Quiles J., Bueno-Cavanillas A., Navarrete-Muñoz E.M.. **Políticas alimentarias para prevenir la obesidad y las principales enfermedades no transmisibles en España: Querer es poder**. *Gac. Sanit.* (2019.0) **33** 584-592. DOI: 10.1016/j.gaceta.2019.05.009
9. Cordain L., Eaton S.B., Sebastian A., Mann N., Lindeberg S., Watkins B.A., O’Keefe J.H., Brand-Miller J.. **Orígenes y evolución de la dieta occidental: Implicaciones para la salud en el siglo XXI**. *J. Clin. Nutr.* (2005.0) **81** 341-354. DOI: 10.1093/ajcn.81.2.341
10. Cena H., Calder P.C.. **Defining a Healthy Diet: Evidence for The Role of Contemporary Dietary Patterns in Health and Disease**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12020334
11. Cussó Segura X., Gamboa G., Pujol-Andreu J.. **The nutritional status of the Spanish population, 1860–2010: An approach to gender and generational differences**. *Nutr. Hosp.* (2018.0) **35** 11-18. DOI: 10.20960/nh.2079
12. Barker D.J.. **The developmental origins of adult disease**. *J. Am. Coll. Nutr.* (2004.0) **23** 588S-595S. DOI: 10.1080/07315724.2004.10719428
13. **WHO Antenatal Care Recommendations for a Positive Pregnancy Experience. Nutritional Interventions Update: Multiple Micronutrient Supplements during Pregnancy**. (2020.0)
14. Marshall N.E., Abrams B., Barbour L.A., Catalano P., Christian P., Friedman J.E., Hay W., Hernandez T.L., Krebs N.F., Oken E.. **The importance of nutrition in pregnancy and lactation: Lifelong consequences**. *Am. J. Obstet. Gynecol.* (2022.0) **226** 607-632. DOI: 10.1016/j.ajog.2021.12.035
15. Black M.H., Sacks D.A., Xiang A.H., Lawrence J.M.. **The relative contribution of prepregnancy overweight and obesity, gestational weight gain, and IADPSG-defined gestational diabetes mellitus to fetal overgrowth**. *Diabetes Care* (2013.0) **36** 56-62. DOI: 10.2337/dc12-0741
16. Shin D., Lee K.W., Song W.O.. **Dietary Patterns during Pregnancy Are Associated with Risk of Gestational Diabetes Mellitus**. *Nutrients* (2015.0) **7** 9369-9382. DOI: 10.3390/nu7115472
17. Langley-Evans S.C.. **Nutrition in early life and the programming of adult disease: A review**. *J. Hum. Nutr. Diet. Off. J. Br. Diet. Assoc.* (2015.0) **28** 1-14. DOI: 10.1111/jhn.12212
18. Martínez-Olcina M., Rubio-Arias J.A., Reche-García C., Leyva-Vela B., Hernández-García M., Hernández-Morante J.J., Martínez-Rodríguez A.. **Eating Disorders in Pregnant and Breastfeeding Women: A Systematic Review**. *Medicina* (2020.0) **56**. DOI: 10.3390/medicina56070352
19. Triunfo S., Lanzone A.. **Impact of maternal under nutrition on obstetric outcomes**. *J. Endocrinol. Investig.* (2015.0) **38** 31-38. DOI: 10.1007/s40618-014-0168-4
20. Koletzko B., Godfrey K.M., Poston L., Szajewska H., van Goudoever J.B., de Waard M., Brands B., Grivell R.M., Deussen A.R., Dodd J.M.. **Nutrition During Pregnancy, Lactation and Early Childhood and its Implications for Maternal and Long-Term Child Health: The Early Nutrition Project Recommendations**. *Ann. Nutr. Metab.* (2019.0) **74** 93-106. DOI: 10.1159/000496471
21. Perng W., Oken E., Dabelea D.. **Developmental overnutrition and obesity and type 2 diabetes in offspring**. *Diabetologia* (2019.0) **62** 1779-1788. DOI: 10.1007/s00125-019-4914-1
22. Sauder K.A., Hockett C.W., Ringham B.M., Glueck D.H., Dabelea D.. **Research: Epidemiology Fetal overnutrition and offspring insulin resistance and β-cell function: The Exploring Perinatal Outcomes among Children (EPOCH) study**. *Diabet. Med. A J. Br. Diabet. Assoc.* (2017.0) **34** 1392-1399. DOI: 10.1111/dme.13417
23. Christians J.K., Lennie K.I., Wild L.K., Garcha R.. **Effects of high-fat diets on fetal growth in rodents: A systematic review**. *Reprod. Biol. Endocrinol. RBE* (2019.0) **17** 39. DOI: 10.1186/s12958-019-0482-y
24. Julia V., Macia L., Dombrowicz D.. **The impact of diet on asthma and allergic diseases**. *Nat. Rev. Immunol.* (2015.0) **15** 308-322. DOI: 10.1038/nri3830
25. Schoenaker D.A., Mishra G.D., Callaway L.K., Soedamah-Muthu S.S.. **El papel de la energía, los nutrientes, los alimentos y los patrones dietéticos en el desarrollo de la diabetes mellitus gestacional: Una revisión sistemática de estudios observacionales**. *Cuid. De La Diabetes* (2016.0) **39** 16-23. DOI: 10.2337/dc15-0540
26. Donazar-Ezcurra M., López-Del Burgo C., Bes-Rastrollo M.. **Prevención primaria de la diabetes mellitus gestacional a través de factores nutricionales: Una revisión sistemática**. *BMC Embarazo Parto* (2017.0) **17**. DOI: 10.1186/s12884-016-1205-4
27. Grandy M., Snowden J.M., Boone-Heinonen J., Purnell J.Q., Thornburg K.L., Marshall N.E.. **Peor calidad de la dieta materna y aumento del peso al nacer**. *J. Matern. Fetal Neonatal Med.* (2018.0) **31** 1613-1619. DOI: 10.1080/14767058.2017.1322949
28. Martínez-Galiano J.M., Amezcua-Prieto C., Salcedo-Bellido I., González-Mata G., Bueno-Cavanillas A., Delgado-Rodríguez M.. **El consumo dietético materno de legumbres, verduras y frutas durante el embarazo, ¿protege contra los pequeños por ¿edad gestacional?**. *BMC Embarazo Parto* (2018.0) **18**. DOI: 10.1186/s12884-018-2123-4
29. Rodríguez-Bernal C.L., Ramón R., Quiles J., Murcia M., Navarrete-Muñoz E.M., Vioque J., Ballester F., Rebagliato M.. **Ingesta dietética en mujeres embarazadas en un área mediterránea española: Tan buena como ¿supuestamente es?**. *Salud Pública Nutr.* (2013.0) **16** 1379-1389. DOI: 10.1017/S1368980012003643
30. Cuervo M., Sayon-Orea C., Santiago S., Martínez J.A.. **Perfiles dietéticos y de salud de mujeres españolas en preconcepción, embarazo y lactancia**. *Nutrientes* (2014.0) **6** 4434-4451. DOI: 10.3390/nu6104434
31. Stephenson J., Heslehurst N., Hall J., Schoenaker D.A.J.M., Hutchinson J., Cade J.E., Poston L., Barrett G., Crozier S.R., Barker M.. **Antes del comienzo: Nutrición y estilo de vida en el período previo a la concepción y su importancia para la salud futura**. *Lanceta* (2018.0) **391** 1830-1841. DOI: 10.1016/S0140-6736(18)30311-8
32. Jardí C., Aparicio E., Bedmar C., Aranda N., Abajo S., March G., Basora J., Arija V.. **The ECLIPSES Study Group Consumo de alimentos durante el embarazo y el posparto. Estudio ECLIPSES**. *Nutrientes* (2019.0) **11**. DOI: 10.3390/nu11102447
33. Stråvik M., Jonsson K., Hartvigsson O., Sandin A., Wold A.E., Sandberg A.S., Barman M.. **Ingesta de alimentos y nutrientes durante el embarazo en relación con las características maternas: Resultados de la cohorte de nacimiento NICE en El norte de Suecia**. *Nutrientes* (2019.0) **11**. DOI: 10.3390/nu11071680
34. Danesi G.. **Un enfoque transcultural para comer juntos: Prácticas de comensalidad entre adultos jóvenes franceses, alemanes y españoles**. *Soc. Cienc. Inf.* (2018.0) **57** 99-120
35. Contreras J., Gracia M.. *Alimentación y Cultura: Perspectivas Antropológicas* (2005.0)
36. Sánchez-Ojeda M.A., Alemany Arrebola I., Gallardo Vigil M.Á.. **The nursing department’s view towards moroccan patients**. *Rev. Da Esc. De Enferm. Da USP* (2017.0) **51** e03227. DOI: 10.1590/s1980-220x2016022703227
37. Rodríguez V.M., Elbusto-Cabello A., Alberdi-Albéniz M., de la Presa-Donado A., Gómez-Pérez de Mendiola F., Portillo-Baquedano M.P., Churruca-Ortega I.. **Validación de nuevos formularios de registro de alimentos precodificados**. *Rev. Española Nutr. Tararear. Dietética* (2014.0) **18** 118. DOI: 10.14306/renhyd.18.3.84
38. Poslusna K., Ruprich J., de Vries J.H.M., Jakubikova M., van’t Veer P.. **Información errónea sobre la ingesta de energía y micronutrientes estimada mediante registros de alimentos y recordatorios de 24 horas, control y métodos de ajuste en la práctica**. *Hermano J. Nutr.* (2009.0) **101** S73-S85. DOI: 10.1017/S0007114509990602
39. **Objetivos nutricionales para la población española: Consenso de la Sociedad Española de Nutrición Comunitaria 2011**. *Rev. Española De Nutr. Comunitaria* (2011.0) **17** 178-199
40. Moreiras O., Carbajal Azcona A., Cabrera L., Cuadrado C.. **Tablas de Composición de Alimentos**. *Guía de Prácticas* (2016.0)
41. Izquierdo Guerrero M.D.L.M.. **Estudio de Hábitos Alimentarios y Conocimientos Nutricionales en Embarazadas de Distintas Áreas de Salud de la Comunidad de Madrid**. *Ph.D. Thesis* (2016.0)
42. Olmedo-Requena R., Gómez-Fernández J., Mozas-Moreno J., Lewis-Mikhael A.-M., Bueno-Cavanillas A., Jiménez-Moleón J.-J.. **Factors associated with adherence to nutritional recommendations before and during pregnancy**. *Women Health* (2018.0) **58** 1094-1111. DOI: 10.1080/03630242.2017.1388332
43. Gao H., Stiller C.K., Scherbaum V., Biesalski H.K., Wang Q., Hormann E., Bellows A.C.. **Dietary intake and food habits of pregnant women residing in urban and rural areas of Deyang City, Sichuan Province, China**. *Nutrients* (2013.0) **5** 2933-2954. DOI: 10.3390/nu5082933
44. Liu F.-L., Zhang Y.-M., Parés G.V., Reidy K.C., Zhao W.-Z., Zhao A., Chen C., Ning C.Y., Zheng Y.-D., Wang P.-Y.. **Nutrient Intakes of Pregnant Women and their Associated Factors in Eight Cities of China: A Cross-sectional Study**. *Chin. Med. J.* (2015.0) **128** 1778-1786. DOI: 10.4103/0366-6999.159354
45. Yang J., Dang S., Cheng Y., Qiu H., Mi B., Jiang Y., Qu P., Zeng L., Wang Q., Li Q.. **Dietary intakes and dietary patterns among pregnant women in Northwest China**. *Public Health Nutr.* (2017.0) **20** 282-293. DOI: 10.1017/S1368980016002159
46. Braarud H.C., Markhus M.W., Skotheim S., Stormark K.M., Frøyland L., Graff I.E., Kjellevold M.. **Maternal DHA Status during Pregnancy Has a Positive Impact on Infant Problem Solving: A Norwegian Prospective Observation Study**. *Nutrients* (2018.0) **10**. DOI: 10.3390/nu10050529
47. Best K.P., Gold M., Kennedy D., Martin J., Makrides M.. **Omega-3 long-chain PUFA intake during pregnancy and allergic disease outcomes in the offspring: A systematic review and meta-analysis of observational studies and randomized controlled trials**. *Am. J. Clin. Nutr.* (2016.0) **103** 128-143. DOI: 10.3945/ajcn.115.111104
48. Marchi J., Berg M., Dencker A., Olander E.K., Begley C.. **Risks associated with obesity in pregnancy, for the mother and baby: A systematic review of reviews**. *Obes. Rev. Off. J. Int. Assoc. Study Obes.* (2015.0) **16** 621-638. DOI: 10.1111/obr.12288
49. Ruiz E., Varela-Moreiras G.. **Adequacy of the dietary intake of total and added sugars in the Spanish diet to the recommendations: ANIBES study**. *Nutr. Hosp.* (2017.0) **34** 45-52. DOI: 10.20960/nh.1571
50. Maslova E., Rytter D., Bech B.H., Henriksen T.B., Olsen S.F., Halldorsson T.I.. **Maternal intake of fat in pregnancy and offspring metabolic health—A prospective study with 20 years of follow-up**. *Clin. Nutr.* (2016.0) **35** 475-483. DOI: 10.1016/j.clnu.2015.03.018
51. Murrin C., Shrivastava A., Kelleher C.C.. **Maternal macronutrient intake during pregnancy and 5 years postpartum and associations with child weight status aged five**. *Eur. J. Clin. Nutr.* (2013.0) **67** 670-679. DOI: 10.1038/ejcn.2013.76
52. Regnault T.R., Gentili S., Sarr O., Toop C.R., Sloboda D.M.. **Fructose, pregnancy and later life impacts**. *Clin. Exp. Pharmacol. Physiol.* (2013.0) **40** 824-837. DOI: 10.1111/1440-1681.12162
53. 53.
Organización Mundial de la Salud (OMS)
Guideline: Sugars Intake for Adults and ChildrenWHOGeneva, Switzerland2015Available online: https://apps.who.int/iris/bitstream/handle/10665/149782/9789241549028_eng.pdf?sequence=1(accessed on 6 January 2023). *Guideline: Sugars Intake for Adults and Children* (2015.0)
54. Ferriols E., Rueda C., Gamero R., Vidal M., Payá A., Carreras R., Flores-le Roux J.A., Pedro-Botet J.. **Relationship between lipid alterations during pregnancy and adverse pregnancy outcomes**. *Clin. Investig. Arterioscler. Publ. Of. Soc. Esp. Arterioscler.* (2016.0) **28** 232-244. DOI: 10.1016/j.arteri.2015.04.003
55. Singh M., Pathak M.S., Paul A.. **A Study on Atherogenic Indices of Pregnancy Induced Hypertension Patients as Compared to Normal Pregnant Women**. *J. Clin. Diagn. Res. JCDR* (2015.0) **9** BC05-8. DOI: 10.7860/JCDR/2015/13505.6241
56. Ortega Anta R.M., Villalobos Cruz T.K., Perea J.M.. **Fuentes alimentarias y adecuación de la ingesta de ácidos grasos omega-3 y omega-6 en una muestra representativa de adultos españoles**. *Nutr. Hosp.* (2013.0) **28** 2236-2246. PMID: 24506406
57. 57.
Fundación Española de Nutrición (FEN)
Libro Blanco de la Nutrición en EspañaFENMadrid, Spain2013Available online: https://www.seedo.es/images/site/documentacionConsenso/Libro_Blanco_Nutricion_Esp-2013.pdf(accessed on 10 January 2023). *Libro Blanco de la Nutrición en España* (2013.0)
58. Stephen A.M., Champ M.M.-J., Cloran S.J., Fleith M., van Lieshout L., Mejborn H., Burley V.J.. **Dietary fibre in Europe: Current state of knowledge on definitions, sources, recommendations, intakes and relationships to health**. *Nutr. Res. Rev.* (2017.0) **30** 149-190. DOI: 10.1017/S095442241700004X
59. Kyrø C., Skeie G., Dragsted L.O., Christensen J., Overvad K., Hallmans G., Johansson I., Lund E., Slimani N., Johnsen N.F.. **Intake of whole grain in Scandinavia: Intake, sources and compliance with new national recommendations**. *Scand. J. Public Health* (2012.0) **40** 76-84. DOI: 10.1177/1403494811421057
60. Carbajal Azcona Á.. (2013.0) 367
61. Blumfield M.L., Hure A.J., Macdonald-Wicks L., Smith R., Collins C.E.. **A systematic review and meta-analysis of micronutrient intakes during pregnancy in developed countries**. *Nutr. Rev.* (2013.0) **71** 118-132. DOI: 10.1111/nure.12003
62. Kocyłowski R., Lewicka I., Grzesiak M., Gaj Z., Sobańska A., Poznaniak J., von Kaisenberg C., Suliburska J.. **Assessment of dietary intake and mineral status in pregnant women**. *Arch. Gynecol. Obstet.* (2018.0) **297** 1433-1440. DOI: 10.1007/s00404-018-4744-2
63. Goletzke J., Buyken A.E., Louie J.C.Y., Moses R.G., Brand-Miller J.C.. **Dietary micronutrient intake during pregnancy is a function of carbohydrate quality**. *Am. J. Clin. Nutr.* (2015.0) **102** 626-632. DOI: 10.3945/ajcn.114.104836
64. Lundqvist A., Johansson I., Wennberg A., Hultdin J., Högberg U., Hamberg K., Sandström H.. **Reported dietary intake in early pregnant compared to non-pregnant women—A cross-sectional study**. *BMC Pregnancy Childbirth* (2014.0) **14**. DOI: 10.1186/s12884-014-0373-3
65. Keats E.C., Haider B.A., Tam E., Bhutta Z.A.. **Multiple-micronutrient supplementation for women during pregnancy**. *Cochrane Database Syst. Rev.* (2019.0) **3** 4905. DOI: 10.1002/14651858.CD004905.pub6
66. Lassi Z.S., Salam R.A., Haider B.A., Bhutta Z.A.. **Folic acid supplementation during pregnancy for maternal health and pregnancy outcomes**. *Cochrane Database Syst. Rev.* (2013.0) **3** 68-96. DOI: 10.1002/14651858.CD006896.pub2
67. Hofmeyr G.J., Manyame S., Medley N., Williams M.J.. **Calcium supplementation commencing before or early in pregnancy, for preventing hypertensive disorders of pregnancy**. *Cochrane Database Syst. Rev.* (2019.0) **9** CD011192. DOI: 10.1002/14651858.CD011192.pub3
68. Willemse J.P.M.M., Meertens L.J.E., Scheepers H.C.J., Achten N.M.J., Eussen S.J., van Dongen M.C., Smits L.J.M.. **Calcium intake from diet and supplement use during early pregnancy: The Expect study I**. *Eur. J. Nutr.* (2019.0) **59** 167-174. DOI: 10.1007/s00394-019-01896-8
69. Hanson M.A., Bardsley A., De-Regil L.M., Moore S.E., Oken E., Poston L., Ma R.C., McAuliffe F.M., Maleta K., Purandare C.N.. **The International Federation of Gynecology and Obstetrics (FIGO) recommendations on adolescent, preconception, and maternal nutrition: “Think Nutrition First”**. *Int. J. Gynecol. Obstet.* (2015.0) **131** S213-S253. DOI: 10.1016/S0020-7292(15)30034-5
70. Ruíz E.. **Aplicación de las Nuevas Tecnologías Para la Estimación de la Ingesta de Energía y Macronutrientes en la Población Española: Estudio ANIBES**. *Ph.D. Thesis* (2017.0)
71. Mohatar-Barba M., López-Olivares M., Fernández-Gómez E., Luque-Vara T., Linares-Manrique M., Enrique-Mirón C.. **Perfiles calóricos y lipídicos en la población española del norte de África**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11081140
72. Dean S.V., Imam A.M., Lassi Z.S., Bhutta Z.A.. **Importance of Intervening in the Preconception Period to Impact Pregnancy Outcomes**. *Matern. Child Nutr. First 1000 Days* (2013.0) **74** 63-73. DOI: 10.1159/000348402
73. Mastroiacovo P., Nilsen R.M., Leoncini E., Gastaldi P., Allegri V., Boiani A., Faravelli F., Ferrazzoli F., Guala A., Madrigali V.. **Prevalence of maternal preconception risk factors: An Italian multicenter survey**. *Ital. J. Pediatr.* (2014.0) **40** 91. DOI: 10.1186/s13052-014-0091-5
|
---
title: Optimizing Levilactobacillus brevis NPS-QW 145 Fermentation for Gamma-Aminobutyric
Acid (GABA) Production in Soybean Sprout Yogurt-like Product
authors:
- Yue Zhang
- Mengjiao Zhu
- Wenjing Lu
- Cen Zhang
- Di Chen
- Nagendra P. Shah
- Chaogeng Xiao
journal: Foods
year: 2023
pmcid: PMC10000865
doi: 10.3390/foods12050977
license: CC BY 4.0
---
# Optimizing Levilactobacillus brevis NPS-QW 145 Fermentation for Gamma-Aminobutyric Acid (GABA) Production in Soybean Sprout Yogurt-like Product
## Abstract
Gamma-aminobutyric acid (GABA) is a non-protein amino acid with various physiological functions. Levilactobacillus brevis NPS-QW 145 strains active in GABA catabolism and anabolism can be used as a microbial platform for GABA production. Soybean sprouts can be treated as a fermentation substrate for making functional products. This study demonstrated the benefits of using soybean sprouts as a medium to produce GABA by *Levilactobacillus brevis* NPS-QW 145 when monosodium glutamate (MSG) is the substrate. Based on this method, a GABA yield of up to 2.302 g L−1 was obtained with a soybean germination time of one day and fermentation of 48 h with bacteria using 10 g L−1 glucose according to the response surface methodology. Research revealed a powerful technique for producing GABA by fermentation with *Levilactobacillus brevis* NPS-QW 145 in foods and is expected to be widely used as a nutritional supplement for consumers.
## 1. Introduction
Gamma-aminobutyric acid (GABA), a four-carbon non-protein and water-soluble amino acid, is the main inhibitory neurotransmitter of the central nervous system [1,2,3,4,5]. It can have beneficial effects on human health and other animals by reducing blood pressure, preventing chronic alcoholic diseases, inhibiting cancer cell proliferation, improving brain function, and promoting insulin [6,7,8]. GABA also demonstrates the potential for lowing blood pressure in spontaneously hypertensive rats (SHR) and hypertensive humans [9,10]. Furthermore, a previous study reported the key role of GABA production in hepatocytes in the dysregulation of glucose regulation and eating behavior associated with obesity [11,12,13]. There has been an increased demand for GABA due to its widespread use in various industries [14].
Concentration of GABA in plant tissues varies between 0.03 and 2.00 μmol g−1, increasing with hypoxia, hydraulic pressure, salt stress, temperature shock, germination, and other biotic stresses [4]. Several microorganisms, including lactic acid bacteria (LAB), such as Levilactobacillus brevis, Lacticaseibacillus paracasei, and Enterococcus raffinosus, have recently been intensively investigated and used in GABA synthesis [15], because they are rich in glutamate decarboxylase and can synthesize GABA.
Plant seed germination is a physiological process that stimulates endogenous enzyme activity and alters biochemical processes [8,16]. According to recent research, soybean sprouts can be utilized as an alternate method to strengthen the nutritional quality of phytochemical content, particularly GABA [7]. Germination of soybean for human consumption would reduce the content of anti-nutritional elements while increasing the number of minerals and phytochemicals such as vitamin E and isoflavone aglycone derivatives [17,18]. In particular, during soybean germination, various free amino acids are produced with protein degradation, providing a natural substrate for GABA synthesis [17].
This study aims to use response surface optimization to investigate the effect of soybean germination treatment and lactic acid bacteria fermentation on the level of GABA in soy milk. The study’s results will provide a favorable theoretical basis for producing products with higher nutritional value.
## 2.1. Materials and Strain
Organic soybeans were purchased from a local supplier. Analytical grade chemical reagents utilized in this work were purchased from Sigma-Aldrich Corp., St. Louis, Missouri, USA. Levilactobacillus brevis NPS-QW 145 was obtained from BD Company (Franklin Lakes, NJ, USA). Six carbon sources, including glucose, lactose, mannose, malactose, amylopectin, and fructose, were purchased from Sigma-Aldrich Corp., St. Louis, MO, USA. Difeo TM lactobacilli MRS broth and Monosodium glutamate (MSG) were purchased from Difco. ( Sparks, MD, USA). All other reagents were of analytical grade.
## 2.2. Preparation of Soybean Sprouts Milk
Germination conditions used in this study were based on Luo’s method [4]. Typically, 200 g of soybeans were selected, washed, and soaked in a $95\%$ ethanol solution for 1 min to remove microorganisms on the surface of soybean seeds. The beans were washed with sterile water and placed in an incubator for germination. Subsequently, the germination status of the beans was observed daily, with the germination length measured as well. Consequently, the bean sprouts were taken out from the incubator on days 0, 1, 3, 4, and 5 to prepare soybean sprout milk. The sprouts were rinsed with clean water and mixed with water in a ratio of 1:2 (soybean sprout: water) before putting the mixture into a grinder for 5 min of pulp grinding. The mixture was then allowed to be filtered, homogenized, and sterilized at 90 °C in a water bath pot before 1 h of boiling. The sterilized mixture was left at room temperature for cooling before fermentation.
## 2.3. Preparation of Fermented Yogurt-like Product
The fermentation method was conducted following the instructions of Xiao and Shah [19] with slight modifications. Firstly, the soybean sprout yogurt-like product made with sprouts of different germination times was autoclaved and then inoculated with $3\%$ Lb. brevis 145 (v/v), 5 g L−1 MSG, and six different monosaccharides (glucose, lactose, mannose, galactose, amylopectin, and fructose) at different concentrations (0, 5, 10, 15, and 20 g L−1) and mixed well. Subsequently, the mixture was fermented in the incubator at 37 °C to observe the coagulation state and compare the GABA concentration in it.
Soybean without germination treatment was used as the blank test. Yogurt prepared from the same quality of milk powder was also fermented with $3\%$ Lb. brevis 145 (v/v), 5 g L−1 MSG, and 10 g L−1 glucose, and then fermented at 37 °C for 48 h. Moreover, the GABA content in the soybean with germination treatment was compared to explore the effect of germination treatment on the GABA content in the soybean.
## 2.4.1. Single-Factor Experiments
Levilactobacillus brevis NPS-QW 145 was used as the fermentation strain in a single-factor experiment. The following factors were examined for their influence on the GABA content of fermented soybean sprouts: types of carbon sources (glucose, lactose, mannose, galactose, amylopectin, and fructose), germination time (0, 1, 3, 4, and 5 d), glucose concentration (0, 5, 10, 15, and 20 g L−1), and fermentation time (12, 24, 48, 72, and 96 h). The GABA concentration was determined using RP-HPLC (Shimadzu model LC-2010A, Shimadzu Corp., Kyoto, Japan).
## 2.4.2. Response Surface Methodology (RSM)
RSM is typically used to investigate optimal experimental conditions since it is a reliable and useful statistical methodology. This experimental method was partially modified according to the method of Zhang et al. [ 14]. Based on the results of the single-factor experiments, glucose concentration, fermentation time, and germination days were selected for the RSM experiment based on a Box-Behnken center combination design (DTD), and the GABA level was treated as the response values. Table 1 shows the three factors and the three levels of the research design.
## 2.5.1. Protein and Peptide Removal from Soybean Sprout Yogurt-like Product
GABA levels were determined according to the Wu and Shah’s method [2]. Reversed-Phase HPLC (RP-HPLC, Shimadzu model LC-2010A, Shimadzu Corp., Kyoto, Japan) was employed to detect the GABA concentration in the fermented soybean sprout yogurt-like product. First, to remove the protein of the soybean sprout milk, a 1 mL aliquot of fermented soymilk samples was diluted five times with sterile H2O, and 250 μL of zinc acetate and ferrous cyanide were added and mixed thoroughly. After standing for 1 h, samples were centrifuged by 5000 g at 25 °C to completely precipitate proteins and peptides. A GABA analysis was performed after samples were stored at 4 °C.
## 2.5.2. Amino acid Derivatization
The obtained supernatant containing GABA was derived. 200 μL of supernatant was mixed with 200 μL of acetonitrile, 200 μL of NaHCO3 (pH 9.8), 200 μL of H2O, and 100 μL 40 g L−1 of Dansyl chloride was added at 60 °C in the dark for 1 h. After derivation, 100 μL of 20 μL mL−1 acetic acids was added to stop the reaction. Subsequently, the sample was centrifuged at 12,000 g at 25 °C for 5 min. Moreover, the supernatant passed through a 0.22 μM filter with a membrane and was stored in a brown vial.
Subsequently, the GABA concentration of the derived sample was analyzed using RP-HPLC, as previously used [2]. The retention time for GABA is shown below at 20 min. Moreover, the standard curve of GABA present in Figure 1 was prepared with 0.01, 0.05, 0.07, 0.1, 0.25, 0.5, 0.7, and 1.75 g L−1 concentrations of GABA standard solution. It can be seen that the peak area was highly correlated with the GABA concentration, R2 = 0.9992, and the relationship between them satisfied the regression equation $y = 4$ × 106 x + 70,120.
According to different experiments, the different integral areas obtained by the samples could be substituted into the formula GABA concentration. RP-HPLC was used to separate and quantify dansyl GABA and dansyl glutamic acid using a Kromasil 5-μm 100A C18 column (250 mm × 4.6 mm; Phenomenex, Torrance, CA, USA).
## 2.6. Determination of pH and Viable Cell Counts in Fermented Soybean Sprout Yogurt-like Product
This method was a combination of Chan and Wu’s research respectively [20,21]. The pH values of the fermented soybean sprout yogurt were measured using a pH meter (250 A Orion Portable pH Meter, US). To measure the viable cell number, 1 mL of the fermented soybean sprout yogurt-like product was dissolved in 9 mL of sterilized normal saline. Subsequently, 1 mL of the uniform solution was taken into Difeo TM lactobacilli MRS broth and incubated at 37 °C for 48 h. The average number of colonies in the Petri dish was multiplied and calculated by the dilution multiple. Generally, 30–300 CFU were chosen to count. The unit of colony numbers was CFU mL−1.
## 2.7. Protein Content of Fermented Soybean Sprout Yogurt-like Product
The fermented soybean sprout yogurt-like product was compared to those made from commercial soybean powder and milk powder. The protein content was determined using the MicroKjeldahl method. A sample of 0.3 g was accurately weighed and then placed in a Kjeldahl tube with a catalyst tablet and 10 mL of concentrated sulfuric acid.
The weighed sample was nitrified in a nitrification furnace at 370 °C for 50 min until the solution turned light green. Next, 40 mL of distilled water was added to the nitrated sample for cooling and then put into the MicroKjeldahl nitrogen determinator for automatic titration. Finally, manual titration was conducted using a 250 mL conical flask with 40 mL of $4\%$ boric acid solution and five drops of the indicator. Three parallel tests were performed for each group of samples. The sample nitrogen content was calculated using the following formula and then converted to crude protein content:[1]%N=((1.4×V)÷1000)⁄g)×100 V = volume (mL) of 0.1 N HCl used in the titration. The value of 1.4 is derived from the fact that 1.0 mL of 0.1 M NH4OH contains 1.4 mg nitrogen.
## 2.8. Texture Analysis
To compare the effects of different soybean germination days, fermentation time and carbon source additions on the texture of the yogurt-like products, a texture analysis was performed on samples with different preparation conditions. The method for textural characterization of the fermented bean sprout yogurt-like product was modified according to Giri’s method [22]. Before measurement, products with different preparation processes were stored at 4 °C for 12 h, restored to room temperature, and about 9 g of samples were weighed. The texture analysis was performed using a Texture Analyzer TAXT2i (Stable Micro Systems, Godalming, Surrey, UK) equipped with a 25 kg load cell and calibrated with a standard dead weight of 5 kg before use. A HDP/SR-TTC probe was unitized for determination. A Texture Expert version 1.20 (Stable Micro Systems) software application measured firmness, stickiness, work of shear, and work of adhesion. The same sample was weighed three times, and the mean value was obtained and recorded.
The specific measurement parameters were test speed: 3.0 mm s−1, measured speed: 10 mm s−1, test distance: 23 mm, trigger force: g, and data acquisition rate: 200 PPS.
## 2.9. Sensory Analysis
This method was slightly modified from Meilgaard’s approach [23]. Briefly, 50 trained panelists were invited to evaluate the appearance, odor, acidity, thickness, fluidity, taste, and overall acceptance of the fermented yogurt-like product using 9-point scores (from 1 to 9). The ratings were presented on a 9-point hedonic scale ranging from 9 (“extremely like”) to 1 (“extremely dislike”).
## 2.10. Statistical Analysis
The data figure was created using the program, Microsoft Excel 2010, and IBM SPSS 25 Statistic was used to analyze the significant differences ($p \leq 0.05$ showed that the difference in the analysis results was significant, and $p \leq 0.01$ indicated that the difference in the analysis results was very significant). The response surface was designed, optimized, and analyzed using Design Export 10.0.7.
## 3.1. The Effect of Various Conditions on GABA Production by Lb. brevis 145 in Soybean Sprout Yogurt-like Product
Legumes primarily metabolize GABA through a short pathway known as a GABA shunt, converting glutamate into succinic acid. This pathway synthesizes GABA from glutamate-by-glutamate decarboxylase (GAD, EC 4.1.1.15). GABA is converted to succinic semialdehyde (SSA) using GABA aminotransferase (GABA-T, EC 2.6.1.19). Then the last step of the shunt pathway is to convert SSA to succinic acid using succinic semialdehyde dehydrogenase (SSADH, EC 1.2.2.16) [24,25]. In the present study, after soybean seed germination, protein was transformed into glutamate and polyamine, which provided a sufficient precursor substance for GABA enrichment. During soybean germination, the content of soluble sugar decreased, and the content of dry matter decreased with the extension of germination time. However, the content of reduced sugar, soluble protein, free amino acid, and GABA increased.
Figure 2A shows that a significant increase ($p \leq 0.05$) in GABA content was detected during soybean germination. GABA content in soybeans initially increased and then decreased with increasing germination time. When the germination time was one day, the GABA content was the most extensive (0.025 g L−1). Compared to raw soybeans, the GABA content increased continuously and significantly by 1.61-fold by the end of germination at one day. This outcome is consistent with the findings of Vann’s study [8], which found that soybean germination significantly increased the GABA content in soybean sprouts. Meanwhile, a previous study [4] reported that the GABA content in germinated soybeans peaked on day 5, which conflicted with the present study. As mentioned above, increased GAD activity could be responsible for higher GABA content. The most suitable explanation for this differential phenomenon is that the GAD activity during germination was also influenced by germination temperature and germination approaches [26,27].
Conversely, Figure 2B shows that GABA-producing bacteria show different preferences for sugars, affecting their growth and GABA production. Compared to lactose, mannose, galactose, amylopectin, and fructose, glucose were significantly different in terms of improving GABA levels ($p \leq 0.05$). This result is consistent with the findings of a previous report by Xiao and Shah [19], which suggested that after fermentation for 24 h, glucose was the main carbon source consumed by Lb. brevis 145.
Furthermore, with increasing fermentation time, the GABA content in the fermentation broth increased initially and then decreased (Figure 2C). The content increased sharply in the first 48 h. After 48 h of continuous culture, the GABA content in the fermentation broth decreased significantly. At 48 h of fermentation, the content of GABA reached its maximum, which was 1.867 g L−1. A possible reason for this was the consumption of MSG and nutrients in the fermentation broth with the extension of fermentation time, and the subsequent cell senescence with decreased GABA content.
An investigation of the effect of different glucose concentrations on GABA production in the soybean sprout yogurt-like product was also performed. Glucose, as the main carbon source of microorganisms, has the energy required for the life activities of bacteria, and constitutes the material basis of bacterial cells and their metabolites [28]. Figure 2D shows that with increasing glucose addition, the GABA content in the fermented bean sprout yogurt-like product also increased initially and then decreased. Briefly, when the glucose addition was 10 g L−1, the maximum GABA content was 2.21 g L−1. However, when the glucose addition continued to increase, the GABA content in the bean sprout yogurt-like product demonstrated an obvious decreasing trend. It could be that when the sugar content in the fermentation medium was too high, the cell metabolic activity produced organic acids, resulting in decreased pH and cell aging. Moreover, when the sugar content in the fermentation medium was in a low range, the bacteria were less affected by changes in sugar metabolites [28].
## 3.2.1. RSM Results
The carbon concentration, fermentation times, and germination days were chosen as the key variables and the focal points for the response surface analysis in order to simulate the fermentation process based on the single-variable optimization (Table 2). Based on the Box-Behnken experimental design results, a quadratic multiple regression fitting was conducted, and a multiple quadratic response surface regression model was established. The obtained quadratic regression equation was as follows:$Y = 2.35$ + 5.9375 × 10−3 × A + 4.1875 × 10−3 × B + 5.125 × 10−3 × C−1.75 × 10−3 × AB + 3.75 × 10−4 × AC + 3.75 × 10−4 × BC−0.060135 × A2−0.046135 × B2−0.04801 × C2[2] where Y represents the GABA concentration, A represents the germination days, B represents the fermentation time, and C represents the glucose concentration.
Table 3 illustrates the results of ANOVA. The regression model F test presented high significance ($p \leq 0.01$), and the R-squared was $95.75\%$, indicating that the model could explain the change in the $95.75\%$ response value. The lack of fit was 0.676 (more than 0.05), which was non-significant. The model had a high degree of fit with the data and a small experimental error. This model and equation could be employed to analyze and predict the amount of GABA extraction.
## 3.2.2. RSM Analysis of the Best-Fermented Parameters
Following the linear regression equation fitted by the RSM, the response surface graph and contour of the model were drawn. The response surface contour map directly reflected the influence of various factors on the response value to find out the best process parameters and the interaction between various parameters. The center point of the smallest ellipse in the contour was the highest point of response surface; the contour map shape reflected the intensity and significance of interaction between the two factors. The contour lines in Figure 3 were oval, corroborating the finding the interaction between fermentation time and the addition of sugar concentration to germination time was significant.
Figure 3 presents the three-dimensional spatial surface diagram of the interaction of two factor independent variables on GABA concentration created by Design Expert 10.0.7 software. The 3D response surface diagrams show that germination days, glucose concentration, and fermentation time had a good interaction, and that their effects were all statistically significant. By analyzing the linear regression equation, it was found that there was a maximum point in the experiment, which was also the maximum point in this study. Technological conditions producing this maximum point could be found through response surface analysis. Thus, the optimal technological conditions for the enrichment of GABA from fermented soybean sprout yogurt-like product were: soybean germination for 0.798 days, fermentation time for 45.490 h, and glucose concentration of 9.691 g L−1. Under these conditions, the predicted value of the GABA mass concentration was 2.287 g L−1. In order to verify the reliability of the regression equation, under the optimized conditions, soybeans germinated for 24 h, fermented for 48 h, and 10 g L−1 of glucose concentration was adopted; the GABA level obtained from the verification test was 2.302 g L−1, and the relative deviation was $0.67\%$ compared to the theoretical prediction value. Therefore, the optimal process conditions of the fermentation system obtained by the response surface optimization method were reliable. Furthermore, the fermented soybean sprout yogurt-like product obtained in the validation test had a uniform solidification state, a strong fermentation flavor, a pure flavor, and no peculiar smell.
## 3.3.1. GABA Concentration in GABA-Rich Yogurt
Figure 4 shows that the soybeans were treated with Lb. brevis 145 after 48 h of fermentation after one day of germination. The GABA content reached a maximum of 2.302 g L−1, which was 1.56 and 3.5 times the GABA content in yogurt-like product fermented with soybean powder and milk powder respectively, implying that soybean germination and fermentation of lactic acid bacteria could significantly increase the GABA content in yogurt, thus producing a functional yogurt-like product rich in GABA.
## 3.3.2. pH and Cell Viability in Fermented Soybean Sprout Yogurt-like Product
According to the Chinese national standard, GB 4789.35, for lactic acid bacteria content in viable products, the lactic acid bacteria content must be higher than 1 × 106 CFU mL−1. Figure 5 illustrates that the number of bacteria after 72 h of fermentation, still up to 8 × 106 CFU mL−1, already met the standard requirement for live bacteria plant yogurt.
Moreover, as the fermentation time increases, acidity elevates due to the production of organic acids in the medium, resulting in a decrease in pH. After fermentation, the pH of the soybean sprout yogurt-like product also showed a downward trend, as illustrated in Figure 5. Finally, the pH of the fermented bean sprout yogurt-like product was maintained at about 4.4, which meets the Chinese national standard requirement (GB 5009.237) for the pH of fermented yogurt products (pH ≤ 4.5).
## 3.3.3. Texture Characteristic and Protein Content in GABA-Rich Yogurt-like Product
Table 4 illustrates the texture characteristics and protein content of fermented yogurt-like product from soybean sprouts, fermented yogurt-like product from soy flour, and fermented yogurt from milk. The texture samples were obtained from samples with intact gel structures after 48 h of fermentation; their work of shear, stickiness, work of adhesion, and firmness was evaluated.
Firmness, or the force required to achieve a certain deformation, is a regularly examined criterion when defining the texture of set-type cultured dairy products. It is the peak force height on the first compression cycle [29,30]. The firmness of fermented soybean sprout yogurt-like product was significantly ($p \leq 0.05$) higher than the other two samples. The increased firmness could be due to the high water binding capacity [31,32].
The quantity of energy required to perform the shear operation is known as the work of shear. It therefore evaluates the sample resistance throughout the penetration of the probe. In the current investigation, the work of shear of the fermented soybean sprout yogurt-like product and the fermented soybean flour yogurt-like product was significantly ($p \leq 0.05$) higher than that of the fermented milk yogurt. However, no significant ($p \leq 0.05$) difference was observed between the fermented soybean sprout yogurt-like product and the fermented soybean flour yogurt-like product.
Stickiness is an essential sensory quality of semisolid food ingredients, defined as a sensation sensed by the tongue and palate [33,34]. Negative stickiness values represent stickiness, while positive values represent the product’s hardness. In the present investigation, no significant ($p \leq 0.05$) difference in stickiness was detected between the fermented soybean sprout yogurt-like product and the fermented soybean flour yogurt-like product, both of which were higher levels of stickiness than the fermented milk yogurt.
To characterize the work of adhesion, the area under the negative peak in penetration was measured. It can also be defined as the work required to overcome the attraction force between the product surface and the probe surface [22]. During the current investigation, as Table 4 illustrates, there was no significant difference in the work of adhesion between the fermented soybean sprout yogurt-like product and the fermented soy flour yogurt-like product ($p \leq 0.05$), both of which were marginally lower compared to the fermented milk yogurt.
Furthermore, according to the Chinese national standard requirement (GB 5009.5), the protein content in soybean products is not allowed to be less than $2.5\%$. Table 4 shows that the protein content of the three products reached the national standard. These results were consistent with the result of Niamah’s study [35]. Notably, the protein level of the fermented soybean sprout yogurt-like product exceeded the national standard by 1.7 times.
## 3.3.4. Sensory Evaluation of Yogurt-like Product Rich in GABA
Table 5 shows the scores for the sensory characteristics of the fermented samples. GABA-rich fermented sprout yogurt-like product had a milky and full-bodied aroma. There were no significant differences ($p \leq 0.05$) in appearance, acidity, fluidity, thickness, or overall acceptance between the bean sprout yogurt-like product and the commercially available yogurt, validating the finding that fermented bean sprout yogurt-like product has prospective market acceptance and consumer acceptance. However, there was a significant difference ($p \leq 0.05$) in odor and taste between the bean sprout yogurt-like product and the commercially available yogurt. Future process optimization will focus on improving these two indicators.
## 4. Conclusions
This study investigated the effect of lactic acid bacteria fermentation of germinated soybeans on the GABA content of yogurt. In soybeans, GABA content increases significantly during the germination and reaches its peak after one day of germination. The highest level of GABA production (2.302 g L−1) of the fermented soybean sprout yogurt-like product was obtained when Lb. brevis 145 was fermented with glucose 10 g L−1 as the sole carbon source for 48 h. The use of germinated soybeans had a significantly positive effect on GABA enrichment. Simultaneously, the fermented soybean sprout yogurt-like product with high GABA content met the requirements of Chinese national standards for yogurt in terms of acidity, protein content, and the number of live bacteria, and it had a better texture than the commercially available yogurt. This provides a prerequisite for producing innovative GABA-enriched yogurt.
## References
1. Wu Q., Shah N.P.. **Gas release-based prescreening combined with reversed-phase HPLC quantitation for efficient selection of high-γ-aminobutyric acid (GABA)-producing lactic acid bacteria**. *J. Dairy Sci.* (2015) **98** 790-797. DOI: 10.3168/jds.2014-8808
2. Wu Q., Shah N.P.. **Restoration of GABA production machinery in**. *Food Microbiol.* (2018) **69** 151-158. DOI: 10.1016/j.fm.2017.08.006
3. Binh T.T.T., Ju W.T., Jung W.J., Park R.D.. **Optimization of γ-amino butyric acid production in a newly isolated**. *Biotechnol. Lett.* (2014) **36** 93-98. DOI: 10.1007/s10529-013-1326-z
4. Luo X., Wang Y., Li Q., Wang D., Xing C., Zhang L., Xu T., Fang F., Wang F.. **Accumulating mechanism of γ-aminobutyric acid in soybean (**. *Int. J. Food Sci. Technol.* (2018) **53** 106-111. DOI: 10.1111/ijfs.13563
5. Wang Y., Liu C., Ma T., Zhao J.. **Physicochemical and functional properties of γ-aminobutyric acid-treated soy proteins**. *Food Chem.* (2019) **295** 267-273. DOI: 10.1016/j.foodchem.2019.05.128
6. Park K.B., Oh S.H.. **Production of yogurt with enhanced levels of gamma-aminobutyric acid and valuable nutrients using lactic acid bacteria and germinated soybean extract**. *Bioresour. Technol.* (2007) **98** 1675-1679. DOI: 10.1016/j.biortech.2006.06.006
7. Shan Y., Man C.X., Han X., Li L., Guo Y., Deng Y., Li T., Zhang L.W., Jiang Y.J.. **Evaluation of improved γ-aminobutyric acid production in yogurt using**. *J. Dairy Sci.* (2015) **98** 2138-2149. DOI: 10.3168/jds.2014-8698
8. Vann K., Techaparin A., Apiraksakorn J.. **Beans germination as a potential tool for GABA-enriched tofu production**. *J. Food Sci. Technol.* (2020) **57** 3947-3954. DOI: 10.1007/s13197-020-04423-4
9. Tung Y.T., Lee B.H., Liu C.F., Pan T.M.. **Optimization of culture condition for ACEI and GABA production by lactic acid bacteria**. *J. Food Sci.* (2011) **76** M585-M591. DOI: 10.1111/j.1750-3841.2011.02379.x
10. Aoki H., Furuya Y., Endo Y., Fujimoto K.. **Effect of γ-aminobutyric acid-enriched tempeh-like fermented soybean (GABA-tempeh) on the blood pressure of spontaneously hypertensive rats**. *Biosci. Biotechnol. Biochem.* (2003) **67** 1806-1808. DOI: 10.1271/bbb.67.1806
11. Geisler C.E., Ghimire S., Bruggink S.M., Miller K.E., Weninger S.N., Kronenfeld J.M., Yoshino J., Klein S., Duca F.A., Renquist B.J.. **A critical role of hepatic GABA in the metabolic dysfunction and hyperphagia of obesity**. *Cell Rep.* (2021) **35** 109301. DOI: 10.1016/j.celrep.2021.109301
12. Abd El-Fattah A., Sakr S., El-Dieb S., Elkashef H.. **Developing functional yogurt rich in bioactive peptides and gamma-aminobutyric acid related to cardiovascular health**. *LWT* (2018) **98** 390-397. DOI: 10.1016/j.lwt.2018.09.022
13. Sharma S., Saxena D.C., Riar C.S.. **Changes in the GABA and polyphenols contents of foxtail millet on germination and their relationship with in vitro antioxidant activity**. *Food Chem.* (2018) **245** 863-870. DOI: 10.1016/j.foodchem.2017.11.093
14. Zhang L., Yue Y., Wang X., Dai W., Piao C., Yu H.. **Optimization of fermentation for γ-aminobutyric acid (GABA) production by yeast**. *Bioprocess Biosyst. Eng.* (2022) **45** 1111-1123. DOI: 10.1007/s00449-022-02702-2
15. Ohmori T., Tahara M., Ohshima T.. **Mechanism of gamma-aminobutyric acid (GABA) production by a lactic acid bacterium in yogurt-sake**. *Process Biochem.* (2018) **74** 21-27. DOI: 10.1016/j.procbio.2018.08.030
16. Guo Y., Yang R., Chen H., Song Y., Gu Z.. **Accumulation of γ-aminobutyric acid in germinated soybean (**. *Eur. Food Res. Technol.* (2012) **234** 679-687. DOI: 10.1007/s00217-012-1678-y
17. Ma Y., Wang P., Gu Z., Sun M., Yang R.. **Effects of germination on physio-biochemical metabolism and phenolic acids of soybean seeds**. *J. Food Compos. Anal.* (2022) **112** 104717. DOI: 10.1016/j.jfca.2022.104717
18. Sęczyk Ł., Świeca M., Gawlik-Dziki U.. **Soymilk enriched with green coffee phenolics—Antioxidant and nutritional properties in the light of phenolics-food matrix interactions**. *Food Chem.* (2017) **223** 1-7. DOI: 10.1016/j.foodchem.2016.12.020
19. Xiao T., Shah N.P.. **Lactic acid produced by Streptococcus thermophilus activated glutamate decarboxylase (GadA) in**. *LWT* (2021) **137** 110474. DOI: 10.1016/j.lwt.2020.110474
20. Chan C.L., Gan R.Y., Shah N.P., Corke H.. **Enhancing antioxidant capacity of**. *Food Biosci.* (2018) **26** 185-192. DOI: 10.1016/j.fbio.2018.10.016
21. Wu Q., Law Y.S., Shah N.P.. **Dairy**. *Sci. Rep.* (2015) **5** 12885. DOI: 10.1038/srep12885
22. Giri A., Kanawjia S.K., Khetra Y.. **Textural and melting properties of processed cheese spread as affected by incorporation of different inulin levels**. *Food Bioprocess Technol.* (2014) **7** 1533-1540. DOI: 10.1007/s11947-013-1235-0
23. Meilgaard M.C., Carr B.T., Civille G.V.. *Sensory Evaluation Techniques* (1999)
24. Rizzello C.G., Cassone A., Di Cagno R., Gobbetti M.. **Synthesis of angiotensin I-converting enzyme (ACE)-inhibitory peptides and γ-aminobutyric acid (GABA) during sourdough fermentation by selected lactic acid bacteria**. *J. Agric. Food Chem.* (2008) **56** 6936-6943. DOI: 10.1021/jf800512u
25. Yang R., Feng L., Wang S., Yu N., Gu Z.. **Accumulation of γ-aminobutyric acid in soybean by hypoxia germination and freeze–thawing incubation**. *J. Sci. Food Agric.* (2016) **96** 2090-2096. DOI: 10.1002/jsfa.7323
26. Hwang C.E., Haque M., Lee J.H., Song Y.H., Lee H.Y., Kim S.C., Cho K.M.. **Bioconversion of γ-aminobutyric acid and isoflavone contents during the fermentation of high-protein soy powder yogurt with**. *Appl. Biol. Chem.* (2018) **61** 409-421. DOI: 10.1007/s13765-018-0366-4
27. Xu J.G., Hu Q.P.. **Changes in γ-aminobutyric acid content and related enzyme activities in Jindou 25 soybean (**. *LWT* (2014) **55** 341-346. DOI: 10.1016/j.lwt.2013.08.008
28. Chew S.Y., Than L.T.L.. **Glucose Metabolism and Use of Alternative Carbon Sources in Medically-Important Fungi**. *Encycl. Mycol.* (2021) **2021** 220-229
29. Meena P.K., Gupta V.K., Meena G.S., Raju P.N., Parmar P.T.. **Application of ultrafiltration technique for the quality improvement of dahi**. *J. Food Sci. Technol.* (2015) **52** 7974-7983. DOI: 10.1007/s13197-015-1951-8
30. Buriti F.C., Castro I.A., Saad S.M.. **Effects of refrigeration, freezing and replacement of milk fat by inulin and whey protein concentrate on texture profile and sensory acceptance of synbiotic guava mousses**. *Food Chem.* (2010) **123** 1190-1197. DOI: 10.1016/j.foodchem.2010.05.085
31. Chen L., Alcazar J., Yang T., Lu Z., Lu Y.. **Optimized cultural conditions of functional yogurt for γ-aminobutyric acid augmentation using response surface methodology**. *J. Dairy Sci.* (2018) **101** 10685-10693. DOI: 10.3168/jds.2018-15391
32. Tárrega A., Costell E.. **Effect of inulin addition on rheological and sensory properties of fat-free starch-based dairy desserts**. *Int. Dairy J.* (2006) **16** 1104-1112. DOI: 10.1016/j.idairyj.2005.09.002
33. Adhikari B., Howes T., Bhandari B.R., Truong V.. **Stickiness in foods: A review of mechanisms and test methods**. *Int. J. Food Prop.* (2001) **4** 1-33. DOI: 10.1081/JFP-100002186
34. Weenen H., Van Gemert L.J., Van Doorn J.M., Dijksterhuis G.B., De Wijk R.A.. **Texture and mouthfeel of semisolid foods: Commercial mayonnaises, dressings, custard desserts and warm sauces**. *J. Texture Stud.* (2003) **34** 159-179. DOI: 10.1111/j.1745-4603.2003.tb01373.x
35. Niamah A.K., Sahi A.A., Al-Sharifi A.S.. **Effect of feeding soy milk fermented by probiotic bacteria on some blood criteria and weight of experimental animals**. *Probiotics Antimicrob. Proteins* (2017) **9** 284-291. DOI: 10.1007/s12602-017-9265-y
|
---
title: Development and Validation of the Breastfeeding Literacy Assessment Instrument
(BLAI) for Obstetric Women
authors:
- María Jesús Valero-Chillerón
- Rafael Vila-Candel
- Desirée Mena-Tudela
- Francisco Javier Soriano-Vidal
- Víctor M. González-Chordá
- Laura Andreu-Pejo
- Aloma Antolí-Forner
- Lledó Durán-García
- Miryam Vicent-Ferrandis
- María Eugenia Andrés-Alegre
- Águeda Cervera-Gasch
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10000890
doi: 10.3390/ijerph20053808
license: CC BY 4.0
---
# Development and Validation of the Breastfeeding Literacy Assessment Instrument (BLAI) for Obstetric Women
## Abstract
Background: Despite international efforts to protect and promote exclusive breastfeeding (EBF) for infants up to six months of age, global rates of EBF continue to fall short of the targets proposed by the WHO for 2025. Previous studies have shown a relationship between the level of health literacy and the duration of EBF, although this relationship was not determinant, probably due to the use of a generic health literacy questionnaire. Therefore, this study aims to design and validate the first specific breastfeeding literacy instrument. Methods: A Breastfeeding Literacy instrument was developed. Content validation was carried out by a group of 10 experts in health literacy, breastfeeding or instrument validation, obtaining a Content Validity index in Scale (S-CVI/Ave) of 0.912. A multicentre cross-sectional study was carried out in three Spanish hospitals to determine the psychometric properties (construct validity and internal consistency). The questionnaire was administered to 204 women during the clinical puerperium. Results: The Kaiser-Meier-Oklin Test (KMO = 0.924) and Bartlett’s Test of Sphericity (X2 = 3119.861; p ≤ 0.001) confirmed the feasibility of the Exploratory Factor Analysis, which explained $60.54\%$ of the variance with four factors. Conclusions: The Breastfeeding Literacy Assessment Instrument (BLAI) consisting of 26 items was validated.
## 1. Introduction
Pregnancy and the postpartum period constitute an important stage in women’s health, in which a series of events take place that require special attention and monitoring by the health system. Although it is a physiological process, it involves a continuum of decision-making in which women need to have sufficient information so that these decisions protect and promote not only their health, but also that of their children.
One of the most important decisions to be made is regarding the feeding the infant will receive. International organisations such as the World Health Organisation and UNICEF recommend exclusive breastfeeding (EBF) for the first six months of an infant’s life and breastfeeding with complementary foods until at least two years of age [1]. The promotion of EBF is an international target in different programmes such as the Comprehensive Implementation Plan on Maternal, Infant, and Young Child Nutrition of the World Health Assembly [2], the United Nations Decade of Action on Nutrition 2016–2025 [3], and the investment framework of the World Bank [4]. However, despite multiple efforts to protect breastfeeding (BF), rates of EBF at six months of infant life remain very low, at around $38\%$ globally [5]. Furthermore, laws to protect breastfeeding remain inadequate in most countries [6]. In Europe, the six-month EBF rate is around $25\%$ [7]. However, in Spain, the six-month EBF rate has varied from $16.8\%$ in 2019 [8] to $39\%$ in 2017 [9]. The data need to be interpreted with caution as the variation in these data is caused by the absence of a unified approach for collecting and monitoring BF information in Spain.
The premature discontinuation of breastfeeding is a complex phenomenon that is influenced by a multitude of factors, including demographic characteristics (e.g., young maternal age, low levels of education and socio-economic status), social considerations (e.g., inadequate workplace support), psychological determinants (e.g., maternal intentions before birth, self-assurance, and engagement in breastfeeding), as well as biological considerations (e.g., infant health concerns, maternal health issues, first-time motherhood, and issues related to lactation) [10,11,12,13]. These considerations contribute to the multifaceted nature of early breastfeeding cessation. However, several studies have shown that, in many cases, early weaning occurs due to maternal decisions or perceptions, which do not always correspond to reality [14]. In the face of these false perceptions, health literacy (HL) has a fundamental role because the primary outcome of having a good level of HL is the ability to make good decisions that promote and protect health [15].
Various authors have broadly defined the concept of HL over time [16]. Despite the lack of consensus on constructing a single definition of this concept, most authors agree that it is multidimensional, complex, and heterogeneous [17]. Sørensen et al. proposed an integrated model of HL that looked at cognitive and social skills that enable the individual to address four competencies (access, understand, appraise, and apply health information) and three domains in which the individual interacts with the health system (health care, disease prevention, and health promotion) [18].
This complex concept of HL has been reformulated and adapted to specific health areas or populations. As a result, it is possible to retrieve a multitude of validated instruments that allow us to generically assess the level of HL, such as the European Health Literacy Survey Questionnaire (HLS-EU-Q) [19] or the test of functional health literacy in adults (TOFHLA) [20]. There are also instruments available that focus on measuring literacy in specific health areas, such as the Literacy Assessment for Diabetes (LAD), which addresses diabetes literacy [21]. Others focus on specific populations, such as the eHealth Literacy Scale (eHEALS), which addresses electronic health literacy in a young population [22]. It is also possible to retrieve the Maternal Health Literacy Inventory in Pregnancy (MHELIP) instrument, which is designed to measure maternal health literacy [23]. However, to our knowledge, no previous instrument has measured breastfeeding literacy (BFL).
Recent studies have suggested that an adequate level of HL may be a protective factor against early BF cessation [12,13,24]. However, these studies use generic HL instruments to determine the relationship between HL and the specific health domain of BF. Specifically, they use the Short Assessment of Health Literacy for Spanish-speaking Adults (SAHLSA) [24] and the Newest Vital Sign (NVS) in its validated version for Spanish-speaking populations [12,13,24]. The main findings of using a generic instrument to explore a particular area of health lack specificity and concreteness in the results obtained, so the authors agree on the need for a specifically validated instrument to measure the level of BFL in women during the perinatal stage [12,13,24].
Therefore, this study aims to design and validate a specific instrument to measure the level of BFL.
## 2.1. Design, Setting, and Participants
A design and validation study of the Breastfeeding Literacy Assessment Instrument (BLAI) was conducted to assess the level of BFL in a Spanish context. The study took place from 1 December 2021 to 30 September 2022.
The project was designed under Organic Law $\frac{03}{2018}$, of 5 December, under the Protection of Personal Data and Guarantee of Digital Rights. First, the instrument was designed by reviewing the literature and content validity by creating a panel of experts. Second, a cross-sectional study was carried out on women during the clinical postpartum period in three hospitals in the Valencian Community (Spain): Hospital Universitario de La Ribera (HULR); Hospital General de Castellón and Hospital Lluís Alcanyís de Xàtiva (Spain) to determine the psychometric properties of the BLAI. Inclusion criteria were: having given birth in one of the participating hospitals and voluntarily agreeing to participate in the study. Exclusion criteria were: having a linguistic barrier that impeded understanding and completion of the data collection form, multiple gestations, or the neonate being admitted to a neonatal care unit. Participants completed an online informed consent form prior to data collection. The Ethics and Research Committees of each participating hospital approved the study. Furthermore, the principles of the Declaration of Helsinki were respected throughout this effort.
According to Anthoine et al. ’s recommendations for instrument validation, a sample size of between five and ten participants per instrument item is recommended [25]. Thus, given that the initial version of the instrument had 28 items, a sample of between 140 and 280 participants was required. However, according to Ferrando y Anguiano-Carrasco, a minimum sample size of 200 participants is recommended to assess the quality of a questionnaire [26]. Therefore, a sample size of at least 200 participants would be sufficient to satisfy both criteria. A non-probabilistic convenience sampling was performed, in which a data collection form was administered consisting of sociodemographic variables (age, country of origin, educational level, perceived socioeconomic status), obstetric variables (parity, feeding doubts before birth, previous BF, variables related to previous BF experience), and the BLAI.
## 2.2. Questionnaire Development and Content Validity
The BLAI was designed based on the definition of the HL concept adapted to the BF context. It was therefore organised into the following dimensions: D1: Access to breastfeeding-related information; D2: Understanding of such information; D3: Appraise the veracity of information related to breastfeeding; D4: Application of that information. The formulation of the items was based on the difficulty in dealing with the situations described, establishing a Likert-type scale with four response options to avoid central tendency errors. The items were developed based on the integrated model of health literacy proposed by Sørensen et al. [ 18]. This model considers the dimensions mentioned above and applies them to healthcare, disease prevention, and health promotion. Likewise, it considers the perspective of the individual’s capacity and the interaction that the individual has with the social and health environment.
Following the development of the first battery of items, a panel of nine experts in breastfeeding, health literacy, and questionnaire development and validation, which included midwives, lactation consultants, and research nurses, was formed. The initial iteration of the survey instrument was presented to a panel of experts for an evaluation of its overall relevance, the appropriateness of individual items within the context of each dimension, and the identification of other item-specific feedback. As many rounds as necessary were carried out until an average congruence percentage (ACP) of 0.9, as recommended by the literature, was reached [27]. For this purpose, the Item Content Validity Index (I-CVI) was calculated using the methodology proposed by Polit and Beck, with considerations given to the level of validity of each item, the probability of agreement due to chance (Pc), and the modified Kappa coefficient [27]. In addition, the overall scale average (S-CVI) was calculated, which determines the mean of the scores of all the I-CVIs and reflects the overall validity of the instrument.
## 2.3. Psychometric Properties
After content validation, the instrument was administered to women in the participating hospitals during the clinical postpartum period, provided they voluntarily agreed to participate in the study.
First, a descriptive analysis of the sample was carried out using the mean, standard deviation, and $95\%$ confidence interval for quantitative variables and absolute and relative frequencies for qualitative variables. After this initial analysis, construct validity was studied using an exploratory factor analysis (EFA). For this purpose, the factor extraction method used was principal axis factorisation, applying an oblique factorial rotation, given the potential correlation between the different factors. The ProMax rotation method was used since a dominant factor was not considered. Previously, the feasibility of the EFA was confirmed with the Kaiser-Mayer-Olkin (KMO) test and Bartlett’s test of sphericity. A factor loading greater than 0.4 was considered to retain items in a given factor [28]. The dimensionality of the instrument was studied using the Kaiser criterion, which considers as many factors as eigenvalues greater than 1 are present [29].
Second, the instrument’s internal consistency and dimensions were determined. Since an ordinal response scale was used, McDonald’s Omega was employed (adequate internal consistency of ω = 0.7–0.9) [30]. Due to the non-normality of the overall scores for each dimension, Spearman’s correlation coefficient was used to investigate the relationship between the different elements of the instrument. A range between 0.50–0.70 was considered a good correlation, and >0.7 was a strong correlation [31].
## 2.4. Inferential Analysis
After studying the instrument’s psychometric properties, an inferential analysis was carried out to explore the association between the level of BFL and the rest of the variables included in the study, using Chi-squared or Fisher’s exact test, depending on the nature of the variables. Participants were first grouped by determining the cut-off points for each of the dimensions of the BLAI questionnaire using cluster analysis. The k-means method was used, forcing two groups to differentiate between inadequate and adequate BFL levels, obtaining statistically significant differences between the two groups.
Statistical analysis was carried out with SPSS v.26, considering a statistical significance level of $p \leq 0.05.$
## 3.1. BLAI Validation Results
An ACP of 0.864 was achieved for content validity through the panel of experts ($$n = 9$$) after the first round. The experts’ contributions to reformulating some items were greatly valued; they added new items to cover certain aspects not contemplated and changed the dimension of others. After conducting a second round, the authors obtained an ACP score of 0.913, which met the percentage recommended by relevant research. After this second round, only minor modifications were made to the wording of the items, resulting in a version of the instrument consisting of 28 items (Access six items; Understand five items, Appraise ten items, Apply seven items). The wording of the items is available in the supplementary material (Table S1), both in the original version in Spanish and in the translated version (not validated) in English.
Regarding the modelling of the instrument through exploratory factor analysis (EFA), it was observed that two items (Access6 and Appraise6) obtained a poor factor loading (<0.4) in the dimension for which they were developed. Moreover, according to theoretical reasoning, these two items had no place in another dimension. In addition, the instrument’s internal consistency slightly increased when these items were removed, so they were eliminated from the instrument, which went from 28 items to 26 items.
Regarding the new 26-item version, KMO (0.924) and Bartlett’s Test of Sphericity (X2 = 3119.861; p ≤ 0.001) confirmed the feasibility of the EFA. The factor analysis explained $60.54\%$ of the variance with a total of four factors, coinciding with the theoretical design of the instrument. Specifically, the first factor (Access) explained $44.02\%$ of the variance and consisted of five items, the second factor (Apply) explained $8.04\%$ of the variance and comprised seven items, the third factor (Appraise) explained $4.38\%$ of the variance and consisted of nine items, and the fourth factor (Understand) explained $4.09\%$ of the variance and consisted of five items. The overall reliability of the questionnaire (ω = 0.949) and of each of the dimensions (Access ω = 0.809; Understand ω = 0.810; Appraise ω = 0.912; Apply ω = 0.873) was excellent. Table 1 shows the results of the content validity, exploratory factor analysis, and reliability of the BLAI.
As also shown in Table 1, the structure matrix demonstrates that most items obtained a higher factor loading for the dimension they were designed for, except for the following seven items that showed a considerable factor loading for two different dimensions. The formulation of the Understand1 item does not fit into the Appraise dimension. The formulation of the Understand3 item could be considered in both the Access and Understand dimensions, although the theoretical reasoning gives it more weight in the Understand dimension. The wording of the Understand2 and Understand4 items means they do not fit into the Access dimension. Finally, Appraise1, Appraise2, and Apply3 cannot be included in the Understand dimension.
Regarding the correlation between the different dimensions, it is observed that all the correlations are good. Specifically, the correlation between the Appraise-Understand and Appraise-Apply dimensions is strong, as they are all statistically significant (Table 2).
Table 3 shows the minimum and maximum scores obtained in each dimension according to the cluster analysis carried out to differentiate between inadequate and adequate BFL. In addition, the descriptive analysis of BLAI for each of the dimensions can also be observed, in which it can be seen that the majority of the participants are in the category of Adequate BFL in all the dimensions, with the Understand dimension having the lowest percentage of women with Adequate BFL ($54.9\%$, $$n = 112$$) and the Apply dimension having the highest percentage of women with Adequate BFL ($66.2\%$, $$n = 135$$).
## 3.2. Descriptive Analysis
A total sample size of 204 participants was reached. The mean maternal age was 32.8 years (SD = 5.143; $95\%$ CI 32.09–33.51). A total of $45.59\%$ ($$n = 93$$) of the deliveries were attended at HULR, $83.8\%$ ($$n = 171$$) of the women were originally from Spain, $50.5\%$ ($$n = 103$$) had a university education, and $85.3\%$ ($$n = 174$$) reported having a medium socioeconomic status (Table 4).
Regarding the type of breastfeeding at discharge, $74\%$ ($$n = 151$$) of the women chose Exclusive Breastfeeding (EBF), $6.4\%$ ($$n = 13$$) mixed breastfeeding, and $19.6\%$ ($$n = 40$$) chose formula feeding. Table 5 shows variables related to the type of breastfeeding chosen during the puerperium. It was observed that $72.7\%$ ($$n = 80$$) of primiparous women chose EBF. Of the women who opted for EBF, $82.3\%$ ($$n = 135$$) had no doubts about the type of breastfeeding, while $38.5\%$ ($$n = 15$$) did have doubts during gestation, although they finally chose EBF. Only one woman reported opting for EBF due to pressure from her environment.
As for the general perception of the previous BF experience ($$n = 82$$), $52.4\%$ ($$n = 44$$) perceived it as a very good experience, and nine of them ($10.7\%$) reported having a regular previous BF experience. Only $45.3\%$ ($$n = 38$$) felt supported at all times by healthcare professionals, and $39.3\%$ ($$n = 33$$) felt supported at all times by family and friends. The $63.4\%$ ($$n = 52$$) fed EBF up to six months or more to their previous child. As for a reason for giving up breastfeeding, $36.9\%$ ($$n = 31$$) of the cases were physiologically weaned, while $20.3\%$ ($$n = 17$$) were weaned because they had started working.
## 3.3. Breastfeeding Literacy Assessment Instrument
Table 6 shows that as the perceived socioeconomic level increases, the percentage of participants with adequate Access BFL increases ($$p \leq 0.016$$). It can also be seen that the percentage of women with adequate Understand BFL or adequate Apply BFL is higher in those women who offer EBF (Understand: $59.6\%$, $$n = 90$$, $$p \leq 0.023$$; Apply: $70.9\%$, $$n = 107$$, $$p \leq 0.026$$), while those who opted for mixed breastfeeding obtained a lower percentage (Understand: $23.01\%$, $$n = 3$$, $$p \leq 0.023$$; Apply: $38.5\%$, $$n = 5$$, $$p \leq 0.026$$). Regarding the Appraise dimension, the percentage of Adequate Appraise BFL is lower among primiparous women ($$p \leq 0.022$$), and the highest percentages are observed among multiparous women of second ($78.9\%$, $$n = 56$$) or subsequent gestations ($65.2\%$, $$n = 15$$), with the differences being statistically significant ($$p \leq 0.018$$). Regarding the Apply dimension, the percentage of women with Adequate Apply BFL is higher among multiparous women of second gestation ($77.5\%$, $$n = 55$$), followed by primiparous women ($60.9\%$; $$n = 67$$). Multiparous women of third or later gestations were the ones with the lowest percentage of Adequate Apply BFL. A comparative analysis of sociodemographic and BF-related variables for each of the dimensions of the BLAI questionnaire can be found in the supplementary material (Tables S2–S5).
## 4. Discussion
The BLAI presents adequate psychometric properties to assess BFL levels in women during the perinatal period, with adequate construct validity and internal consistency. The exploratory factor analysis explains $60.54\%$ of the variance with four domains, coinciding with the four dimensions covered by the concept of HL (Access, Understand, Appraise, and Apply) developed by Sørensen et al. [ 18].
It is worth mentioning that, during the instrument’s modelling, a number of items had a slightly higher loading in dimensions for which they were not designed. However, after thoroughly examining each item to evaluate the feasibility of assigning it to alternative dimensions, the research team determined that it was more appropriate to retain these items within their original dimensions, as the theoretical alignment was more convincing in these dimensions. In addition, two items were removed (Access6, Appraisse6) due to their poor factor loadings. The internal consistency of the BLAI slightly increased after their deletion.
As for the dimensionality study of the instrument, the EFA was run without determining a number of factors to extract, allowing the statistical programme to determine the number of factors based on the Kaiser criterion of eigenvalues greater than 1 [29]. This is the default method in the statistical programme used, and it is possible to retrieve scientific evidence that casts doubt on its practical usefulness, as has been reported by other authors [32,33]. However, the resulting factor structure coincided with the number of dimensions for which the instrument was created. Today, there are other, more commonly used methods to corroborate the appropriate number of factors, such as parallel analysis or the ratio of the first-to-second eigenvalue. However, we have not found a universally accepted criterion. For example, in the case of eigenvalues, there is no criterion for the ratio to be accepted, some authors propose four [34], others five [35], but none seem to be based on empirical reasoning. Therefore, it is essential that future studies consider other analyses for studying dimensionality.
While it is true that the use of a single criterion may lead to an overestimation or an underestimation of the actual number of factors, over-extraction leads to fewer measurement errors [36]. Moreover, it would not be appropriate to treat as unidimensional a construct of which the theoretical foundation is based on more than one factor, even if the multidimensionality is moderate. In the present instrument, an overall score of the construct would tend to lean towards the mean of the possible score range, and would not allow for discerning which competence/s the subject presents, and which others lower the mean score of the construct and would need to be addressed by a practitioner. Therefore, treating the construct in an unidimensional way would diminish its usefulness in practice. However, in order to obtain an instrument with a solid factor structure supported by theoretical and statistical reasoning, it is of utmost importance to progress with the validation process, with larger samples and different methods of studying dimensionality, in order to confirm or refute the factor structure that supports the theoretical reasoning.
In terms of the percentage of variance explained by each of the factors, it can be seen that the Access dimension is the one that explains the highest percentage of variance, followed by the Apply dimension. This may be because these dimensions are more manageable for women, while the Understand and Apply dimensions may be more complex due to the reflection involved in these situations. In other words, the general population can access information related to a given topic (Access) and apply the information they have accessed (Apply). However, people who are not experts in an area may find it more challenging to reflect on whether they adequately understand the information they have accessed (Understand), as well as to assess whether the source of information is reliable or may contain information that is not scientifically supported (Appraise). It is important that this finding is taken into account when addressing any health education, specifically in the area of BF, with the aim of training mothers-to-be, and even health professionals, to reflect on the information accessed in order to increase confidence when making health decisions based on the knowledge they have acquired. Future studies could address this necessary line of research.
As evidenced by the findings of this study, the BLAI questionnaire demonstrates utility in identifying areas where perinatal women may require additional competencies to access, understand, appraise and apply information about BF, not only for self-care purposes but also to prevent occurrences that may impede BF, as well as to foster successful initiation and continuation of BF. Similarly, it would be interesting in future studies to use the BLAI questionnaire to measure the effect of BF training or antenatal education on BF. Similarly, future studies should consider confirmatory factor analysis to confirm the current four-dimensional factor structure, as the evidence does not recommend using the same sample to address all validation phases of a newly created instrument, as this would lead to optimistic results [37]. In fact, we are currently continuing to collect data in order to be able to carry out the confirmatory factor analysis. However, this is the first publication derived from the design and validation of the instrument based on solid theoretical reasoning, so it is interesting to make its existence known, as well as its first psychometric properties.
This study is a continuation of previous studies that addressed the relationship between health literacy measured by generic instruments and BF [12,13,38]. It has not been possible to retrieve in the literature another validated instrument to address the level of BFL, which makes it challenging to contrast results in the present study. On the one hand, concerning age, the present study did not find a statistically significant association with the level of BFL, in line with the results of Vila-Candel et al., in which the study also showed no significant association with the level of LH [12]. On the other hand, Valero-Chillerón et al. did find that the mean age among mothers with an adequate level of BFL was higher than those with a limited level of BFL [13].
In the present study, no statistically significant association was observed between educational level and BFL level in any of the dimensions that comprise the questionnaire, in contrast to other studies that obtained such an association between HL level measured with generic instruments and educational level [12,13]. This may be due to the fact that two completely different phenomena; the level of education academically trains you in a certain area, whereas the level of breastfeeding literacy explores the individual’s ability to access information related to breastfeeding, understand that information, evaluate the quality of the information accessed, and apply that information in the specific area of breastfeeding. It is possible that a higher level of education may enhance an individual’s competence in certain areas of daily life, but it may not be sufficient to establish statistically significant relationships across all dimensions of the BFL concept. Another discrepancy is observed for parity. In the present study, a significant association was observed between Appraise BFL and the number of children; whereas this association was not significant in previous studies for HL levels [12,13]. In addition, Valero-Chillerón et al. observed an association between the country of origin and the level of HL, while this association could not be observed in the present study regarding the level of BFL, perhaps due to the low participation of women whose country of origin was not Spain [13]. In line with the findings of Sørensen et al., low socioeconomic status is related to low levels of HL, and, as in the present study, with Inadequate Access BFL [38].
It has not been possible to retrieve any study in which a statistically significant association was found between HL level measured with a generic instrument and maintenance of EBF at six months. However, Vila-Candel et al. did find a statistical association between LH level and maintenance of EBF at one, two, and four months of infant life, although they did not re-measure at six months [12]. Moreover, all studies seem to confirm the multi-causality derived from early breastfeeding cessation [12,13,24,39]. This is why it may not be appropriate to address this relationship using a generic instrument to give sufficient weight to the level of HL on the duration of EBF, and it may be advisable to use a specific instrument to assess the level of BFL. Future studies should address this aspect to confirm the results obtained.
It was observed that the percentage of women who opted for mixed breastfeeding had the lowest percentage of adequate understanding and adequate Apply BFL. This may be a chance finding due to the limited percentage of this category in the present study. Contrasting these results in future studies conducted with larger samples would be interesting. It is worth mentioning that the rates of EBF and mixed feeding are similar to those reported in the study by Chertok et al., and point to an increase in the numbers of mixed breastfeeding and formula feeding after the SARS-CoV-2 pandemic, due to the lack of support for breastfeeding during the pandemic, among other factors [40].
We must recognise several limitations in our study and cautiously interpret the results. Firstly, it should be noted that since we could not retrieve any previous instruments that measure the level of BFL or any other measurement method that could be used as a gold standard reference, it was not possible to study convergent validity. Secondly, it was challenging to randomise the study sample, so convenience sampling was used. Thirdly, it is necessary to advance the process of analysing the dimensionality of the instrument. The methods used in the present study need to be tested against more objective criteria in larger samples, minimising additional survey items to the BLAI questionnaire to try to avoid possible response bias among participants, in order to confirm the factor structure.
Despite the limitations, we believe that the good psychometric properties of the instrument suggest that its use should be considered, as it is the first validated instrument to measure the level of BFL. Previous studies have found that the percentage of women with limited HL was significantly higher among mothers who did not reach four months [12] or six months of EBF than among those who did reach EBF at these follow-up points [13,24]. Therefore, it is interesting to study the relationship between the level of BFL using the BLAI questionnaire and maintenance of EBF at six months, as well as to study the explanatory power of the instrument. Future studies will also allow us to contrast the results obtained and explore the possibility of refining the instrument or the suitability of maintaining the current version. Similarly, future studies could adapt and validate the current version of the instrument among health science professionals and students.
## 5. Conclusions
The Breastfeeding Literacy Assessment Instrument (BLAI) can be used as a valid questionnaire to assess women’s literacy during the perinatal period to access, understand, appraise, and apply information related to BF, both in the sphere of self-care and the prevention of problems that negatively impact on BF, as well as the promotion of the adequate establishment and maintenance of EBF. However, it would be interesting to use the BLAI questionnaire in future studies to corroborate its validity and reliability.
## References
1. 1.
World Health Organization
United Nations Children’s Fund
Global Strategy for Infant and Young Child FeedingWHOGeneva, Switzerland2003. *Global Strategy for Infant and Young Child Feeding* (2003.0)
2. **Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition**
3. **The United Nations Decade of Action on Nutrition 2016–2025**
4. Bank W.. **An Investment Framework for Nutrition: Reaching the Global Targets for Stunting, Anemia, Breastfeeding and Wasting**
5. **Breastfeeding Policy Brief. Global Nutrition Targets 2025**
6. Network I.B.F.A.. *Laws to Protect Breastfeeding Inadequate in Most Countries* (2016.0)
7. **Infants Exclusively Breastfed for the First Six Months of Life (%)**
8. Cabedo R., Manresa J.M., Cambredó M.V., Montero L., Reyes A., Gol R.. **Tipos de Lactancia Materna y Factores Que Influyen En Su Abandono Hasta Los 6 Meses**. *Matronas Profesión* (2019.0) **20** 54-61
9. **Tipo de Lactancia Según Sexo y Clase Social Basada En La Ocupación de La Persona de Referencia**. *Población de 6 Meses a 4 Años.*
10. Oribe M., Lertxundi A., Basterrechea M., Begiristain H., Santa Marina L., Villar M., Dorronsoro M., Amiano P., Ibarluzea J.. **Prevalencia y Factores Asociados Con La Duración de La Lactancia Materna Exclusiva Durante Los 6 Primeros Meses En La Cohorte INMA de Guipúzcoa**. *Gac. Sanit.* (2015.0) **29** 4-9. DOI: 10.1016/j.gaceta.2014.08.002
11. Ramiro González M.D., Ortiz Marrón H., Arana Cañedo-Argüelles C., Esparza Olcina M.J., Cortés Rico O., Terol Claramonte M., Ordobás Gavín M.. **Prevalence of Breastfeeding and Factors Associated with the Start and Duration of Exclusive Breastfeeding in the Community of Madrid among Participants in the ELOIN**. *An. Pediatría* (2018.0) **89** 32-43. DOI: 10.1016/j.anpedi.2017.09.002
12. Vila-Candel R., Soriano-Vidal F.J., Mena-Tudela D., Quesada J.A., Castro-Sánchez E.. **Health Literacy of Pregnant Women and Duration of Breastfeeding Maintenance: A Feasibility Study**. *J. Adv. Nurs.* (2021.0) **77** 703-714. DOI: 10.1111/jan.14625
13. Valero-Chillerón M.J., Mena-Tudela D., Cervera-Gasch Á., González-Chordá V.M., Soriano-Vidal F.J., Quesada J.A., Castro-Sánchez E., Vila-Candel R.. **Influence of Health Literacy on Maintenance of Exclusive Breastfeeding at 6 Months Postpartum: A Multicentre Study**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph19095411
14. Niño M.R., Silva E.G., Atalah S.E.. **Factores Asociados a La Lactancia Materna Exclusiva**. *Rev. Chil. Pediatría* (2012.0) **83** 161-169. DOI: 10.4067/S0370-41062012000200007
15. Dumenci L., Matsuyama R., Riddle D.L., Cartwright L.A., Perera R.A., Chung H., Siminoff L.A.. **Measurement of Cancer Health Literacy and Identification of Patients with Limited Cancer Health Literacy**. *J. Health Commun.* (2014.0) **19** 205-224. DOI: 10.1080/10810730.2014.943377
16. Liu C., Wang D., Liu C., Jiang J., Wang X., Chen H., Ju X., Zhang X.. **What Is the Meaning of Health Literacy? A Systematic Review and Qualitative Synthesis**. *Fam. Med. Community Heal.* (2020.0) **8** e000351. DOI: 10.1136/fmch-2020-000351
17. Okan O., Bauer U., Levin-Zamir D., Pinheiro P., Sørensen K.. *International Handbook of Health Literacy* (2019.0)
18. Sørensen K., Van den Broucke S., Fullam J., Doyle G., Pelikan J., Slonska Z., Brand H.. **Health Literacy and Public Health: A Systematic Review and Integration of Definitions and Models**. *BMC Public Health* (2012.0) **12**. DOI: 10.1186/1471-2458-12-80
19. Sørensen K., Pelikan J.M., Röthlin F., Ganahl K., Slonska Z., Doyle G., Fullam J., Kondilis B., Agrafiotis D., Uiters E.. **Health Literacy in Europe: Comparative Results of the European Health Literacy Survey (HLS-EU)**. *Eur. J. Public Health* (2015.0) **25** 1053-1058. DOI: 10.1093/eurpub/ckv043
20. Parker R.M., Baker D.W., Williams M.V., Nurss J.R.. **The Test of Functional Health Literacy in Adults**. *J. Gen. Intern. Med.* (1995.0) **10** 537-541. DOI: 10.1007/BF02640361
21. Nath C.R., Sylvester S.T., Yasek V., Gunel E.. **Development and Validation of a Literacy Assessment Tool for Persons With Diabetes**. *Diabetes Educ.* (2001.0) **27** 857-864. DOI: 10.1177/014572170102700611
22. Norman C.D., Skinner H.A.. **EHEALS: The EHealth Literacy Scale**. *J. Med. Internet Res.* (2006.0) **8** e27. DOI: 10.2196/jmir.8.4.e27
23. Taheri S., Tavousi M., Momenimovahed Z., Direkvand-Moghadam A., Tiznobaik A., Suhrabi Z., Taghizadeh Z.. **Development and Psychometric Properties of Maternal Health Literacy Inventory in Pregnancy**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0234305
24. Valero-Chillerón M.J., González-Chordà V.M., Cervera-Gasch Á., Vila-Candel R., Soriano-Vidal F.J., Mena-Tudela D.. **Health Literacy and Its Relation to Continuing with Breastfeeding at Six Months Post-partum in a Sample of Spanish Women**. *Nurs. Open* (2021.0) **8** 3394-3402. DOI: 10.1002/nop2.885
25. Anthoine E., Moret L., Regnault A., Sébille V., Hardouin J.-B.. **Sample Size Used to Validate a Scale: A Review of Publications on Newly-Developed Patient Reported Outcomes Measures**. *Health Qual. Life Outcomes* (2014.0) **12** 2. DOI: 10.1186/s12955-014-0176-2
26. Ferrando P.J., Anguiano-Carrasco C.. **El Análisis Factorial Como Técnica de Investigación en Psicología**. *Pap. Psicólogo* (2010.0) **31** 18-33
27. Polit D.F., Beck C.T.. **The Content Validity Index: Are You Sure You Know What’s Being Reported? Critique and Recommendations**. *Res. Nurs. Health* (2006.0) **29** 489-497. DOI: 10.1002/nur.20147
28. Lloret-Segura S., Ferreres-Traver A., Hernández-Baeza A., Tomás-Marco I.. **El Análisis Factorial Exploratorio de Los Ítems: Una Guía Práctica, Revisada y Actualizada**. *Ann. Psychol.* (2014.0) **30** 1151-1169. DOI: 10.6018/analesps.30.3.199361
29. Kaiser H.F.. **The Application of Electronic Computers to Factor Analysis**. *Educ. Psychol. Meas.* (1960.0) **20** 141-151. DOI: 10.1177/001316446002000116
30. Campo-Arias A., Oviedo H.C.. **Propiedades Psicométricas de Una Escala: La Consistencia Interna: [Revisión]**. *Rev. Salud Pública* (2008.0) **10** 831-839. DOI: 10.1590/S0124-00642008000500015
31. Hazra A., Gogtay N.. **Biostatistics Series Module 6: Correlation and Linear Regression**. *Indian J. Dermatol.* (2016.0) **61** 593. DOI: 10.4103/0019-5154.193662
32. Watkins M.W.. **Exploratory factor analysis: A guide to best practice**. *J. Black Psychol.* (2018.0) **44** 219-246. DOI: 10.1177/0095798418771807
33. Rogers P.. **Best practices for your exploratory factor analysis: A factor tutorial**. *Rev. De Adm. Contemp.* (2022.0) **26** e210085. DOI: 10.1590/1982-7849rac2022210085.por
34. Reeve B.B., Hays R.D., Bjorner J.B., Cook K.F., Crane P.K., Teresi J.A., Thissen D., Revicki D.A., Weiss D.J., Hambleton R.K.. **Psychometric Evaluation and Calibration of Health-Related Quality of Life Item Banks**. *Med. Care* (2007.0) **45** S22-S31. DOI: 10.1097/01.mlr.0000250483.85507.04
35. Lizasoain Hernández L., Joaristi Olariaga L.. **Análisis de La Dimensionalidad En Modelos de Valor Añadido: Estudio de Las Pruebas de Matemáticas Empleando Métodos No Paramétricos Basados En TRI**. *Rev. Educ.* (2009.0) **348** 175-194
36. Reise S.P., Waller N.G., Comrey A.L.. **Factor Analysis and Scale Revision**. *Psychol. Assess.* (2000.0) **12** 287-297. DOI: 10.1037/1040-3590.12.3.287
37. Streiner D.L., Kottner J.. **Recommendations for Reporting the Results of Studies of Instrument and Scale Development and Testing**. *J. Adv. Nurs.* (2014.0) **70** 1970-1979. DOI: 10.1111/jan.12402
38. Sørensen K., Van den Broucke S., Pelikan J.M., Fullam J., Doyle G., Slonska Z., Kondilis B., Stoffels V., Osborne R.H., Brand H.. **Measuring Health Literacy in Populations: Illuminating the Design and Development Process of the European Health Literacy Survey Questionnaire (HLS-EU-Q)**. *BMC Public Health* (2013.0) **13**. DOI: 10.1186/1471-2458-13-948
39. Graus T.M., Brandstetter S., Seelbach-Göbel B., Melter M., Kabesch M., Apfelbacher C., Fill Malfertheiner S., Ambrosch A., Arndt P., Baessler A.. **Breastfeeding Behavior Is Not Associated with Health Literacy: Evidence from the German KUNO-Kids Birth Cohort Study**. *Arch. Gynecol. Obstet.* (2021.0) **304** 1161-1168. DOI: 10.1007/s00404-021-06038-2
40. Chertok I.A., Artzi-Medvedik R., Arendt M., Sacks E., Otelea M.R., Rodrigues C., Costa R., Linden K., Zaigham M., Elden H.. **Factors Associated with Exclusive Breastfeeding at Discharge during the COVID-19 Pandemic in 17 WHO European Region Countries**. *Int. Breastfeed. J.* (2022.0) **17** 83. DOI: 10.1186/s13006-022-00517-1
|
---
title: 3-Phenyllactic Acid and Polyphenols Are Substances Enhancing the Antibacterial
Effect of Methylglyoxal in Manuka Honey
authors:
- Marcus Thierig
- Jana Raupbach
- Diana Wolf
- Thorsten Mascher
- Kannan Subramanian
- Thomas Henle
journal: Foods
year: 2023
pmcid: PMC10000891
doi: 10.3390/foods12051098
license: CC BY 4.0
---
# 3-Phenyllactic Acid and Polyphenols Are Substances Enhancing the Antibacterial Effect of Methylglyoxal in Manuka Honey
## Abstract
Manuka honey is known for its unique antibacterial activity, which is due to methylglyoxal (MGO). After establishing a suitable assay for measuring the bacteriostatic effect in a liquid culture with a time dependent and continuous measurement of the optical density, we were able to show that honey differs in its growth retardingeffect on *Bacillus subtilis* despite the same content of MGO, indicating the presence of potentially synergistic compounds. In model studies using artificial honey with varying amounts of MGO and 3-phenyllactic acid (3-PLA), it was shown that 3-PLA in concentrations above 500 mg/kg enhances the bacteriostatic effect of the model honeys containing 250 mg/kg MGO or more. It has been shown that the effect correlates with the contents of 3-PLA and polyphenols in commercial manuka honey samples. Additionally, yet unknown substances further enhance the antibacterial effect of MGO in manuka honey. The results contribute to the understanding of the antibacterial effect of MGO in honey.
## 1. Introduction
Honey is a food item known for its antibacterial activity, which is due to physical factors such as high osmolarity and low pH value as well defined antibacterial compounds such as hydrogen peroxide, produced by glucose oxidase, and antibacterial peptides from the honeybee such as “bee-defensin” [1,2,3]. Compared with “conventional” honeys, such as linden or rapeseed honey, the pronounced antibacterial activity of manuka honey (Leptospermum scoparium) is due to high concentrations of methylglyoxal (MGO), which is formed by dehydration of dihydroxyacetone in the nectar during maturation [4,5]. While MGO is present in conventional honeys in amounts up to 24.1 mg/kg [6], the MGO levels in manuka honey reach up to 1541 mg/kg, depending on the location of the bee hive [7,8,9]. On the other hand, manuka honey is classified as a “low-peroxide” honey due to its low glucose oxidase activity [10].
Besides the use as a food item, honey and, especially manuka honey, is used as a non-adherent dressing in medical wound management, in which the mentioned properties help to stimulate tissue regeneration and to keep the wound sterile [11,12]. A standard method for the determination of the antibacterial activity of (manuka) honey is the agar diffusion assay. Thereby, honey solution is placed into a cavity of an agar plate and inoculated with a culture of Staphylococcus aureus. After 24 h of incubation, the antibacterial effect of the honey solution can be estimated based on the inhibition zones around the samples and by comparing the effect caused by the honey samples with standard solutions of an antibacterial agent, e.g., phenol [13,14]. This technique has the drawback that the agar plates cannot be evaluated precisely and that no statements about bacteriostatic or bactericidal effects are possible. For the classification of the antibacterial activity of manuka honey, Swift et al. [ 2014] established a growth assay in liquid cultures, in which it has been shown that MGO has a bacteriostatic effect on S. aureus [15]. Besides that, a quantitative evaluation of the antibacterial effect of MGO in honey on specific bacteria can be found rarely in the literature, as well as with respect to the variety of bioactive substances in honey besides MGO, such as hydrogen peroxide [1].
Additionally, it has already been shown that MGO and manuka honey have a synergistic effect on the antibacterial and antiviral activity of antimicrobial substances. For example, the addition of linezolid, an oxazolidinone-based antibiotic inhibiting the protein biosynthesis of Gram-positive bacteria, reduced the MIC of MGO against S. aureus in a checkerboard broth microdilution assay by a factor of four in concentrations below its minimal inhibitory concentration (MIC) [16]. Similar effects were observable for S. aureus biofilms when adding Rifampicin to manuka honey or on influenza viruses (H1N1) in Madin–Darby canine kidney cells when adding the antiviral agents Zanamivir or Oseltamivir to manuka honey [17,18]. On the other hand, little can be found in the literature on whether the antibacterial effect of MGO itself can be enhanced by naturally occurring compounds present in manuka honey. It has been shown that the growth delay of S. aureus and *Pseudomonas aeruginosa* were higher when treated with manuka honey in concentrations above 250 mg/kg and α-cyclodextrin, compared with the honey solutions without α-cyclodextrin [15]. Furthermore, the only substances native to (manuka) honey discussed to have an antibacterial effect themselves are syringic acid and 3,4,5-trimethoxybenzoic acid [19]. Manuka and non-manuka honeys also contain polyphenolic compounds in concentrations up to 2967 mg gallic acid equivalents (GAE)/kg, which provide an antioxidant activity in the honey [20,21]. Synthetic derivatives of gallic acid show an antibacterial activity against E. coli, S. aureus and *Bacillus subtilis* [22]. So far, it is unknown if syringic acid, 3,4,5-trimethoxybenzoic acid or naturally occurring polyphenolic compounds enhance the effect of MGO in manuka honey. Since manuka honey is also rich in other organic acids with structural similarities, such as 3-phenyllactic acid (3-PLA), 4-hydroxyphenyllactic acid and 2-methoxybenzoic acid, we hypothesized that these substances can influence the antibacterial effect of manuka honey. As 3-PLA is a marker substance for manuka honey, manuka honey has to contain at least 500 mg/kg 3-PLA per definition [23]. Due to the high 3-PLA contents up to 1400 mg/kg in manuka honey, which are also in the same order of magnitude as MGO, and its relevance as a marker substance for manuka honey, it will be considered in this study [24]. Therefore, the aim of this study was to evaluate possible synergistic effects of MGO, 3-PLA and polyphenols on the antibacterial activity of manuka honey.
## 2.1. Honey Samples
In this study, four commercially available manuka honeys, all labeled for the purpose of wound healing treatments; two manuka honey labeled with MGO250+ (containing 270 mg/kg MGO) and MGO400+ (containing 444 mg/kg MGO) and a cornflower honey, as references; and a manuka honey labeled with MGO30+ (containing 72 mg/kg MGO) as a basis for spike experiments were included. After purchase, all samples were stored at 4 °C until analysis. As a blank matrix and for dilution of the honey samples, artificial honey prepared following Deng et al. [ 2018] was used in the assay to obtain constant osmotic pressure [25]. In brief, 44 g of fructose, 37 g of glucose and 2 g sucrose were dissolved in 17 g water. The suspension was slightly heated to 45 °C and stirred until full dissolved.
## 2.2. Chemicals
Methanol (HPLC grade) and acetonitrile (LC-MS grade) were purchased from VWR (Darmstadt, Germany). Methylglyoxal, ortho-phenylenediamine, 2-methylchinoxaline, gallic acid, 3-phenyllactic acid, forchlorfenuron, 1,3-dihydroxyacetone-dimer and Folin–Ciocalteu-reagent were obtained from Sigma Aldrich/Merck (Steinheim, Germany). Formic acid, acetic acid, fructose, glucose, sucrose and LB-broth were obtained from Carl Roth (Karlsruhe, Germany). Sodium carbonate, sodium dihydrogenphosphate and disodium hydrogenphosphate were purchased from Grüssing (Filsum, Germany). 3-Deoxyglucosone (3-DG) was synthesized according to Henle and Bachmann [1996] [26]. Double-distilled water (Bi 18E double distillation system, QCS, Maintal, Germany) was used for HPLC solvents.
## 2.3. Quantification of MGO in Manuka Honeys and in Model Solutions
The quantification of MGO was performed according to Atrott et al. [ 2012] with slight modifications [5]. Briefly, 150 µL ortho-phenylenediamine (OPD) solution ($1\%$ in phosphate-buffered saline (PBS)) was added to 650 µL of a $2.5\%$ solution on honey in 0.5 M PBS with pH 6.5 or to a mixture of 150 µL model solution and 500 µL PBS, respectively, for an overnight incubation at room temperature in the absence of light. After membrane filtration (0.45 µm), the samples were analyzed via HPLC-UV. The analyses were performed using a system containing a pump, including an online-degasser and a mixing chamber P 6.1 L, an autosampler AS 6.1 L, a column thermostat CT 2.1 and a diode array detector DAD 2.1 L, all from Knauer (Berlin, Germany). The separation of the quinoxalines was achieved on a column filled with Eurospher C-18 material (250 × 4.6 mm, 5 µm particle size, with integrated precolumn, Knauer, Berlin, Germany) as the stationary phase and a mobile phase containing $0.075\%$ acetic acid as solvent A and a mixture of $20\%$ solvent A and $80\%$ methanol as solvent B. The gradient started with $40\%$ solvent B for 1 min, was elevated to $100\%$ B within 20 min, was changed back to $40\%$ B in 4 min and was held there for an additional 5 min. The separation was performed with a flow rate of 0.9 mL/min; at a temperature of 30 °C, 20 µL of the sample was injected. The detection of the peaks was conducted by measuring the UV-absorbance at 312 nm. Quantification was achieved with an external calibration with commercial MGO solution, of which the MGO content was determined after derivatization with OPD by comparison with 2-methylquinoxaline standard.
## 2.4. Extraction of Honey Proteins
To extract the high molecular fraction of the honeys containing the honey protein, honey samples were prepared according to the protocol published by Hellwig et al. [ 2017], with slight modifications [27]. Approx. 5 g of honey was dissolved in 10 mL of water and transferred into a dialysis tube (MWCO 14 kDa, Sigma, Steinheim, Germany), followed by dialysis against water. During the 2 days of dialysis, the water was changed twice a day. Afterwards, the retentates were freeze-dried and stored at −18 °C until sampling. About 20 mg of honey protein was obtained of each sample.
## 2.5. Quantification of 3-Phenyllactic Acid in Manuka Honeys
To determine the content of 3-phenyllactic acid, the honey samples were prepared according to the protocol published by the NZ Ministry for Primary Industries [2017] with slight modifications [28]. Briefly, 1.5 g honey was dissolved in 10 mL of a solution consisting of acetonitrile, formic acid and water (10:1:90 v/v). The samples were shaken for 20 min with an overhead shaker until complete dissolution. After centrifugation (3000 g, 10 min) and membrane filtration (0.45 µm), 50 µL solution was mixed with 940 µL extraction solution and 10 µL internal standard solution (forchlorfenuron, 10 mg/L in $1\%$ formic acid in acetonitrile). This solution was injected in the HPLC-MS/MS system, consisting of a binary pump G1312A, an online-degasser G1379B, an autosampler G1329A, a column thermostat G1316A and a mass spectrometer with electrospray ion source G6410A, all from Agilent Technologies (Santa Clara, CA, USA). Separation was achieved with a Kinetex 100 C18 column (2.1 × 50 mm, 1.7 µm, with precolumn, Phenomenex, Torrance, CA, USA) as a stationary phase and a mobile phase containing $0.1\%$ formic acid in water as solvent A and $0.1\%$ formic acid in acetonitrile as solvent B. The gradient started with $5\%$ solvent B for 0.75 min, was elevated to $15\%$ B within 1.25 min and $70\%$ B within 2 min before a final increase to $98\%$ B within 2 min. The concentration was held for 2 min, changed back to $5\%$ B within 1 min and finally held for 7 min. The separation was performed with a flow rate of 0.2 mL/min at a temperature of 40 °C. In total, 5 µL of the sample was injected. For quantification, MRM transitions (see Table 1) were recorded and an external calibration with standard solutions of 3-phenyllactic acid (concentrations between 0.025 and 1 mg/L) was used. General working conditions of the mass spectrometer were 350 °C gas temperature, 11 L/min gas flow, 35 psi nebulizer pressure and 4000 V capillary voltage.
## 2.6. Estimation of the Polyphenol Content in Honey via Folin–Ciocalteu Method
The determination of total polyphenols was achieved using the method of Singleton et al. [ 1999], with slight modifications [29]. In total, 20 µL of sample solution ($15\%$ w/v in water) or calibration solution (gallic acid, dissolved in water, concentrations ranging between 5 and 200 mg/L) and 100 µL of 0.2 N Folin reagent solution (Sigma Aldrich, Steinheim, Germany) were pipetted into a cavity of a 96-well plate. After 5 min, 80 µL of a 75 g/L sodium carbonate solution was added. After a reaction time of 120 min, the absorbance of the resulting blue dye was measured at 760 nm against a blank value containing water instead of sample or calibration solution.
## 2.7. Determination of the Bacteriostatic Effect of Manuka Honey Solutions against Bacillus subtilis
The determination of the bacteriostatic effect of manuka honey solutions against B. subtilis W168 was performed according to Jenkins and Cooper [2012], with modifications [30]. Therefore, an overnight culture was prepared by inoculating approx. 10 mL liquid LB-broth with a colony forming unit of the bacterial strain. The next day, the OD600 was measured of a 1:10 (v/v) solution of the culture against LB-broth with a spectrophotometer UV-3100PC (VWR, Darmstadt, Germany). To evaluate the antibacterial effect, $30\%$ solutions of the honey samples or of artificial honey, respectively, were prepared with liquid LB-broth and sterile filtered (0.2 µm). To realize different amounts of methylglyoxal in the samples, while maintaining the sugar concentration at the same level, honey solutions were diluted with artificial honey solution. This also ensured a constant osmotic pressure in each diluted sample. For the assay, 105 µL of the diluted or undiluted honey sample solution was pipetted into a cavity of a 96-well plate. In total, 105 µL of the solution of artificial honey was used as blank. An aliquot of the 1:10 diluted overnight culture was placed into each cavity, such that the resulting OD600 was calculated to be 0.05. Finally, LB-broth was added to make a volume of 210 µL, such that the assay solution contained $15\%$ (w/v) in total. To prove sterile conditions, 210 µL of liquid LB-broth was tested as well as blank. To obtain growth curves, the samples were incubated at 37 °C for 24 h while being shaken continuously. The OD600 was measured every 5 min using a Biotek EPOCH 2 microplate reader (Agilent, Santa Clara, CA, USA). To calculate the bacteriostatic effect, the end of the bacterial lag phase was defined as the OD600 value 5-fold higher than the initial OD600 value. Assuming a proportionality between the OD600 and the cell count, this factor of 5 corresponds to two-generation growth cycles of the bacteria. To calculate a growth arrest or delay, the time of the lag phase measured for bacterial growth in the honey sample was divided by the lag time obtained for the bacterial growth in artificial honey on the respective 96-well plate as control. The calculated growth delay gives a statement about the bacteriostatic effect, i.e., the increase in the duration of the lag-phase due to the presence of antibacterial compounds. If the bacteria did not grow during the whole measurement, the effect was defined to be bactericidal.
## 3.1. Measurement of the Antibacterial Activity of Honeys
To evaluate the antibacterial effect of MGO, *Bacillus subtilis* was chosen as a bacterial model strain. Due to the absence of glutathione in cells of B. subtilis, and the production of bacillithiol instead, it has a lower capacity to detoxify MGO [31]. Therefore, enzymatic intracellular MGO degradation can be neglected. Additionally, the antibacterial activity of hydrogen peroxide formed by glucose oxidase in honeys can be ruled as an antibacterial factor since B. subtilis is able to degrade hydrogen peroxide due to its catalase activity. These assumptions were tested by measuring growth curves of B. subtilis in presence of $15\%$ solutions of artificial honey, cornflower honey and manuka honeys labeled MGO250+ and MGO400+, respectively. Cornflower honey, which is well known for a high glucose oxidase activity, showed a slight inhibiting effect with a growth delay of 2.3 when compared with artificial honey (see Figure 1). Furthermore, the addition of hydrogen peroxide to artificial honey in honey-relevant concentrations did not lead to a delayed growth of B. subtilis significantly (see Figure S1). In contrast, manuka honey MGO250+ showed a growth delay of 5.3. This confirms that hydrogen peroxide only plays a minor role in the antibacterial effect of honey on B. subtilis, and MGO in manuka honey can be assumed as the main inhibiting compound. The inhibiting effect of manuka honey MGO250+ on the growth of B. subtilis is clearly observable (Figure 1).
The strength of the antibacterial effect is dependent on the MGO content of the investigated honeys. Higher MGO contents lead to longer a lag phase, after which the bacteria start to grow. The bacteriostatic, but not bactericidal, effect is likely due to chemical or microbial degradation of MGO during the measurement. MGO is able to react within the Maillard reaction with lysine and arginine side chains of the proteins in liquid medium [32], which leads to a reduction of the MGO content. Besides its weak glyoxalase system, B. subtilis may degrade MGO via other pathways, e.g., with aldo-keto reductase to acetol [33]. If the MGO level drops below a certain concentration, the bacteria are able to start growing. On the other hand, in the presence of MGO400+ honey bacteria did not grow during the measurement. For the purpose of this study, this is considered to be a bactericidal effect, even though it cannot be ruled out that the strain would have grown after a longer incubation period.
Therefore, this model allows quantification of the antibacterial activity of different honeys. In particular, comparative statements between (manuka) honeys are possible, especially when compared with a reference honey. Principally, this assay is also transferable to other bacterial strains. However, for the evaluation of the bacteriostatic effect, the activity of glyoxalase, catalase and other enzymes responsible for the degradation of antibacterial compounds have to be considered.
## 3.2. Antibacterial Activity of Commercial Manuka Honey Samples
To evaluate the antibacterial activity of commercial manuka honey samples, the assay was applied to four commercial manuka honeys which were labeled for wound healing purposes. Besides using the $30\%$ honey solutions for measurements (resulting in a $15\%$ solution in the assay), all honeys were also diluted to $2\%$, $5\%$, $10\%$, $15\%$ and $20\%$ with a $30\%$ solution of artificial honey in liquid medium prior to the addition of the liquid culture and LB-medium. The dilution was obtained with artificial honey to achieve varying MGO levels but to keep constant osmotic pressure in the assays. Thereby, every sample contains the same amount of sugars, which is necessary to compare the antibacterial effect of diluted honey samples [3]. It can be seen that higher MGO contents in the assay lead to higher growth delays (Figure 2, Table S1). In addition, it is noticeable that similar contents of MGO in the honeys did not necessarily lead to the same growth delays and, conversely, no conclusion can be drawn from the growth delay to the MGO content in the assay. Comparing honey one with honey three, for instance, an adjustment to 30 µg MGO per mL (corresponding to a $30\%$ solution of a honey containing 100 mg/kg MGO) resulted in GD values of four and six, respectively. In reverse, to obtain a growth delay of five, a MGO concentration of 24 µg/mL of honey one or 31 µg/mL of honey four is needed.
As mentioned above, hydrogen peroxide as a second antibacterial agent in honeys can be excluded due to active catalase in the bacterial cell. Additionally, different pH values of the honeys can also be excluded, since measurements of the antibacterial activity of artificial honey spiked with MGO (pH~6.5) did not differ when the pH value of artificial honey was adjusted to five with gluconic acid. Therefore, neither the glucose oxidase activity nor the pH values of the honeys explain the differences in growth delay. The effect of MGO on bacterial growth has to be enhanced or reduced by other honey ingredients.
## 3.3. Determination of Synergistic Effects in Manuka Honey
The next aim of the study was to analyze which compounds, besides MGO, might be relevant for the antibacterial activity. Therefore, artificial honey spiked with MGO was used as a model system, to which potential synergistic substances were added in honey at relevant concentrations. The following substances were chosen as potential synergists: dihydroxyacetone (DHA) as the precursor substance of MGO in manuka honey; isolated manuka honey protein; gallic acid as a representative for phenolic compounds in honey; 3-phenyllactic acid (3-PLA) as a marker substance of manuka honey, occurring in similar concentrations as MGO and considered to have antibacterial effects on Gram-positive bacteria [34]; and 3-desoxyglucosone (3-DG) as another abundant dicarbonyl compound in honey.
Except for 3-PLA, none of the compounds tested were found to enhance the antibacterial activity of MGO (see Figures S2 and S3 in the supplementary material). Honey protein especially did not show any particular effect. For further studies, structural changes and a loss of a possible antibacterial effect during the protein extraction should be considered.
Whereas 3-PLA in honey-relevant concentrations ranging up to 2000 mg/kg alone or added to artificial honey containing 100 mg/kg MGO did not lead to a delay in the growth curves, adding 3-PLA to artificial honeys with 250 mg MGO/kg or higher clearly increased the antibacterial activity caused solely by MGO. As an example, the addition of 2000 mg/kg 3-PLA to artificial honey with 400 mg/kg MGO increased the growth delay from 4.06 to 5.05. ( Figure 3, Table S2).
To simulate real honey samples, the experiment was repeated with a manuka honey naturally containing 72 mg/kg MGO. This manuka honey was spiked with different amounts of MGO up to an additional 400 mg/kg and 3-PLA up to 2000 mg/kg. Concerning the antibacterial activity, the same effect was observed as in the artificial honeys: the addition of 3-PLA resulted in a dose-dependent increased growth delay when MGO concentrations of 322 mg/kg or higher were present, e.g., the addition of 2000 mg/kg 3-PLA to the honey containing 472 mg/kg MGO increased the growth delay from 5.64 to 6.85 (Figure 4, Tables S2 and S4). In the presence of lower MGO concentrations, 3-PLA did not show a growth delay enhancing effect.
There are two explanatory approaches for the synergistic effect of 3-PLA with MGO. Firstly, MGO is stabilized by 3-PLA in the medium. MGO is a reactive substance which can react with proteins in the liquid medium. Therefore, the MGO concentration in the assay decreases over time. To test this, MGO was diluted to a concentration of 120 mg/L (corresponding to a $30\%$ solution of a honey containing 400 mg/kg MGO) with LB medium and in LB medium containing 600 mg/L 3-PLA (corresponding to a $30\%$ solution of a honey containing 2000 mg/kg 3-PLA). The samples were incubated without the presence of B. subtilis. The MGO content was analyzed at 0 h, 1 h, 3 h, 5 h, 8 h and 24 h during the 24 h incubation at 37 °C.
While the MGO level in the sample without 3-PLA dropped from 120 mg/L to 7 mg/L within 24 h, the MGO content in the sample with 3-PLA decreased to 35 mg/L (Figure 5, Table S3). Therefore, a higher apparent MGO concentration in the assay leads to longer lag-times of the bacteria. The mechanism of how MGO is stabilized by 3-PLA is still unknown.
Besides an extracellular effect, intracellular effects might be relevant as well for the synergistic effect of 3-PLA. It has been shown in the literature that 3-PLA in high concentrations (>10 mg/mL) damages or alters the cell wall of Gram-positive bacteria, e.g., Listeria monocytogenes, due to the loss of cell wall rigidity [34]. With regard to manuka honey, 3-PLA might also interact with bacterial cell walls in honey-relevant concentrations (~100 µg/mL) without leading to cell death, but to “softening” the cell wall. This could lead to a higher susceptibility of the cell towards MGO by increasing intracellular MGO concentrations.
Besides 3-PLA, gallic acid showed similar properties enhancing the antibacterial effect of MGO. When added to artificial honeys containing no MGO, gallic acid did not have a growth delaying effect. On the other hand, when added to artificial honey containing 250 mg MGO/kg or more, higher levels of gallic acid lead to a higher growth delay at the same MGO concentration (Figure 6). Additionally, in the artificial honey containing 400 mg/kg MGO, the increase in gallic acid from 1500 mg/kg to 2000 mg/kg resulted in a bactericidal effect. In this assay, gallic acid was used as a representative for the polyphenolic compounds in (manuka) honey. Despite the fact that honeys contain rather small amounts of up to 66 mg/kg of gallic acid [35], the content of polyphenols expressed as gallic acid equivalents (GAE) is within the range of our model honeys, as manuka honeys containing 2170 mg GAE/kg have been described in the literature [20].
In order to check whether these results are an explanation for the differences in the antibacterial properties between honeys with similar MGO contents (Figure 2), the contents of 3-PLA and polyphenols, expressed as gallic acid equivalents (GAE), were measured (Table 2).
It is noticeable that the two honeys with the highest 3-PLA and GAE content are also the honeys with the highest growth delay against B. subtilis. As an example, to obtain a growth delay of about five with “honey 1” (containing 734 mg/kg 3-PLA and 636 mg/kg GAE), a MGO concentration of 24 µg/mL is needed, while “honey 4” (334 mg/kg PLA and 386 mg/kg GAE) obtains a growth delay of five at 31 µg/mL, which might be due to different amounts of 3-PLA and GAE in the assay. Nevertheless, the exact quantitative contribution to the antibacterial effect of manuka honey, especially in models containing more than one synergist, and the specific mechanism of action of the synergistic effect have to be investigated in further studies.
To verify the synergistic effect of 3-PLA, the antibacterial activities of a manuka honey containing 259 mg/kg MGO and 467 mg/kg 3-PLA, an artificial honey spiked to the same MGO concentration and another spiked artificial honey with the same MGO and 3-PLA concentrations were compared. It was confirmed that 3-PLA enhances the effect of MGO above a concentration of 34.1 µg/mL, corresponding to a $30\%$ solution of a honey containing 113 mg/kg MGO. ( Figure 7, Table S5) Restrictively, this enhancing effect is not enough to reach the antibacterial level of the commercial manuka honey sample. Whereas a MGO concentration of 27.3 µg/mL obtained by MGO-spiked artificial honey leads to a growth delay of 3.2 without 3-PLA and 3.4 in the presence of 3-PLA, the same MGO level obtained by manuka honey leads to a growth delay of 6.2 (Figure 7). Therefore, it can be concluded that polyphenolic compounds, as well as additional compounds, presumably other organic acids such as 4-hydroxyphenyllactic acid, 2-methoxybenzoic acid, syringic acid or 3,4,5-trimethoxybenzoic acid, or yet unknown substances in manuka honey, enhance the effect of MGO.
## 4. Conclusions
For the first time, we demonstrated that 3-phenyllactic acid, a marker compound of manuka honey occurring in concentrations up to 1400 mg/kg, enhances the antibacterial activity of artificial honeys containing 250 mg/kg MGO or more against the model bacterium B. subtilis, even if 3-phenyllactic acid alone does not show any bacteriostatic effect in honey-relevant concentrations. This is due to higher apparent MGO concentrations in the assay due to MGO stabilization by 3-PLA. Additionally, first results indicate that polyphenols, tested with gallic acid as a substitute with similar chemical properties, also enhance the antibacterial effect of MGO. Therefore, 3-PLA and polyphenols as synergists of MGO may also be considered for the qualitative evaluation of manuka honey besides their chemical benefits. Nevertheless, the differences in the antibacterial activities between artificial honey spiked with MGO and commercial manuka honey with the same amount of MGO cannot be explained uniquely by 3-PLA and GAE. Other substances in (manuka) honey appear to enhance the effect of MGO as well.
The results of this study contribute to the understanding of the antibacterial mechanism of MGO itself and in manuka honey. The results further support the application of manuka honey beyond its use as food item, e.g., to the use of manuka honey for functional and medical applications such as wound healing. However, the assay presented in this study should be transferred to other bacteria, especially those without a proper glyoxalase system, such as S. aureus [31], as well as to other Gram-positive and Gram-negative bacteria and other microbiota to obtain more general data about the mechanism of action of the antimicrobial activity of methylglyoxal and manuka honey.
## References
1. White J.W., Subers M.H., Schepartz A.I.. **The Identification of Inhibine, the Antibacterial Factor in Honey, as Hydrogen Peroxide and Its Origin in a Honey Glucose-Oxidase System**. *Biochim. Biophys. Acta Proteins Proteom.* (1962) **73** 57-70. DOI: 10.1016/0926-6569(63)90108-1
2. Kwakman P.H.S., te Velde A.A., de Boer L., Speijer D., Vandenbroucke-Grauls C.M.J.E., Zaat S.A.J.. **How Honey Kills Bacteria**. *FASEB J.* (2010) **24** 2576-2582. DOI: 10.1096/fj.09-150789
3. Bogdanov S.. **Nature and Origin of the Antibacterial Substances in Honey**. *LWT Food Sci. Technol.* (1997) **30** 748-753. DOI: 10.1006/fstl.1997.0259
4. Mavric E., Wittmann S., Barth G., Henle T.. **Identification and Quantification of Methylglyoxal as the Dominant Antibacterial Constituent of Manuka (Leptospermum Scoparium) Honeys from New Zealand**. *Mol. Nutr. Food Res.* (2008) **52** 483-489. DOI: 10.1002/mnfr.200700282
5. Atrott J., Haberlau S., Henle T.. **Studies on the Formation of Methylglyoxal from Dihydroxyacetone in Manuka (Leptospermum Scoparium) Honey**. *Carbohydr. Res.* (2012) **361** 7-11. DOI: 10.1016/j.carres.2012.07.025
6. Terio V., Bozzo G., Ceci E., Savarino A.E., Barrasso R., Di Pinto A., Mottola A., Marchetti P., Tantillo G., Bonerba E.. **Methylglyoxal (MGO) in Italian Honey**. *Appl. Sci.* (2021) **11**. DOI: 10.3390/app11020831
7. Weigel K.U., Opitz T., Henle T.. **Studies on the Occurrence and Formation of 1,2-Dicarbonyls in Honey**. *Eur. Food Res. Technol.* (2004) **218** 147-151. DOI: 10.1007/s00217-003-0814-0
8. Stephens J.M., Schlothauer R.C., Morris B.D., Yang D., Fearnley L., Greenwood D.R., Loomes K.M.. **Phenolic Compounds and Methylglyoxal in Some New Zealand Manuka and Kanuka Honeys**. *Food Chem.* (2010) **120** 78-86. DOI: 10.1016/j.foodchem.2009.09.074
9. Williams S., King J., Revell M., Manley-Harris M., Balks M., Janusch F., Kiefer M., Clearwater M., Brooks P., Dawson M.. **Regional, Annual, and Individual Variations in the Dihydroxyacetone Content of the Nectar of Mānuka (Leptospermum Scoparium) in New Zealand**. *J. Agric. Food Chem.* (2014) **62** 10332-10340. DOI: 10.1021/jf5045958
10. Majtan J., Bohova J., Prochazka E., Klaudiny J.. **Methylglyoxal May Affect Hydrogen Peroxide Accumulation in Manuka Honey Through the Inhibition of Glucose Oxidase**. *J. Med. Food* (2014) **17** 290-293. DOI: 10.1089/jmf.2012.0201
11. Visavadia B.G., Honeysett J., Danford M.H.. **Manuka Honey Dressing: An Effective Treatment for Chronic Wound Infections**. *Br. J. Oral Maxillofac. Surg.* (2008) **46** 55-56. DOI: 10.1016/j.bjoms.2006.09.013
12. Bischofberger A.S., Dart C.M., Perkins N.R., Kelly A., Jeffcott L., Dart A.J.. **The Effect of Short- and Long-Term Treatment with Manuka Honey on Second Intention Healing of Contaminated and Noncontaminated Wounds on the Distal Aspect of the Forelimbs in Horses**. *Vet. Surg.* (2013) **42** 154-160. DOI: 10.1111/j.1532-950X.2012.01083.x
13. Adams C.J., Boult C.H., Deadman B.J., Farr J.M., Grainger M.N.C., Manley-Harris M., Snow M.J.. **Isolation by HPLC and Characterisation of the Bioactive Fraction of New Zealand Manuka (Leptospermum Scoparium) Honey**. *Carbohydr. Res.* (2008) **343** 651-659. DOI: 10.1016/j.carres.2007.12.011
14. Cokcetin N.N., Pappalardo M., Campbell L.T., Brooks P., Carter D.A., Blair S.E., Harry E.J.. **The Antibacterial Activity of Australian Leptospermum Honey Correlates with Methylglyoxal Levels**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0167780
15. Swift S., Chepulis L.M., Uy B., Radcliff F.J.. **Enhanced Antibacterial Activity of MGOTM Manuka Honey Complexed with A- Cyclodextrin (Manuka Honey with CycloPowerTM)**. *Funct. Foods Health Dis.* (2014) **4** 172-181. DOI: 10.31989/ffhd.v4i5.13
16. Hayes G., Wright N., Gardner S.L., Telzrow C.L., Wommack A.J., Vigueira P.A.. **Manuka Honey and Methylglyoxal Increase the Sensitivity of Staphylococcus Aureus to Linezolid**. *Lett. Appl. Microbiol.* (2018) **66** 491-495. DOI: 10.1111/lam.12880
17. Liu M.Y., Cokcetin N.N., Lu J., Turnbull L., Carter D.A., Whitchurch C.B., Harry E.J.. **Rifampicin-Manuka Honey Combinations Are Superior to Other Antibiotic-Manuka Honey Combinations in Eradicating Staphylococcus Aureus Biofilms**. *Front. Microbiol.* (2018) **8** 2653. DOI: 10.3389/fmicb.2017.02653
18. Watanabe K., Rahmasari R., Matsunaga A., Haruyama T., Kobayashi N.. **Anti-Influenza Viral Effects of Honey In Vitro: Potent High Activity of Manuka Honey**. *Arch. Med. Res.* (2014) **45** 359-365. DOI: 10.1016/j.arcmed.2014.05.006
19. Russell K.M., Molan P.C., Wilkins A.L., Holland8 P.T.. **Identification of Some Antibacterial Constituents of New Zealand Manuka Honey Hamilton, New Zealand, and Ministry of Agriculture and Fisheries**. *Food Chem.* (1990) **38** 10-13. DOI: 10.1021/jf00091a002
20. Goslinski M., Nowak D., Klebukowska L.. **Antioxidant Properties and Antimicrobial Activity of Manuka Honey versus Polish Honeys**. *J. Food Sci. Technol.* (2020) **57** 1269-1277. DOI: 10.1007/s13197-019-04159-w
21. Mahnot N.K., Saikia S., Mahanta C.L.. **Quality Characterization and Effect of Sonication Time on Bioactive Properties of Honey from North East India**. *J. Food Sci. Technol.* (2019) **56** 724-736. DOI: 10.1007/s13197-018-3531-1
22. Khatkar A., Nanda A., Kumar P., Narasimhan B.. **Synthesis, Antimicrobial Evaluation and QSAR Studies of Gallic Acid Derivatives**. *Arab. J. Chem.* (2017) **10** S2870-S2880. DOI: 10.1016/j.arabjc.2013.11.014
23. 23.
Ministry for Primary Industries
Criteria for Identifying Mānuka Honey: A Summary of the Mānuka Honey Science ProgrammeMinistry for Primary IndustriesWellington, New Zealand2017Volume 7. *Criteria for Identifying Mānuka Honey: A Summary of the Mānuka Honey Science Programme* (2017) **Volume 7**
24. Rückriemen J., Henle T.. **Pilot Study on the Discrimination of Commercial Leptospermum Honeys from New Zealand and Australia by HPLC–MS/MS Analysis**. *Eur. Food Res. Technol.* (2018) **244** 1203-1209. DOI: 10.1007/s00217-018-3036-1
25. Deng J., Liu R., Lu Q., Hao P., Xu A., Zhang J., Tan J.. **Biochemical Properties, Antibacterial and Cellular Antioxidant Activities of Buckwheat Honey in Comparison to Manuka Honey**. *Food Chem.* (2018) **252** 243-249. DOI: 10.1016/j.foodchem.2018.01.115
26. Henle T., Bachmann A.. **Synthesis of Pyrraline Reference Material**. *Z. Für Lebensm. Unters. Und Forsch.* (1996) **202** 72-74. DOI: 10.1007/BF01229689
27. Hellwig M., Rückriemen J., Sandner D., Henle T.. **Unique Pattern of Protein-Bound Maillard Reaction Products in Manuka (Leptospermum Scoparium) Honey**. *J. Agric. Food Chem.* (2017) **65** 3532-3540. DOI: 10.1021/acs.jafc.7b00797
28. 28.
Ministry for Primary Industries
Determination of Four Chemical Characterisation Compounds in Honey by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS)Ministry for Primary IndustriesWellington, New Zealand2017Volume 2130. *Determination of Four Chemical Characterisation Compounds in Honey by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS)* (2017) **Volume 2** 1-30
29. Singleton V., Orthofer R., Lamuela-Raventós R.M.. **Analysis of Total Phenols and Other Oxidation Substrates and Antioxidants by Means of Folin-Ciocalteu Reagent**. *Methods Enzymol.* (1999) **299** 152-178
30. Jenkins R.E., Cooper R.. **Synergy between Oxacillin and Manuka Honey Sensitizes Methicillin-Resistant Staphylococcus Aureus to Oxacillin**. *J. Antimicrob. Chemother.* (2012) **67** 1405-1407. DOI: 10.1093/jac/dks071
31. Ferguson G.P., Tötemeyer S., MacLean M.J., Booth I.R.. **Methylglyoxal Production in Bacteria: Suicide or Survival?**. *Arch. Microbiol.* (1998) **170** 209-218. DOI: 10.1007/s002030050635
32. Hellwig M., Henle T.. **Baking, Ageing, Diabetes: A Short History of the Maillard Reaction**. *Angew. Chem. Int. Ed. Engl.* (2014) **53** 10316-10329. DOI: 10.1002/anie.201308808
33. Chandrangsu P., Dusi R., Hamilton C.J., Helmann J.D.. **Methylglyoxal Resistance in Bacillus Subtilis: Contributions of Bacillithiol-Dependent and Independent Pathways**. *Mol. Microbiol.* (2014) **91** 706-715. DOI: 10.1111/mmi.12489
34. Dieuleveux V., Lemarnier S., Gueguen M.. **Antimicrobial Spectrum and Target Site of D-3-Phenyllactic Acid**. *Int. J. Food Microbiol.* (1998) **40** 177-183. DOI: 10.1016/S0168-1605(98)00031-2
35. Cheung Y., Meenu M., Yu X., Xu B., Cheung Y., Meenu M.. **Phenolic Acids and Flavonoids Profiles of Commercial Honey from Different Floral Sources and Geographic Sources Phenolic Acids and Flavonoids Profiles of Commercial Honey from Different Floral Sources and Geographic Sources**. *Int. J. Food Prop.* (2019) **22** 290-308. DOI: 10.1080/10942912.2019.1579835
|
---
title: Peptidomics Study of Plant-Based Meat Analogs as a Source of Bioactive Peptides
authors:
- Shuguang Wang
- Mouming Zhao
- Hongbing Fan
- Jianping Wu
journal: Foods
year: 2023
pmcid: PMC10000916
doi: 10.3390/foods12051061
license: CC BY 4.0
---
# Peptidomics Study of Plant-Based Meat Analogs as a Source of Bioactive Peptides
## Abstract
The demand for plant-based meat analogs (PBMA) is on the rise as a strategy to sustain the food protein supply while mitigating environmental change. In addition to supplying essential amino acids and energy, food proteins are known sources of bioactive peptides. Whether protein in PBMA affords similar peptide profiles and bioactivities as real meat remains largely unknown. The purpose of this study was to investigate the gastrointestinal digestion fate of beef and PBMA proteins with a special focus on their potential as precursors of bioactive peptides. Results showed that PBMA protein showed inferior digestibility than that in beef. However, PBMA hydrolysates possessed a comparable amino acid profile to that of beef. A total of 37, 2420 and 2021 peptides were identified in the gastrointestinal digests of beef, Beyond Meat and Impossible Meat, respectively. The astonishingly fewer peptides identified from beef digest is probably due to the near-full digestion of beef proteins. Almost all peptides in Impossible Meat digest were from soy, whereas $81\%$, $14\%$ and $5\%$ of peptides in Beyond Meat digest were derived from pea, rice and mung proteins, respectively. Peptides in PBMA digests were predicted to exert a wide range of regulatory roles and were shown to have ACE inhibitory, antioxidant and anti-inflammatory activities, supporting the potential of PBMA as a source of bioactive peptides.
## 1. Introduction
A growing global population poses critical challenges in sustaining protein supply under already constrained resources and alarming concerns over climate change. Among various strategies towards sustainable protein production such as cellular agriculture (i.e., cultured meat), alternative proteins (i.e., terrestrial plant, insect and seaweed) and valorization of agricultural by-products [1,2], developing plant-based meat analogs (PBMA) is an attractive solution to replace traditional livestock production [3]. The market shares of alternative proteins remain low when compared with meat, even though governments and innovative companies increasingly advertise these alternatives to traditional meat products or dishes, such as plant-based burgers [4]. One major hurdle is consumer acceptance; in comparison, insects showed the lowest acceptance, followed by cultured meat, while terrestrial plant-based alternatives have the highest acceptance level [5]. The consumer acceptance of alternative proteins showed to be closely relevant to the drivers of taste and health, the color and aroma inherited, familiarity, food neophobia and disgust [1,2].
Since the successful launch of Beyond Meat and Impossible Meat, the market of PBMA has been on the rise; the global plant protein-based meat market is estimated to be approximately USD 21 billion by 2025 [6]. From a nutritional point of view, PBMA has unique advantages: its negligible cholesterol content, low fat content and high protein content with a well-balanced amino acids pattern [7,8]. McClements et al. reported that PBMA burgers contained fewer calories, cholesterol and fat than conventional beef burgers, despite nearly equal protein content [9]. However, there are continuous debates over the health implications of PMBA due to the addition of additives and the use of highly processed ingredients [3,8]. The health benefits of plant foods are likely compromised in PBMA. There is a need to develop clean labels and minimally processed products. For instance, the clean-labelled ProDiem™Refresh *Soy is* characterized by its sustainable and optimized nutrients to simulate/fulfill a protein intake similar to egg/milk [10].
A wide range of alternative proteins is explored for use in PBMA, especially those from grains and legumes, such as soy, pea, wheat, mung and lentil [11]. However, terrestrial plant proteins commonly possess inferior digestibility to that of livestock proteins, which challenges the nutritional profile of protein in meat analogs [12]. For example, Xie et al. reported that real meat (pork and beef) exhibited higher digestibility than that of PBMA during simulated gastrointestinal digestion, and the digestibility of PBMA depends on the origin and structure of proteins as well as the method of protein processing [13]. Food proteins are known as good sources of bioactive peptides. Bioactive peptides usually consist of 2–20 amino acids in length that are encrypted in their parent proteins and can exert regulatory roles once released in certain scenarios, including the gastrointestinal tract [1]. Given its increasing role in human dietary patterns, it is imperative to understand the potential of PBMA as the precursor of bioactive peptides. For instance, Chen et al. showed the formation of higher molecular weight and higher hydrophobicity in PBMA-derived peptides (soy and wheat proteins) than in chicken breast [14]. Xie et al. reported a larger number of peptides were identified from real meat than those of PBMA after simulated gastrointestinal digestion [13].
However, PBMA used in previous studies was prepared experimentally; research on commercial PBMA, especially from Beyond Meat and Impossible Meat, two major producers, are rarely reported. Simultaneously, systematic studies on the gastrointestinal fate, especially peptide profile and bioactivities after gastrointestinal digestion of PBMA, are still insufficiently understood. Meanwhile, there is no doubt that the peptide fragments released from real meat and PBMA are diverse due to their different parent protein sequences. Thus, the potential health benefits of these peptide fragments released from real meat and PBMA will also differ. Additionally, peptidomics and bioinformatics are emerging tools for identifying and predicting peptide profiling, bioavailability and bioactivity of bioactive peptides [15]. Hence, exploration of the digestibility and peptide profile after gastrointestinal digestion with the aid of peptidomics and bioinformatics will facilitate our understanding of the potential health benefits of PBMA.
The purpose of this study was to compare the in vitro gastrointestinal digestion fate of beef and PBMA (from Beyond Meat and Impossible Meat) with a special focus on their potential as precursors of bioactive peptides through assessing digestibility and peptide profiles and evaluate the relationship between peptide features and biofunctions (angiotensin-converting enzyme (ACE) inhibition, antioxidant and anti-inflammation).
## 2.1. Materials
Cooked patties of beef hamburger and Beyond Meat hamburger were bought from A&W (Edmonton, Alberta, Canada), and cooked patties of Impossible Meat burger were bought from Burger King (Edmonton, AB, Canada). ACE (from rabbit lung), hippuryl-His-Leu (HHL), pepsin (porcine gastric mucosa), pancreatin (porcine pancreas), 2,4,6-trinitrobenzenesulfonic acid (TNBS), cytochrome C, aprotinin, vitamin B12, (glycine)3, dithiothreitol (DTT) and angiotensin II (Ang II) were obtained from Sigma (Oakville, ON, Canada). Vascular smooth muscle A7r5 cell line was purchased from ATCC (Manassas, VA, USA). Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), 4-(2-68 hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) and non-essential amino acids (NEAA) were obtained from Gibco Invitrogen (Burlington, ON, Canada). Dihydroethidium (DHE) was purchased from Biotium (Fremont, CA, USA). Solvents used for UPLC were of chromatographic grade. Other chemicals applied were of analytical grade.
## 2.2. Preparation of Beef and PBMA Gastrointestinal Digests
The cooked beef patties and plant-based patties (Beyond Meat and Impossible Meat) in this study were bought from stores. Minced beef and PBMA were suspended in ddH2O and then exposed to two-step simulated gastrointestinal digestion [16]. Briefly, beef and PBMA ($5\%$ protein, w/v) were hydrolyzed by pepsin ($1\%$ protease/substrate, w/w protein) at pH 2.0 and 37 °C for 2.0 h, and then the digests were adjusted to pH 7.5 for another 2.0 h of hydrolysis with pancreatin ($1\%$ protease/substrate, w/w protein). Hydrolysis was terminated by heating the slurry at 95 °C for 10 min to inactive the proteases. Subsequently, the mixtures were centrifuged (8000× g, 15 min, 4 °C) to collect the supernatants, which were filtered by qualitative filter paper before being lyophilized to obtain the hydrolysates including BfP (cooked beef-pepsin), BfPP (cooked beef-pepsin-pancreatin), ByP (cooked Beyond Meat-pepsin), ByPP (cooked Beyond Meat-pepsin-pancreatin), ImP (cooked Impossible Meat-pepsin) and ImPP (cooked Impossible Meat-pepsin-pancreatin).
## 2.3. Molecular Weight Distribution
The molecular weight distribution of beef and PBMA hydrolysates were performed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and size exclusion chromatography according to the methods of Laemmli et al. [ 17] and Fan et al. [ 18], respectively. Briefly, for SDS-PAGE, beef and PBMA hydrolysates were initially dissolved in water at a concentration of 10 mg/mL and then diluted using a 2 × Laemmli sample buffer containing $5\%$ β-mecaptoethanol at a volume ratio of 1:1. The prepared beef and PBMA hydrolysates were heated to 95 °C for 5 min before 20 µL of them were loaded to $16.5\%$ Mini-Protean Tris-Tricine gel in a Mini-PROTEAN Tetra Cell with a PowerPac *Basic electrophoresis* apparatus (Bio-Rad, CA, USA) at a constant 150 V voltage. Gels were stained by Coomassie brilliant blue R250 dye and further destained by destaining buffer (ddH2O:methanol:acetic acid = 5:4:1, v/v/v), and then were scanned through an Alpha Innotech gel scanner (San Leandro, CA, USA). On the other hand, the molecular weight distribution was analyzed by size exclusion chromatography connecting with an AKTA explorer 10XT system (GE Healthcare, Uppsala, Sweden) with a Superdex peptide $\frac{10}{300}$ GL column. Beef and PBMA hydrolysates were dissolved in $30\%$ ACN containing $0.1\%$ TFA. Subsequently, 100 µL beef and PBMA hydrolysates at a concentration of 1 mg/mL were injected into the Superdex peptide $\frac{10}{300}$ GL column and eluted at an isocratic gradient with a flow rate of 0.5 mL/min. Peaks were monitored at 220 nm. The molecular weight was calibrated by a protein marker mixture in SDS-PAGE, whereas aprotinin, cytochrome C, (glycine)3 and vitamin B12 were used as molecular weight markers in size exclusion chromatography.
## 2.4. Degree of Hydrolysis (DH) and Amino Acid Compositions
The DH of beef and PBMA hydrolysates were evaluated using the TNBS method [19]. The amino acids analysis of beef and PBMA hydrolysates were determined according to the method of Zheng et al. [ 20].
## 2.5. Identification of Peptides by LC-MS/MS
The gastrointestinal-digested beef and PBMA hydrolysates were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) on an Atlantis dC18 UPLC column (Waters, Milford, MA, USA) using a nano-Acquity RP-UPLC system, coupled with a Micromass Quadrupole Time-of-Flight (Q-TOF) premier mass spectrometer (Bruker, Bremen, Germany), as previously described [16]. Solvents were chromatographic grade acetonitrile (mobile phase B) and H2O (mobile phase A) containing $0.1\%$ formic acid. The gradient program was set as $1\%$–$60\%$–$95\%$ mobile phase B according to 0–2–40–55 min. Mass spectra were set in the positive-ion mode. The quadrupole ion energy was set at 4.0 eV, while the collision-inducing dissociation energy was set at 8–50 eV. The parameters for the ESI interface were as follows: 180 °C drying gas temperature, 8.0 L/min drying gas flow and 1.5 bar ESI nebulizer pressure. Data were interpreted by searching Mascot. The major parent protein sequences of beef, pea, soy, mungbean, rice and potato were obtained from the UniProtKB [21].
## 2.6. ACE Inhibition Assay
ACE inhibition was measured by referring to the method of Wu et al. [ 22]. ACE, HHL, beef and PBMA hydrolysates were dissolved and diluted with 100 mM potassium phosphate buffer containing 300 mM NaCl (pH 8.3). Substrate HHL (50 μL, 5 mM) and beef/PBMA hydrolysate (10 μL) were initially mixed and preincubated at 37 °C for 5 min in a 2 mL polypropylene centrifuge tube, and then 20 μL of preincubated ACE (37 °C, 2 mU) was added and reacted for another half an hour by an Eppendorf Thermomixer R (Brinkmann Instruments, NY, USA). The reaction was terminated by further adding 1 M HCl (125 μL) and then analyzed using an UPLC system combined with an Acquity BEH C18 column (1.7 μm, 2.1 mm × 50 mm). Solvents were chromatographic grade acetonitrile (mobile phase B) and H2O (mobile phase A) containing $0.05\%$ formic acid. Samples (5 μL) were eluted at a flow rate of 0.245 mL/min, and the gradient program was set as $5\%$–$60\%$–$60\%$–$5\%$ B according to 0–3.5–4.2–5 min. Absorbance was monitored at 220 nm. Hippuric acid was identified and quantified through its standard curve. The IC50 value represents the concentration of PBMA hydrolysates when inhibiting ACE activity by $50\%$.
## 2.7. Desalting Protocol, Cell Culture and Cytotoxicity
Before incubation with A7r5 cells, beef and PBMA hydrolysates were desalted according to the method described previously by Fan et al. [ 18]. Briefly, beef and PBMA hydrolysates were dissolved in ddH2O and then loaded into a Sep-Pak 35cc tC18 cartridge (Waters, MA, USA). Firstly, the cartridge was washed with ddH2O at the volume of two column volumes for salt removal. Subsequently, ACN was added to wash the cartridge, and the ACN eluent was collected, vacuum evaporated and freeze-dried.
A7r5 cells were cultured with DMEM medium containing $10\%$ FBS, 25 mM HEPES and $1\%$ penicillin-streptomycin in a cell incubator at 37 °C, $5\%$ CO2 and $100\%$ humidity. The culture media were changed every two days. The cytotoxicity of beef and PBMA hydrolysates against A7r5 cells was measured through an alamarBlue assay, as depicted by Fan et al. [ 18]. A7r5 cells were initially sown in a 96-well plate, and cells were treated with 1.0 mg/mL of beef and PBMA hydrolysates for 24 h when reaching $80\%$ of confluency, and then the medium was replaced with 200 µL of $10\%$ alamarBlue solution for another 4 h. Finally, the solution (150 µL) was transferred into an opaque 96-well plate for fluorescence signal detection, with an emission wavelength at 590 nm and excitation wavelength at 560 nm.
## 2.8. Superoxide Detection
Superoxide in A7r5 cells was investigated by the Dihydroethidium (DHE) staining method [23]. A7r5 cells were pre-incubated with hydrolysates (1.0 mg/mL) for 1 h before the addition of Ang II (1 µM) for 0.5 h. Subsequently, DHE (20 µM) was added and treated for another 30 min. After that, cells were triple-washed with non-phenol-red DMEM, and the fluorescence intensity was measured by an Olympus IX81 fluorescent microscope (Olympus, Tokyo, Japan). Each data was comprised of two or three random fields. The mean fluorescence intensity was obtained using ImageJ software (National institutes of health, Bethesda, MD, USA).
## 2.9. Western Blotting
A7r5 cells were pre-incubated with beef and PBMA hydrolysates (1.0 mg/mL) for 1 h before adding Ang II (1 µM) for 24 h. After the treatment, cells were scraped and lysed in boiling Laemmle’s buffer containing 50 mM DTT and $0.2\%$ Triton-X-100, and then cell samples were loaded onto a $9\%$ separating gel and transferred to a nitrocellulose membrane for specific antibodies incubation. Bands of cyclooxygenase-2 (COX-2; Abcam, Toronto, ON, Canada) and inducible nitric oxide synthase (iNOS; BD Biosciences, San Jose, CA, USA) were normalized to GAPDH (ab8245, Abcam). The fluorescent bands were visualized by adding corresponding secondary antibodies, and the signals were detected using Licor Odyssey BioImager (Licor Biosciences, Lincoln, NE, USA).
## 2.10. Statistical Analysis
SPSS 17.0 (SPSS Inc., Chicago, IL, USA) was applied to statistical treatment with ANOVA analysis followed by the Duncan post hoc test. Data were expressed as mean ± standard deviation. Differences were considered statistically significant at $p \leq 0.05.$
## 3.1. Molecular Weight Distribution, DH and Amino Acid Compositions of Beef and PBMA Digests
Figure 1A shows that the pepsin and/or pancreatin treatments cause a substantial decrease/disappearance in the intensity of large-molecular-weight protein bands, which is due to the degradation of proteins into peptides/free amino acids. Likewise, the results of size exclusion chromatography further demonstrated that the small-molecular-weight fractions in beef and PBMA hydrolysates increased rapidly from gastric digestion to the intestinal digestion phase (Figure 1B), which dominated peptide composition in BfPP, ByPP and ImPP due to further extensive hydrolysis. Furthermore, DH data were consistent with the results shown in SDS-PAGE and size exclusion chromatography (Table 1). The DH of ByPP and ImPP increased gradually during in vitro digestion, being $4.92\%$ and $6.09\%$ after gastric digestion, and further increased to $7.94\%$ and $7.48\%$ after intestinal digestion, respectively. Beef hydrolysate had higher DH than PBMA throughout digestion. The gastrointestinal digestion fate of real meat and PBMA are hypothesized to be different due to the diversities in the structures and compositions of the raw material. Particularly, PBMA contains different sources of proteins as compared with real meat, as well as a variety of food additives which may affect protein digestion [24,25]. Moreover, the processing technologies in PBMA production may result in the formation of structures that negatively impact protein digestion. For instance, the dense mesh structure or aligned fibrils of proteins formation under the thermal–mechanical treatment largely impair the digestibility of proteins in PBMA [26]. A better swelling capacity of beef promotes penetration of gastrointestinal proteases, whereas the bulkiness of storage proteins, protein aggregates and the presence of antinutritional factors in beans limit the digestion of PBMA [27]. Our results are consistent with previous research. For instance, Xie et al. demonstrated that real pork and beef showed higher digestibility than PBMA [13], and the study of McClements et al. also reported the inferior digestibility of PBMA [8].
Amino acid composition is an indicator of the nutritional value of protein hydrolysates [28]. Essential amino acids (EAA) refer to amino acids which cannot be auto-synthesized by the human body, or the rate of synthesis is inadequate to meet the biological needs of the body. Thus, they need to be supplied by food protein intake. Normally, Val, Leu, Ile, Phe, Lys, His, Thr and Met are considered the eight EAA of individuals. Table 1 and Figure 2 shows that the total amino acid compositions of the three hydrolysates ranged from 67.56–87.64 g/100 g. Gastrointestinal digestion of beef had the highest content of amino acids, whereas ByPP and ImPP had a relatively low content of amino acids. However, the content of EAA in PBMA hydrolysate was comparable to the beef counterpart. The contents of EAA in ByPP and ImPP were 42.91 g/100 g and 41.60 g/100 g, whereas a higher value (46.04 g/100 g) was found in BfPP. EAA cannot be synthesized by mammals and must be obtained from food. EAA have important regulatory effects in many physiological events [29,30]. On the other hand, PBMA hydrolysates also contain a high level of non-EAA. Of which, Glu, Gly and Ala were abundant in beef hydrolysate, whereas Asp and Arg content was lower. In particular, no Glu was detected in PBMA hydrolysates. There is no compelling evidence to support that synthesis of non-EAA in the body could satisfy the requirement of physiological activities [31]. Thus, the content of non-EAA should still be taken into consideration when evaluating the nutritional value of proteins. From the amino acids profile, PBMA hydrolysates were expected to possess comparable nutritional properties to that of beef hydrolysate.
## 3.2. Effects of Gastrointestinal Digestion on Peptide Profiles of Beef and PBMA Hydrolysates
LC-MS/MS was used to identify the peptide profiles of beef and PBMA hydrolysates in this study, with the purpose of following the generation of peptides during in vitro gastrointestinal digestion and their relationship with bioactivities. To identify the potential bioactive peptides and predict their chemical properties, peptidomics and bioinformatics approaches were applied. Additionally, a peptide fragment may recur multiple times in its parent protein sequences, which can impact the theoretical content of peptides; therefore, this variation was also considered.
A total of 37, 2420 and 2021 peptides were identified in BfPP, ByPP and ImPP, respectively, indicating that gastrointestinal digestion had a significant impact on peptide release (Figure 3A). Among them, the abundant peptide fragments in ByPP were mainly derived from pea protein ($81\%$), followed by rice protein ($14\%$) and mung protein ($5\%$). Almost all peptides identified in ImPP originated from soy protein. These results were consistent with the declaration of protein origins in their formulas. Even though beef hydrolysate had the highest DH, surprisingly, much fewer peptides were identified therein. This is probably because beef protein is more easily digested into free amino acids by gastrointestinal proteases or beef-derived peptides showing stronger hydrophilic properties, which were washed away from the reverse phase column prior to sequence identification. It is worth noting that the amino acid composition among proteins largely dictates the extent of digestion, such as Phe, Tyr, Trp Lys, and Arg, which are the cleavage sites of gastrointestinal proteases [32]. Unfortunately, the peptide fragments released from in silico hydrolysis (pepsin and trypsin) in Supplementary Table S1 show a weak correlation with peptides identified by LC-MS/MS, suggesting the gaps between in silico hydrolysis and actual enzymatic hydrolysis. Particularly, in silico mimic hydrolysis is performed under ideal conditions where all proteins are fully digested, whereas the food matrix and processing conditions have a major impact on the digestibility of food proteins. Similarly, discrepancies between virtual and actual hydrolysis were also reported by others [33,34].
Generally, it is normally accepted that small peptides in protein hydrolysates possess better biological activities [16,24]. PeptideRanker is widely used to predict the potential bioactivity of peptides. A total of 5, 798 and 555 potent peptides were selected from BfPP, ByPP and ImPP based on the following filter conditions: peptide length < 20, molecular weight <3 kDa and PeptideRanker scores >0.2. Parent proteins, peptide sequences, repeat numbers, PeptideRanker scores, CPPpred scores, potential bioactive peptides and biological function of these potent peptides are listed in Supplementary Tables S2–S7. Additionally, Figure 3B shows the distribution of selected peptides in each sample according to their protein origins. Globulin, including legumin and vicilin, is one of the major storage proteins in peas [10]. Almost half of the peptides that occurred in ByPP were from legumin and vicilin in peas. The remaining half was also derived from other storage proteins such as provicilin and convicilin in peas, glutelin and globulin in rice and globulin and glycinin in mung. On the other hand, peptides identified from ImPP were mostly derived from glycinin and conglycinin.
Additionally, the number of small peptides (peptide length < 10) released by gastrointestinal proteases were 222 and 166 in ByPP and ImPP, accounting for $35.24\%$ and $29.96\%$ of the total peptides identified, respectively. Peptides released in ByPP were repeated more frequently than those in ImPP. The potential bioactivities of peptides were predicted by calculating molecular weight, PeptideRanker scores and CPPpred scores. PeptideRanker is used to predict peptide bioactivities, and CPPpred predicts the ability of a peptide to go across the cell membrane [34]. As shown in Figure 3D, peptides in ImPP have higher PeptideRanker scores than those in ByPP. Additionally, most peptides in ByPP and ImPP had strong cell penetration capacity. These results indicated that gastrointestinal digestion could effectively release bioactive peptides from PBMA.
Recently, lifestyle-related chronic diseases have triggered a series of global public health concerns, leading to growing interest in researching food bioactives, including bioactive peptides, as alternatives for treatment. To further clarify and predict the potential biological functions of beef and PBMA hydrolysates, the screened peptides with active probability were compared to the reported active sequences in the BIOPEP database (Supplementary Tables S2–S7). Peptides shared the same sequence with the reported bioactive sequences in the BIOPEP database, implying that they exhibit the same biological functions. Bioactive peptides in PBMA hydrolysates were predicted to exert a wide range of regulatory roles, including amelioration of cardiovascular diseases (including hypertension, diabetes, obesity and hyperlipemia), antioxidation, anti-inflammation, anticancer and neuroprotection (Figure 3E). Taken together, our results suggest that PBMA is a good precursor of bioactive peptides with various biological functions.
## 3.3. ACE Inhibition, Antioxidant and Anti-Inflammation of Beef and PBMA Hydrolysates
After predicting the bioactivities of peptides identified from beef and PBMA digests, we further determined ACE inhibitory, antioxidant and anti-inflammatory activities. ACE is a target of blood pressure reduction [35], and amelioration of oxidative stress and inflammatory responses have been considered key preventive strategies against various chronic diseases [36,37,38].
Hypertension is widely known as a risk factor for cardiovascular diseases, and the renin–angiotensin system (RAS) plays a pivotal role in blood pressure regulation [39]. ACE activates the RAS and converts angiotensin (Ang) I into Ang II, which is a potent vasoconstrictor to trigger hypertension. Figure 4 shows in vitro ACE inhibition of beef and PBMA digests. ByPP showed the highest ACE inhibition, with an IC50 value of 0.16 ± 0.03 mg/mL, followed by that of ImPP and BfPP (IC50: 0.20 ± 0.05 and 0.26 ± 0.05 mg/mL, respectively). Evidently, the results of ACE inhibition were consistent with the biological function prediction by in silico approach (Figure 3E). Similarly, a previous study also showed that PBMA-derived digests showed ACE inhibitory activity [13].
Oxidative stress triggers various kinds of damage to cells and further disrupts cellular function [40]. Sustained and aberrant oxidative stress contributes to vascular dysfunction, thereby causing hypertension, type 2 diabetes, atherosclerosis and other chronic diseases [41]. Vascular smooth muscle cells (A7r5) are a well-established model for evaluating health benefits, including relief of vascular dysfunction, anti-inflammation and antioxidation. In this study, antioxidant and anti-inflammatory activities in Ang II-induced A7r5 cells were studied. All hydrolysates showed no cytotoxicity against A7r5 cells. Treatment of beef and PBMA hydrolysates significantly lowered superoxide levels in Ang II-stimulated A7r5 cells, especially for ByPP and ImPP (Figure 5). Fan et al. found that spent hen-derived peptides exhibited antioxidant effects by acting as direct radical scavengers or mediating endogenous antioxidant enzymes in Ang II-stimulated A7r5 cells [42]. Similarly, egg white-derived peptide IRW was also demonstrated to exhibit an antioxidant effect in A7r5 cells against Ang II stimulation [39]. In our study, the remarkable inhibition of superoxide generation ($p \leq 0.05$) in A7r5 cells indicated that PBMA was a good precursor of antioxidant peptides.
Vascular inflammation is an underlying cause of hypertension and cardiovascular diseases. COX2 and iNOS are two proinflammatory mediators in vascular smooth muscle cells [38]; thus, the expression of these two proteins in A7r5 cells was detected to evaluate the anti-inflammatory activity of beef and PBMA hydrolysates. As shown in Figure 4, iNOS and COX2 expression levels surged in A7r5 cells upon Ang II insult ($p \leq 0.05$), whereas the hydrolysates treatment significantly inhibited their protein expressions. Similarly, peptides VVHPKESF and IRW could attenuate Ang II-induced inflammation in A7r5 cells [43,44]. These findings suggested the formation of anti-inflammatory peptides by gastrointestinal digestion from PBMA.
## 4. Conclusions
This study mimicked the protein digestion of beef and PBMA through an in vitro gastrointestinal tract and further investigated the peptide profile and biological bioactivity by combining peptidomics, bioinformatics and wet lab experiments. Results obtained in SDS-PAGE, size exclusion chromatography and DH showed that gastrointestinal proteases were able to degrade beef and PBMA proteins. Notably, PBMA protein exhibited inferior digestibility than that of beef, as reported previously. From the amino acids profile, PBMA hydrolysates were expected to possess comparable nutritional properties to beef hydrolysate. A total of 37, 2420 and 2021 peptides were identified in the gastrointestinal digests of beef, Beyond Meat and Impossible Meat, respectively. The astonishingly fewer peptides identified from beef digest is probably due to the near-full digestion of beef proteins. The analysis of peptide profiles indicated that PBMA could be considered a good precursor of bioactive peptides with widespread biological functions, including amelioration of cardiovascular diseases (including hypertension, diabetes, obesity and hyperlipemia), antioxidation, anti-inflammation, anticancer and neuroprotection. Furthermore, PBMA hydrolysates exhibited great ACE inhibition, antioxidant and anti-inflammation in test tube experiments and A7r5 cells. The current results underscored the promise of generating bioactive peptides from PBMA.
## References
1. Wang S., Zhao M., Fan H., Wu J.. **Emerging proteins as precursors of bioactive peptides/hydrolysates with health benefits**. *Curr. Opin. Food Sci.* (2022) **48** 100914. DOI: 10.1016/j.cofs.2022.100914
2. Hadi J., Brightwell G.. **The safety of alternative proteins: Technological, environmental and regulatory aspects of cultured meat, plant-based meat, insect protein and single-cell protein**. *Foods* (2021) **10**. DOI: 10.3390/foods10061226
3. Yang H., Shen Y., Li Y.. **Physicochemical and functional properties of texturized vegetable proteins and cooked patty textures: Comprehensive characterization and correlation analysis**. *Foods* (2022) **11** 2619. PMID: 36076805
4. Onwezen M.C., Bouwman E.P., Reinders M.J., Dagevos H.. **A systematic review on consumer acceptance of alternative proteins: Pulses, algae, insects, plant-based meat alternatives, and cultured meat**. *Appetite* (2021) **159** 105058. DOI: 10.1016/j.appet.2020.105058
5. Hwang J., You J., Moon J., Jeong J.. **Mechanisms factors affecting consumers’ alternative meats buying intentions: Plant-based meat alternative and cultured meat**. *Sustainability* (2020) **12**. DOI: 10.3390/su12145662
6. Bohrer B.M.. **An investigation of the formulation and nutritional composition of modern meat analogue products**. *Food Sci. Hum. Wellness* (2019) **8** 320-329. DOI: 10.1016/j.fshw.2019.11.006
7. Singh M., Trivedi N., Enamala M.K.. **Plant-based meat analogue (PBMA) as a sustainable food: A concise review**. *Eur. Food Res. Technol.* (2021) **247** 2499-2526. DOI: 10.1007/s00217-021-03810-1
8. Zhou H., Hu Y., Tan Y.. **Digestibility and gastrointestinal fate of meat versus plant-based meat analogs: An in vitro comparison**. *Food Chem.* (2021) **364** 130439. DOI: 10.1016/j.foodchem.2021.130439
9. McClements D.. **Future foods: Is it possible to design a healthier and more sustainable food supply**. *Nutr. Bull.* (2020) **45** 341-354. DOI: 10.1111/nbu.12457
10. Sridhar K., Bouhallab S., Croguennec T., Renard D., Lechevalier V.. **Recent trends in design of healthier plant-based alternatives: Nutritional profile, gastrointestinal digestion, and consumer perception**. *Crit. Rev. Food Sci.* (2022) 2081666. DOI: 10.1080/10408398.2022.2081666
11. Zhang T., Dou W., Zhang X.. **The development history and recent updates on soy protein-based meat alternatives**. *Trends Food Sci. Technol.* (2021) **109** 702-710. DOI: 10.1016/j.tifs.2021.01.060
12. Khalesi M., FitzGerald R.J.. **In vitro digestibility and antioxidant activity of plant protein isolate and milk protein concentrate blends**. *Catalysts* (2021) **11**. DOI: 10.3390/catal11070787
13. Xie Y., Cai L., Zhao D.. **Real meat and plant-based meat analogues have different in vitro protein digestibility properties**. *Food Chem.* (2022) **387** 132917. DOI: 10.1016/j.foodchem.2022.132917
14. Chen D., Rocha-Mendoza D., Shan S.. **Characterization and cellular uptake of peptides derived from in vitro digestion of meat analogues produced by a sustainable extrusion process**. *J. Agric. Food Chem.* (2022) **70** 8124-8133. DOI: 10.1021/acs.jafc.2c01711
15. Mora L., Escudero E., Toldra F.. **Characterization of the peptide profile in Spanish Teruel, Italian Parma and Belgian dry-cured hams and its potential bioactivity**. *Food Res. Int.* (2016) **89** 638-646. DOI: 10.1016/j.foodres.2016.09.016
16. Fan H., Wang J., Liao W., Wu J.. **Identification and characterization of gastrointestinal-resistant angiotensin-converting enzyme inhibitory peptides from egg white proteins**. *J. Agric. Food Chem.* (2019) **67** 7147-7156. DOI: 10.1021/acs.jafc.9b01071
17. Laemmli U.K.. **Cleavage of structural proteins during the assembly of the head of bacteriophage T4**. *Nature* (1970) **227** 680-685. DOI: 10.1038/227680a0
18. Fan H., Yu W., Liao W., Wu J.. **Spent hen protein hydrolysate with good gastrointestinal stability and permeability in Caco-2 cells shows antihypertensive activity in SHR**. *Foods* (2020) **9**. DOI: 10.3390/foods9101384
19. Adler-Nissen J.. **Determination of the degree of hydrolysis of food protein hydrolysates by trinitrobenzenesulfonic acid**. *J. Agric. Food Chem.* (1979) **27** 1256-1262. DOI: 10.1021/jf60226a042
20. Suarez L.M.. **Optimization of enzymatic hydrolysis for preparing cassava leaf hydrolysate with antioxidant activity**. *Food Bioprocess. Tech.* (2021) **14** 2181-2194. DOI: 10.1007/s11947-021-02693-0
21. **Universal Protein Knowledgebase**. (2022)
22. Wu J., Aluko R., Muir A.. **Improved method for direct high-performance liquid chromatography assay of angiotensin-converting enzyme-catalyzed reactions**. *J. Chromatogr. A* (2002) **950** 125-130. DOI: 10.1016/S0021-9673(02)00052-3
23. Wang X., Bhullar K.S., Fan H., Wu J.. **Regulatory effects of a pea-derived peptide Leu-Arg-Trp (LRW) on dysfunction of rat aortic vascular smooth muscle cells against angiotensin II stimulation**. *J. Agric. Food Chem.* (2020) **68** 3947-3953. DOI: 10.1021/acs.jafc.0c00028
24. Bakhsh A.. **A novel approach for tuning the physicochemical, textural, and sensory characteristics of plant-based meat analogs with different levels of methylcellulose concentration**. *Foods* (2021) **10**. DOI: 10.3390/foods10030560
25. De Marchi M.. **Detailed characterization of plant-based burgers**. *Sci. Rep.* (2021) **11** 2049. DOI: 10.1038/s41598-021-81684-9
26. Chen D., Jones O.G., Campanella O.H.. **Plant protein-based fibers: Fabrication, characterization and potential food applications**. *Crit. Rev. Food Sci.* (2021) 1-25. DOI: 10.1080/10408398.2021.2004991
27. Mulla M.Z., Subramanian P., Dar B.N.. **Functionalization of legume proteins using high pressure processing: Effect on technofunctional properties and digestibility of legume proteins**. *LWT-Food Sci. Technol.* (2022) **158** 113106. DOI: 10.1016/j.lwt.2022.113106
28. Ozorio L., Mellinger-Silva C., Cabral L.M.C.. **The influence of peptidases in intestinal brush border membranes on the absorption of oligopeptides from whey protein hydrolysate: An ex vivo study using an ussing chamber**. *Foods* (2020) **9**. DOI: 10.3390/foods9101415
29. Pekala J., Patkowska-Sokola B., Bodkowski R.. **L-carnitine-metabolic functions and meaning in humans’ life**. *Curr. Drug Metab.* (2011) **12** 667-678. DOI: 10.2174/138920011796504536
30. Yoshizawa F.. **Notable functions of branched chain amino acids as biological regulators**. *J. Pharmacol. Sci.* (2011) **115** 39
31. Wu G., Wu Z., Dai Z.. **Dietary requirements of “nutritionally non-essential amino acids” by animals and humans**. *Amino Acids.* (2013) **44** 1107-1113. DOI: 10.1007/s00726-012-1444-2
32. Gallego M., Mauri L., Aristoy M.C.. **Antioxidant peptides profile in dry-cured ham as affected by gastrointestinal digestion**. *J. Funct. Foods* (2020) **69** 103956. DOI: 10.1016/j.jff.2020.103956
33. Fu Y., Young J., Lokke M., Lametsch R., Aluko R.E., Therkildsen M.. **Revalorisation of bovine collagen as a potential precursor of angiotensin 1-converting enzyme (ACE) inhibitory peptides based on in silico and in vitro protein digestions**. *J. Funct. Foods* (2016) **24** 196-206. DOI: 10.1016/j.jff.2016.03.026
34. Wang S., Su G., Fan J., Zhao M., Wu J.. **Arginine-containing peptides derived from walnut protein against cognitive and memory impairment in scopolamine-induced zebrafish: Design, release, and neuroprotection**. *J. Agric. Food Chem.* (2022) **70** 11579-11590. DOI: 10.1021/acs.jafc.2c05104
35. Udenigwe C.C., Mohan A.. **Mechanisms of food protein-derived antihypertensive peptides other than ACE inhibition**. *J. Funct. Foods* (2014) **8** 45-52. DOI: 10.1016/j.jff.2014.03.002
36. Fleenor B.S., Seals D.R., Zigler M.L.. **Superoxide-lowering therapy with TEMPOL reverses arterial dysfunction with aging in mice**. *Aging Cell* (2012) **11** 269-276. DOI: 10.1111/j.1474-9726.2011.00783.x
37. Wang S., Su G., Zhang X.. **Characterization and exploration of potential neuroprotective peptides in walnut (**. *J. Agric. Food Chem.* (2021) **69** 2773-2783. DOI: 10.1021/acs.jafc.0c07798
38. Wang S., Sun-Waterhouse D., Waterhouse G.I.N.. **Effects of food-derived bioactive peptides on cognitive deficits and memory decline in neurodegenerative diseases: A review**. *Trends Food Sci. Technol.* (2021) **116** 712-732. DOI: 10.1016/j.tifs.2021.04.056
39. Fan H., Xu Q., Hong H., Wu J.. **Stability and transport of spent hen-derived ACE-inhibitory peptides IWHHT, IWH, and IW in human intestinal Caco-2 cell monolayers**. *J. Agric. Food Chem.* (2018) **66** 11347-11354. DOI: 10.1021/acs.jafc.8b03956
40. Touyz R., Schiffrin E.. **Reactive oxygen species in vascular biology: Implications in hypertension**. *Histochem. Cell Biol.* (2004) **122** 339-352. DOI: 10.1007/s00418-004-0696-7
41. Odegaard A.O., Jacobs D.R., Sanchez O.A.. **Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes**. *Cardiovasc. Diabetol.* (2016) **15** 51. DOI: 10.1186/s12933-016-0369-6
42. Fan H., Bhullar K.S., Wu J.. **Spent hen muscle protein-derived RAS regulating peptides show antioxidant activity in vascular cells**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10020290
43. Liao W., Fan H., Wu J.. **Egg white-derived antihypertensive peptide IRW (Ile-Arg-Trp) inhibits angiotensin II-stimulated migration of vascular smooth muscle cells via angiotensin type I receptor**. *J. Agric. Food Chem.* (2018) **66** 5133-5138. DOI: 10.1021/acs.jafc.8b00483
44. Fan H., Liao W., Davidge S.T., Wu J.. **Chicken muscle-derived ACE2 upregulating peptide VVHPKESF inhibits angiotensin II-stimulated inflammation in vascular smooth muscle cells via the ACE2/Ang (1−7)/MasR axis**. *J. Agric. Food Chem.* (2022) **70** 6397-6406. DOI: 10.1021/acs.jafc.1c07161
|
---
title: Maternal and Intrauterine Influences on Feto-Placental Growth Are Accompanied
by Sexually Dimorphic Changes in Placental Mitochondrial Respiration, and Metabolic
Signalling Pathways
authors:
- Esteban Salazar-Petres
- Daniela Pereira-Carvalho
- Jorge Lopez-Tello
- Amanda N. Sferruzzi-Perri
journal: Cells
year: 2023
pmcid: PMC10000946
doi: 10.3390/cells12050797
license: CC BY 4.0
---
# Maternal and Intrauterine Influences on Feto-Placental Growth Are Accompanied by Sexually Dimorphic Changes in Placental Mitochondrial Respiration, and Metabolic Signalling Pathways
## Abstract
Adverse maternal environments such as small size, malnutrition, and metabolic conditions are known to influence fetal growth outcomes. Similarly, fetal growth and metabolic alterations may alter the intrauterine environment and affect all fetuses in multiple gestation/litter-bearing species. The placenta is the site of convergence between signals derived from the mother and the developing fetus/es. Its functions are fuelled by energy generated by mitochondrial oxidative phosphorylation (OXPHOS). The aim of this study was to delineate the role of an altered maternal and/or fetal/intrauterine environment in feto-placental growth and placental mitochondrial energetic capacity. To address this, in mice, we used disruptions of the gene encoding phosphoinositol 3-kinase (PI3K) p110α, a growth and metabolic regulator to perturb the maternal and/or fetal/intrauterine environment and study the impact on wildtype conceptuses. We found that feto-placental growth was modified by a perturbed maternal and intrauterine environment, and effects were most evident for wildtype males compared to females. However, placental mitochondrial complex I+II OXPHOS and total electron transport system (ETS) capacity were similarly reduced for both fetal sexes, yet reserve capacity was additionally decreased in males in response to the maternal and intrauterine perturbations. These were also sex-dependent differences in the placental abundance of mitochondrial-related proteins (e.g., citrate synthase and ETS complexes), and activity of growth/metabolic signalling pathways (AKT and MAPK) with maternal and intrauterine alterations. Our findings thus identify that the mother and the intrauterine environment provided by littermates modulate feto-placental growth, placental bioenergetics, and metabolic signalling in a manner dependent on fetal sex. This may have relevance for understanding the pathways leading to reduced fetal growth, particularly in the context of suboptimal maternal environments and multiple gestation/litter-bearing species.
## 1. Introduction
To meet fetal growth requirements, the placenta adapts and changes functionally under normal and altered intrauterine environments [1,2,3,4,5,6]. These changes are fundamental to maintain fetal growth trajectory and development and are aided by the endocrine output from the placenta that encourage physiological changes in the mother and ensure nutrients are available for transfer to the fetus [7,8,9,10]. From the perspective of the placenta, the intrauterine environment is a site of convergence between signals derived from the mother and those from the developing fetus or fetuses in the case of multiple gestation/litter-bearing species [3,11,12]. Mouse and human placentas are relatively similar in exchange, endocrine function, and morphology, as both are haemochorial in nature. However, the murine placenta is divided in two distinguished regions: the junctional zone (responsible for endocrine production) and the labyrinth zone (LZ; substrate exchange) [13,14,15], whereas in the human placenta, the villous syncytiotrophoblast performs both functions. Nevertheless, comparative proteomic and transcriptomic analyses of mouse placental LZ and human placental villous samples showed that over $80\%$ of genes implicated in mouse placental phenotypes are co-expressed in both species [16]. Studies performed on mice and humans demonstrate that under unfavourable health conditions, maternal adaptations may fail, leading to abnormal nutrient partitioning and pregnancy complications. Under these adverse conditions, placental development may also be compromised, yet, in certain situations, the placenta may also be capable of maintaining or even increasing nutrient supply to optimize fetal growth in that prevailing environment [17,18,19,20,21,22,23]. However, further work is required to understand the role of maternal and fetal signals in conditioning the intrauterine environment and to uncover the cellular and molecular pathways that may modulate placental metabolic functions [24].
Adaptations in placental transport capacity could be related to changes in mitochondrial function. This is because the placenta relies heavily on energy (ATP) that is generated primarily by mitochondria. *Mitochondria* generate ATP via oxidative phosphorylation (OXPHOS) and reducing substrates derived from β-oxidation and the tricarboxylic acid cycle [25]. The energy generated by mitochondria is used for placental metabolism, growth, morphological remodelling, and to actively transport a range of substrates from the mother to the fetus for growth and development [26,27,28,29,30]. Prior work has reported that placental mitochondrial bioenergetic capacity alters developmentally to meet the increasing fetal demands for growth toward term in humans and animal models [31,32]. Moreover, there are changes in placental mitochondrial function in human pregnancies and rodents exposed to unfavourable maternal environments, which includes changes in OXPHOS and mitochondrial-related proteins [31,33,34,35,36,37,38,39]. Finally, accumulating findings are demonstrating that placental adaptive responses, and mitochondrial function specifically, are different for female and male fetuses [1,26,39,40,41]. However, it remains unclear how the maternal and intrauterine environments interact to modulate placental mitochondrial function and, consequently, how this interaction relates to the placental support of female and male fetal growth.
To address these key deficiencies in knowledge, this study used disruptions of the gene encoding phosphoinositol 3-kinase (PI3K) p110α (Pik3ca) in mice as a tool to perturb the intrauterine environment and assess the impact on feto-placental development, placental mitochondrial respiration, and metabolic signalling pathways of female and male wildtype fetuses. The PI3K signalling pathway is a critical signalling pathway that regulates cell metabolism and growth [42,43,44,45,46,47,48]. Moreover, this pathway mediates the metabolic effects of insulin [49,50,51] by promoting glucose uptake and glycogen synthesis [52,53]. This is highly relevant since previous work has found that PI3K-p110α signalling is required for mediating metabolic adaptations in the mother that support fetal glucose transfer during pregnancy [24,54,55]. Other work has also shown that PI3K-p110α signalling regulates placental LZ formation [21,56]. Finally, PI3K-p110α signalling deficiency in the fetus causes growth stunting and mal-formed placentas of both males and females, and the presence of PI3K-p110α mutants would be expected to influence the intrauterine environment and, hence, the development of wildtype siblings [21,24,56].
## 2.1. Animals and Experimental Design
Mice were housed at the University of Cambridge Animal Facility and the study was undertaken abiding by the UK Home Office Animals (Scientific Procedures) Act 1986 after approval from the University of Cambridge ethics committee (UK HOL PP6324596). Mice were allowed to drink and feed ad libitum and were housed under a 12:12 h light-dark cycle. This study employed wildtype (WT) mice and mice with partial inactivation of the PI3K isoform p110α, which was induced via heterozygous inheritance of a dominant negative mutation in Pik3ca (Pik3ca-D933A; mice are defined as α/+ in the text). *The* generation of the α/+ mutant mice has previously been reported [49] and they have been on a C57BL/6 background for more than 10 generations. Female mice were virgin and aged 4-months when they were mated with males. Three types of crosses of mice were generated in this study (Figure 1). Briefly, WT females were mated with WT males (WT x WT) to generate pregnancies with control WT litters. WT females were mated with α/+ males (WT x α/+) to generate pregnancies with litters containing α/+ conceptuses (adverse intrauterine environment). Finally, α/+ females were mated with WT males (α/+ x WT) to generate pregnancies with litters containing α/+ conceptuses within a mother who was α/+ (adverse intrauterine and maternal environment) [24,54]. The detection of a mating plug in the female vagina was used to indicate gestational day 1 (Gd1).
## 2.2. Tissue Collection and Genotyping
Pregnant dams were killed by cervical dislocation on Gd18 for retrieval of the gravid uterus. Fetuses and placentas were dissected free of fetal membranes and individually weighed. After weighing the placenta, the placental labyrinth LZ was micro-dissected from the endocrine junctional zone. The LZ was then weighed and either immediately snap frozen or placed in cryopreservation media prior to snap freezing (0.21 M mannitol, 0.07 M sucrose, $30\%$ DMSO, pH 7.5). These LZ samples were stored at −80 °C for subsequent molecular and mitochondrial respiratory analyses, respectively. Fetal tails were taken to determine sex (*Sry* gene F: 5′-GTGGGTTCCTGTCCCACTGC-3′, R: 5′-GGCCATGTCAAGCGCCCCAT-3′ and autosomal PCR control gene F: 5′-TGGTTGGCATTTTATCCCTAGAAC-3′, R: 5′-GCAACATGGCAACTGGAAACA-3′) and α/+ genotype (F: 5′-TTCAAGCACTGTTTCAGCT-3′ and R: 5′-TTATGTTCTTGCTCAAGTCCTA-3′) [55]. Only WT conceptuses were analysed in this study.
## 2.3. Placental LZ Mitochondrial Respirometry
Mitochondrial respiratory capacity was examined in placental LZ samples using high resolution respirometry (Oxygraph 2k respirometer; Oroboros Instruments, Innsbruck, Austria), using a sequential substrate, inhibitor, uncoupler titration (SUIT) protocol as reported previously [1]. In brief, thawed LZ samples (in sucrose solution, pH 7.5) were permeabilized using saponin (5 mg/ mL, Sigma-Aldrich, Gillingham, UK) in biopsy preservation medium (BIOPS; pH 7.1, containing 10 mM Ca-EGTA buffer, 0.1µM free Ca2+, 1 mM free Mg2+, 20 mM imidazole, 20 mM taurine, 50 mM K-MES, 0.5 mM DTT, 6.56 mM MgCl2, 5.77 mM ATP, and 15 mM phosphocreatine). Following washes in respiratory medium (MiR05; pH 7.1 solution containing 20 mM HEPES, 0.46 mM EGTA, 2.1 mM free Mg2+, 90 mM K+, 10 mM Pi, 20 mM taurine, 110 mM sucrose, 60 mM lactobionate, and 1 g/L BSA), 15–20 mg of LZ sample was analysed at 37 °C in a pre-calibrated Oxygraph-2k respirometer chamber. Oxygen concentration was kept between 250 μM and 300 μM, and real-time acquisition and assessment of oxygen consumption was obtained using the SUIT protocol [1] and DatLab software (V7, Oroboros Instruments). To provide information about outer membrane integrity, exogenous cytochrome c (10 µM) was added during maximum OXPHOS capacity (after succinate addition to activate CI and CII pathways in the presence of ADP), and LZ samples showing a >$30\%$ increase in oxygen consumption were excluded (a total of 6 placentas from different groups were excluded from the study), as suggested by others [57]. Respiration was expressed as oxygen consumption per mg of placental LZ tissue.
## 2.4. Western Blot Analysis
Protein abundance was determined in placental LZ samples (representative for each sex and genotype) using Western blotting as previously described [1]. Briefly, proteins were extracted using RIPA buffer (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with Mini EDTA-free protease inhibitor cocktail mix (Roche, Basel, Switzerland, CH). Proteins were separated using electrophoresis and transferred onto 0.2 μm nitrocellulose membranes (Bio-Rad Laboratories, Hercules, CA, USA). Membranes were blocked in tris-buffered saline with Tween 20 (TBS-T) plus $5\%$ milk or fetal bovine serum for one hour at room temperature and subsequently incubated overnight with primary antibodies described in Supplementary Table S1. The day after, membranes were washed in TBS-T and incubated with secondary antibodies (NA934 or NA931; 1:10,000). Protein bands were visualized via enhanced chemiluminescence using SuperSignal™ West Femto Substrate (Thermo Fisher Scientific, MA, USA). Signal intensity was determined using ImageJ, version 2.1.0 software, and proteins were normalized to protein loading as informed by Ponceau S staining [58].
## 2.5. Statistical Analysis
Statistical analyses were performed using GraphPad Prism version 9 (GraphPad, CA, USA). Outliers were detected by the Prisms Grubbs’ test. To analyse the effect of the mating cross (WT x WT, WT x α/+, and α/+ x WT) on WT conceptus development and LZ mitochondrial respirometry, each sex was analysed separately by one-way ANOVA followed by Tukey post hoc tests. Litter composition (sex and α/+ genotype) was analysed using Chi-squared analysis. Data are shown as mean ± SEM with individual datapoints. Data were considered statistically significant at values of $p \leq 0.05.$ Any tendency for a significant effect ($p \leq 0.07$) are stated with specific p value in the text.
## 3.1. Littermate and/or Maternal p110α Deficiency Alters Feto-Placental Growth of WT Fetuses in a Sex-Specific Manner
We used our three parental crosses (female x male: WT x WT, WT x α/+, and α/+ x WT, shown in Figure 1) to assess if littermate and/or maternal p110α deficiency affects the growth of WT conceptuses. In particular, by comparing to WT x WT, pregnancies generated by the WT x α/+ cross informed on the impact of adverse intrauterine conditions (presence of α/+ littermates in utero), whilst those created by mating α/+ females with WT mates informed on the combined effect of an adverse intrauterine and an adverse maternal environment [24,54]. Comparing WT x α/+ and α/+ x WT pregnancies allowed us to deduce the influence of an adverse maternal environment due to α/+ deficiency on WT conceptus growth. We found there was no effect of the parental cross on litter size or sex ratio and there was also no difference in the percentage of α/+ pups within the litter between WT x α/+ and α/+ x WT pregnancies (Supplementary Table S2).
By analysing each fetal sex separately, we found that WT female fetuses from α/+ x WT pregnancies were growth-restricted when compared to those from either the WT x WT or WT x α/+ pregnancies (Figure 2A). Further, WT male fetuses from WT x α/+ pregnancies were significantly heavier when compared to those from the WT x WT and α/+ x WT pregnancies (Figure 2A). Placental weight of WT female fetuses did not vary between WT x WT, WT x α/+, and α/+ x WT pregnancies. In contrast, WT male placental weight was greater in α/+ x WT pregnancies compared to WT x WT and WT x α/+ pregnancies (Figure 2B). Similar to placental weight, no differences were detected in the LZ weight between groups for WT female fetuses, but LZ weight for WT male fetuses in α/+ x WT pregnancies was heavier when compared to WT x WT pregnancies (Figure 2C). Since our study is focused on responses of the placental LZ, which determines nutrient supply to the fetus for growth, we calculated the ratio between fetal weight and placental LZ weight. This calculation revealed that fetal weight as a proportion of LZ weight was not different among the groups in females but was lower in males from the α/+ x WT group when compared to WT x WT and WT x α/+ groups (Figure 2D). Collectively, these findings suggest that littermate and maternal p110α deficiency modifies WT conceptus growth. However, the specific nature of these changes depends on fetal sex.
## 3.2. Littermate and/or Maternal p110α Deficiency Alters Mitochondrial Bioenergetics in the Placental LZ of WT Fetuses of Both Sexes
To understand how littermate and maternal p110α deficiency modifies WT conceptus growth, we then assessed placental LZ mitochondrial function in WT fetuses from our three pregnancy groups (WT x WT, WT x α/+, and α/+ x WT). While there were no differences in Complex (C) CILEAK and CIOXPHOS in both WT females and males between our different experimental groups (Figure 3A,B), maximal respiratory capacity through CI and II (CI + CIIOXPHOS) was lower in the LZ of both female and male WT fetuses from WT x α/+ and α/+ x WT pregnancies compared to WT x WT pregnancies (Figure 3C). This was related to lower CII-associated oxygen consumption in WTs of both sexes in WT x α/+ and α/+ x WT versus WT x WT pregnancies (Figure 3D). It was also related to a reduction in oxygen consumption by the total ETS for female and male WTs in WT x α/+ and α/+ x WT litters (Figure 3E). Mitochondrial reserve capacity was profoundly decreased in the placental LZ of WT males from WT x α/+ and α/+ x WT pregnancies (∼−$60\%$ and ∼−$73\%$, respectively, compared to WT x WT), meanwhile, no differences were detected for WT female fetuses (Figure 3F). There was a greater contribution of CI-associated leak respiration to total ETS capacity in the placental LZ of both sexes in the α/+ x WT group compared to the WT x WT group (Figure 3G). However, in the OXPHOS state, the contribution of CI to total ETS was increased only in the placental LZ of WT females, and not males and this was observed for both the WT x α/+ and α/+ x WT compared to WT x WT pregnancies (Figure 3H). There were no differences in oxygen flux associated with fatty acid oxidation and CIV activity for female or male WT fetuses between the three crosses (Supplementary Figure S1). Taken together, these results indicate that placental LZ mitochondrial bioenergetics of both female and male WT fetuses is modulated by littermate and maternal p110α deficiency.
## 3.3. Littermate and/or Maternal p110α Deficiency Alters the Expression of Mitochondrial-Related Proteins in the Placental LZ of WT Fetuses in a Sex-Specific Manner
To provide information on the molecular changes mediating alterations in mitochondrial bioenergetics with littermate and maternal p110α deficiency, Western blotting was used to quantify the abundance of individual ETS complexes and additional mitochondrial-related proteins in the placental LZ of WT fetuses in our three pregnancy groups (WT x WT, WT x α/+, and α/+ x WT). There was a decreased abundance of CI and CII proteins in the placental LZ of WT females in both WT x α/+ and α/+ x WT pregnancies, compared to WT x WT (Figure 4A). In WT males, CI was instead significantly increased in both WT x α/+ and α/+ x WT pregnancies compared to WT x WT pregnancies and no difference in CII levels between the three pregnancy groups was found (Figure 4B). There was an increased abundance of CIII protein in WT females from the WT x α/+ compared to the WT x WT group, but again, no changes were seen in males (Figure 4A,B). CIV protein expression was decreased in the LZ of both WT female and male fetuses from α/+ x WT pregnancies when compared to WT x α/+. No differences were detected in CV protein abundance between the three groups regardless of fetal sex (Figure 4A,B).
The abundance of citrate synthase, an indicator of mitochondrial density, and the abundance of PGC-1α, a transcription factor that promotes mitochondrial biogenesis, were altered in the LZ of male but not female WT fetuses, presenting with an increase for both proteins in WT x α/+ and α/+ x WT pregnancies, compared to WT x WT (Figure 4C,D). In contrast, the abundance of PPARγ, another transcription factor that regulates mitochondrial biogenesis, was reduced in the placental LZ of WT female fetuses of WT x α/+ pregnancies when compared with WT x WT, whilst no differences were observed for males (Figure 4E). Finally, we evaluated the abundance of UCP2, a protein that uncouples oxygen consumption from ATP synthesis, and found UCP2 was increased in the placental LZ of males, but not females, from WT x α/+ pregnancies, when compared to WT x WT (Figure 4F). Taken together, these data indicate that there are sex-dependent changes in the LZ abundance of key mitochondrial regulatory proteins in WT fetuses exposed to littermate and/or maternal p110α deficiency.
## 3.4. Littermate and/or Maternal p110α Deficiency Alters the Abundance of Key Growth and Metabolic Signalling Proteins in a Manner That Depends on Fetal Sex
To further understand the mechanisms underlying the sex-specific changes in placental LZ profile in WT fetuses exposed to littermate and maternal p110α deficiency, we evaluated the expression of key growth and metabolic signalling proteins in our three pregnancy groups (WT x WT, WT x α/+, and α/+ x WT: Figure 5A,B). The selected proteins are known to regulate cellular bioenergetics by linking endocrine signals with mitochondrial function [1]. In WT female fetuses, the LZ abundance of total AKT was not affected, but the level of active phosphorylated AKT was decreased in both WT x α/+ and α/+ x WT pregnancies compared to WT x WT (Figure 5C,D). Meanwhile, WT males showed an increased abundance of total AKT in addition to reduced AKT activation only in α/+ x WT pregnancies compared to WT x WT and/or WT x α/+ (Figure 5C,D). WT females in α/+ x WT pregnancies exhibited increased total MAPK $\frac{44}{42}$ abundance compared to those from WT x WT and WT x α/+ pregnancies, but no change was seen in the level of active phosphorylated protein. Furthermore, no changes in total or activated MAPK $\frac{44}{42}$ were seen for the placental LZ of WT males, with similar values observed for all three pregnancy groups. Total p38 MAPK was also greater in the placental LZ of WT females from α/+ x WT pregnancies, but phosphorylated active levels were reduced in both WT x α/+ and α/+ x WT pregnancies compared to WT x WT. In contrast, in males, only changes in p38 MAPK were observed between the WT x α/+ and α/+ x WT pregnancy groups, with a tendency for increased total p38 MAPK, and a significant decrease in activated p38 MAPK. Together, these data suggest that growth and metabolic signalling proteins in the WT placenta are affected by both littermate and maternal p110α deficiency. Moreover, the nature of these effects depends on fetal sex.
## 4. Discussion
*Using* genetic disruption of p110α signalling as a tool, we demonstrated the important influence of the maternal and intrauterine environments on feto-placental growth and placental mitochondrial respiration/metabolism. Specifically, by comparing to pregnancies where both the mother and all fetuses are WT, placental LZ respiratory capacity of WT fetuses of both sexes varied when either littermates and/or the mother was p110α deficient. Moreover, there were alterations in placental LZ mitochondrial bioenergetic capacity that were associated with sex-specific changes in the abundance of mitochondrial-related, growth, and metabolic proteins in the placenta of WTs exposed to littermate and/or maternal p110α deficiency. Together, these data may have relevance for understanding divergent fetal outcomes in pregnancies associated with different gestational conditions. They also support the concept of developing treatments that are targeted to the placenta, which are tailored to the sex of the fetus.
In previous studies done by our laboratory, we demonstrated that conceptus p110α deficiency leads to reduced fetal and placental growth [21,24,56]. Furthermore, α/+ dams have metabolic imbalances before and during pregnancy, which may relate to the altered fetal outcomes observed in this study and in our previous work [24,54,56]. In particular, compared to WT mothers carrying only WT fetuses in the litter, α/+ dams are lighter and unable to shift their glucose handling during pregnancy to favour fetal growth [54]. The novelty of the current work is the finding that WT males in a WT mother who carried α/+ fetuses (WT x α/+) are heavier than those from WT-only pregnancies (WT x WT). Moreover, this effect is lost if the pregnancy was carried by a mutant α/+ dam (α/+ x WT). These data are consistent with other work showing the important influence of genetically altered littermates on the intrauterine growth of their siblings [21,59]. This study therefore extends those findings to show that the influence of an altered intrauterine environment caused by sharing a litter with a mutant fetus (in this case α/+ fetus), is dependent on fetal sex. It is well established that males grow faster than females in utero [1,60,61,62,63]. In addition, recent data indicate that males are more growth-impaired than females in response to fetal p110α deficiency [56]. Therefore, one interpretation may be that WT males can grow more when there is less competition for maternally-supplied metabolic substrates from α/+ littermates. However, this innate ability of WT males to enhance their growth is hindered when the maternal ability to support the pregnancy is compromised by p110α deficiency.
We also found that mitochondrial oxygen consumption in the placental LZ of WT fetuses is responsive to the environment provided by the mother and/or littermates carrying the α/+ mutation. Overall, changes in LZ mitochondrial OXPHOS capacity for WT fetuses was similar for those gestated with p110α deficient littermates (WT x α/+) and/or p110α deficient mother (α/+ x WT), compared to WTs from entire WT pregnancies (WT x WT). This indicates that littermate, and largely not maternal, p110α deficiency impacts mitochondrial respiratory capacity in the placental LZ of WT fetuses. For instance, placental LZ CIIOXPHOS and total ETS were greater for WT fetuses from either WT x α/+ or α/+ x WT pregnancies compared to WT x WT. However, the effect of p110α deficiency to increase LZ CILeak flux control ratio (CILeak/total ETS) was only significant for WT fetuses from α/+ x WT pregnancies compared to WT x WT (values for WT x α/+ pregnancies were intermediate between the two groups). Moreover, some sex-specific changes were found in the effect of littermate p110α deficiency. In particular, LZ CIOXPHOS flux control (CIOXPHOS/total ETS) ratio was elevated only in WT females, and reserve capacity lower only for WT males, in WT x α/+ and α/+ x WT compared to WT x WT pregnancies. Earlier work has demonstrated that overall, both fetal and maternal p110α deficiency results in defective placental endocrine output, namely altered expression of placental lactogen genes [24]. Other work has also reported sex-dependent differences in placental capacity to produce placental lactogens, as well as sex steroids in response to genetic manipulations [64,65]. Placental hormones can exert local effects with resultant impacts on the placental support of fetal development [65,66]. Hence, changes in placental endocrine output may modulate feto-placental phenotype, including mitochondrial respiration of WT littermates, and how this may be modified by fetal sex requires further work. Female and male fetuses also vary in their circulating hormones [67,68], and this could serve as an additional mechanism behind sexually-dimorphic placental mitochondrial phenotypes as reported in several gestational pathologies [69,70,71,72,73,74,75].
Sex-specific differences in WT fetuses exposed to littermate and/or maternal p110α deficiency were related to molecular changes in the placental LZ. For instance, WT females presented a decreased CI and CII protein abundance in both WT x α/+ and α/+ x WT pregnancies, which may explain their reduced mitochondrial respiratory capacity. However, CIII abundance was also up-regulated for WT females from the WT x α/+ parental cross. In contrast, abundance of CI was elevated in the LZ of WT males of both WT x α/+ and α/+ x WT pregnancies and no differences were found for CII or CIII mitochondrial complexes, which could explain the changes in mitochondrial OXPHOS regardless of the parental cross. Interestingly, CIV was similarly affected in both WT females and males, presenting a decreased protein expression only when the mother was p110α deficient (α/+ x WT), when compared to pregnancies generated by a father who was p110α deficient (WT x α/+). Other studies conducted on placentas from adverse maternal gestational environments such as malnutrition, hypoxia, preeclampsia, obesity and metabolic diseases have also reported changes in the abundance of mitochondrial complexes, but few if at all have explored if changes are sex-specific [31,33,37,69,76,77,78,79,80,81,82]. Together these data highlight there is an important role for maternal and intrauterine p110α deficiency in modulating the expression of mitochondrial respiratory complexes by the WT male and female placental LZ.
There were also sex-specific changes in the levels of mitochondria-related proteins in the placental LZ of WT fetuses exposed to littermate and/or maternal p110α deficiency. For instance, abundance of citrate synthase and PGC1α were increased only in WT males from both WT x α/+ and α/+ x WT pregnancies, suggesting a compensatory elevation in mitochondrial density and biogenesis which is in line with higher CI levels compared to WT x WT pregnancies. Additionally, PPAR and uncoupling protein UCP2 protein abundance were altered in WT females and WT males, respectively and only in the group with mutated littermates (WT x α/+). The relevance of these changes though are unknown, especially given that mitochondrial fatty acid oxidation and oxygen consumption in LEAK state were not different for the placental LZ of females or males regardless of the parental cross (no differences between WT x WT, WT x α/+ and α/+ x WT). The abundance and activity of AKT, MAPK $\frac{44}{42}$ and p38MAPK signalling proteins in the LZ of WT fetuses were also sex-specifically impacted by littermate and/or maternal p110α deficiency. However, unlike the changes in LZ respiratory capacity and mitochondria-related proteins, the impacts were more pronounced for WT fetuses if the mother plus littermates carried the p110α mutation (the α/+ x WT cross). Compared to both WT x WT and WT x α/+pregnancies, WT females presented increased levels of MAPK $\frac{44}{42}$ and p38MAPK, whilst males showed elevated AKT levels. Interestingly, both WT females and males presented decreased levels of activated AKT (phosphorylation/total protein ratio) in α/+ x WT pregnancies, but for females this was also observed for the WT x α/+ cross. AKT signalling is important for placental growth and transport functional capacity [26,83] and recent work has shown that signalling via AKT in the placental LZ can vary in a sex-dependant manner [1,3]. The relevance of our current findings though is unclear, as placental LZ weight was unchanged, or even greater (for males in the α/+ x WT cross) in pregnancies where there was a deficiency in p110α (WT x α/+ or α/+ x WT). However, our data may have implications for previous work showing that WT placentas exposed to different p110α deficiencies vary in their glucose and amino acid transport in vivo [21]. Finally, activation of p38MAPK was decreased in the LZ of WT female fetuses exposed to both littermate and maternal p110α deficiency (i.e. WT x α/+ and α/+ x WT pregnancies compared to WT x WT), but increased specifically in the LZ of WT male fetuses in WT x α/+ compared to α/+ x WT. The significance of sex-specific changes in the WT placenta to littermate and maternal p110α deficiency is unclear.
While our study has multiple strengths, it also has certain limitations. For example, the evaluation of maternal food intake would be very helpful to understand how maternal metabolism and nutrient resources can impact on the changes observed in the placental LZ. Another limitation is that we only examined one gestational age and the changes observed in this study are likely to be a result of placental adaptations occurring earlier. Therefore, future experiments should evaluate additional gestational days to assess the ontogeny of sex-specific adaptations in placenta function. Lastly, our study was only focused on the LZ and we did not study the endocrine junctional zone. Therefore, future work should investigate the changes occurring in this placental region, which is also critical for the success of the pregnancy. Indeed, we have recently shown in mice that small for gestational age fetuses have changes in the function of the junctional zone [84]. Therefore, it is plausible that additional sex-specific adaptations are occurring in this placental region.
To conclude, our data emphasize the significance of the maternal and intrauterine environment in the control of fetal development. Furthermore, modulation of fetal development by the maternal and intrauterine environment is accompanied by changes in placental formation, energetic capacity and abundance of proteins regulating mitochondrial density, lipid metabolism, growth and nutrient handling of female and male fetuses. Our finding may have relevance for understanding the pathways leading to reduced fetal growth in suboptimal maternal environments, as well as for fetal outcomes in the context of multiple gestations/litter bearing species [3].
## References
1. Salazar-Petres E., Carvalho D.P., Lopez-Tello J., Sferruzzi-Perri A.N.. **Placental structure, function and mitochondrial phenotype relate to fetal size in each fetal sex in mice**. *Biol. Reprod.* (2022) **106** 1292-1311. DOI: 10.1093/biolre/ioac056
2. Coan P.M., Angiolini E., Sandovici I., Burton G.J., Constância M., Fowden A.L.. **Adaptations in placental nutrient transfer capacity to meet fetal growth demands depend on placental size in mice**. *J. Physiol.* (2008) **586** 4567-4576. DOI: 10.1113/jphysiol.2008.156133
3. Sferruzzi-Perri A.N., Lopez-Tello J., Salazar-Petres E.. **Placental adaptations supporting fetal growth during normal and adverse gestational environments**. *Exp. Physiol.* (2022) **108** 1-27. DOI: 10.1113/EP090442
4. Burton G.J., Fowden A.L., Burton G.J.. **The placenta: A multifaceted, transient organ**. *Philos. Trans. R. Soc. B Biol. Sci.* (2015) **370** 20140066. DOI: 10.1098/rstb.2014.0066
5. Zhang S., Regnault T.R.H., Barker P.L., Botting K.J., McMillen I.C., McMillan C.M., Roberts C.T., Morrison J.L.. **Placental adaptations in growth restriction**. *Nutrients* (2015) **7** 360-389. DOI: 10.3390/nu7010360
6. Sandovici I., Hoelle K., Angiolini E., Constância M.. **Placental adaptations to the maternal-fetal environment: Implications for fetal growth and developmental programming**. *Reprod. Biomed. Online* (2012) **25** 68-89. DOI: 10.1016/j.rbmo.2012.03.017
7. Napso T., Yong H.E.J., Lopez-Tello J., Sferruzzi-Perri A.N.. **The role of placental hormones in mediating maternal adaptations to support pregnancy and lactation**. *Front. Physiol.* (2018) **9** 1-39. DOI: 10.3389/fphys.2018.01091
8. Costa M.A.. **The endocrine function of human placenta: An overview**. *Reprod. Biomed. Online* (2015) **32** 14-43. DOI: 10.1016/j.rbmo.2015.10.005
9. Stern C., Schwarz S., Moser G., Cvitic S., Jantscher-Krenn E., Gauster M., Hiden U.. **Placental endocrine activity: Adaptation and disruption of maternal glucose metabolism in pregnancy and the influence of fetal sex**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms222312722
10. Simpson S., Smith L., Bowe J.. **Placental peptides regulating islet adaptation to pregnancy: Clinical potential in gestational diabetes mellitus**. *Curr. Opin. Pharmacol.* (2018) **43** 59-65. DOI: 10.1016/j.coph.2018.08.004
11. Burton G.J., Jauniaux E., Charnock-Jones D.S.. **The influence of the intrauterine environment on human placental development**. *Int. J. Dev. Biol.* (2010) **54** 303-311. DOI: 10.1387/ijdb.082764gb
12. Dimasuay K.G., Boeuf P., Powell T.L., Jansson T.. **Placental responses to changes in the maternal environment determine fetal growth**. *Front. Physiol.* (2016) **7** 1-9. DOI: 10.3389/fphys.2016.00012
13. Woods L., Perez-Garcia V., Hemberger M., Brett K.E., Ferraro Z.M., Yockell-Lelievre J., Gruslin A., Adamo K.B.. **Maternal–Fetal nutrient transport in pregnancy pathologies: The role of the placenta**. *Int. J. Mol. Sci.* (2014) **15** 16153-16185. PMID: 25222554
14. Hemberger M., Hanna C.W., Dean W.. **Mechanisms of early placental development in mouse and humans**. *Nat. Rev. Genet.* (2020) **21** 27-43. DOI: 10.1038/s41576-019-0169-4
15. Schmidt A., Morales-Prieto D.M., Pastuschek J., Fröhlich K., Markert U.R.. **Only humans have human placentas: Molecular differences between mice and humans**. *J. Reprod. Immunol.* (2015) **108** 65-71. DOI: 10.1016/j.jri.2015.03.001
16. Cox B., Kotlyar M., Evangelou A.I., Ignatchenko V., Ignatchenko A., Whiteley K., Jurisica I., Adamson S.L., Rossant J., Kislinger T.. **Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology**. *Mol. Syst. Biol.* (2009) **5** 1-15. DOI: 10.1038/msb.2009.37
17. Coan P.M., Vaughan O.R., Sekita Y., Finn S.L., Burton G.J., Constancia M., Fowden A.L.. **Adaptations in placental phenotype support fetal growth during undernutrition of pregnant mice**. *J. Physiol.* (2010) **3** 527-538. DOI: 10.1113/jphysiol.2009.181214
18. Ganguly A., McKnight R.A., Raychaudhuri S., Shin B.C., Ma Z., Moley K., Devaskar S.U.. **Glucose transporter isoform-3 mutations cause early pregnancy loss and fetal growth restriction**. *Am. J. Physiol.—Endocrinol. Metab.* (2007) **292** E1241-E1255. DOI: 10.1152/ajpendo.00344.2006
19. Constância M., Angiolini E., Sandovici I., Smith P., Smith R., Kelsey G., Dean W., Ferguson-smith A., Sibley C.P., Reik W.. **Adaptation of nutrient supply to fetal demand in the mouse involves interaction between the Igf2 gene and placental transporter systems**. *Proc. Natl. Acad. Sci. USA* (2005) **102** 19219-19224. DOI: 10.1073/pnas.0504468103
20. Wyrwoll C.S., Seckl J.R., Holmes M.C.. **Altered placental function of 11β-hydroxysteroid dehydrogenase 2 knockout mice**. *Endocrinology* (2009) **150** 1287-1293. DOI: 10.1210/en.2008-1100
21. López-Tello J., Pérez-García V., Khaira J., Kusinski L.C., Cooper W.N., Andreani A., Grant I., de Liger E.F., Lam B.Y.H., Hemberger M.. **Fetal and trophoblast PI3K p110α have distinct roles in regulating resource supply to the growing fetus in mice**. *Elife* (2019) **8** 1-25. DOI: 10.7554/eLife.45282
22. James-Allan L.B., Teal S., Powell T.L., Jansson T.. **Changes in Placental Nutrient Transporter Protein Expression and Activity Across Gestation in Normal and Obese Women**. *Reprod. Sci.* (2020) **27** 1758-1769. DOI: 10.1007/s43032-020-00173-y
23. Borges M.H., Pullockaran J., Catalano P.M., Baumann M.U., Zamudio S., Illsley N.P.. **Human placental GLUT1 glucose transporter expression and the fetal insulin-like growth factor axis in pregnancies complicated by diabetes**. *Biochim. Biophys. Acta—Mol. Basis Dis.* (2019) **1865** 2411-2419. DOI: 10.1016/j.bbadis.2019.06.002
24. Sferruzzi-Perri A.N., López-Tello J., Fowden A.L., Constancia M.. **Maternal and fetal genomes interplay through phosphoinositol 3-kinase (PI3K)-p110α signaling to modify placental resource allocation**. *Proc. Natl. Acad. Sci. USA* (2016) **113** 11255-11260. DOI: 10.1073/pnas.1602012113
25. Lu M., Sferruzzi-Perri A.N.. **Placental mitochondrial function in response to gestational exposures**. *Placenta* (2021) **104** 124-137. DOI: 10.1016/j.placenta.2020.11.012
26. Aye I.L.M.H., Aiken C.E., Charnock-Jones D.S., Smith G.C.S.. **Placental energy metabolism in health and disease—Significance of development and implications for preeclampsia**. *Am. J. Obstet. Gynecol.* (2022) **226** S928-S944. DOI: 10.1016/j.ajog.2020.11.005
27. Martinez F., Olvera-Sanchez S., Esparza-Perusquia M., Gomez-Chang E., Flores-Herrera O., Jornayvaz F.R., Shulman G.I., Katic M., Kennedy A.R., Leykin I.. **Multiple functions of syncytiotrophoblast mitochondria**. *Essays Biochem.* (2010) **103** 69-84. DOI: 10.1016/j.steroids.2015.09.006
28. Waker C.A., Albers R.E., Pye R.L., Doliboa S.R., Wyatt C.N., Brown T.L., Mayes D.A.. **AMPK Knockdown in Placental Labyrinthine Progenitor Cells Results in Restriction of Critical Energy Resources and Terminal Differentiation Failure**. *Stem Cells Dev.* (2017) **26** 808-817. DOI: 10.1089/scd.2016.0252
29. Bartho L.A., Fisher J.J., Walton S.L., Perkins A.V., Cuffe J.S.M.. **The effect of gestational age on mitochondrial properties of the mouse placenta**. *Reprod. Fertil.* (2022) **3** 19-29. DOI: 10.1530/RAF-21-0064
30. Murray A.J.. **Oxygen delivery and fetal-placental growth: Beyond a question of supply and demand?**. *Placenta* (2012) **33** e16-e22. DOI: 10.1016/j.placenta.2012.06.006
31. Sferruzzi-Perri A.N., Higgins J.S., Vaughan O.R., Murray A.J., Fowden A.L.. **Placental mitochondria adapt developmentally and in response to hypoxia to support fetal growth**. *Proc. Natl. Acad. Sci. USA* (2019) **116** 1621-1626. DOI: 10.1073/pnas.1816056116
32. Holland O.J., Hickey A.J.R., Alvsaker A., Moran S., Hedges C., Chamley L.W., Perkins A.V.. **Changes in mitochondrial respiration in the human placenta over gestation**. *Placenta* (2017) **57** 102-112. DOI: 10.1016/j.placenta.2017.06.011
33. Rebelato H.J., Esquisatto M.A.M., Moraes C., Amaral M.E.C., Catisti R.. **Gestational protein restriction induces alterations in placental morphology and mitochondrial function in rats during late pregnancy**. *J. Mol. Histol.* (2013) **44** 629-637. DOI: 10.1007/s10735-013-9522-7
34. Sobrevia L., Valero P., Grismaldo A., Villalobos-Labra R., Pardo F., Subiabre M., Armstrong G., Toledo F., Vega S., Cornejo M.. **Mitochondrial dysfunction in the fetoplacental unit in gestational diabetes mellitus**. *Biochim. Biophys. Acta—Mol. Basis Dis.* (2020) **1866** 165948. DOI: 10.1016/j.bbadis.2020.165948
35. Mayeur S., Lancel S., Theys N., Lukaszewski M.-A., Duban-Deweer S., Bastide B., Hachani J., Cecchelli R., Breton C., Gabory A.. **Maternal calorie restriction modulates placental mitochondrial biogenesis and bioenergetic efficiency: Putative involvement in fetoplacental growth defects in rats**. *Am. J. Physiol. Endocrinol. Metab.* (2013) **304** 14-22. DOI: 10.1152/ajpendo.00332.2012
36. Yung H.W., Colleoni F., Dommett E., Cindrova-Davies T., Kingdom J., Murray A.J., Burton G.J.. **Noncanonical mitochondrial unfolded protein response impairs placental oxidative phosphorylation in early-onset preeclampsia**. *Proc. Natl. Acad. Sci. USA* (2019) **116** 18109-18118. DOI: 10.1073/pnas.1907548116
37. Mandò C., De Palma C., Stampalija T., Anelli G.M., Figus M., Novielli C., Parisi F., Clementi E., Ferrazzi E., Cetin I.. **Placental mitochondrial content and function in intrauterine growth restriction and preeclampsia**. *Am. J. Physiol.—Endocrinol. Metab.* (2014) **306** 404-413. DOI: 10.1152/ajpendo.00426.2013
38. Holland O.J., Cuffe J.S.M., Dekker Nitert M., Callaway L., Kwan Cheung K.A., Radenkovic F., Perkins A.V.. **Placental mitochondrial adaptation in preeclampsia associated with progression to term delivery**. *Cell Death Dis.* (2018) **9** 1150. DOI: 10.1038/s41419-018-1190-9
39. Abbade J., Klemetti M.M., Farrell A., Ermini L., Gillmore T., Sallais J., Tagliaferro A., Post M., Caniggia I.. **Increased placental mitochondrial fusion in gestational diabetes mellitus: An adaptive mechanism to optimize feto-placental metabolic homeostasis?**. *BMJ Open Diabetes Res. Care* (2020) **8** e000923. DOI: 10.1136/bmjdrc-2019-000923
40. Napso T., Hung Y.P., Davidge S.T., Care A.S., Sferruzzi-Perri A.N.. **Advanced maternal age compromises fetal growth and induces sex-specific changes in placental phenotype in rats**. *Sci. Rep.* (2019) **9** 1-15. DOI: 10.1038/s41598-019-53199-x
41. Napso T., Lean S.C., Lu M., Mort E.J., Desforges M., Moghimi A., Bartels B., El-Bacha T., Fowden A.L., Camm E.J.. **Diet-induced maternal obesity impacts feto-placental growth and induces sex-specific alterations in placental morphology, mitochondrial bioenergetics, dynamics, lipid metabolism and oxidative stress in mice**. *Acta Physiol.* (2022) **234** e13795. DOI: 10.1111/apha.13795
42. Engelman J.A., Luo J., Cantley L.C.. **The evolution of phosphatidylinositol 3-kinases as regulators of growth and metabolism**. *Nat. Rev. Genet.* (2006) **7** 606-619. DOI: 10.1038/nrg1879
43. Bettedi L., Yan A., Schuster E., Alic N., Foukas L.C.. **Increased mitochondrial and lipid metabolism is a conserved effect of Insulin/PI3K pathway downregulation in adipose tissue**. *Sci. Rep.* (2020) **10** 3418. DOI: 10.1038/s41598-020-60210-3
44. Gao M., Wang J., Wang W., Liu J., Wong C.W.. **Phosphatidylinositol 3-kinase affects mitochondrial function in part through inducing peroxisome proliferator-activated receptor γ coactivator-1β expression**. *Br. J. Pharmacol.* (2011) **162** 1000-1008. DOI: 10.1111/j.1476-5381.2010.01105.x
45. Yu J.S.L., Cui W.. **Proliferation, survival and metabolism: The role of PI3K/AKT/ mTOR signalling in pluripotency and cell fate determination**. *Development* (2016) **143** 3050-3060. DOI: 10.1242/dev.137075
46. Tu-Sekine B., Kim S.F.. **The Inositol Phosphate System—A Coordinator of Metabolic Adaptability**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23126747
47. Cardoso S., López I.P., Piñeiro-Hermida S., Pichel J.G., Moreira P.I.. **Igf1r deficiency modulates brain signaling pathways and disturbs mitochondria and redox homeostasis**. *Biomedicines* (2021) **9**. DOI: 10.3390/biomedicines9020158
48. Zhang D., Wei Y., Huang Q., Chen Y., Zeng K., Yang W., Chen J., Chen J.. **Important Hormones Regulating Lipid Metabolism**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27207052
49. Foukas L.C., Claret M., Pearce W., Okkenhaug K., Meek S., Peskett E., Sancho S., Smith A.J.H., Withers D.J., Vanhaesebroeck B.. **Critical role for the p110 a phosphoinositide-3-OH kinase in growth and metabolic regulation**. *Nature* (2006) **441** 3-7. DOI: 10.1038/nature04694
50. Knight Z.A., Gonzalez B., Feldman M.E., Zunder E.R., Goldenberg D.D., Williams O., Loewith R., Stokoe D., Balla A., Toth B.. **A Pharmacological Map of the PI3-K Family Defines a Role for p110 a in Insulin Signaling**. *Cell* (2006) **125** 733-747. DOI: 10.1016/j.cell.2006.03.035
51. Taniguchi C.M., Kondo T., Sajan M., Luo J., Bronson R., Asano T., Farese R., Cantley L.C., Kahn C.R.. **Divergent regulation of hepatic glucose and lipid metabolism by phosphoinositide 3-kinase via Akt and PKC l/z**. *Cell Metab.* (2006) **3** 343-353. DOI: 10.1016/j.cmet.2006.04.005
52. Sopasakis V.R., Liu P., Suzuki R., Kondo T., Winnay J., Tran T.T., Asano T., Smyth G., Sajan M.P., Farese R.V.. **Short Article Specific Roles of the p110 a Isoform of Phosphatidylinsositol 3-Kinase in Hepatic Insulin Signaling and Metabolic Regulation**. *Cell Metab.* (2010) **11** 220-230. DOI: 10.1016/j.cmet.2010.02.002
53. Pérez-García V., Redondo-Muñoz J., Kumar A., Carrera A.C.. **Cell activation-induced phosphoinositide 3-kinase alpha/beta dimerization regulates PTEN activity**. *Mol. Cell Biol.* (2014) **34** 3359-3373. DOI: 10.1128/MCB.00167-14
54. Lopez-Tello J., Salazar-Petres E., Webb L., Fowden A.L., Sferruzzi-Perri A.N.. **Ablation of PI3K-p110alpha Impairs Maternal Metabolic Adaptations to Pregnancy**. *Front. Cell Dev. Biol.* (2022) **10** 928210. DOI: 10.3389/fcell.2022.928210
55. Makarova E.N., Kochubei E.D., Bazhan N.M.. **Regulation of food consumption during pregnancy and lactation in mice**. *Neurosci. Behav. Physiol.* (2010) **40** 263-267. DOI: 10.1007/s11055-010-9253-0
56. Pereira-Carvalho D., Salazar-Petres E., Lopez-Tello J., Sferruzzi-Perri A.N.. **Maternal and Fetal PI3K-p110α Deficiency Induces Sex-Specific Changes in Conceptus Growth and Placental Mitochondrial Bioenergetic Reserve in Mice**. *Vet. Sci.* (2022) **9**. DOI: 10.3390/vetsci9090501
57. Djafarzadeh S., Jakob S.M.. **High-resolution respirometry to assess mitochondrial function in permeabilized and intact cells**. *J. Vis. Exp.* (2017) **2017** 1-11
58. Romero-Calvo I., Ocón B., Martínez-Moya P., Suárez M.D., Zarzuelo A., Martínez-Augustin O., de Medina F.S.. **Reversible Ponceau staining as a loading control alternative to actin in Western blots**. *Anal. Biochem.* (2010) **401** 318-320. DOI: 10.1016/j.ab.2010.02.036
59. Tunster S.J., Creeth H.D.J., John R.M.. **The imprinted Phlda2 gene modulates a major endocrine compartment of the placenta to regulate placental demands for maternal resources**. *Dev. Biol.* (2016) **409** 251-260. DOI: 10.1016/j.ydbio.2015.10.015
60. Kalisch-Smith J.I., Simmons D.G., Dickinson H., Moritz K.M.. **Review: Sexual dimorphism in the formation, function and adaptation of the placenta**. *Placenta* (2017) **54** 10-16. DOI: 10.1016/j.placenta.2016.12.008
61. Eriksson J.G., Kajantie E., Osmond C., Thornburg K., Barker D.J.P.. **Boys live dangerously in the womb**. *Am. J. Hum. Biol. Counc.* (2010) **22** 330-335. DOI: 10.1002/ajhb.20995
62. Bartels H.C., Geraghty A.A., O’Brien E.C., Kranidi A., Mehegan J., Yelverton C., McDonnell C.M., McAuliffe F.M.. **Fetal Growth Trajectories and Their Association with Maternal, Cord Blood, and 5-year Child Adipokines**. *J. Nutr. Metab.* (2020) **2020** 4861523. DOI: 10.1155/2020/4861523
63. Broere-Brown Z.A., Baan E., Schalekamp-Timmermans S., Verburg B.O., Jaddoe V.W.V., Steegers E.A.P.. **Sex-specific differences in fetal and infant growth patterns: A prospective population-based cohort study**. *Biol. Sex Differ.* (2016) **7** 1-9. DOI: 10.1186/s13293-016-0119-1
64. Aykroyd B.R.L., Tunster S.J., Sferruzzi-Perri A.N.. **Loss of imprinting of the Igf2-H19 ICR1 enhances placental endocrine capacity via sex-specific alterations in signalling pathways in the mouse**. *Development* (2022) **149** dev199811. DOI: 10.1242/dev.199811
65. Aykroyd B.R.L., Tunster S.J., Sferruzzi-Perri A.N.. **Igf2 deletion alters mouse placenta endocrine capacity in a sexually dimorphic manner**. *J. Endocrinol.* (2020) **246** 93-108. DOI: 10.1530/JOE-20-0128
66. Sferruzzi-Perri A.N., Sandovici I., Constancia M., Fowden A.L.. **Placental phenotype and the insulin-like growth factors: Resource allocation to fetal growth**. *J. Physiol.* (2017) **15** 5057-5093. DOI: 10.1113/JP273330
67. Wen Q., Cheng C.Y., Liu Y.X.. **Development, function and fate of fetal Leydig cells**. *Semin. Cell Dev. Biol.* (2016) **59** 89-98. DOI: 10.1016/j.semcdb.2016.03.003
68. Biason-Lauber A., Chaboissier M.C.. **Ovarian development and disease: The known and the unexpected**. *Semin. Cell Dev. Biol.* (2015) **45** 59-67. DOI: 10.1016/j.semcdb.2015.10.021
69. Hebert J.F., Myatt L.. **Placental mitochondrial dysfunction with metabolic diseases: Therapeutic approaches**. *Biochim. Biophys. Acta—Mol. Basis Dis.* (2021) **1867** 165967. DOI: 10.1016/j.bbadis.2020.165967
70. Muralimanoharan S., Guo C., Myatt L., Maloyan A.. **Sexual dimorphism in MIR-210 expression and mitochondrial dysfunction in the placenta with maternal obesity**. *Int. J. Obes.* (2015) **39** 1274-1281. DOI: 10.1038/ijo.2015.45
71. Mishra J.S., Blesson C.S., Kumar S.. **Testosterone decreases placental mitochondrial content and cellular bioenergetics**. *Biology* (2020) **9**. DOI: 10.3390/biology9070176
72. Cuffe J.S.M., Dickinson H., Simmons D.G., Moritz K.M.. **Sex specific changes in placental growth and MAPK following short term maternal dexamethasone exposure in the mouse**. *Placenta* (2011) **32** 981-989. DOI: 10.1016/j.placenta.2011.09.009
73. Evans L.S., Myatt L.. **Sexual dimorphism in the effect of maternal obesity on antioxidant defense mechanisms in the human placenta**. *Placenta* (2017) **51** 64-69. DOI: 10.1016/j.placenta.2017.02.004
74. Jiang S., Teague A.M., Tryggestad J.B., Aston C.E., Lyons T., Chernausek S.D.. **Effects of maternal diabetes and fetal sex on human placenta mitochondrial biogenesis**. *Placenta* (2017) **57** 26-32. DOI: 10.1016/j.placenta.2017.06.001
75. Ganguly E., Kirschenman R., Spaans F., Holody C.D., Phillips T.E.J., Case C.P., Murphy M.P., Lemieux H., Davidge S.T.. **Nanoparticle-encapsulated antioxidant improves placental mitochondrial function in a sexually dimorphic manner in a rat model of prenatal hypoxia**. *FASEB J.* (2021) **35** 1-16. DOI: 10.1096/fj.202002193R
76. Vangrieken P., Al-Nasiry S., Bast A., Leermakers P.A., Tulen C.B.M., Schiffers P.M.H., van Schooten F.J., Remels A.H.V.. **Placental Mitochondrial Abnormalities in Preeclampsia**. *Reprod. Sci.* (2021) **28** 2186-2199. DOI: 10.1007/s43032-021-00464-y
77. Fisher J.J., Bartho L.A., Perkins A.V., Holland O.J.. **Placental mitochondria and reactive oxygen species in the physiology and pathophysiology of pregnancy**. *Clin. Exp. Pharmacol. Physiol.* (2020) **47** 176-184. DOI: 10.1111/1440-1681.13172
78. Hastie R., Lappas M.. **The effect of pre-existing maternal obesity and diabetes on placental mitochondrial content and electron transport chain activity**. *Placenta* (2014) **35** 673-683. DOI: 10.1016/j.placenta.2014.06.368
79. Muralimanoharan S., Maloyan A., Myatt L.. **Mitochondrial function and glucose metabolism in the placenta with gestational diabetes mellitus: Role of miR-143**. *Clin. Sci.* (2016) **130** 931-941. DOI: 10.1042/CS20160076
80. Luo Z., Luo W., Li S., Zhao S., Sho T., Xu X., Zhang J., Xu W., Xu J.. **Reactive oxygen species mediated placental oxidative stress, mitochondrial content, and cell cycle progression through mitogen-activated protein kinases in intrauterine growth restricted pigs**. *Reprod. Biol.* (2018) **18** 422-431. DOI: 10.1016/j.repbio.2018.09.002
81. Ramírez-Emiliano J., Fajardo-Araujo M.E., Zúñiga-Trujillo I., Pérez-Vázquez V., Sandoval-Salazar C., Órnelas-Vázquez J.K.. **Mitochondrial content, oxidative, and nitrosative stress in human full-term placentas with gestational diabetes mellitus**. *Reprod. Biol. Endocrinol.* (2017) **15** 1-8. DOI: 10.1186/s12958-017-0244-7
82. Marín R., Chiarello D.I., Abad C., Rojas D., Toledo F., Sobrevia L.. **Oxidative stress and mitochondrial dysfunction in early-onset and late-onset preeclampsia**. *Biochim. Biophys. Acta—Mol. Basis Dis.* (2020) **1866** 165961. DOI: 10.1016/j.bbadis.2020.165961
83. Burton G.J., Jauniaux E.. **Expert Reviews Pathophysiology of placental-derived fetal growth restriction**. *Am. J. Obstet. Gynecol.* (2018) **218** S745-S761. DOI: 10.1016/j.ajog.2017.11.577
84. Lopez-Tello J., Sferruzzi-Perri A.N.. **Characterization of placental endocrine function and fetal brain development in a mouse model of small for gestational age**. *Front. Endocrinol.* (2023) **14** 1-11. DOI: 10.3389/fendo.2023.1116770
|
---
title: 'Parental Ethnicity and Adolescent Development: Evidence from a Nationally
Representative Dataset'
authors:
- Lidan Lyu
- Danyang Sheng
- Yu Chen
- Yu Bai
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10000958
doi: 10.3390/ijerph20053799
license: CC BY 4.0
---
# Parental Ethnicity and Adolescent Development: Evidence from a Nationally Representative Dataset
## Abstract
Adolescent developmental outcomes can vary significantly by differences in ethnicity. While previous studies have examined the impacts of adolescents’ own ethnicity on their development, little research has been conducted about the impacts of the ethnicity of both parents as an important family background factor which is likely to expose adolescents to a variety of growth environments. Using nationally representative data from the China Family Panel Studies (CFPS) surveys, we examine the relationship between parental ethnicity (including both monoethnic families and interethnic families with intermarried Han and ethnic minority groups) and adolescent developmental outcomes, measured by academic performance, cognitive development, and health. Our results show that adolescents with interethnic parents had higher scores in literacy and mathematics tests than those of monoethnic non-Han parents, but their scores were not statistically significantly different from those in monoethnic Han families. Adolescents with interethnic parents also performed better in fluid intelligence assessments and had lower obesity rates than those with monoethnic ethnic minority parents. Our results further suggest that socioeconomic status, parental education, and education expectations partially mediate the association between interethnic parents and adolescent development. Moreover, parental ethnic composition acts as a potential moderator that influences the effects of parents’ non-agricultural work on adolescent development. Our study expands the growing body of empirical evidence on the relationship between parental ethnicity and adolescent development and is conducive to policy recommendations for interventions in the development of adolescents with ethnic minority parents.
## 1. Introduction
The ethnicity of parents, as an important family attribute, can have a profound influence on the growth and development of adolescents and children. It is widely reported that adolescents from ethnic minority families, as compared to adolescents from other family backgrounds, are disadvantaged in terms of their educational performance, nutritional status, and mental health [1]. In recent years, there has been a rising trend of mixed-ethnic intermarriages. However, it is relatively unknown about the influence of families with interethnic parents on adolescent development. It is likely that interethnic couples might need to resolve differences in values, social norms, and lifestyle choices associated with their respective ethnicities. The collision and fusion of different cultural values and social norms for interethnic parents may influence their children’s development in terms of academic performance, cognitive ability, and even physical fitness [2].
As a multiethnic country with 55 ethnic minorities and the Han majority, China provides a useful case for studying this issue. According to *Chinese census* data, the proportion of ethnic minorities in the Chinese population has risen from $6.7\%$ in 1982 to $8.9\%$ in 2020, more than doubling on an absolute scale [3]. The proportion of ethnic minorities among children has experienced an even faster growth rate, rising from $7.6\%$ in 1982 to $11.0\%$ in 2020 [4]. From data collected for the seventh census in 2020, the number of interethnic households in China has increased to 14.38 million. Interethnic marriages are increasingly common in China, and more than 26 million people are married outside of their own ethnic groups [5]. A majority of interethnic marriages are between Han and non-Han ethnic minorities, and all 55 ethnic minorities record incidences of interethnic marriages with the Han. For nine ethnic minorities, the number of interethnic marriages with the Han actually exceeds the number of intraethnic marriages within their respective ethnic groups [6,7].
In light of this trend, the goal of this study was to investigate the variations in developmental outcomes of adolescents from families with different ethnic compositions, drawing on nationally representative data from the China Family Panel Studies (CFPS). We used academic performance, cognitive ability, and physical health to measure adolescent development. We further explored the underlying mechanisms driving the observed differences. Specifically, we compared adolescents from monoethnic Han families (Han–Han), monoethnic ethnic minority families (Minority–Minority), and interethnic families involving the Han (Han–Minority). Our research focuses on two specific questions: [1] Does heterogeneity in parental ethnic composition drive differences in adolescent development? [ 2] How do interethnic marriages, directly and indirectly, affect adolescent development? These are important topics to study because the findings based on China will broaden the scope of research in the literature on the relationship between family background and adolescent development and enrich our understanding of the impact of interethnic marriages on adolescent development. Moreover, our study provides significant insights into interethnic integration and offers useful policy recommendations for the building of inter-embedded multiethnic communities, which are important strategic initiatives for multiethnic countries.
## 2. Related Studies and Hypotheses
Findings from a large number of studies reveal that interethnic marriages can enhance children’s and adolescents’ development in many dimensions. From a biological perspective, children of interethnic Han and non-Han parents may have better physical health due to exogamous unions leading to a very low degree of consanguinity [8]. Related empirical evidence shows that children of interethnic Mongolian–Han couples have better fitness and physical constitution in comparison to children in the same age group but from monoethnic Mongolian or Han families [9]. Interethnic marriages also mitigate the relatively common occurrence of consanguineous marriage found in a number of remotely located ethnic minority groups [10]. However, there are significant cross-sectional differences in educational performance and opportunities for children from different ethnic groups. For example, Chinese children from ethnic minority groups tend to underperform relative to Han children in terms of school attendance, participation in higher education, or academic achievement [6,7]. Strikingly, children from interethnic families tend to outperform their contemporaries from monoethnic families; there is evidence to suggest that children from interethnic families have equal or even better access to learning opportunities [7]. In spite of this, some studies argue that there is no direct relationship between interethnic marriages and children’s academic performance. Instead, the deciding factors that are considered to influence the educational outcome are a child’s personal attributes and the extent of parental support and involvement in their children’s education [9]. In light of the existing literature, this paper proposes Hypothesis 1: there are cross-sectional variations in the adolescent developmental outcomes in terms of academic performance, cognitive ability, and physical health from different parental ethnicity compositions, and adolescents from interethnic families might experience better development due to their growing environments.
Establishing the mechanism of how parental ethnicity affects adolescent development requires numerous considerations. Conjectures and empirical evidence based on social capital theory, cultural capital theory, and human capital theories have long asserted that the socioeconomic background of the family and the extent of parental investment in their children are directly related to adolescent development and access to opportunities [11]. In the literature on this topic, poverty is considered to be a key driver of school dropout rates for ethnic minority children [12]. Children’s education is deemed a consumer product, and poor families cannot afford high education costs, while rich families spend more on their children’s education. Hannum [2002] uses a 1992 Chinese national sample survey to conclude that family poverty is a significant reason for the underperformance of ethnic minority children in school attendance and academic achievements compared to their Han contemporaries [7]. The mechanism of this effect is that ethnic minority children are more likely to drop out of school to earn an income. Furthermore, the level of parental educational attainment positively influences children’s educational performance. Parental investment in education is also positively correlated with children’s educational achievement. Research on the educational investment of ethnic minority children shows that, compared with Han families, ethnic minority families tend to invest less in their children’s education. Using a sample of school dropout rates in children from different ethnic backgrounds in Gansu Province, Sun and Xu [2010] propose that differences in ethnic cultures shape the parents’ perception of whether formal school education is important [13]. They find that parents from ethnic minority groups often view the learning of ethnic culture and receiving school education as mutually incompatible. Their perception is reinforced by local cultural practices specific to an ethnic group, leading to higher dropout rates of ethnic minority children in comparison to Han children. To explore whether such relationships exist, this paper proposes Hypothesis 2: parental educational expectations and education investment have a mediating effect between parents’ ethnic identity and adolescent educational performance.
Parental ethnicity may affect adolescent development through socioeconomic status (SES) and educational expectations. At the same time, variations in parental ethnic composition could also drive differences in the relationship between SES and educational expectation and adolescent growth outcomes. To further understand this heterogeneity and the complex paths of the relationship, this paper incorporates interaction terms to examine the moderating effects of differences in parental ethnic composition on the effect of family SES, parental educational expectations, and investment on children’s health and educational outcomes. The paper proposes Hypothesis 3: the ethnic composition of parents will modify the influence on adolescent development from family socioeconomic status, parental educational expectations, and parental investment.
## 3.1. Data
This article is based on data from the China Family Panel Studies (CFPS). The CFPS is a nationally representative survey conducted across 25 provinces or their administrative equivalents, including municipalities and autonomous regions across the country. The CFPS 2010 baseline survey data tracks family members and their gene members identified as of 2010, including their newborn and adopted children, using multistage probability proportional to size (PPS) sampling methods. The population sample in the survey represents approximately $95\%$ of the total population in China and is consistent with the demographic characteristics (such as age and gender) from the 2010 census [14].
As of 2022, there were five waves of published CFPS survey data spanning 2010, 2012, 2014, 2016, and 2018. For adolescent respondents aged from 10 to 15, the CFPS rotates two sets of aptitude tests across waves. The first set tests aptitude in literacy and mathematics and was used in waves 2010, 2014, and 2018. The second set tests memory and number series and was used in waves 2012 and 2016. Basic demographic characteristics, such as respondents’ height, weight, and age, were collected in each wave.
We use pooled cross-sectional data rather than panel data to analyze adolescents’ developmental outcomes, measured by academic performance, cognitive ability, and health. There are two reasons. First, we focus on adolescents aged between 10 and 15 only. The participants in the base year of 2010 were between 14 and 19 years old in the 2014 wave, and most of them were not in our target group in 2014. None of them were included in our study in the 2016 and 2018 waves. Second, as two sets of aptitude tests rotate across waves, there is a four-year gap between the same set of aptitude tests. Therefore, it is difficult to form appropriate panel data for our research purposes. Instead, we combine the five waves of published CFPS data based on information from the child questionnaire, which is used to merge with information on their parents and family collected in the adult, family, and household finance questionnaires, respectively. We only keep the most recent observation for the same adolescent. The steps for processing the data used in our study are summarized below.
Step 1: Data from each wave are processed separately. Each respondent in the child questionnaire is identified by a unique personal code ‘pid’ and also a family code ‘fid’, which allows for the child to be matched with the child’s parents, family, community, and household financial information collected in their respective and separate questionnaires. In the end, we obtain five separate waves of data with adolescents’ personal information, family, and socioeconomic status.
Step 2: The five waves of data from Step 1 are merged using the unique personal code ‘pid’ for each child from the child questionnaire. We match wave-by-wave, beginning with the 2010 wave as the baseline and merging it with the 2012 wave. For those adolescents with more than one observation, we only keep the most recent one. The process is repeated for all subsequent waves until information from all five waves is merged.
Step 3: The merged data are checked for errors and duplicates, and a pooled cross-sectional sample is formed. Given that the study requires data on parental ethnicity, as well as adolescent developmental characteristics, including academic performance, cognitive abilities, and physical health, respondents with missing or inappropriate information for these characteristics are removed from the sample, resulting in a total sample size of 4165 adolescents.
## 3.2.1. Parental Ethnicity
Parental ethnicity is determined by ethnicity information reported in the adult questionnaire matched to each adolescent by the child’s unique identifier and family identifier. In our study, we focused on three groups of adolescent households: households with monoethnic Han–Han parents ($86.1\%$), households with interethnic Han–Minority parents ($4.6\%$), and households with monoethnic Minority–Minority parents ($9.0\%$), as shown in Table 1. Interethnic marriages involving non-Han minorities also exist. However, the sample size of 16 observations of adolescents from such interethnic minority households is so small that we do not include them in the analysis.
## 3.2.2. Adolescent Development
Adolescents’ overall developmental status is measured by three factors: academic performance, cognitive development, and physical health. The CFPS has two sets of adolescent aptitude tests (literacy and mathematics vs. memory and number series) for respondents based on questions from the US Health and Retirement Survey (HRS). Existing research suggests that scores for literacy and mathematics tests capture crystallized intelligence, while scores on memory and number series tests capture fluid intelligence [15]. Therefore, we interpret CFPS scores on the literacy/word test (0–34 points) and mathematical test (0–24 points) to reflect adolescents’ crystallized intelligence and scores on the memory test (0–10 points) and number series test (0–15 points) to reflect fluid intelligence. Since crystallized intelligence tends to increase with age and years of schooling, we control for adolescents’ years of schooling in related analyses. On the other hand, fluid intelligence remains relatively stable over time. Physical health is measured by BMI z-scores following the WHO’s definition, in which adolescents with BMI below the 15th percentile are defined as underweight, those between the 15th and 85th percentiles as normal, those between the 85th and 95th percentiles as overweight, and those at or above the 95th percentile as obese. The obesity rate for children in China has been increasing rapidly since 2010, making childhood obesity a serious concern [16]. As a result, our analysis treats the obese and overweight categories as one group and treats the normal and underweight categories as the control group to shed light on the factors influencing overweight and obesity in adolescents.
## 3.2.3. Family Socioeconomic Status and Child Investment
Family socioeconomic status is a function of family income, parent’s employment status, parental education attainment, and hukou type. Family income is measured by the annual comparable gross household income from the CFPS data. Parental employment status is determined by whether the parents are working and whether they are engaged in agricultural work. In the CFPS adult questionnaire, question QG3 asks, “Do you currently have a job?” If the answer is affirmative, the corresponding response is recorded as “Yes”; if otherwise, it is recorded as “No”. The questionnaire further provides a binary response variable that records whether individuals are engaged in agricultural vs. non-agricultural work. The level of parental education attainment or years of schooling is measured by the maximum of either parent’s years of schooling. In China, a household’s hukou type is closely linked to a family’s socioeconomic status. According to the hukou (household registration) system, Chinese citizens have been registered with either agricultural (rural) or non-agricultural (urban) hukou status at a particular place since they were born. People with different hukou statuses are entitled to different social benefits and services. We, therefore, include the father’s and mother’s hukou status in our analysis of family socioeconomic characteristics. To assess parental investment, we use parental educational expectations and household spending on education as measures. Previous studies have used the attendance at junior college as the threshold to define whether parents have high or low expectations on their children’s educational outcomes [17]. We follow the literature in measuring parental expectations by using the CFPS response variable on whether the parents expect their children to obtain at least a junior college diploma. The CFPS also asks parents about the household’s total expenditure on their children’s education over the previous 12 months and whether their children attended after-school tutoring. The proportion of family income spent on education reflects the importance that parents place on their children’s education. We, therefore, use their responses to educational expenditure and after-school tutoring attendance to measure parental investment in their children’s education.
## 3.2.4. Population Demographics
We also construct basic demographic information on the adolescents, such as gender (male or female), age, family size, number of siblings, and regional characteristics (town or country), and labeled the year of the wave corresponding to when each observation is collected.
## 3.3. Statistical Analysis
Stata 16.0 is used for the data analysis. We analyze the relationship between parental ethnicity with adolescent development along three dimensions. First, we investigate whether the parental ethnic composition is directly related to adolescent development to explain variations in educational performance, cognitive development, and physical health among adolescents in order to address Hypothesis 1. Second, we examine whether family socioeconomic status and parents’ educational expectations and investment are potential mediating mechanisms that explain the relationship between parental ethnic composition and adolescent development in order to address Hypothesis 2. Third, regarding Hypothesis 3, we explore the role of parental ethnic composition as a potential moderator variable that affects the association between adolescent development and family socioeconomic status, parents’ educational expectations, and parental investment (see Figure 1).
The empirical analysis in this paper is therefore divided into three steps based on the theoretical framework and data characteristics:[1]Use the multiple linear regression model and the multinomial logit model to test the impact of parental ethnic composition on adolescent educational/cognitive development and health, respectively. With this methodology, we can control for factors such as family socioeconomic status and educational expectations in order to examine the robustness of the impact of ethnic composition on adolescent development. Before performing the regression analysis, the multicollinearity diagnosis is carried out on the explanatory variables. The variance inflation factor (VIF) of each variable is less than 10, indicating that there is no serious multicollinearity problem.[2]Use the mediation model to test whether family socioeconomic status, parents’ educational expectations and investment, and children’s health status are intervening variables that transmit mediating effects of parents’ ethnic composition on adolescents’ educational performance. We interpret model parameter estimates to evaluate the statistical significance and direction of the path coefficients.[3]Create interaction terms between parental ethnic composition with other explanatory variables to evaluate whether parental ethnic composition acts as a potential moderator that changes the effects of other explanatory variables on adolescent development.
## 4.1. Parental Ethnicity and Development of Adolescents: Comparisons between Adolescents of Different Parental Ethnicity
Table 2 shows the descriptive statistics of adolescent attributes across different parental ethnic compositions. Among adolescents from monoethnic Han–Han families (Han–Han adolescents), interethnic Han–Minority families (Han–Minority adolescents), and monoethnic Minority–Minority families (Minority–Minority adolescents), we observe heterogeneity in developmental measures. Education-related outcomes are reported in terms of the various aptitude test scores. Han–Han adolescents perform the best in literacy (Wordtest score), while Han–Minority adolescents rank highest in mathematics (Mathtest score); both groups are very close in both tests, with differences of only 0.18 points and 0.13 points in the Wordtest and Mathtest scores, respectively. Both scores for Minority–Minority adolescents are significantly lower. Their average Wordtest and Mathtest scores are 4.42 points and 1.76 points less than the Han–Han group and 4.24 points and 1.89 points less than the Han–Minority group. The Han–Minority adolescents also perform relatively well in fluid intelligence as measured by their memory and number series test scores, which differ from the Han–Han adolescents by only 0.24 points and 0.26 points but are 0.48 points and 1.85 points higher than Minority–Minority adolescents. Minority–Minority adolescents also differ from the other two groups in physical health, proxied by their BMI z-scores. The proportion of obese adolescents in Minority–Minority families is $5.52\%$ higher than those in Han–Han families, while the proportion of obesity in Han–Minority adolescents is $4.41\%$ higher than Han–Han adolescents.
There is also a disparity in family socioeconomic attributes. The average family income of Han–Minority families is 41,257 yuan, which is 5400 yuan less than Han–Han families but 10,311 yuan higher than Minority–Minority families. Han–Minority parents have the highest average years of education, with an average of 8.83 years, which is 0.11 years more than Han–Han parents and 2.86 years more than Minority–Minority parents. Han–Minority parents are similar to Han–Han parents in terms of the proportion of employment in non-agricultural jobs by either or both of the parents, which are $65.75\%$ and $65.66\%$, respectively; for the Minority–Minority group, the proportion is the lowest at $43.01\%$. Over a quarter of Han–Minority parents hold non-rural hukou by either or both parents, the highest proportion among the three groups. The proportion of Han–Han parents is slightly lower at $24.92\%$, while Minority–Minority parents hold the least at less than $10\%$.
All three groups have similar expectations of their children’s educational attainment. Han–Minority parents have the highest expectations, with $79.56\%$ expecting their children to obtain at least a junior college diploma. $76.41\%$ of Han–Han parents report similar expectations and only $71.77\%$ of Minority–Minority parents do so. Consistent with the higher expectations, Han–Minority parents also invest more in their children’s education, spending $10.76\%$ of the family income on related expenditures in the past 12 months; in comparison, related expenditures by Han–Han and Minority–Minority families are $8.39\%$ and $7.14\%$, respectively. More of the Han–Minority adolescents attend after-school tutoring ($24.86\%$) compared to the other two groups ($18.30\%$ of Han–Han adolescents and $5.38\%$ of Minority–Minority adolescents); attendance of the Minority–Minority group is notably low and less than a quarter of the Han–Minority group.
## 4.2.1. Parental Ethnicity and Adolescent Academic Performance
Columns 2 and 3 of Table 3 report the relationship between parental ethnicity and adolescent academic achievement. After controlling for family socioeconomic factors, educational expectations and expenditure, and adolescents’ individual characteristics, parental ethnicity is significantly and positively related to adolescent academic performance. The parental ethnic composition is associated with a p-value of less than 0.05 in explaining both literacy (Wordtest) and mathematics (Mathtest) scores. Han–Minority adolescents outperformed Minority–Minority adolescents on the literacy test by 2.2 points ($p \leq 0.001$) and the math test by 1.03 points ($p \leq 0.05$). Han–Han adolescents also scored significantly better on both tests than the Minority–Minority group. If the reference group is switched to the Han–Han adolescents, the differences between the Han–Han and Han–Minority groups become statistically insignificant, whereas the Minority–Minority adolescents scored significantly lower in both literacy and math scores.
Parental education attainment, non-agricultural employment, parental educational expectations for their children, and after-school tutoring attendance are all significantly and positively correlated with literacy and math test scores. This result indicates that higher socioeconomic status and higher parental investment, as reflected by educational expectations and education investment, have a positive and significant impact on adolescent academic performance.
Columns 4 and 5 of Table 3 show the relationship between parental ethnicity and adolescent cognitive development. In contrast to academic performance, there is no statistically significant difference in memory test scores between Han–Minority adolescents and Minority–Minority adolescents. Han–Han adolescents score 0.524 points higher in the memory test compared to Minority–Minority adolescents ($p \leq 0.001$). On the other hand, Han–Minority adolescents score 1.196 points higher in the number series test than the Minority–Minority group ($p \leq 0.001$). Han–Han adolescents also score higher than the Minority–Minority group. Most family socioeconomic factors and parental investment do not show a significantly strong relationship with memory test scores ($p \leq 0.1$), thus confirming the view that fluid intelligence is a cognitive ability that is a function of neural development. In contrast, family income, non-agricultural employment, non-rural hukou, educational expectations, and educational investment are all significantly and positively related to number series test scores, which also improve with age and years of education. Parental ethnicity clearly has a significant impact on adolescent fluid intelligence.
Column 6 of Table 3 presents the relationship between parental ethnicity and adolescent obesity. The incidence of obesity does not appear to be significantly different between Han–Minority adolescents compared to Minority–Minority adolescents. However, Han–Han adolescents exhibit a lower incidence at only $55\%$ of that of Minority–Minority adolescents ($p \leq 0.001$). When the reference group is switched to the Han–Han adolescents, there is no significant difference in the incidence of obesity between Han–Han vs. Han–Minority adolescents. Family socioeconomic attributes do not appear to be related to the incidence of adolescent obesity.
## 4.2.2. Mediating Role of Household Characteristic between Parental Ethnicity and Adolescent Development
We note that there exist significant differences in the household characteristics across the three groups of parental ethnicities, and family socioeconomic factors have a statistically significant relationship with adolescent developmental outcomes. As a result, we used the causal-steps approach to test whether family socioeconomic factors act as mediators between parental ethnic composition and adolescent development. We used bootstrapping to test the significance of the indirect effect to relax the Sobel test’s inherent assumption of normal distribution.
Table 4 presents the results of the mediation analysis. Given that socioeconomic factors, as well as educational expectation and investment, are not significantly related to the memory test score and the incidence of obesity ($p \leq 0.05$), we only show the mediating analysis for academic performance and number series test score. We find that the bias-corrected bootstrap confidence for the product of path coefficients of family income and share of family income spent on education contains zero. In contrast, those for the remaining family socioeconomic factors, as well as educational expectation and investment, do not contain zero, thus indicating that the mediation effect from these factors is significantly different from zero. As expected, parental ethnicity affects adolescent academic performance (literacy and math test scores) through family socioeconomic factors, as well as educational expectations and investment. In terms of number series test scores, which are a measure of fluid intelligence that is relatively stable over time and age, the effect of parental ethnicity on Han–Minority and Han–Han adolescents’ scores also operates via mediating pathways through the extent of parental education attainment and level of family income. For Han–Han adolescents, the mediating effect of whether parents have non-agricultural employment is not significant.
## 4.2.3. Moderating Role of Parental Ethnicity on the Development of Adolescent
Table 5 shows the results of the moderation test of parental ethnicity on the relationship between family socioeconomic status and adolescents’ academic performance. We add multiple interaction terms between parental ethnic composition and parents’ years of schooling, family income, non-agricultural work, and non-agricultural hukou to analyze the extent to which parental ethnic composition has a moderating effect on literacy and mathematics test scores. The results show that, for the literacy test (Wordtest), statistically significant coefficients are found on the interaction terms between Han–Han adolescents and parents’ years of schooling, non-agricultural work, and educational expectations, thus suggesting the existence of a significant moderation effect. Compared to adolescents from Minority–Minority families, the more years of schooling obtained by the parents of Han–Han families, the higher these Han–Han adolescents score on the word test. In contrast, being adolescents from Han–Han families weakens the positive influence that non-agricultural work and higher education expectation have on word test scores. This negative effect is also consistent in the set of results from the mathematics test scores (Mathtest), in which the coefficients of interaction terms for Han–Han parents vs. non-rural hukou, and Han–Han vs. education expectation are all significantly negatively, suggesting that having Han–Han parents reduces the positive effect of non-agricultural work and non-rural hukou on adolescents’ performance on the mathematics test. The findings are consistent with earlier work by Hong Yanbi [2010].
Table 6 reports the results of the moderation test of parental ethnicity on the relationship between family socioeconomic status and adolescents’ cognitive development. Based on the analysis on both the memory and number series tests, the interaction term between parental ethnicity and non-agricultural work is the only term to show statistical significance. For the memory test component, the coefficient on the Han–Han parents interacted with non-agricultural work is negative, suggesting that compared to the Minority–Minority group from families with at least one parent being employed in non-agricultural jobs, the general positive effect of non-agricultural work on adolescent memory ability is weakened for those with Han–Han parents. For the number series test, the interaction term of Han–Minority parents with non-agricultural work has a statistically significant and positive coefficient, indicating that parental ethnicity positively moderates the effect of non-agricultural work on adolescents’ numerical logic. Specifically, compared to the Minority–Minority group, adolescents from Han–Minority families for which at least one parent holds non-agricultural work have better numerical logic results. Since we do not find statistically significant relationships connecting family socioeconomic factors with adolescent obesity rates, a moderation test along this dimension is not conducted.
## 5. Conclusions
Using data from China’s nationally representative CFPS survey across five waves, this study conducted a set of empirical analyses on the influence of parental ethnicity composition on adolescent development, as well as the underlying mechanism. By exploring the differences in the developmental outcomes of adolescents from monoethnic families with Han–Han parents, interethnic families with Han–Minority parents, and monoethnic families with Minority–Minority parents, this study established that, after controlling for differences in family socioeconomic factors and individual characteristics, there are significant associations between parental ethnicity composition and adolescent academic performance, cognitive development, and health. Adolescents from families with interethnic Han–Minority parents perform better in literacy, mathematics, and number series tests in comparison to adolescents from families with interethnic Minority–Minority parents. Adolescents from interethnic Han–Minority families have similar educational outcomes as those from monoethnic Han–Han families.
This study contributes to existing knowledge and practice in three aspects. First, the analysis of the impacts of the ethnicity of both parents as an important family characteristic on adolescent educational performance provides depth and breadth to existing research that focuses only on the ethnicity of adolescents. Adopting the perspective of the family and parental ethnicity beyond the adolescent’s individual characteristics allows for a more robust investigation into how a range of attributes associated with different ethnic groups can influence adolescent development. Second, the study explores the pathways between parental ethnicity and adolescent development for a detailed analysis of the direct and indirect effects. We find that parental ethnicity has moderating effects on some family socioeconomic factors, as well as parental education expectations and investment. We further show that the heterogeneity in developmental outcomes of adolescents from families with different parental ethnicity compositions could be partly explained by indirect mediating influences from differences in family socioeconomic status, parental education expectations, and parental investment in children’s education. Third, our findings can be applied in practice with important policy implications. Interethnic marriages are proliferating in numbers. By understanding the factors that influence the educational performance of adolescents from families with different ethnic compositions and delineating the pathways of such influences, we can better appreciate the effects of interethnic marriages on adolescent development, as we find adolescents from interethnic Han–Minority families have better academic performance and cognitive development. On the other hand, our results indicate that more efforts are required to support adolescents from monoethnic Minority–Minority families, whose relative family socioeconomic status, as well as parental expectation and investment, is weaker compared to that of the other two groups. Notably, either or both parents being employed in non-agricultural jobs has a positive impact on adolescent educational performance. Policy initiatives can target family socioeconomic status and parental investment to support and enhance the developmental outcomes of adolescents from ethnic minority families. Efforts should be made to accelerate the economic and social development in ethnic minority areas and to improve the quality of life for minority families. It would be useful to develop policy initiatives aimed at improving the non-agricultural employment rate of ethnic minorities, which would improve their household income. Moreover, the government should promote the education programs for ethnic minorities and pay more attention to the cultivation of talents among ethnic minorities.
Our study advances the existing understanding of the relationship between parental ethnicity and adolescent development, but it has limitations. Firstly, our sample population is restricted to adolescents aged 10 to 15 who are the target group in the CFPS survey. As a result of the relatively narrow age range, the sample cannot represent adolescents of all age groups in China. Secondly, this study treats ethnic minorities as an aggregate group, given the limited sample size. The conclusions drawn, therefore, reflect the average effects on the general ethnic minority population, while we keep in mind that there exist differences in religious beliefs, cultural values, and socioeconomic status among different ethnic minority groups in China. Future research could explore the impacts of such differences among different ethnic minorities on adolescent development. In addition, there are large disparities in the population size across different ethnic minority groups. The sample of ethnic minorities in this study may not be nationally representative of all ethnic minorities. Finally, while two different assessment outcomes are used to measure academic performance and cognitive development to ensure a more comprehensive measurement of adolescent developmental outcomes, health is measured only through adolescent obesity. Future research can provide a more systematic and detailed analysis of this dimension by incorporating both physical and mental health attributes.
## References
1. Hannum E., Xie Y.. **Ethnic Stratification in Northwest China: Occupational Differences between Han Chinese and National Minorities in Xinjiang, 1982–1990**. *Demography* (1998) **35** 323-333. DOI: 10.2307/3004040
2. Hong Y.. **Home Language and Educational Attainments of Ethnic Minorities in Western China**. *Chin. Educ. Soc.* (2010) **43** 24-35. DOI: 10.2753/CED1061-1932430102
3. Wu X.. **Continuty and Change: A Preliminary Study on the Development of Minority Population from Seventh Census**. *Popul. Dev.* (2022) **28** 127-139
4. Lyu L.. **Ethnic Children’s Demographics, HouseholdCharacters and Education Status**. *Popul. Dev.* (2016) **22** 83-93
5. Huang F., Duan C., Bi Z.. **Ten Major Trends in Interethnic Marriages in China Since the Reform**. *Popul. Res.* (2022) **46** 20-35
6. Yuan T.. **Chen Batell On the Research Orientation of School Ethnography, A Theoretical Discussion on the Reasons of Minority Children’s Academic Success and Failure**. *Ethn. Educ. Res.* (2007) **18** 5-14. DOI: 10.15946/j.cnki.1001-7178.2007.04.011
7. Hannum E.. **Educational Stratification by Ethnicity in China: Enrollment and Attainment in the Early Reform Years**. *Demography* (2002) **39** 95-117. DOI: 10.1353/dem.2002.0005
8. Du J.. **Looking at ethnic blending and development from the perspective of interethnic marriages**. *J. South-Cent. Univ. Natl. (Humanit. Soc. Sci. Ed.)* (2018) **38** 17-20
9. Chen G., Liu S., Zhu Q.. **Research on the physical characteristics of college students from interethnic Mongolian and Han families**. *J. Anthropol.* (1992) **6** 171-175. DOI: 10.16359/j.cnki.cn11-1963/q.1992.02.010
10. Zhong M.. **The Impact of Interethnic Marriages on Ethnic Minorities with Smaller Population Sizes, The Case of the Yugur Ethnic Minority**. *J. South Cent. Minzu Univ. (Humanit. Soc. Sci. Ed.)* (2012) **32** 38-43. DOI: 10.19898/j.cnki.42-1704/c.2012.02.009
11. Lu M., Cui M., Shi Y., Chang F., Mo D., Rozelle S., Johnson N.. **Who Drops out from Primary Schools in China? Evidence from Minority-Concentrated Rural Areas**. *Asia Pac. Educ. Rev.* (2016) **17** 235-252. DOI: 10.1007/s12564-016-9421-1
12. Yang Y., Wang H., Zhang L., Sylvia S., Luo R., Shi Y., Wang W., Rozelle S.. **The Han-Minority Achievement Gap, Language, and Returns to Schools in Rural China**. *Econ. Dev. Cult. Chang.* (2015) **63** 319-359. DOI: 10.1086/679070
13. Baicai S., Jingjian X.. **Why Ethnic Minority Children Are More Likely to Drop Out of School: A Cultural Capital Perspective: Evidence from Ethnic Minority Rural Communities in the Northwest**. *Chin. Educ. Soc.* (2010) **43** 31-46. DOI: 10.2753/CED1061-1932430502
14. Xie Y., Hu J., Zhang C.. **Tracking Survey of Chinese Families, Concept and Practice**. *Society* (2014) **34** 1-32. DOI: 10.15992/j.cnki.31-1123/c.2014.02.003
15. Huang G., Xie Y.. **The Impact of Cognitive Ability and Non-cognitive Ability on the Youth’s Labor Income Returns**. *Chin. Youth Stud.* (2017) **252** 56-64. DOI: 10.19633/j.cnki.11-2579/d.2017.02.009
16. Zhang N., Ma G.. **Interpretation of “China Childhood Obesity Report”**. *Nutr. J.* (2017) **39** 530-534. DOI: 10.13325/j.cnki.acta.nutr.sin.2017.06.005
17. Liu B., Zhang Y., Li J.. **Socioeconomic Status, Cultural Concepts and Family Educational Expectations, Youth Studies**. *JCEPS* (2014) **399** 46-55
|
---
title: 'Indigenous Eye Health in the Americas: The Burden of Vision Impairment and
Ocular Diseases'
authors:
- João Marcello Furtado
- Arthur Gustavo Fernandes
- Juan Carlos Silva
- Sandra Del Pino
- Carolina Hommes
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10000964
doi: 10.3390/ijerph20053820
license: CC BY 4.0
---
# Indigenous Eye Health in the Americas: The Burden of Vision Impairment and Ocular Diseases
## Abstract
Review of the burden of vision impairment and blindness and ocular disease occurrence in Indigenous Peoples of the Americas. We systematically reviewed findings of the frequency of vision impairment and blindness and/or frequency of ocular findings in Indigenous groups. The database search yielded 2829 citations, of which 2747 were excluded. We screened the full texts of 82 records for relevance and excluded 16. The remaining 66 articles were examined thoroughly, and 25 presented sufficient data to be included. Another 7 articles derived from references were included, summing a total of 32 studies selected. When considering adults over 40 years old, the highest frequencies of vision impairment and blindness in Indigenous Peoples varied from $11.1\%$ in high-income North America to $28.5\%$ in tropical Latin America, whose rates are considerably higher than those in the general population. Most of the ocular diseases reported were preventable and/or treatable, so blindness prevention programs should focus on accessibility to eye examinations, cataract surgeries, control of infectious diseases, and spectacles distribution. Finally, we recommend actions in six areas of attention towards improving the eye health in Indigenous Peoples: access and integration of eye services with primary care; telemedicine; customized propaedeutics; education on eye health; and quality of data.
## 1. Introduction
Vision impairment and blindness are estimated to affect more than 339 million people worldwide, with 43.3 million people blind and 295.3 million people having moderate to severe visual impairment (MSVI), representing a prevalence of 5.25 cases of blindness per 1000 persons ($95\%$ CI: 4.58–5.87) and 35.8 cases of MSVI per 1000 persons ($95\%$ CI: 32.4–39.2) [1]. Cataract, glaucoma, under-corrected refractive errors, age-related macular degeneration, and diabetic retinopathy are the main causes of blindness, while the main causes of MSVI are uncorrected refractive errors, cataract, age-related macular degeneration, glaucoma, and diabetic retinopathy [2]. In the Americas, the estimates vary substantially across the different Global Burden of Disease (GBD) regions, with blindness estimates ranging from 1.93 cases per 1000 people in southern Latin America (i.e., Argentina, Chile, and Uruguay) to 7.40 cases per 1000 in tropical Latin America (i.e., Brazil and Paraguay) [1].
Most global estimates, however, do not include data from Indigenous Peoples and other ethnic groups, even though those groups are expected to present higher frequencies of ocular diseases and vision loss [3,4,5]. As a result, the burden of vision impairment and blindness may be underestimated, and the public health policies derived from it may insufficiently attend the demand in those minority groups. Including those groups in population-based sample sizes is often challenging due to the low number of individuals in comparison with the overall population and/or due to the low response from those specific groups even when they are included in the sampling [6,7]. Developing and implementing services designed to prioritize reaching groups in situations of vulnerability, as with Indigenous Peoples, with quality and affordable eye services was recently listed as one of the main challenges in global eye health [8].
Indigenous individuals can, on certain occasions, be considered one of the most disadvantaged and marginalized populations worldwide [9]. A recent systematic review of vision loss among Indigenous populations has shown a lack of data on the burden of vision loss in most countries and has pointed out the importance of improving the quality and number of research about eye health and eye care in Indigenous communities [10]. Different Indigenous groups from different nations have unique characteristics in language, culture, environmental risk factors, and political autonomy, yet, as a result of the colonization process, many face similar health disparities and social disadvantages [11]. Indigenous groups currently account for around $17\%$ of those living in extreme poverty in Latin America, even though they represent less than $8\%$ of the population [12].
It is estimated that in 2010 there were at least 44.8 million Indigenous persons in Latin America, representing 826 Indigenous Peoples mainly concentrated in Mexico (seventeen million people) and Peru (seven million), followed by Guatemala and Bolivia (six million in each) [13]. While they are the majority of the population in Bolivia ($62\%$) and Guatemala ($60\%$), they represent less than $2\%$ in Brazil, Colombia, Venezuela, and the Caribbean [14,15]. In the United States (USA), 6.6 million Alaskan Natives and Native American Indians ($2\%$ of the general population) live in 567 tribes, and 326 Indian reserves are officially recognized by the federal government [16]. In Canada, $5\%$ of the total population is identified as Indigenous, summing 1.8 million individuals from First Nations, Métis, and Inuit groups [17].
The purpose of the current study is to conduct a review of the burden of vision impairment and blindness and ocular disease occurrence in the Indigenous Peoples of the Americas while comparing it to the estimates based on non-indigenous populations and identifying gaps in the literature.
## 2. Materials and Methods
We systematically reviewed findings on frequencies of vision impairment and blindness or frequencies of ocular findings such as cataract, under-corrected refractive errors, glaucoma, age-related macular degeneration, diabetic retinopathy, pterygium, trachoma, and onchocerciasis in Indigenous populations in Americas. We searched for any study evaluating eye health not limiting the sources to population-based data. The search combined terms related to three concept areas: population (Indigenous), outcome (vision impairment/blindness and ocular findings), and study site (the Americas). Term selection was based on previous systematic reviews and combined key terms adapted for each database and medical subject headings (MeSH) as applicable. We searched for studies in any language, indexed from 1 January 2000 to 1 November 2022.
We screened the selected papers in terms of [1] reporting frequencies of vision impairment or blindness or frequencies of ocular diseases; [2] reporting results for an indigenous population; and [3] reporting data from populations resident in any region of the Americas. We excluded articles that did not include an Indigenous group, were iterations, were program evaluations or experimental studies, not primary studies, were from the gray literature, or used identical data sources as prior studies. Because many studies on Indigenous Peoples have not reported response rates, we did not impose any minimum response rate limit. Self-reported outcome data were not included.
The following information was extracted from each selected study: author, year of publication, country, Indigenous group, study design, sample size, individuals age, main outcome, method for visual acuity and definitions for vision impairment and blindness, frequency of vision impairment and blindness, and/or frequency of ocular diseases. The results were presented separately according to the GBD regions classification (Table 1).
We presented the results as descriptive tables for frequencies of vision impairment and blindness and for frequencies of ocular diseases in the population. As most of the studies adopted different criteria for definitions of vision impairment and blindness and varied the measurement method (i.e., uncorrected, presenting vision, and best-corrected vision acuity), we could not standardize estimates and summarize the findings per region and therefore presented descriptive data along with the specificities of each estimate.
## 3. Results
The database search yielded 2829 citations, of which 2747 were excluded. We screened the full texts of 82 records for relevance and excluded 16. The remaining 66 articles were examined thoroughly, and 25 presented sufficiently data to be included in the current review. Another 7 articles derived from references were included, summing a total of 32 studies selected. Figure 1 shows the flowchart of records selection.
Out of the 32 selected studies, 14 ($43.75\%$) were conducted in tropical Latin America (13 in Brazil and 1 in Paraguay), 12 ($37.50\%$) in high-income North America (8 in the USA and 4 in Canada), 4 (12.50) in central Latin America (2 in Colombia, 1 in Mexico, and 1 in Venezuela), 1 ($3.12\%$) in Andean Latin America (Ecuador), and 1 ($3.12\%$) in the Caribbean (Haiti). No studies from southern Latin America were included.
A total of 11 studies ($34.37\%$) reported frequencies of vision impairment and blindness, with most of them from high-income North America. No studies from Andean Latin America, the Caribbean, or southern Latin America presented data on vision impairment and blindness. A great variability of vision acuity measurement methods, as well as vision impairment and blindness definitions, was observed. Table 2 shows the frequencies of vision impairment and blindness according to the GBD region along with study population Indigenous group and age, and categories’ definitions.
Despite the differences in the vision impairment and blindness definitions, it is a clear significant difference in the frequencies between high-income North America and tropical Latin American countries. When considering adults over 40 years old and the BCVA method, the highest frequencies of vision impairment and blindness in high-income North *America sum* $11.1\%$ [27] while in the tropical Latin America it can reach $28.5\%$ [19].
A total of 26 studies ($81.25\%$) reported frequencies of ocular diseases, with most of them from tropical Latin America and high-income North America. Trachoma was the main condition evaluated, discussed in nine studies ($34.61\%$), with six in tropical Latin America and three in Central America. Cataract was evaluated in seven studies ($26.92\%$), three in high-income North America, three in tropical Latin America, and one in the Caribbean. Interestingly, the six studies evaluating diabetic retinopathy ($23.07\%$) were from high-income North America. Pterygium was evaluated in five studies ($19.23\%$), with four from tropical Latin America and one from the Caribbean. Table 3 shows the frequencies of ocular diseases according to the GBD region along with study population Indigenous group and ages.
## 4. Discussion
This study presents an overall panorama of the ocular health in Indigenous Peoples in the America. The main limitation, however, is the shortage of data. The low number of records retrieved from our literature review reflects the scarcity of studies focused on eye health in Indigenous populations in the Americas. Out of the 33 countries in the Americas, only 7 ($21\%$) had data on vision impairment/blindness and/or ocular disease in Indigenous groups. The lack of studies is particularly more evident in Andean Latin America, where a high percentage of the population self-identify as Indigenous and yet is underrepresented [14]. No studies were found for southern Latin America, which is the sub-region with the lowest frequencies of Indigenous Peoples in the general population. The most recent worldwide estimates of vision impairment and blindness, however, have included data from most countries in the Americas, indicating availability of population-based surveys and therefore reinforcing the misrepresentation of Indigenous Peoples in these calculations. While part of these studies might have included Indigenous groups in their samples, most of them have used the RAAB (Rapid Assessment of Avoidable Blindness) methodology, which is a format that does not disaggregate information on ethnicity further limiting the analysis of burden of disease in Indigenous populations specifically and the comparisons between Indigenous and non-indigenous groups [50].
Most studies on frequency of vision impairment and blindness were conducted in high-income North America. According to the GBD, the prevalence of moderate to severe vision impairment (MSVI: VA < $\frac{20}{63}$ to VA ≥ $\frac{20}{400}$) and blindness (VA < $\frac{20}{400}$) in the general population aged 50 years and older in the region was $3.28\%$ and $0.40\%$, respectively [1]. Despite the different criteria for classification, the frequency of vision impairment and blindness in the Indigenous populations evaluated were higher than those presented by the GBD, with values in older adults ranging from $3.10\%$ [28] to $12.80\%$ [25] for vision impairment and $0.30\%$ [28] to $1.90\%$ [27] for blindness.
Tropical Latin *America is* one of the sub-regions with the highest estimated rates of MSVI ($10.60\%$) and blindness ($2.71\%$) in older adults in the Americas [1]. A recent study performed with residents from the Xingu Indigenous Park in Brazil following the same GDB criteria of classification has shown frequencies of MSVI and blindness substantially higher than those calculated for the general population, reaching $22.58\%$ and $5.92\%$, respectively, in adults 45 years and older [19].
The only study from central Latin America evaluated individuals 20 years and older in Mexico and found a prevalence of presenting vision acuity <$\frac{20}{60}$ in $10\%$ of the population [18]. The estimates for MSVI and blindness considering best-corrected vision acuity in adults 50 years and older in the region were $10.70\%$ and $1.83\%$ [1], but due to the different criteria of measurement and definitions, we are not able to make direct comparisons.
*The* general estimates of vision impairment and blindness for Andean Latin America (MSVI: $13.00\%$; blindness: $2.20\%$), the Caribbean (MSVI: $8.22\%$; blindness: $1.74\%$), and southern Latin American (MSVI: $6.59\%$; blindness: $0.66\%$) could not be compared to Indigenous Peoples due to the lack of studies on these groups in those specific countries [1].
In 2020, cataract and under-corrected refractive error composed $50\%$ of all global blindness and $75\%$ of all global MSVI [2]. Other causes included glaucoma, age-related macular degeneration, and diabetic retinopathy, being the five conditions mostly studied in the general population. Diabetic retinopathy was the smallest contributor to blindness in 2020 among those, however, it was the only cause of blindness that showed a global increase in prevalence from 1990 to 2020, particularly in the high-income North America sub-region [2]. While the data retrieved from studies using Indigenous populations cover extensive age ranges and do not necessarily represent the disease frequency or the cause of MSVI and blindness, a differential pattern of disease focus is observed among the sub-regions. While $66.7\%$ of the studies from high-income North America have presented data on diabetic retinopathy, none of the studies from the other region have evaluated this condition.
The cataract rates in older adults, regardless of vision acuity status, have varied from $12.2\%$ in Northwestern and Alaskan Natives in the USA [28] to $54.5\%$ in groups from the Xingu Indigenous Park in Brazil [19]. These values are sensitive to the population’s access to cataract surgeries, which may explain the high frequency of disease in Indigenous populations with limited access to specialized eye health services. Few studies evaluated refractive errors, with rates reaching up to $62\%$ in Brazilian communities [43]. The effective cataract surgical coverage (eCSC) and the effective refractive error coverage (eREC) are indicators requested by the WHO in order to meet the 2030 Sustainable Development Goals [51]. eCSC refers to the proportion of people who have received cataract surgery and have a resultant good quality outcome relative to the number of people in need of cataract surgery [52]. Similarly, eREC refers to the proportion of people who have received refractive correction and have a resultant good quality outcome relative to the number of people in need of refractive correction [53]. These indicators are ideal to not only track changes in the uptake and quality of eye care services, but also to contribute to monitoring progress towards universal health care in general [54]; however, none of the studies using Indigenous populations in the Americas have reported eCSC or eREC. A previous analysis of *Indigenous versus* non-indigenous groups in Australia has shown that eCSC was significantly better in non-indigenous Australians than in Indigenous Australians ($88.5\%$ vs. $51.6\%$) [55].
Pterygium is a condition commonly evaluated in the studies as its occurrence is associated with geographic locations characterized by low latitude and high ultraviolet exposure. In that sense, studies from the Caribbean, central and tropical Latin America have reported frequencies from $12.8\%$ [40] to $27.1\%$ [43]. The population profile is a determinant for pterygium development, so people who have an outdoor lifestyle tend to be more likely to develop the disease due to the direct UV exposure. The disease is also highly prevalent in non-indigenous populations in equatorial areas with prevalence reaching up to $58.8\%$ [56].
Ocular infectious diseases are highly associated with living style, access to clean water, and basic sanitation, and therefore can be highly prevalent in Indigenous communities [57]. Trachoma and onchocerciasis were evaluated in $73\%$ of the studies from central and tropical Latin America reflecting the concern about such conditions in these regions. Onchocerciasis was identified in two studies in Brazil, affecting up to $68.6\%$ of a Yanomami community [35]. Trachoma was identified in both central and tropical Latin America with frequencies ranging from $6.9\%$ [31] to $41.8\%$ [34]. Moreover, one study in Brazil evaluated parasitic keratitis in Arawak, Tukano, and Maku peoples finding a frequency of $17.2\%$ [43].
Historically, onchocerciasis was formerly prevalent in 13 foci in Brazil, Colombia, Ecuador, Guatemala, Mexico, and Venezuela [58]. In response, the Pan American Health Organization (PAHO) established the Onchocerciasis Elimination Program for the Americas (OEPA) in 1992 with the main purpose to guide countries to achieve the goal of eliminating onchocerciasis in Latin America [59]. *In* general, the strategy included six-monthly mass administration of ivermectin (Mectizan®, Merck & Co. Inc., Rahway/NJ, USA) with coverage equal to or higher than $85\%$ of the eligible population [59]. The onchocerciasis elimination program in Latin American countries has been ongoing since 1996 [60]. To date, onchocerciasis transmission has been eliminated from 11 of the 13 previously endemic disease foci in Latin America, and four out of six endemic countries have been verified as eliminated by PAHO (Colombia, Ecuador, Guatemala, and Mexico) [61].
Trachoma is the world’s leading infectious cause of blindness and is endemic in several parts of the world [62]. Mexico was the first country in the Americas to eliminate trachoma as a public health problem, as validated by PAHO in 2017, but this is still a concern in four countries in Andean, central, and tropical Latin America: Brazil, Colombia, Guatemala, and Peru [63,64]. PAHO/WHO support countries to implement the SAFE strategy (i.e., surgery, antibiotics, facial cleanliness, and environmental improvement), a program that consists of surgery to treat advanced trachoma (trichiasis), antibiotics (azithromycin) to clear the agent of infection, facial hygiene, and environmental improvements to reduce transmission from one person to another [63]. While the strategy adherence might be more challenging in Indigenous communities, the example from Mexico reinforces the importance of partnership with local leader authorities who will enhance the population’s trust in the program and improve the outcomes [64].
Other conditions observed in the reviewed studies include glaucoma and under-corrected refractive errors. Glaucoma was present in a relatively small proportion of the populations of Brazil and the USA but at a high frequency of $19.1\%$ in Haiti [30]. The high frequency of glaucoma in Haiti could be influenced by the nonvariation in race and the higher environment temperature [30,65]. *In* general, the high rates of cataract and under-corrected refractive errors reflect the poor access of the Indigenous populations to specialized care. The access is likely associated with education and economic status, which are factors that could not be evaluated in the current revision due to the lack of information in the selected studies [66,67].
There are significant disparities in the number and distribution of ophthalmologists in American countries as they tend to be concentrated in more developed cities, leaving remote areas, where most Indigenous Peoples are concentrated, with a low density of ophthalmologists [66]. Due to a lack of access to and utilization of eye care services, Indigenous Peoples in the Amazon may combine several social determinants of blindness and visual impairment, such as ethnicity, place of residence (rural remote areas), socioeconomic status (poverty), and education (low levels of schooling). In Guatemala, with a high percentage of Indigenous population and high prevalence of blindness [67], the determinant “place of living” might not be as important as in the Amazon, but others are present among Indigenous groups. More recently, social, political, and economic crises have motivated intense migratory movements and refugee requests in Latin American countries, with an increasing number of Indigenous individuals living in public or self-managed shelters or even on the street in extreme poverty. These conditions represent an extra challenge to address, not only visual, but the general health care needs of such groups [68,69,70].
Improving Indigenous eye health in the *Americas is* particularly challenging and mainly due to limited access and inequalities in care. More than achieving universal health coverage in a country, equity should be prioritized, otherwise, socially advantaged groups will be more likely to use the new or improved services [71,72]. Specific actions include the following: [1] access: increasing the number of clinic sites, rural locations, and eye care sessions, not only with ophthalmologists, but also with other eye health practitioners as optometrists, ophthalmic technologists, and/or trained nurses should improve the number of patient seen, dispensing spectacles, and surgery referrals [72,73]; [2] integration with family medicine/primary care: several communities have general health programs with systemic condition screening and could include ocular health screening tools into their practice to detect and timely refer cases of vision impairment and blindness for specialized care [19,72,74]; [3] telemedicine: several telemedicine protocols in ophthalmology focused on diabetes retinopathy, glaucoma, and cataract have been shown to be effective in populations living in remote areas and should be used as models towards Indigenous population groups [75,76,77]; [4] customized propaedeutics: specific techniques should be indicated to populations living in remote areas, for example, manual small incision cataract surgery (MSICS) techniques in resource-constrained health care settings such as Indigenous communities [78]; [5] education on eye health: by promoting basic knowledge on eye health, the population can better understand the importance of seeking timely treatment, improving visual outcomes [79,80]; [6] quality data: more studies focused on Indigenous population’s eye health should be performed with appropriate methodology and collection of key indicators such as eCSC and eREC, and studies performed in the general population should collect data on the participants’ ethnicity/race [52,53].
## 5. Conclusions
Despite the shortage of data, our findings show a higher frequency of vision impairment and blindness in the Indigenous population when compared to worldwide estimates for all sub-regions in the Americas. Most of the ocular diseases reported are preventable and/or treatable, so blindness prevention programs should focus on accessibility to eye examinations, cataract surgeries, control of infectious diseases, and spectacles distribution. Finally, more epidemiological studies with Indigenous populations using higher methodologic quality and consistent indicators are recommended in order to understand the burden of diseases and optimize developed programs focused on these groups.
## References
1. **Vision Loss Expert Group of the Global Burden of Disease Study. Trends in prevalence of blindness and distance and near vision impairment over 30 years: An analysis for the Global Burden of Disease Study**. *Lancet Glob. Health* (2021.0) **9** e130-e143. DOI: 10.1016/S2214-109X(20)30425-3
2. **Vision Loss Expert Group of the Global Burden of Disease Study. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: The right to sight: An analysis for the Global Burden of Disease Study**. *Lancet Glob. Health* (2021.0) **9** e144-e160. PMID: 33275949
3. Asleh S.A., Chowers I.. **Ethnic background as a risk factor for advanced age related macular degeneration in Israel**. *Isr. Med. Assoc. J.* (2007.0) **9** 656-658. PMID: 17939627
4. Gilbert C.E., Shah S.P., Jadoon M.Z., Bourne R., Dineen B., Khan M.A., Johnson G.J., Khan M.D.. **Poverty and blindness in Pakistan: Results from the Pakistan national blindness and visual impairment survey**. *BMJ* (2008.0) **336** 29-32. DOI: 10.1136/bmj.39395.500046.AE
5. Fisher D.E., Shrager S., Shea S.J., Burke G.L., Klein R., Wong T.Y., Klein B.E., Cotch M.F.. **Visual impairment in white, Chinese, black, and Hispanic participants from the Multi-Ethnic Study of Atherosclerosis cohort**. *Ophthalmic Epidemiol.* (2015.0) **22** 321-332. DOI: 10.3109/09286586.2015.1066395
6. Redwood S., Gill P.S.. **Under-representation of minority ethnic groups in research–call for action**. *Br. J. Gen. Pract.* (2013.0) **63** 342-343. DOI: 10.3399/bjgp13X668456
7. Gill P.S., Plumridge G., Khunti K., Greenfeld S.. **Under-representation of minority ethnic groups in cardiovascular research: A semi-structured interview study**. *Fam. Pract.* (2013.0) **30** 233-241. DOI: 10.1093/fampra/cms054
8. Ramke J., Evans J.R., Habtamu E., Mwangi N., Silva J.C., Swenor B.K., Congdon N., Faal H.B., Foster A., Friedman D.S.. **Grand Challenges in global eye health: A global prioritisation process using Delphi method**. *Lancet Healthy Longev.* (2022.0) **3** e31-e41. DOI: 10.1016/S2666-7568(21)00302-0
9. Stephens C., Porter J., Nettleton C., Willis R.. **Disappearing, displaced, and undervalued: A call to action for indigenous health worldwide**. *Lancet* (2006.0) **367** 2019-2028. DOI: 10.1016/S0140-6736(06)68892-2
10. Foreman J., Keel S., van Wijngaarden P., Bourne R.A., Wormald R., Crowston J., Taylor H.R., Dirani M.. **Prevalence and causes of visual loss among the indigenous peoples of the world: A systematic review**. *JAMA Ophthalmol.* (2018.0) **136** 567-580. DOI: 10.1001/jamaophthalmol.2018.0597
11. Gracey M., King M.. **Indigenous health part 1: Determinants and disease patterns**. *Lancet* (2009.0) **374** 65-75. DOI: 10.1016/S0140-6736(09)60914-4
12. **Policy on Ethnicity and Health**
13. **Pan-American Health Organization Strategy and Plan of Action on Ethnicity and Health (PAHO, 2019–2025)**
14. Montenegro R.A., Stephens C.. **Indigenous health in Latin America and the Caribbean**. *Lancet* (2006.0) **367** 1859-1869. DOI: 10.1016/S0140-6736(06)68808-9
15. Barreto S.M., Miranda J.J., Figueroa J.P., Schmidt M.I., Munoz S., Kuri-Morales P.P., Silva J.B.. **Epidemiology in Latin America and the Caribbean: Current situation and challenges**. *Int. J. Epidemiol.* (2012.0) **41** 557-571. DOI: 10.1093/ije/dys017
16. **American Indians by the Numbers from the U.S**
17. **Component of Statistics Canada Catalogue No. 11-001-X**
18. Corona A.J., Corona M.E.J., Ponce-de-Leon S., Chavez-Rodriguez M., Graue-Hernandez E.O.. **Social Determinants and Their Impact on Visual Impairment in Southern Mexico**. *Ophthalmic Epidemiol.* (2015.0) **22** 342-348. DOI: 10.3109/09286586.2014.949009
19. Fernandes A.G., Alves M., Nascimento R.A.E., Valdrighi N.Y., de Almeida R.C., Nakano C.T.. **Vision impairment and blindness in the Xingu Indigenous Park-Brazil**. *Int. J. Equity Health* (2021.0) **20** 197. DOI: 10.1186/s12939-021-01536-w
20. Carter M.J., Lansingh V.C., Schacht G., Río del Amo M., Scalamogna M., France T.D.. **Visual acuity and refraction by age for children of three different ethnic groups in Paraguay**. *Arq. Bras. Oftalmol.* (2013.0) **76** 94-97. DOI: 10.1590/S0004-27492013000200008
21. Salum T.G.B., Rodrigues M.L.V.. **Saúde ocular dos povos indígenas do Brasil**. *Medicina* (2012.0) **49** 265-272
22. Redher J.R., Sobral Neto H., Carvalho F., Lima V.L., Pereira R., Barreiro J., Angelucci R.I.. **Prevalência e causas de cegueira e baixa acuidade visual entre grupos indígenas da Amazônia Legal**. *Arq. Méd. ABC* (2001.0) **25** 59-62
23. Woodward M.A., Hughes K., Ballouz D., Hirth R.A., Errickson J., Newman-Casey P.A.. **Assessing Eye Health and Eye Care Needs Among North American Native Individuals**. *JAMA Ophthalmol.* (2022.0) **140** 134-142. DOI: 10.1001/jamaophthalmol.2021.5507
24. Aljied R., Aubin M.J., Buhrmann R., Sabeti S., Freeman E.E.. **Prevalence and determinants of visual impairment in Canada: Cross-sectional data from the Canadian Longitudinal Study on Aging**. *Can. J. Ophthalmol.* (2018.0) **53** 291-297. DOI: 10.1016/j.jcjo.2018.01.027
25. McClure T.M., Choi D., Becker T., Cioffi G.A., Mansberger S.L.. **The effect of visual impairment on vision-related quality of life in American Indian/Alaska Natives**. *Ophthalmic Epidemiol.* (2009.0) **16** 128-135. DOI: 10.1080/09286580902745428
26. Harvey E.M., Dobson V., Miller J.M.. **Prevalence of high astigmatism, eyeglass wear, and poor visual acuity among Native American grade school children**. *Optom. Vis. Sci.* (2006.0) **83** 206-212. DOI: 10.1097/01.opx.0000214333.84822.71
27. Lee E.T., Russell D., Morris T., Warn A., Kingsley R., Ogola G.. **Visual impairment and eye abnormalities in Oklahoma Indians**. *Arch. Ophthalmol.* (2005.0) **123** 1699-1704. DOI: 10.1001/archopht.123.12.1699
28. Mansberger S.L., Romero F.C., Smith N.H., Johnson C.A., Cioffi G.A., Edmunds B., Choi D., Becker T.M.. **Causes of visual impairment and common eye problems in Northwest American Indians and Alaska Natives**. *Am. J. Public Health* (2005.0) **95** 881-886. DOI: 10.2105/AJPH.2004.054221
29. Del Brutto O.H., Mera R., Recalde B.Y., Rumbea D.A., Costa A.F., Viteri E.. **Hypertensive Retinopathy and All-Cause Mortality in Older Adults of Amerindian Ancestry. A Population-based Longitudinal Prospective Study**. *High Blood Press. Cardiovasc. Prev.* (2021.0) **28** 613-618. DOI: 10.1007/s40292-021-00481-7
30. Duong H.V., Westfield K.C., Jones L.S., Mitchell J., Carr T.. **A survey of ocular diseases in an isolated rural Haitian community: A retrospective evaluation**. *J. Natl. Med. Assoc.* (2012.0) **104** 536-543. DOI: 10.1016/S0027-9684(15)30220-0
31. López Y.A., Talero S.L., León Donado J.P., Álvarez Á.M., Magris M., Hernández T., Bermúdez M., Villalobos N., Saboyá-Díaz M.I.. **Trachoma Rapid Assessments in Venezuela, an Example of the Integration of Data Gathering with Service Delivery in Hard-to-reach Populations**. *Ophthalmic Epidemiol.* (2022.0) **29** 100-107. DOI: 10.1080/09286586.2021.1904512
32. Miller H.A., López de Mesa C.B., Talero S.L., Meza Cárdenas M., Ramírez S.P., Moreno-Montoya J., Porras A., Trujillo-Trujillo J.. **Prevalence of trachoma and associated factors in the rural area of the department of Vaupés, Colombia**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0229297
33. Miller H., Gallego G., Rodríguez G.. **Evidencia clínica de tracoma en indígenas colombianos del departamento de Vaupés [Clinical evidence of trachoma in Colombian Amerindians of the Vaupés Province]**. *Biomedica* (2010.0) **30** 432-439. DOI: 10.7705/biomedica.v30i3.277
34. Freitas H.S., Medina N.H., Lopes M.F., Soares O.E., Teodoro M.T., Ramalho K.R., Caligaris L.S., Mörschbächer R., de Menezes M.N., Luna E.J.. **Trachoma in Indigenous Settlements in Brazil, 2000–2008**. *Ophthalmic Epidemiol.* (2016.0) **23** 354-359. DOI: 10.3109/09286586.2015.1131305
35. Herzog-Neto G., Jaegger K., Nascimento E.S., Marchon-Silva V., Banic D.M., Maia-Herzog M.. **Ocular onchocerciasis in the Yanomami communities from Brazilian Amazon: Effects on intraocular pressure**. *Am. J. Trop. Med. Hyg.* (2014.0) **90** 96-98. DOI: 10.4269/ajtmh.13-0357
36. Neto G.H., Jaegger K., Marchon-Silva V., Calvão-Brito R.H., Vieira J.B., Banic D.M., Maia-Herzog M.. **Eye disease related to onchocerciasis: A clinical study in the Aratha-ú, Yanomami Tribe, Roraima State, Brazil**. *Acta Trop.* (2009.0) **112** 115-119. DOI: 10.1016/j.actatropica.2009.07.006
37. Cruz A.A., Medina N.H., Ibrahim M.M., Souza R.M., Gomes U.A., Goncalves G.F.. **Prevalence of trachoma in a population of the upper Rio Negro basin and risk factors for active disease**. *Ophthalmic Epidemiol.* (2008.0) **15** 272-278. DOI: 10.1080/09286580802080090
38. Piccinin M.R.M., Cunha J.F., Almeida H.P., Bach C.C., de Oliveira Dossa A.C., da Silva R.F., Pessoa V.F.. **Baixa prevalência de discromatopsia, pela 4a edição do teste pseudoisocromático HRR (Hardy, Rand e Rittler), da população indígena de etnia terena da aldeia lalima na região de Miranda: Mato Grosso do Sul [Low prevalence of dyschromatopsia using the fourth edition of HRR (Hardy, Rand and Rittler) pseudoisochromatic plate test among the Indian population of Lalima village, Terena]**. *Arq. Bras. Oftalmol.* (2007.0) **70** 259-269. PMID: 17589697
39. Paula J.S., Thorn F., Cruz A.A.. **Prevalence of pterygium and cataract in indigenous populations of the Brazilian Amazon rain forest**. *Eye* (2006.0) **20** 533-536. DOI: 10.1038/sj.eye.6701917
40. Reis A.C.P.P., Chaves C., Cohen J.M., Belfort F., Oliveira N.P., Belfort Jr R.. **Detecção de tracoma e doenças corneanas em índios da região do Alto Rio Negro**. *Arq. Bras. Oftalmol.* (2002.0) **65** 79-81. DOI: 10.1590/S0004-27492002000100015
41. Paula J.S., Medina N.H., Cruz A.A.. **Trachoma among the Yanomami Indians**. *Braz. J. Med. Biol. Res.* (2002.0) **35** 1153-1157. DOI: 10.1590/S0100-879X2002001000007
42. Alves A.P., Medina N.H., Cruz A.A.. **Trachoma and ethnic diversity in the Upper Rio Negro Basin of Amazonas State, Brazil**. *Ophthalmic Epidemiol.* (2002.0) **9** 29-34. DOI: 10.1076/opep.9.1.29.1716
43. Garrido C., Campos M.. **First report of presumed parasitic keratitis in Indians from the Brazilian Amazon**. *Cornea* (2000.0) **19** 817-819. DOI: 10.1097/00003226-200011000-00011
44. Fonda S.J., Bursell S.E., Lewis D.G., Clary D., Shahon D., Silva P.S.. **Prevalence of Diabetic Eye Diseases in American Indians and Alaska Natives (AI/AN) as Identified by the Indian Health Service’s National Teleophthalmology Program Using Ultrawide Field Imaging (UWFI)**. *Ophthalmic Epidemiol.* (2022.0) **29** 672-680. DOI: 10.1080/09286586.2021.1996611
45. Maple-Brown L.J., Cunningham J., Zinman B., Mamakeesick M., Harris S.B., Connelly P.W., Shaw J., O’Dea K., Hanley A.J.. **Cardiovascular disease risk profile and microvascular complications of diabetes: Comparison of Indigenous cohorts with diabetes in Australia and Canada**. *Cardiovasc. Diabetol.* (2012.0) **11** 30. DOI: 10.1186/1475-2840-11-30
46. Rudnisky C.J., Wong B.K., Virani H., Tennant M.T.S.. **Risk factors for progression of diabetic retinopathy in Alberta First Nations communities**. *Can. J Ophthalmol.* (2017.0) **52** S19-S29. DOI: 10.1016/j.jcjo.2017.09.023
47. Butt A.L., Lee E.T., Klein R., Russell D., Ogola G., Warn A., Kingsley R.M., Yeh J.. **Prevalence and risks factors of age-related macular degeneration in Oklahoma Indians: The Vision Keepers Study**. *Ophthalmology* (2011.0) **118** 1380-1385. DOI: 10.1016/j.ophtha.2010.11.007
48. McClure T.M., Choi D., Wooten K., Nield C., Becker T.M., Mansberger S.L.. **The impact of eyeglasses on vision-related quality of life in American Indian/Alaska Natives**. *Am. J. Ophthalmol.* (2011.0) **151** 175-182. DOI: 10.1016/j.ajo.2010.06.043
49. Maberley D., Cruess A.F., Barile G., Slakter J.. **Digital photographic screening for diabetic retinopathy in the James Bay Cree**. *Ophthalmic Epidemiol.* (2002.0) **9** 169-178. DOI: 10.1076/opep.9.3.169.1517
50. Mactaggart I., Limburg H., Bastawrous A., Burton M.J., Kuper H.. **Rapid Assessment of Avoidable Blindness: Looking back, looking forward**. *Br. J. Ophthalmol.* (2019.0) **103** 1549-1552. DOI: 10.1136/bjophthalmol-2019-314015
51. **Report of the 2030 Targets on Effective Coverage of Eye Care**
52. Ramke J., Gilbert C.E., Lee A.C., Ackland P., Limburg H., Foster A.. **Effective cataract surgical coverage: An indicator for measuring quality-of-care in the context of Universal Health Coverage**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0172342
53. McCormick I., Mactaggart I., Bastawrous A., Burton M.J., Ramke J.. **Effective refractive error coverage: An eye health indicator to measure progress towards universal health coverage**. *Ophthalmic Physiol. Opt.* (2020.0) **40** 1-5. DOI: 10.1111/opo.12662
54. Fernandes A.G., Ferraz A.N., Lemos R.S., Watanabe S.E.S., Berezovsky A., Salomão S.R.. **Trends in cataract surgical treatment within the Brazilian national public health system over a 20-year period: Implications for Universal Eye Health as a global public health goal**. *PLoS Glob. Public Health* (2022.0) **2**. DOI: 10.1371/journal.pgph.0000328
55. Keel S., Xie J., Foreman J., Taylor H.R., Dirani M.. **Population-based assessment of visual acuity outcomes following cataract surgery in Australia: The national eye health survey**. *Br. J. Ophthalmol.* (2018.0) **102** 1419-1424. DOI: 10.1136/bjophthalmol-2017-311257
56. Fernandes A.G., Salomão S.R., Ferraz N.N., Mitsuhiro M.H., Furtado J.M., Muñoz S., Cypel M.C., Cunha C.C., Vasconcelos G.C., Sacai P.Y.. **Pterygium in adults from the Brazilian Amazon Region: Prevalence, visual status and refractive errors**. *Br. J. Ophthalmol.* (2020.0) **104** 757-763. DOI: 10.1136/bjophthalmol-2019-314131
57. Ali S.H., Foster T., Hall N.L.. **The Relationship between Infectious Diseases and Housing Maintenance in Indigenous Australian Households**. *Int. J. Environ. Res. Public Health* (2018.0) **15**. DOI: 10.3390/ijerph15122827
58. **Progress towards eliminating onchocerciasis in the WHO Region of the Americas: Elimination of transmission in the north-east focus of the Bolivarian Republic of Venezuela**. *Wkly. Epidemiol. Rec.* (2017.0) **92** 617-623. PMID: 29028167
59. Sauerbrey M.. **The Onchocerciasis Elimination Program for the Americas (OEPA)**. *Ann. Trop. Med. Parasitol.* (2008.0) **102** 25-29. DOI: 10.1179/136485908X337454
60. Nicholls R.S., Duque S., Olaya L.A., López M.C., Sánchez S.B., Morales A.L., Palma G.I.. **Elimination of onchocerciasis from Colombia: First proof of concept of river blindness elimination in the world**. *Parasit. Vectors* (2018.0) **11** 237. DOI: 10.1186/s13071-018-2821-9
61. Lakwo T., Oguttu D., Ukety T., Post R., Bakajika D.. **Onchocerciasis Elimination: Progress and Challenges**. *Res. Rep. Trop. Med.* (2020.0) **11** 81-95. DOI: 10.2147/RRTM.S224364
62. Saboyá-Díaz M.I., Betanzos-Reyes A.F., West S.K., Muñoz B., Castellanos L.G., Espinal M.. **Trachoma elimination in Latin America: Prioritization of municipalities for surveillance activities**. *Rev. Panam. Salud Publica* (2019.0) **43** e93. DOI: 10.26633/RPSP.2019.93
63. **Trachoma in the Americas**. (2019.0)
64. Quesada-Cubo V., Damián-González D.C., Prado-Velasco F.G., Fernández-Santos N.A., Sánchez-Tejeda G., Correa-Morales F., Domínguez-Zárate H., García-Orozco A., Saboyá-Díaz M.I., Sánchez-Martín M.J.. **The elimination of trachoma as a public health problem in Mexico: From national health priority to national success story**. *PLoS Negl. Trop. Dis.* (2022.0) **16**. DOI: 10.1371/journal.pntd.0010660
65. Weale R.A.. **Ethiniticy and glaucoma: Higher environmental temperature may accelerate the onset, and increase the prevalence, of primary open angle glaucoma**. *Med. Hypotheses* (2007.0) **69** 432-437. DOI: 10.1016/j.mehy.2006.12.020
66. Hong H., Mújica O.J., Anaya J., Lansingh V.C., López E., Silva J.C.. **The Challenge of Universal Eye Health in Latin America: Distributive inequality of ophthalmologists in 14 countries**. *BMJ Open* (2016.0) **6** e012819. DOI: 10.1136/bmjopen-2016-012819
67. Chávez G.M.S., de Barrios A.R.S., Pojoy O.L.F., de Reyes A.R.M.H., Melgar M.Y., Melgar J.F.Y., Régil M.L., Hernandez C.A.M., Chanquin V.A.M., Diaz E.. **National survey of blindness and visual impairment in Guatemala, 2015**. *Arq. Bras. Oftalmol.* (2019.0) **82** 91-97. DOI: 10.5935/0004-2749.20190029
68. Ponce P., Muñoz R., Stival M.. **Pueblos indígenas, VIH y políticas públicas en Latinoamérica: Una exploración en el panorama actual de la prevalencia epidemiológica, la prevención, la atención y el seguimiento oportuno**. *Salud Colect.* (2017.0) **13** 537-554. DOI: 10.18294/sc.2017.1120
69. De Macedo J.N., Sousa Júnior O.V., Biazussi H.M., Pereira B.G.. **Venezuelanos no Brasil: Direitos dos Imigrantes e a Saúde Pública Local**. *Interfaces Cient. Direito* (2019.0) **7** 73-82
70. Abreu I.N., Lopes F.T., Lima C.N.C., Barbosa A.D.N., de Oliveira L.R., Fujishima M.A., Freitas F.B., Dos Santos M.B., de Lima V.N., Cayres-Vallinoto I.M.V.. **HTLV-1 and HTLV-2 Infection Among Warao Indigenous Refugees in the Brazilian Amazon: Challenges for Public Health in Times of Increasing Migration**. *Front. Public Health.* (2022.0) **10** 833169. DOI: 10.3389/fpubh.2022.833169
71. Gwatkin D.R., Ergo A.. **Universal health coverage: Friend or foe of health equity?**. *Lancet* (2011.0) **377** 2160-2161. DOI: 10.1016/S0140-6736(10)62058-2
72. Burton M.J., Ramke J., Marques A.P., Bourne R.R.A., Congdon N., Jones I., Ah Tong B.A.M., Arunga S., Bachani D., Bascaran C.. **The Lancet Global Health Commission on Global Eye Health: Vision beyond 2020**. *Lancet Glob. Health* (2021.0) **9** e489-e551. DOI: 10.1016/S2214-109X(20)30488-5
73. Napper G., Fricke T., Anjou M.D., Jackson A.J.. **Breaking down barriers to eye care for Indigenous people: A new scheme for delivery of eye care in Victoria**. *Clin. Exp. Optom.* (2015.0) **98** 430-434. DOI: 10.1111/cxo.12325
74. Spurr S., Bullin C., Bally J., Trinder K., Khan S.. **Nurse-led diabetic retinopathy screening: A pilot study to evaluate a new approach to vision care for Canadian Aboriginal peoples**. *Int. J. Circumpolar Health* (2018.0) **77** 1422670. DOI: 10.1080/22423982.2017.1422670
75. Avidor D., Loewenstein A., Waisbourd M., Nutman A.. **Cost-effectiveness of diabetic retinopathy screening programs using telemedicine: A systematic review**. *Cost Eff. Resour. Alloc.* (2020.0) **18** 16. DOI: 10.1186/s12962-020-00211-1
76. Gan K., Liu Y., Stagg B., Rathi S., Pasquale L.R., Damji K.. **Telemedicine for Glaucoma: Guidelines and Recommendations**. *Telemed. J. E-Health* (2020.0) **26** 551-555. DOI: 10.1089/tmj.2020.0009
77. Askarian B., Ho P., Chong J.W.. **Detecting Cataract Using Smartphones**. *IEEE J. Transl. Eng. Health Med.* (2021.0) **9** 3800110. DOI: 10.1109/JTEHM.2021.3074597
78. Bernhisel A., Pettey J.. **Manual small incision cataract surgery**. *Curr. Opin. Ophthalmol.* (2020.0) **31** 74-79. DOI: 10.1097/ICU.0000000000000624
79. Muir K.W., Santiago-Turla C., Stinnett S.S., Herndon L.W., Allingham R.R., Challa P., Lee P.P.. **Health literacy and adherence to glaucoma therapy**. *Am. J. Ophthalmol.* (2006.0) **142** 223-226. DOI: 10.1016/j.ajo.2006.03.018
80. Williams A.M., Muir K.W., Rosdahl J.A.. **Readability of patient education materials in ophthalmology: A single-institution study and systematic review**. *BMC Ophthalmol.* (2016.0) **16**. DOI: 10.1186/s12886-016-0315-0
|
---
title: Ginsenoside Rg2 Promotes the Proliferation and Stemness Maintenance of Porcine
Mesenchymal Stem Cells through Autophagy Induction
authors:
- Lina Che
- Caixia Zhu
- Lei Huang
- Hui Xu
- Xinmiao Ma
- Xuegang Luo
- Hongpeng He
- Tongcun Zhang
- Nan Wang
journal: Foods
year: 2023
pmcid: PMC10000966
doi: 10.3390/foods12051075
license: CC BY 4.0
---
# Ginsenoside Rg2 Promotes the Proliferation and Stemness Maintenance of Porcine Mesenchymal Stem Cells through Autophagy Induction
## Abstract
Mesenchymal stem cells (MSCs) can be used as a cell source for cultivated meat production due to their adipose differentiation potential, but MSCs lose their stemness and undergo replicative senescence during expansion in vitro. Autophagy is an important mechanism for senescent cells to remove toxic substances. However, the role of autophagy in the replicative senescence of MSCs is controversial. Here, we evaluated the changes in autophagy in porcine MSCs (pMSCs) during long-term culture in vitro and identified a natural phytochemical, ginsenoside Rg2, that could stimulate pMSC proliferation. First, some typical senescence characteristics were observed in aged pMSCs, including decreased EdU-positive cells, increased senescence-associated beta-galactosidase activity, declined stemness-associated marker OCT4 expression, and enhanced P53 expression. Importantly, autophagic flux was impaired in aged pMSCs, suggesting deficient substrate clearance in aged pMSCs. Rg2 was found to promote the proliferation of pMSCs using MTT assay and EdU staining. In addition, Rg2 inhibited D-galactose-induced senescence and oxidative stress in pMSCs. Rg2 increased autophagic activity via the AMPK signaling pathway. Furthermore, long-term culture with Rg2 promoted the proliferation, inhibited the replicative senescence, and maintained the stemness of pMSCs. These results provide a potential strategy for porcine MSC expansion in vitro.
## 1. Introduction
With the growth of the world population and the economic development of developing countries, the demand for meat has increased rapidly [1,2,3]. It is estimated that by 2050, the global population will reach 9.5 billion [4]. To meet people’s demand for animal-based protein, the global meat production in 2050 is expected to increase to $169\%$ of that in 2018 [5]. It is clear that traditional animal agriculture based on livestock and meat production methods cannot maintain the growth in the meat demand and will further exacerbate environmental stress. Recently, cultivated meat (CM), also known as in vitro meat, clean meat, cell-based meat, or cultured meat, was used as an alternative source of animal protein, providing a possible solution to these problems. In fact, meat is a set of complex muscle tissues, with a structure with specific characteristics and properties. Therefore, compared with the term “cultivated meat,” “food made with cultured animal cells” could describe this food more accurately at the current stage of development. Cultured animal cell food, as an important subfield of cellular agriculture, is produced in vitro using stem cells and tissue engineering, without sacrificing animals [6,7]. According to the ex ante life cycle assessment (LCA) of commercial-scale CM production in 2030 [8], compared to the traditional production of chicken, pork, and beef, it is estimated that industrialized CM production could reduce land use by $64\%$, $67\%$, and 55–$90\%$, respectively. The carbon footprint of CM production is similar to that of chicken, which is significantly lower than that of pork and beef, and can be reduced by $43\%$ and 67–$92\%$, respectively. Food made with cultured animal cells is also beneficial to food security and animal welfare.
The primary types of cell sources for cultured animal cell food production mainly include pluripotent stem cells, such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), and adult stem cells, such as myosatellite cells and mesenchymal stem cells (MSCs) [6,9,10]. MSCs from a variety of animals, including chicken, pig, and bovines, have been shown to differentiate into adipocytes [11] and myocytes [12,13], thus producing fat and muscle, respectively. MSCs can be obtained mainly from bone marrow and also from other tissues, including adipose, umbilical cord, and placental tissues. Recent studies have focused on the use of MSCs for food made with cultured animal cells [14,15,16]. Zagury et al. constructed a three-dimensional fat-rich, edible engineered tissue by loading bovine MSCs within alginate hydrogel [14]. Machour et al. reported a “print-and-grow” approach using κ-carrageenan-based microgels (CarGrow), which was expected to be used in the production of CM [15]. MSCs printed and grown within CarGrow exhibited higher viability and proliferation capability compared to the control group. Hanga et al. developed a strategy for the expansion of bovine MSCs based on microcarriers [16]. Stem cell harvesting is the basis of cultured animal cell food production, and therefore, long-term culture and amplification of cells in vitro are required to obtain enough cells. However, the proliferative capacity of MSCs in vitro is limited. Long-term culture of MSCs in vitro leads to loss of stemness [17], and the cells undergo replicative senescence [18,19], which is accompanied by a decline in the differentiation potential [20], DNA damage response [19], anti-oxidation ability [21], and immune regulation ability [22,23]. Similar to MSCs derived from humans and mice, MSCs derived from porcine bone marrow or adipose tissue also suffer from replicative senescence after long-term in vitro passaging [11,23,24]. Therefore, it is of positive significance to study the biological characteristics of porcine MSCs’ (pMSCs) in vitro expansion and explore possible effective strategies to promote proliferation, maintain stemness, and inhibit replicative senescence.
Macroautophagy (hereafter referred to as autophagy) is a process that produces energy and macromolecular precursors for cellular renovation by degrading unnecessary or dysfunctional cell components, which is essential for maintaining cell, tissue, and organ homeostasis [25]. Activation of autophagy also helps to remove oxidized and damaged proteins and prevent the accumulation of toxic substances [26]. Aged MSCs are characterized by high levels of reactive oxygen species and accumulation of toxic or oxidized metabolites [27]. Recently, it has been reported that activation of autophagy can prevent radiation-induced ROS production and DNA damage in MSCs and therefore contributes to the preservation of stemness [28]. Additionally, blocking autophagy has been found to lead to ROS accumulation and stemness loss, suggesting that autophagy plays a crucial role in the maintenance of MSC stemness [28]. Similarly, Garcia-Prat et al. reported the important role of basal autophagy in preserving stemness in muscle satellite cells [29]. Compared to young quiescent satellite cells, autophagic activity in aged cells has been found to be impaired, while reactivation of autophagy could restore cellular stemness, rescue the proliferative defect, and reduce senescence [29]. However, the role of autophagy in the senescence of MSCs is still not fully understood, and results from the literature are controversial. For instance, autophagy has been reported to be activated in aged bone marrow MSCs (BMMSCs) due to the observed increase in autophagy-related gene expression [30]. Conversely, some recent investigations have stated that senescent BMMSCs have low or defective autophagy [31,32,33]. Therefore, it is imperative to explore the regulatory role of autophagy in porcine MSC stemness maintenance and senescence, which is critical for improving stem cell in vitro expansion.
Ginsenoside Rg2 is a biactive natural component of ginseng. The contents of Rg2 in the root of red ginseng (RG) are reported to be from 0.6 mg/g [34] to 1.1 mg/g [35]. Fermentation of ginseng with *Rhizopus oligosporus* increases the contents of Rg2 from 0.85 mg/g to 2.05 mg/g [36]. Black ginseng fermented with *Saccharomyces cerevisiae* contains 2.86 μg/mL of Rg2 [37]. Rg2 has been shown to have a variety of pharmacological effects, including anti-oxidant [38], anti-inflammatory [38], anti-cancer [39], cardiovascular protection [40], and neuro-protection [41,42] activities. Our previous study confirmed that Rg2 can activate autophagy in multiple mouse tissues and effectively improve cognitive impairment in mice with Alzheimer disease [41]. Recently, the other two ginsenosides, Rg1 [43] and Rg3 [44], were found to increase human BMMSC proliferation and suppress senescence in vitro. Moreover, it is reported that Rg1 is also able to improve the proliferative capacity of hematopoietic stem cells [45] and neural stem cells [46]. However, the effect of ginsenoside Rg2 on the proliferation and senescence of MSCs is unclear.
In this study, the senescence characteristics and autophagic activities of porcine MSCs during long-term culture in vitro were evaluated. Next, using a D-galactose (D-gal)-induced accelerated senescence model, we investigated the effect of ginsenoside Rg2 on the proliferation, senescence, and stemness of porcine MSCs and explored its potential mechanisms. Furthermore, whether Rg2 can stimulate the proliferation and maintain the stemness of porcine MSCs during long-term culture in vitro was also assessed.
## 2.1. Experimental Animals
In this study, 1–3-day-old pigs were obtained from the Tianjin Fushengyuan livestock farm. Animal experiments were performed in accordance with the guidelines established by the Institutional Animal Care and Use Committee at Tianjin University of Science & Technology.
## 2.2. Isolation and Culture of pMSCs
pMSCs were isolated from the femur and tibia of pigs according to the reported method with minor modifications [47]. Briefly, the femur and tibia were retrieved and rinsed twice with phosphate-buffered saline (PBS) containing $3\%$ penicillin/streptomycin. After both ends of the femur and tibia were cut, the marrow was flushed out by inserting a syringe needle into the cut surface and centrifuged for 5 min at 1000 rpm at room temperature. The cells were resuspended in Dulbecco’s modified Eagle’s medium/F12 (DMEM/F12; Gibco, New York, NY, USA) containing $10\%$ fetal bovine serum (FBS; AusGeneX, Gold Coast, Australia) and cultured at 37 °C in a humidified atmosphere containing $5\%$ CO2. Porcine MSCs were passaged with digestion with $0.25\%$ trypsin containing $0.02\%$ EDTA when they reached 80–$90\%$ confluence. Cellular morphology was observed and photographed using a phase-contrast microscope (Nikon Eclipse Ti, Nikon, Tokyo, Japan).
## 2.3. Flow Cytometry
To identify cellular surface immunophenotypes, porcine MSCs were digested and washed twice with PBS. The cells were labeled with antibodies against PerCP-CD45 (Cat.#: 642275; BD Biosciences, New York, NY, USA), APC-CD44 (Cat.#: 103011; BioLegend, San Diego, CA, USA), FITC-CD90 (Cat.#: 328107; BioLegend, CA, USA), and PE-CD34 (Cat.#: 343605; BioLegend, CA, USA) for 30 min. After washing twice with PBS, the labeled cells were analyzed using a flow cytometer (BD Biosciences, New York, NY, USA).
To measure the intracellular reactive oxygen species (ROS) level, cells were incubated with 10 μM of DCFH-DA (Nanjing Jiancheng Biotechnology, Nanjing, China) at 37 °C for 1 h, followed by washing and resuspending with PBS. Fluorescence was analyzed via flow cytometry (BD Biosciences, New York, NY, USA) with excitation at 500 nm and emission at 525 nm.
## 2.4. Adipogenic and Osteogenic Differentiation of MSCs
For adipogenic differentiation, porcine MSCs (1 × 105/well) at P4 were seeded in 6-well plates until they reached $70\%$–$80\%$ confluence. These cells were first cultured in adipogenic induction medium for 3 days and sequentially in maintenance medium for another 3 days. Next, the two media were replaced alternately until 21 days. Adipogenic induction medium is composed of DMEM/F-12 supplemented with $10\%$ FBS, 10 μM of dexamethasone (Solarbio, Beijing, China), 200 μM of indomethacin (Solarbio, Beijing, China), and 10 μM of insulin (Solarbio, Beijing, China), while maintenance medium is composed of basal medium supplemented with 0.2 nM of insulin. After 21 days, the cells were fixed with $4\%$ paraformaldehyde and then stained with oil red O (Solarbio, Beijing, China).
For osteogenic differentiation, porcine MSCs (1 × 105/well) at P4 were seeded in 6-well plates until they reached 80–$90\%$ confluence. The medium was replaced with osteogenic induction medium. Osteogenic induction medium is composed of basal medium supplemented with 0.1 μM of dexamethasone (Solarbio, Beijing, China), 10 μM of β-glycerophosphate (Coolaber, Beijing, China), and 50 μM of vitamin C (Solarbio, Beijing, China). The media were changed every 2–3 days. After 21 days, the cells were fixed with $4\%$ paraformaldehyde and then stained with alizarin red S (Solarbio, Beijing, China).
## 2.5. Senescence-Associated β-Galactosidase (SA-β-Gal) Staining
To evaluate cellular senescence, β-gal activity was analyzed using a SA-β-Gal staining kit (Biyuntian, Beijing, China), following the manufacturer’s instructions. Briefly, pMSCs at P5, P10, and P15 were plated in a 6-well plate, fixed with fixative solution for 15 min at room temperature, and washed three times with PBS. The cells were incubated overnight with freshly prepared staining solution at 37 °C in the absence of CO2. After washing with $70\%$ ethanol, the aging cells were dyed blue. The number of these blue cells was counted under a inverted phase-contrast microscope (Nikon, Tokyo, Japan).
## 2.6. Cell Proliferation Assay
Cellular proliferation was detected according to the instructions of a Click-iT EdU (5-Ethynyl-2′-deoxyuridine) Cell Proliferation Kit (Meilunbio, Dalian, China). pMSCs at P5, P10, and P15 were plated in a 24-well plate and cultured overnight. For labeling cells with EdU, an equal volume of 2× EdU solution was added to the cells, and the cells were incubated at 37 °C for 2 h. The samples were then fixed and permeabilized. The nuclei were stained using the Hoechst 33342 (Meilunbio, Dalian, China) fluorescent stain. Digital images were acquired using a laser confocal microscope (OLYMPUS, Tokyo, Japan), and the number of EdU-positive cells were calculated using Image-Pro Plus 5.1 software (MEDIA CYBERNETICS, Silver Spring, MD, USA). EdU incorporation (the ratio of EdU-labeled cells to total cells) indicated the cellular proliferation rate.
## 2.7. Quantitative Real-Time PCR (qRT-PCR)
Total RNA was isolated from porcine MSCs using Trizol reagent (Invitrogen, Carlsbad, CA, USA), and reverse transcription of the RNA sample to cDNA was carried out using M-MLV reverse transcriptase (Promega, Madison, WI, USA). qRT-PCR was performed on a Applied Biosystems StepOneTM RT-PCR system (Applied Biosystems, Foster City, CA, USA) with the Fast SYBR1 Green Master Mix obtained from Applied Biosystems. Primers for each targeted mRNA were designed and are listed in Table 1. The 2−ΔΔCt method was used to calculate the relative expression levels of target genes, and GAPDH was used as an internal control.
## 2.8. Drug Administration
To monitor autophagic flux, pMSCs at P5, P10, and P15 were treated with 150 nM of bafilomycin A1 (Santa Cruz Biotechnology, Santa Cruz, CA, USA) for 2 h, and then, the protein samples were collected for LC3II detection. To assess the effect of Rg2 on cellular proliferation, Rg2 (Shanghai Yuanye Bio-Technology Co., Shanghai, China) was dissolved in DMSO and provided to the cells. pMSCs were treated with 25 μM, 50 μM, and 100 μM of Rg2 in DMEM/F-12 containing $1\%$, $5\%$, and $10\%$ FBS for 24, 48, and 72 h, followed by MTT and EdU staining assays. To investigate the effect of Rg2 on D-gal-induced senescence, pMSCs were pre-treated for 24 h with 20 g/L of D-gal and then incubated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h, followed by MTT, EdU staining, SA-β-gal activity, and Western blot assays.
## 2.9. Western Blot
pMSCs treated under different conditions were collected and then lysed with RIPA buffer along with PMSF protease inhibitor. The primary antibodies used for immunodetection included anti-OCT4 (Cat.#: AF0226; Affinity Biosciences, Changzhou, China), anti-p53 (Cat.#: AF0879; Affinity Biosciences, Changzhou, China; Cat.#: 10442-1-AP; Proteintech, Wuhan, China), anti-p62 (Cat.#: ab109012; Abcam, Cambridge, MA, USA), anti-LC3-I/II (Cat.#: NB100-2220; Novusbio, CO, USA), anti-p-AMPK (Cat.#: AF3423; Affinity Biosciences, Changzhou, China), anti-AMPK (Cat.#: sc74461; Santa Cruz Biotechnology, Santa Cruz, CA, USA), and anti-β-actin (Cat.#: sc8432; Signalway Antibody, Baltimore, MD, USA). The specific protein bands were visualized with the Odyssey Infrared Imaging System (LI-COR, Lincoln, Dearborn, MI, USA). The band density was analyzed using Image-Pro Plus 5.1 software (MEDIA CYBERNETICS, Silver Spring, MD, USA) using β-actin as an internal control and then normalized to the vehicle control.
## 2.10. Cell Viability Assay
For the detection of cellular viability, pMSCs (5 × 103/well) were seeded in a 96-well plate with 100 μL of the medium, followed by MTT assay. The cells were treated with 5 mg/mL of 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetrazolium (MTT; Solarbio, Beijing, China) solution (10 μL per well) and then incubated for 4 h. The medium was then discarded, and 100 μL of dimethyl sulfoxide (DMSO) was added to each well. The absorbance of each well was measured using a Synergy 4 plate reader (Bioteck, Winooski, VT, USA) with a wavelength of 490 nm. Absorbance was directly proportional to the number of surviving cells.
## 2.11. Measurement of Malondialdehyde (MDA) Contents and Superoxide Dismutase (SOD) Activities
After pre-incubation with 20 g/L of D-gal for 24 h, pMSCs were incubated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h, and then, the cells were collected and lysed. The contents of MDA and the activities of SOD were detected using commercial available kits (Solarbio, Beijing, China) according to the manufacturer’s instructions.
## 2.12. Statistical Analysis
All data are shown as the mean ± SD, and all experiments were repeated at least three times. Statistical analysis was conducted using Microsoft Excel and GraphPad Prism 6. Two-tailed, unpaired Student’s t-tests were performed to determine statistical significance when comparing two groups, and one-way ANOVA followed by a Dunnett multiple-comparison test was used when comparing more than two groups. A p-value of <0.05 was considered statistically significant.
## 3.1. Isolation, Culture, and Identification of pMSCs
The primary cells isolated from porcine bone marrow were cultured in basic medium for 12 h and adhered to the wall. After 3–5 days, the cells began to fuse, and the rate of cell fusion reached $65\%$–$70\%$ within 1 week. As shown in Figure 1A, porcine MSCs at P2 and P5 showed a spindle shape and strong proliferative capacity, while the cells after several passages gradually showed the characteristics of aging, such as a flat body, hypertrophy, and weak refraction. Almost all cells lost their ability of proliferation beyond passage 20. To identify the immunophenotypes of the primary cells isolated, cellular surface markers CD34, CD44, CD45, and CD90 were analyzed in the cells at P3 using flow cytometry. The isolated porcine MSCs were strongly positive for CD44 (96.27 ± $0.13\%$) and CD90 (98.79 ± $0.05\%$) but negative for the hematopoietic lineage markers CD34 (0.08 ± $0.02\%$) and CD45 (0.04 ± $0.01\%$); see Figure 1B. In addition, the multi-lineage differentiation ability of MSCs to adipocytes and osteoblasts was studied. Lipid droplets and positive oil red O staining were observed in the pMSCs exposed to adipogenic differentiation medium for 21 days (Figure 1C), while calcified nodules and positive alizarin red S staining appeared in the cells exposed to osteogenic differentiation medium (Figure 1D). These results indicated that the primary cells isolated from porcine bone marrow were mesenchymal stem cells.
## 3.2. Reduced Proliferation Potential and Stemness in pMSCs after Long-Time Culture
It is known that MSCs show reduced proliferation capacity and undergo replicative senescence with cellular expansion in vitro [48]. Here, some age-related changes were observed in pMSCs at P10 and P15 compared to the cells at P5. With an increase in the number of passages, the proportion of SA-β-gal-staining-positive cells significantly scaled up (Figure 2A,B), yet the proportion of EdU-positive cells decreased notably (Figure 2C,D). Next, we detected the mRNA levels of the stemness gene OCT4 and the proliferative marker Ki67 in pMSCs at P5, P10, and P15. The mRNA levels of OCT4 and Ki67 significantly decreased in pMSCs at higher passage numbers (Figure 2E,F). Consistent with the change in the OCT4 mRNA level, the protein level of OCT4 was downregulated in pMSCs at higher passage numbers (Figure 2G,H and Figure S1). Furthermore, a prominent increase in the protein level of the aging-related marker p53 was observed in pMSCs at P10 and P15 compared to the counterparts at P5 (Figure 2G,I and Figure S1).
## 3.3. Impaired Autophagic Flux and Elevated ROS in pMSCs after Long-Time Culture
The relationship between MSC senescence and autophagy remains unclear and debatable [26]. To investigate the relationship between the autophagy and replicative senescence of pMSCs, the expression of microtubule-associated protein 1 light chain 3 (LC3) and cargo protein SQSTM1/p62 was tested in pMSCs at P5, P10, and P15 using Western blot. As shown in Figure 3A–C and Figure S2, the relative protein levels of LC3-II and P62 in pBMSC significantly increased in aged pMSCs (P10 and P15) compared to young cells (P5). The increase in LC3-II indicates the combined results of increased autophagosome synthesis (activated autophagy induction) or suppressed autophagosome degradation (blockage of autophagic flux), while the accumulation of P62 indicates suppressed autophagic flux. To further distinguish between these two possibilities, BafA1 was used to block autophagosome–lysosome fusion (Figure 3D). Treatment with BafA1 for 2 h resulted in a noticeable accumulation of LC3-II in young pMSCs at P5, suggesting activated autophagic flux. Compared with BafA1-treated cells at P5, BafA1-treated pMSCs at P10 exhibited a further increase in LC3-II levels (Figure 3E,F and Figure S2), suggesting that in the early stages of aging, pMSCs can promote autophagy induction to remove toxic substrates. Although increased autophagosome synthesis was observed in pMSCs at P10, the accumulation of P62 in these cells (Figure 3A and Figure S2) suggested a defect in the later stages of autophagy (a potential inhibition of autophagosome degradation). Combined with markedly enhanced P62 levels, these results illustrate that senescent pMSCs activate autophagy at an early stage in response to oxidative stress, but the weakened autophagic flux makes it insufficient for them to completely remove toxic substances. Importantly, there was no significant difference in LC3-II levels between P5 and P15 pMSCs along with BafA1, but a profound increase in LC3-II levels was observed in P15 pMSCs without BafA1, compared to P5 cells, indicating that in the late stages of aging, autophagic flux is further impaired in pMSCs (Figure 3E,F and Figure S2).
Oxidative stress can cause oxidative damage to organelles and proteins, leading to cell senescence [49]. Correspondingly, we detected the ROS levels in pMSCs at P5, P10, and P15 using flow cytometry. As shown in Figure 3G,H, compared with young cells at P5, aged pMSCs at P10 and P15 showed a marked increase in ROS levels. These results further indicate the attenuated ability of senescent cells to scavenge ROS.
## 3.4. Ginsenoside Rg2 Promoted the Proliferation of pMSCs
To evaluate the stimulatory effect of ginsenoside Rg2 on the proliferation of porcine MSCs, cells at P6 were treated with different concentrations of Rg2 (25, 50, and 100 μM) in DMEM/F12 containing $1\%$, $5\%$, and $10\%$ FBS for 24 h, 48 h, and 72 h, and then, MTT assay was carried out. Our data showed that ginsenoside Rg2 at a concentration of 25–100 μM exhibits no cytotoxicity and that cellular viability increased remarkably with increasing Rg2 concentration (Figure 4A–C). In addition, 100 μM of Rg2 showed the most significant proliferative effect under the condition of $1\%$ serum (Figure 4A). Furthermore, the number of EdU-staining-positive cells markedly increased in pMSCs treated with 50 and 100 μM of Rg2 (Figure 4D,E). Our data showed that Rg2 can promote the proliferation of pMSCs in a concentration- and time-dependent manner.
## 3.5. Ginsenoside Rg2 Reversed D-Gal-Induced Senescence and Maintained Stemness in pMSCs
Next, the anti-senescence effect of Rg2 was assessed using a model of accelerated aging induced by D-gal. After pre-incubation with 20 g/L of D-gal for 24 h, pMSCs were treated with 100 μM of Rg2 in the presence/absence of D-gal for another 24 h and subsequently subjected to MTT, EdU staining, and SA-β-gal staining assays. Consistent with a previous study [50], we found that D-gal treatment significantly inhibited cell viability (Figure 5A), reduced the numbers of EdU-positive cells (Figure 5D,E) and increased the percentage of SA-β-gal-positive cells (Figure 5B,C). These changes mediated by D-gal were reversed by the administration of 100 μM of Rg2 (Figure 5A–E). Moreover, decreased OCT4 levels induced by D-gal were rescued by the addition of Rg2 (Figure 5F,G and Figure S3), suggesting that Rg2 can contribute to the maintenance of pMSC stemness. Similarly, Rg2 significantly inhibited the D-gal-caused increase in the protein expression of P53 in pMSCs (Figure 5F,H and Figure S3). These results indicated that treatment with Rg2 effectively prevents the pro-senescence effects of D-gal on pMSCs.
## 3.6. Ginsenoside Rg2 Protected pMSCs against Oxidative Stress
To determine whether Rg2 can delay the senescence of pMSCs by reducing ROS levels, intracellular ROS levels were assessed in Rg2-treated pMSCs using flow cytometry. D-gal treatment markedly stimulated the production of ROS in pMSCs, whereas the effect was attenuated by the administration of Rg2 (Figure 6A,B). Furthermore, MDA contents and SOD activities were detected in Rg2-treated pMSCs. The addition of Rg2 dramatically inhibited the D-gal-stimulated increase in MDA contents (Figure 6C). SOD activities were significantly downregulated in D-gal-stimulated pMSCs, while Rg2 treatment partly reversed the D-gal-induced reduction in SOD activities (Figure 6D). These results indicated that Rg2 prevents the senescence of pMSCs by increasing SOD activities and reducing ROS and MDA levels.
## 3.7. Ginsenoside Rg2 Induced Autophagy in pMSCs via the AMPK Signaling Pathway
To demonstrate whether the positive effect of Rg2 on the anti-senescence and stemness maintenance of pMSCs is related to autophagy induction, the protein expression of LC3 and P62 was tested in Rg2-treated pMSCs with/without D-gal using Western blot. Compared with the D-gal group, Rg2-treated cells showed increased LC3II expression and reduced P62 levels, indicating the activation of autophagy (Figure 7A–C and Figure S4). Our previous study confirmed that Rg2 can activate autophagy in multiple types of cells via the AMPK signaling pathway [41], but it is unknown whether Rg2 can activate the AMPK signaling pathway in porcine MSCs. Thus, we detected the expression of p-AMPK and AMPK in Rg2-treated pMSCs with/without D-gal using Western blot. As shown in Figure 7D,E and Figure S4, the relative protein level of p-AMPK/AMPK significantly increased in the Rg2 group compared to the D-gal group. These results further confirmed that autophagy activated by Rg2 can play a critical role in the anti-senescence and stemness maintenance of pMSCs via the AMPK signaling pathway.
## 3.8. Ginsenoside Rg2 Improved Longevity of pMSCs during Long-Term Culture
As Rg2 could maintain the stemness of pMSCs and stimulate proliferation, we next checked the effects of Rg2 on pMSCs during long-term culture. First, we checked the protein expression of OCT4 and P53 in pMSCs at P5, P10, and P15 in the presence/absence of Rg2. The protein expression of OCT4 significantly decreased in pMSCs at higher passage numbers, whether in Rg2-treated pMSCs or in cells without Rg2 (Figure 8A,B and Figure S5). However, higher OCT4 protein expression was observed in Rg2-treated cells compared to the control group. Similarly, Rg2 treatment also resulted in low expression of P53 protein (Figure 8A,C and Figure S5). Consistent with these results, the percentage of EdU-positive cells remarkably decreased in pMSCs at higher passage numbers, whereas the administration of Rg2 upregulated a percentage of EdU-positive cells (Figure 8D,E). Meanwhile, we found that the administration of Rg2 downregulated the numbers of SA-β-gal-positive cells (Figure 8D,F). Taking together, long-term culture of pMSCs with Rg2 can help maintain stemness and promote proliferation, as well as inhibit aging.
## 4. Discussion
MSCs have the potential of self-renewal and multi-directional differentiation, including adipocytes and muscle cells [11,51,52,53,54], and thus are considered one of the most advantageous seed cells for cultured animal cell food [55]. However, the replicative senescence of porcine MSCs during in vitro expansion limits their application in the large-scale industrial production of cultured animal cell food [56]. Therefore, it is of great significance to explore an effective method to promote the proliferation and delay the senescence of porcine MSCs.
An increasing amount of evidence indicates that basal autophagy serves as a key mechanism to regulate the proliferation, differentiation, and stemness maintenance of adult stem cells, including MSCs [57,58], muscle stem cells (MuSCs) [29], and hematopoietic stem cells (HSCs) [59]. Human MSCs have been demonstrated to possess constitutive autophagic flux due to the observed LC3 conversion (LC3-I to LC3-II) [57,58]. Accumulation of cellular damage during senescence activates stem cell autophagic flux to remove toxic material and maintain their stemness. Emerging evidence has revealed that the activation of autophagy can eliminate ROS and oxidative proteins in aged MSCs, thus keeping their stemness and genomic integrity [28,60,61].
The role of autophagy in MSC aging seems puzzling due to some contrary reports. Zheng et al. observed the increased expression of autophagy-related protein, including LC3-II, ATG7, and ATG12, in aging rat MSCs, thus considering that autophagy is activated during cellular senescence [30]. However, the increase in LC3-II is the result of the combination of autophagosome formation and blockage of autophagic degradation. Thus, it is necessary to analyze autophagic flux by blocking autophagy with bafilomycin A1. Contrary to activated autophagy in aged MSCs [30], more studies support that autophagy activity is impaired during aging [31,32,33]. Compared with young BMMSCs, aged cells showed reduced expression of Atg7, Beclin1, and LC3II/I and the accumulation of P62, as well as fewer autophagosomes [32]. After chloroquine (CQ) treatment, young BMMSCs possessed more LC3 dots compared to aged cells, indicating that aged BMMSCs might be characterized by impaired autophagy [32]. In addition, autophagy markedly decreased in aged BMMSCs under normoxic and hypoxic conditions [31]. Here, we found that although the expression of LC3II increased in aged pMSCs compared to young counterparts, p62 proteins accumulated, suggesting the potential blockage of autophagic flux. Accordingly, the number of autophagosomes first increased and then decreased during pMSC senescence, confirmed by the addition of bafilomycin A1. Autophagic flux is significantly impaired due to the blockage of autophagic degradation in P15 pMSCs compared with P5 cells. These results indicate that in the early stage of senescence, pMSCs need to activate autophagy in response to oxidative stress and damaged proteins, while in the late stage of senescence, cells display a decline in autophagy function, thus leading to reduced clearance ability. In line with the impaired ROS clearance during senescence, increased ROS levels were observed in aged pMSCs. Our data indicate that the ability of aged pMSCs to remove toxic substrates might be defective.
The activation of autophagy could protect MSCs from oxidative stress, thus resisting aging and promoting proliferation. The autophagic agonist rapamycin has been reported to alleviate the senescent features of aged MSCs [32,62]. Hypoxia can promote the self-renewal and proliferation of MSCs by activating autophagy [63,64]. Contrarily, the inhibition of autophagy could promote aging in MSCs. The autophagic inhibitor 3-methyladenine (3-MA) aggravates the aging of MSCs [32,62]. It is reported that blocking autophagy with kynurenine accelerates senescence in mice BMMSCs via the aryl hydrocarbon receptor pathway [65].
Our previous study confirmed the positive effect of ginsenoside Rg2 on autophagy induction [41]. However, it is not clear whether Rg2 has a retarding effect on MSC aging. Here, we demonstrated that Rg2 promotes the proliferation of porcine MSCs and slows down senescence by activating autophagy. Similar to our results, a number of natural and synthetic compounds that can activate autophagy have been demonstrated to inhibit the senescence of MSCs and increase their proliferative potential [62,66,67]. Autophagy induced by curcumin protects canine BMMSCs against replicative senescence during in vitro expansion, defined by the increased colony-forming unit–fibroblastic (CFU-F) capacity and decreased SA-β-gal activities [62]. A combination of 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), an AMPK activator, and nicotinamide (NAM), an activator of sirtuin1 (SIRT1), showed the protective effect of anti-senescence and proliferation promotion in MSCs [61]. Camphorquinone [67] and licochalcone D [66] have been reported to be able to activate autophagy via the adenosine-monophosphate-activated protein kinase (AMPK) signal pathway, therefore alleviating the H2O2-induced senescence of human BMMSCs in vitro and also inhibiting D-gal-induced aging in mice in vivo.
Reactive oxygen species are known to be important risk factors affecting the aging of mesenchymal stem cells [68,69,70]. It is known that ROS production increases with age, leading to oxidative DNA damage and decreased proliferation of stem cells [71]. D-gal induced accelerated senescence has been used as a conventional experimental model to study cell senescence [72,73]. Previous studies have also shown that D-gal can significantly induce senescence in MSCs by promoting ROS production [74]. Here, we also found that the percentage of SA-β-gal-positive cells significantly increased and the number of EdU-positive cells remarkably decreased in D-gal-treated pMSCs, whereas the changes were reversed with Rg2. Rg2 treatment also inhibited D-gal-induced upregulation of P53 expression and downregulation of OCT4 expression, suggesting that Rg2 prevents D-gal-mediated senescence in porcine MSCs. A recent report showed that Rg2 could delay D-gal-induced brain aging and recover impaired memory function in mice by increasing mitochondrial autophagy flux and relieving oxidative stress [42]. Similarly, ginsenoside Rg1 has been shown to have protective effects in multiple tissues of mice with D-gal-induced aging through attenuating oxidative stress [75,76,77]. Upregulating autophagy with by rapamycin has been shown to inhibit ROS generation and attenuate senescence caused by D-gal in rat BMMSCs [78]. Furthermore, our data demonstrated that Rg2 can protect porcine MSCs against the oxidative stress signal triggered by D-gal, as evidenced by the enhanced SOD activity and reduced MDA and ROS levels. This result was coincident with a previous finding that Rg2 effectively inhibits oleic acid and palmitic acid (OA&PA)-induced ROS generation in mouse primary hepatocytes [79]. The combined treatment of Rg2 and Rh1 has been found to significantly suppress LPS-induced excessive ROS accumulation in HepG2 cells [38].
One of the major regulators of autophagy is the adenosine-monophosphate-activated protein kinase (AMPK) signaling pathway [80]. AMPK can inhibit the activation of mammalian target of rapamycin (mTOR) through phosphorylating raptor, while mTOR functions as a critical negative regulator of autophagy by inhibiting Unc-51-like kinase 1 (ULK1) activation [81,82]. In addition, AMPK can trigger autophagy by directly phosphorylating ULK1 at multiple sites, such as S317, S467, and S777. [ 83,84]. The AMPK-mediated activation of autophagy has been reported to ameliorate D-gal-induced senescence in multiple tissues, including the heart [66,67], hippocampus [66,85,86], kidney [87], and skeletal muscle [88]. In human BMMSCs, licochalcone D or camphorquinone can induce autophagy and reduce H2O2-induced senescence via the AMPK signal pathway [66,67]. Rg2 has been reported to activate the AMPK signal in multiple cell lines, including 3T3-L1 preadipocytes [89], HepG2 cells [90], MCF-7 cells [39], Neuro2A cells [41], and PC12 cells [41]. Similarly, our data confirmed that pre-incubation with Rg2 significantly upregulates LC3-II expression and activates authophagy in D-gal-treated pMSCs via the AMPK signaling pathway.
In addition, we found that 100 μM of Rg2 can significantly enhance the proliferative capacity of porcine MSCs and inhibit replicative senescence during long-term culture in vitro. A recent study focused on the positive effect of Rg2 on the proliferation of induced-pluripotent-stem-cell-derived endothelial cells (iPSC-ECs) for clinical application [91]. Similar to the concentration of Rg2 used in our study, 10–200 μM of Rg2 was found to remarkably upregulate the EdU-positive cellular number of iPSC-ECs after three passages [91]. Mechanically, the stimulatory effect of Rg2 on iPSC-EC proliferation depends on mTOR-independent AMPK/ULK1-mediated autophagy. Furthermore, two recent studies on the use of Rg2 in the development of functional foods reported that the working concentration of Rg2 in the cells is approximately 80 μM [35,89], which is similar to the concentration of Rg2 (25–100 μM) used in our study.
## 5. Conclusions
Taken together, our findings suggest that in the early stage of senescence, pMSCs enhance autophagosome formation in respond to oxidative stress, while in the late stage, aged cells display impaired autophagic flux, thus leading to reduced clearance ability. Furthermore, ginsenoside Rg2 improves the longevity of porcine MSCs by inducing AMPK-mediated protective autophagy. Ginsenoside Rg2 may be an effective protector of MSC senescence induced by oxidative stress. These findings highlight the positive role of Rg2 in porcine MSC expansion in vitro.
## References
1. Arshad M.S., Javaid M., Sohaib M., Saeed F., Imran A., Yildiz F.. **Tissue engineering approaches to develop cultured meat from cells: A mini review**. *Cogent Food Agric.* (2017) **3** 1320814. DOI: 10.1080/23311932.2017.1320814
2. Gerbens-Leenes P.W., Nonhebel S., Krol M.S.. **Food consumption patterns and economic growth. Increasing affluence and the use of natural resources**. *Appetite* (2010) **55** 597-608. DOI: 10.1016/j.appet.2010.09.013
3. Stephens D.N., Dunsford I., Silvio L.D., Ellis D.M., Glencross A., Sexton D.A.. **Bringing cultured meat to market: Technical, socio-political, and regulatory challenges in Cellular Agriculture**. *Trends Food Sci. Technol.* (2018) **78** 155-166. DOI: 10.1016/j.tifs.2018.04.010
4. Ong K.J., Johnston J., Datar I., Sewalt V., Shatkin J.A.. **Food Safety Considerations and Research Priorities for the Cultured Meat and Seafood Industry**. *Compr. Rev. Food Sci. Food Saf.* (2021) **20** 5421-5448. DOI: 10.1111/1541-4337.12853
5. Warner R.D.. **Review: Analysis of the process and drivers for cellular meat production**. *Animal* (2019) **13** 3041-3058. DOI: 10.1017/S1751731119001897
6. Reiss J., Robertson S., Suzuki M.. **Cell Sources for Cultivated Meat: Applications and Considerations throughout the Production Workflow**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22147513
7. Baldermann S., Wikandari R., Manikharda A., Ningrum M.J.. **Application of cell culture technology and genetic engineering for production of future foods and crop improvement to strengthen food security**. *Bioengineered* (2021) **12** 11305-11330. DOI: 10.1080/21655979.2021.2003665
8. Sinke P., Swartz E., Sanctorum H., van der Giesen C., Odegard I.. **Ex-ante life cycle assessment of commercial-scale cultivated meat production in 2030**. *Int. J. Life Cycle Assess.* (2023) **28** 234-254. DOI: 10.1007/s11367-022-02128-8
9. Ozhava D., Bhatia M., Freman J., Mao Y.. **Sustainable Cell Sources for Cultivated Meat**. *J. Biomed. Res. Environ. Sci.* (2022) **3** 1382-1388. DOI: 10.37871/jbres1607
10. Knezic T., Janjusevic L., Djisalov M., Yodmuang S., Gadjanski I.. **Using Vertebrate Stem and Progenitor Cells for Cellular Agriculture, State-of-the-Art, Challenges, and Future Perspectives**. *Biomolecules* (2022) **12**. DOI: 10.3390/biom12050699
11. Lee A.Y., Lee J., Kim C.L., Lee K.S., Lee S.H., Gu N.Y., Kim J.M., Lee B.C., Koo O.J., Song J.Y.. **Comparative studies on proliferation, molecular markers and differentiation potential of mesenchymal stem cells from various tissues (adipose, bone marrow, ear skin, abdominal skin, and lung) and maintenance of multipotency during serial passages in miniature pig**. *Res. Vet. Sci.* (2015) **100** 115-124. DOI: 10.1016/j.rvsc.2015.03.010
12. Pérez-Serrano R., González-Dávalos M., Lozano-Flores C., Shimada A., Antaramian A., Varela-Echavarría A., Mora O.. **PPAR Agonists Promote the Differentiation of Porcine Bone Marrow Mesenchymal Stem Cells into the Adipogenic and Myogenic Lineages**. *Cells Tissues Organs* (2016) **203** 153-172. DOI: 10.1159/000447628
13. Ramírez-Espinosa J.J., González-Dávalos L., Shimada A., Piña E., Varela-Echavarria A.. **Bovine (**. *Cells Tissues Organs* (2015) **201** 51-64. DOI: 10.1159/000440878
14. Zagury Y., Ianovici I., Landau S., Lavon N., Levenberg S.. **Engineered marble-like bovine fat tissue for cultured meat**. *Commun. Biol.* (2022) **5** 927. DOI: 10.1038/s42003-022-03852-5
15. Machour M., Hen N., Goldfracht I., Safina D., Davidovich-Pinhas M., Bianco-Peled H., Levenberg S.. **Print-and-Grow within a Novel Support Material for 3D Bioprinting and Post-Printing Tissue Growth**. *Adv. Sci.* (2022) **9** e2200882. DOI: 10.1002/advs.202200882
16. Hanga M.P., Ali J., Moutsatsou P., de la Raga F.A., Hewitt C.J., Nienow A., Wall I.. **Bioprocess development for scalable production of cultivated meat**. *Biotechnol. Bioeng.* (2020) **117** 3029-3039. DOI: 10.1002/bit.27469
17. Jiang T., Xu G., Wang Q., Yang L., Zheng L., Zhao J., Zhang X.. **In vitro expansion impaired the stemness of early passage mesenchymal stem cells for treatment of cartilage defects**. *Cell Death Dis.* (2017) **8** e2851. DOI: 10.1038/cddis.2017.215
18. Kim J., Kim Y., Choi H., Kwon A., Jekarl D.W., Lee S., Jang W., Chae H., Kim J.R., Kim J.M.. **Ubiquitin C decrement plays a pivotal role in replicative senescence of bone marrow mesenchymal stromal cells**. *Cell Death Dis.* (2018) **9** 139. DOI: 10.1038/s41419-017-0032-5
19. Yu J., Shi J., Zhang Y., Zhang Y., Huang Y., Chen Z., Yang J.. **The replicative senescent mesenchymal stem / stromal cells defect in DNA damage response and anti-oxidative capacity**. *Int. J. Med. Sci.* (2018) **15** 771-781. DOI: 10.7150/ijms.24635
20. Turinetto V., Vitale E., Giachino C.. **Senescence in Human Mesenchymal Stem Cells: Functional Changes and Implications in Stem Cell-Based Therapy**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17071164
21. Estrada J.C., Torres Y., Benguria A., Dopazo A., Roche E., Carrera-Quintanar L., Perez R.A., Enriquez J.A., Torres R., Ramirez J.C.. **Human mesenchymal stem cell-replicative senescence and oxidative stress are closely linked to aneuploidy**. *Cell Death Dis.* (2013) **4** e691. DOI: 10.1038/cddis.2013.211
22. Zhang J., Yao H., Wu M., Li Y., Yang K.. **Nrf2 modulates immunosuppressive ability and cellular senescence of human umbilical cord mesenchymal stem cells**. *Biochem. Biophys. Res. Commun.* (2020) **526** 1021-1027. DOI: 10.1016/j.bbrc.2020.03.175
23. Vacanti V., Kong E., Suzuki G., Sato K., Lee T.. **Phenotypic changes of adult porcine mesenchymal stem cells induced by prolonged passaging in culture**. *J. Cell. Physiol.* (2010) **205** 194-201. DOI: 10.1002/jcp.20376
24. Pokrywczynska M., Maj M., Kloskowski T., Buhl M., Balcerczyk D., Jundzill A., Szeliski K., Rasmus M., Drewa T.. **Molecular Aspects of Adipose-Derived Stromal Cell Senescence in a Long-Term Culture: A Potential Role of Inflammatory Pathways**. *Cell Transpl.* (2020) **29** 963689720917341. DOI: 10.1177/0963689720917341
25. Mizushima N., Komatsu M.. **Autophagy: Renovation of cells and tissues**. *Cell* (2013) **147** 728-741. DOI: 10.1016/j.cell.2011.10.026
26. Sbrana F.V., Cortini M., Avnet S., Perut F., Columbaro M., Milito A.D., Baldini N.. **The Role of Autophagy in the Maintenance of Stemness and Differentiation of Mesenchymal Stem Cells**. *Stem Cell Rev.* (2016) **12** 621-633. DOI: 10.1007/s12015-016-9690-4
27. Weng Z., Wang Y., Ouchi T., Liu H., Qiao X., Wu C., Zhao Z., Li L., Li B.. **Mesenchymal Stem/Stromal Cell Senescence: Hallmarks, Mechanisms, and Combating Strategies**. *Stem Cells Transl. Med.* (2022) **11** 356-371. DOI: 10.1093/stcltm/szac004
28. Hou J., Han Z.p., Jing Y.y., Yang X., Zhang S.s., Sun K., Hao C., Meng Y., Yu F.h., Liu X.Q.. **Autophagy prevents irradiation injury and maintains stemness through decreasing ROS generation in mesenchymal stem cells**. *Cell Death Dis.* (2013) **4** e844. DOI: 10.1038/cddis.2013.338
29. García-Prat L., Martínez-Vicente M., Perdiguero E., Ortet L., Rodríguez-Ubreva J., Rebollo E., Ruiz-Bonilla V., Gutarra S., Ballestar E., Serrano A.L.. **Autophagy maintains stemness by preventing senescence**. *Nature* (2016) **534** S3-S4. DOI: 10.1038/nature19415
30. Zheng Y., Hu C.J., Zhuo R.H., Lei Y.S., Han N.N., He L.. **Inhibition of autophagy alleviates the senescent state of rat mesenchymal stem cells during long-term culture**. *Mol. Med. Rep.* (2014) **10** 3003-3008. DOI: 10.3892/mmr.2014.2624
31. Yang M., Wen T., Chen H., Deng J., Yang C., Zhang Z.. **Knockdown of insulin-like growth factor 1 exerts a protective effect on hypoxic injury of aged BM-MSCs: Role of autophagy**. *Stem Cell Res. Ther.* (2018) **9** 284. DOI: 10.1186/s13287-018-1028-5
32. Yang M., Meng Q., Ying A., Zhang L., Rui Y., Doro D.H., Liu W., Yan J.. **Autophagy controls mesenchymal stem cell properties and senescence during bone aging**. *Aging Cell* (2018) **17** e12709. DOI: 10.1111/acel.12709
33. Liu Z.Z., Hong C.G., Hu W.B., Chen M.L., Duan R., Li H.M., Yue T., Cao J., Wang Z.X., Chen C.Y.. **Autophagy receptor OPTN (optineurin) regulates mesenchymal stem cell fate and bone-fat balance during aging by clearing FABP3**. *Autophagy* (2021) **17** 2766-2782. DOI: 10.1080/15548627.2020.1839286
34. Kang O.J., Kim J.S.. **Comparison of Ginsenoside Contents in Different Parts of Korean Ginseng (Panax ginseng C.A. Meyer)**. *Prev. Nutr. Food Sci.* (2016) **21** 389-392. DOI: 10.3746/pnf.2016.21.4.389
35. Wang F., Park J.S., Ma Y., Ma H., Lee Y.J., Lee G.R., Yoo H.S., Hong J.T., Roh Y.S.. **Ginseng Saponin Enriched in Rh1 and Rg2 Ameliorates Nonalcoholic Fatty Liver Disease by Inhibiting Inflammasome Activation**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13030856
36. Lee G., Nguyen T.T.H., Lim T.Y., Lim J., Park B., Lee S., Mok I.K., Pal K., Lim S., Kim D.. **Fermented Wild Ginseng by Rhizopus oligosporus Improved l-Carnitine and Ginsenoside Contents**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25092111
37. Bak M.J., Jeong W.S., Kim K.B.. **Detoxifying effect of fermented black ginseng on H**. *Int. J. Mol. Med.* (2014) **34** 1516-1522. DOI: 10.3892/ijmm.2014.1972
38. Nguyen T., Huynh D., Jin Y., Jeon H., Heo K.S.. **Protective effects of ginsenoside-Rg2 and -Rh1 on liver function through inhibiting TAK1 and STAT3-mediated inflammatory activity and Nrf2/ARE-mediated antioxidant signaling pathway**. *Arch. Pharmacal Res.* (2021) **44** 241-252. DOI: 10.1007/s12272-020-01304-4
39. Hj A., Dtnha B., Nb A., Tlln A., Ksh A.. **Ginsenoside-Rg2 affects cell growth via regulating ROS-mediated AMPK activation and cell cycle in MCF-7 cells**. *Phytomedicine* (2021) **85** 153549. DOI: 10.1016/j.phymed.2021.153549
40. Gou D., Pei X., Wang J., Wang Y., Hu C., Song C., Cui S., Zhou Y.. **Antiarrhythmic effects of ginsenoside Rg2 on calcium chloride–induced arrhythmias without oral toxicity**. *J. Ginseng Res.* (2020) **44** 717-724. DOI: 10.1016/j.jgr.2019.06.005
41. Fan Y., Wang N., Rocchi A., Zhang W., Vassar R., Zhou Y., He C.. **Identification of natural products with neuronal and metabolic benefits through autophagy induction**. *Landes Biosci.* (2017) **13** 41-56. DOI: 10.1080/15548627.2016.1240855
42. Zhang J.J., Chen K.C., Zhou Y., Wei H., Qi M.H., Wang Z., Zheng Y.N., Chen R.X., Liu S., Li W.. **Evaluating the effects of mitochondrial autophagy flux on ginsenoside Rg2 for delaying D-galactose induced brain aging in mice**. *Phytomedicine* (2022) **104** 154341. DOI: 10.1016/j.phymed.2022.154341
43. Wang Z., Wang L., Jiang R., Li C., Wang Y.. **Ginsenoside Rg1 prevents bone marrow mesenchymal stem cell senescence via NRF2 and PI3K/Akt signaling**. *Free Radic. Biol. Med.* (2021) **174** 182-194. DOI: 10.1016/j.freeradbiomed.2021.08.007
44. Hong T., Kim M.Y., Da Ly D., Park S.J., Eom Y.W., Park K.S., Baik S.K.. **Ca(2+)-activated mitochondrial biogenesis and functions improve stem cell fate in Rg3-treated human mesenchymal stem cells**. *Stem Cell Res. Ther.* (2020) **11** 467. DOI: 10.1186/s13287-020-01974-3
45. He F., Yao G.. **Ginsenoside Rg1 as a Potential Regulator of Hematopoietic Stem/Progenitor Cells**. *Stem Cells Int.* (2021) **17** 849. DOI: 10.1155/2021/4633270
46. Si Y.C., Li Q., Xie C.E., Niu X., Yu C.Y.. **Chinese herbs and their active ingredients for activating xue (blood) promote the proliferation and differentiation of neural stem cells and mesenchymal stem cells**. *Chin. Med.* (2014) **9** 13. DOI: 10.1186/1749-8546-9-13
47. Nishimura M., Nguyen L., Watanabe N., Fujita Y., Sawamoto O., Matsumoto S.. **Development and characterization of novel clinical grade neonatal porcine bone marrow-derived mesenchymal stem cells**. *Xenotransplantation* (2019) **26** e12501. DOI: 10.1111/xen.12501
48. Wagner W., Horn P., Castoldi M., Diehlmann A., Bork S., Saffrich R., Benes V., Blake J., Pfister S., Eckstein V.. **Replicative senescence of mesenchymal stem cells: A continuous and organized process**. *PLoS ONE* (2008) **3**. DOI: 10.1371/journal.pone.0002213
49. Nakamura T., Naguro I., Ichijo H.. **Iron homeostasis and iron-regulated ROS in cell death, senescence and human diseases**. *Biochim. Biophys. Acta Gen. Subj.* (2019) **1863** 1398-1409. DOI: 10.1016/j.bbagen.2019.06.010
50. Wang J., Liu L., Ding Z., Luo Q., Ju Y., Song G.. **Exogenous NAD+ Postpones the D-Gal-Induced Senescence of Bone Marrow-Derived Mesenchymal Stem Cells via Sirt1 Signaling**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10020254
51. Zahedi M., Parham A., Dehghani H., Kazemi Mehrjerdi H.. **Equine bone marrow-derived mesenchymal stem cells: Optimization of cell density in primary culture**. *Stem Cell Investig.* (2018) **5** 31. DOI: 10.21037/sci.2018.09.01
52. Rink B.E., Amilon K.R., Esteves C.L., French H.M., Watson E., Aurich C., Donadeu F.X.. **Isolation and characterization of equine endometrial mesenchymal stromal cells**. *Stem Cell Res. Ther.* (2017) **8** 166. DOI: 10.1186/s13287-017-0616-0
53. Tjempakasari A., Suroto H., Santoso D.. **Mesenchymal Stem Cell Senescence and Osteogenesis**. *Medicina* (2021) **58**. DOI: 10.3390/medicina58010061
54. Wang J.J., Zhang W.X., Wang K.F., Zhang S., Han X., Guan W.J., Ma Y.H.. **Isolation and biological characteristics of multipotent mesenchymal stromal cells derived from chick embryo intestine**. *Br. Poult. Sci.* (2018) **59** 521-530. DOI: 10.1080/00071668.2018.1490495
55. Zhang G., Zhao X., Li X., Sun X., Zhou J., Du G., Chen J.. **Application of cell culture techniques in cultured meat-a review**. *Sheng Wu Gong Cheng Xue Bao* (2019) **35** 1374-1381. DOI: 10.13345/j.cjb.190138
56. Li X., Zhang G., Zhao X., Sun X., Zhou J., Du G., Chen J.. **Prospects of process and bioreactors for large scale cultured meat production**. *Chin. J. Process Eng.* (2020) **20** 3-11. DOI: 10.12034/j.issn.1009-606X.219179
57. Oliver L., Hue E., Priault M., Vallette F.M.. **Basal autophagy decreased during the differentiation of human adult mesenchymal stem cells**. *Stem Cells Dev.* (2012) **21** 2779-2788. DOI: 10.1089/scd.2012.0124
58. Salemi S., Yousefi S., Constantinescu M.A., Fey M.F., Simon H.U.. **Autophagy is required for self-renewal and differentiation of adult human stem cells**. *Cell Res.* (2012) **22** 432-435. DOI: 10.1038/cr.2011.200
59. Mortensen M., Watson A.S., Simon A.K.. **Lack of autophagy in the hematopoietic system leads to loss of hematopoietic stem cell function and dysregulated myeloid proliferation**. *Autophagy* (2011) **7** 1069-1070. DOI: 10.4161/auto.7.9.15886
60. Bu W., Hao X., Yang T., Wang J., Liu Q., Zhang X., Li X., Gong Y., Shao C.. **Autophagy Contributes to the Maintenance of Genomic Integrity by Reducing Oxidative Stress**. *Oxid. Med. Cell Longev.* (2020) **2020** 2015920. DOI: 10.1155/2020/2015920
61. Khorraminejad-Shirazi M., Sani M., Talaei-Khozani T., Dorvash M., Attar A.. **AICAR and nicotinamide treatment synergistically augment the proliferation and attenuate senescence-associated changes in mesenchymal stromal cells**. *Stem Cell Res. Ther.* (2020) **11** 45. DOI: 10.1186/s13287-020-1565-6
62. Deng J., Ouyang P., Li W., Zhong L., Gu C., Shen L., Cao S., Yin L., Ren Z., Zuo Z.. **Curcumin Alleviates the Senescence of Canine Bone Marrow Mesenchymal Stem Cells during In Vitro Expansion by Activating the Autophagy Pathway**. *Int J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms222111356
63. Lee Y., Jung J., Cho K.J., Lee S.K., Park J.W., Oh I.H., Kim G.J.. **Increased SCF/c-kit by hypoxia promotes autophagy of human placental chorionic plate-derived mesenchymal stem cells via regulating the phosphorylation of mTOR**. *J. Cell. Biochem.* (2012) **114** 79-88. DOI: 10.1002/jcb.24303
64. Li L., Li L., Zhang Z., Jiang Z.. **Hypoxia promotes bone marrow-derived mesenchymal stem cell proliferation through apelin/APJ/autophagy pathway**. *Acta Biochim. Biophys. Sin.* (2015) **47** 362-367. DOI: 10.1093/abbs/gmv014
65. Dka B., Aea B., Rtb C., Tm C., Tb C., Neab K., Gka B., Ps C., Apcd E., Xmsfg H.. **Kynurenine inhibits autophagy and promotes senescence in aged bone marrow mesenchymal stem cells through the aryl hydrocarbon receptor pathway—ScienceDirect**. *Exp. Gerontol.* (2020) **130** 110805. DOI: 10.1016/j.exger.2019.110805
66. Maharajan N., Ganesan C.D., Moon C., Jang C.H., Oh W.K., Cho G.W.. **Licochalcone D Ameliorates Oxidative Stress-Induced Senescence via AMPK Activation**. *Multidiscip. Digit. Publ. Inst.* (2021) **22**. DOI: 10.3390/ijms22147324
67. Maharajan N., Cho G.W.. **Camphorquinone Promotes the Antisenescence Effect via Activating AMPK/SIRT1 in Stem Cells and D-Galactose-Induced Aging Mice**. *Antioxidants* (2021) **10**. DOI: 10.3390/antiox10121916
68. Feng X., Xing J., Feng G., Huang D., Lu X., Liu S., Tan W., Li L., Gu Z.. **p16(INK4A) mediates age-related changes in mesenchymal stem cells derived from human dental pulp through the DNA damage and stress response**. *Mech. Ageing Dev.* (2014) **141** 46-55. DOI: 10.1016/j.mad.2014.09.004
69. Zhang D.Y., Pan Y., Zhang C., Yan B.X., Yu S.S., Wu D.L., Shi M.M., Shi K., Cai X.X., Zhou S.S.. **Wnt/beta-catenin signaling induces the aging of mesenchymal stem cells through promoting the ROS production**. *Mol. Cell Biochem.* (2013) **374** 13-20. DOI: 10.1007/s11010-012-1498-1
70. Wu J., Niu J., Li X., Wang X., Guo Z., Zhang F.. **TGF-β1 induces senescence of bone marrow mesenchymal stem cells via increase of mitochondrial ROS production**. *BMC Dev. Biol.* (2014) **14**. DOI: 10.1186/1471-213X-14-21
71. Basciano L., Nemos C., Foliguet B., de Isla N., de Carvalho M., Tran N., Dalloul A.. **Long term culture of mesenchymal stem cells in hypoxia promotes a genetic program maintaining their undifferentiated and multipotent status**. *BMC Cell Biol.* (2011) **12**. DOI: 10.1186/1471-2121-12-12
72. Zhang S., Dong Z., Peng Z., Lu F.. **Anti-aging effect of adipose-derived stem cells in a mouse model of skin aging induced by D-galactose**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0097573
73. He Z.H., Li M., Fang Q.J., Liao F.L., Zou S.Y., Wu X., Sun H.Y., Zhao X.Y., Hu Y.J., Xu X.X.. **FOXG1 promotes aging inner ear hair cell survival through activation of the autophagy pathway**. *Autophagy* (2021) **17** 4341-4362. DOI: 10.1080/15548627.2021.1916194
74. Zhang D., Yan B., Yu S., Zhang C., Wang B., Wang Y., Wang J., Yuan Z., Zhang L., Pan J.. **Coenzyme Q10 inhibits the aging of mesenchymal stem cells induced by D-galactose through Akt/mTOR signaling**. *Oxid. Med. Cell Longev.* (2015) **2015** 867293. DOI: 10.1155/2015/867293
75. Chen L., Yao H., Chen X., Wang Z., Xiang Y., Xia J., Liu Y., Wang Y.. **Ginsenoside Rg1 Decreases Oxidative Stress and Down-Regulates Akt/mTOR Signalling to Attenuate Cognitive Impairment in Mice and Senescence of Neural Stem Cells Induced by d-Galactose**. *Neurochem. Res.* (2018) **43** 430-440. DOI: 10.1007/s11064-017-2438-y
76. Li J., Cai D., Yao X., Zhang Y., Chen L., Jing P., Wang L., Wang Y.. **Protective Effect of Ginsenoside Rg1 on Hematopoietic Stem/Progenitor Cells through Attenuating Oxidative Stress and the Wnt/beta-Catenin Signaling Pathway in a Mouse Model of d-Galactose-induced Aging**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17060849
77. Hou J., Ma R., Zhu S., Wang Y.. **Revealing the Therapeutic Targets and Mechanism of Ginsenoside Rg1 for Liver Damage Related to Anti-Oxidative Stress Using Proteomic Analysis**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms231710045
78. Zhang D., Chen Y., Xu X., Xiang H., Shi Y., Gao Y., Wang X., Jiang X., Li N., Pan J.. **Autophagy inhibits the mesenchymal stem cell aging induced by D-galactose through ROS/JNK/p38 signalling**. *Clin. Exp. Pharmacol. Physiol.* (2019) **47** 466-477. DOI: 10.1111/1440-1681.13207
79. Cheng B., Gao W., Wu X., Zheng M., Gao Y.. **Ginsenoside Rg2 Ameliorates High-Fat Diet-Induced Metabolic Disease through SIRT1**. *J. Agric. Food Chem.* (2020) **68** 4215-4226. DOI: 10.1021/acs.jafc.0c00833
80. Han D., Jiang L., Gu X., Huang S., Pang J., Wu Y., Yin J., Wang J.. **SIRT3 deficiency is resistant to autophagy-dependent ferroptosis by inhibiting the AMPK/mTOR pathway and promoting GPX4 levels**. *J. Cell Physiol.* (2020) **235** 8839-8851. DOI: 10.1002/jcp.29727
81. Gwinn D.M., Shackelford D.B., Egan D.F., Mihaylova M.M., Shaw R.J.. **AMPK Phosphorylation of Raptor Mediates a Metabolic Checkpoint**. *Mol. Cell* (2008) **30** 214-226. DOI: 10.1016/j.molcel.2008.03.003
82. Lin M., Hua R., Ma J., Zhou Y., Quan S.. **Bisphenol A promotes autophagy in ovarian granulosa cells by inducing AMPK/mTOR/ULK1 signalling pathway**. *Environ. Int.* (2021) **147** 106298. DOI: 10.1016/j.envint.2020.106298
83. Mao K., Klionsky D.J.. **AMPK activates autophagy by phosphorylating ULK1**. *Circ. Res.* (2011) **108** 787-788. DOI: 10.1161/RES.0b013e3182194c29
84. Kim J., Kundu M., Viollet B., Guan K.L.. **AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1**. *Nat. Cell Biol.* (2011) **13** 132-141. DOI: 10.1038/ncb2152
85. Ameen O., Samaka R.M., Abo-Elsoud R.A.A.. **Metformin alleviates neurocognitive impairment in aging via activation of AMPK/BDNF/PI3K pathway**. *Sci. Rep.* (2022) **12** 17084. DOI: 10.1038/s41598-022-20945-7
86. Lu S., Zhou J., Yang C., Zhang X., Shi Y., Liu J., Yan X., Liang J., Liu X., Luo L.. **gamma-Glutamylcysteine ameliorates D-gal-induced senescence in PC12 cells and mice via activating AMPK and SIRT1**. *Food Funct.* (2022) **13** 7560-7571. DOI: 10.1039/D2FO01246D
87. Zhu M., Shen W., Li J., Jia N., Xiong Y., Miao J., Xie C., Chen Q., Shen K., Meng P.. **AMPK Activator O304 Protects Against Kidney Aging Through Promoting Energy Metabolism and Autophagy**. *Front. Pharm.* (2022) **13** 836496. DOI: 10.3389/fphar.2022.836496
88. Kou X., Li J., Liu X., Yang X., Fan J., Chen N.. **Ampelopsin attenuates the atrophy of skeletal muscle from d-gal-induced aging rats through activating AMPK/SIRT1/PGC-1α signaling cascade**. *Biomed. Pharmacother. Biomed. Pharmacother.* (2017) **90** 311-320. DOI: 10.1016/j.biopha.2017.03.070
89. Liu H., Liu M., Jin Z., Yaqoob S., Zheng M., Cai D., Liu J., Guo S.. **Ginsenoside Rg2 inhibits adipogenesis in 3T3-L1 preadipocytes and suppresses obesity in high-fat-diet-induced obese mice through the AMPK pathway**. *Food Funct.* (2019) **10** 3603-3614. DOI: 10.1039/C9FO00027E
90. Yuan H.D., Kim D.Y., Quan H.Y., Su J.K., Mi S.J., Chung S.H.. **Ginsenoside Rg2 induces orphan nuclear receptor SHP gene expression and inactivates GSK3β via AMP-activated protein kinase to inhibit hepatic glucose production in HepG2 cells**. *Chem. Biol. Interact.* (2012) **195** 35-42. DOI: 10.1016/j.cbi.2011.10.006
91. Hekman K.E., Koss K.M., Ivancic D.Z., He C., Wertheim J.A.. **Autophagy Enhances Longevity of Induced Pluripotent Stem Cell-Derived Endothelium via mTOR-Independent ULK1 Kinase**. *Stem Cells Transl. Med.* (2022) **11** 1151-1164. DOI: 10.1093/stcltm/szac069
|
---
title: Artificial Diets with Selective Restriction of Amino Acids and Very Low Levels
of Lipids Induce Anticancer Activity in Mice with Metastatic Triple-Negative Breast
Cancer
authors:
- Emilio Guillén-Mancina
- Julio José Jiménez-Alonso
- José Manuel Calderón-Montaño
- Víctor Jiménez-González
- Patricia Díaz-Ortega
- Estefanía Burgos-Morón
- Miguel López-Lázaro
journal: Cancers
year: 2023
pmcid: PMC10000978
doi: 10.3390/cancers15051540
license: CC BY 4.0
---
# Artificial Diets with Selective Restriction of Amino Acids and Very Low Levels of Lipids Induce Anticancer Activity in Mice with Metastatic Triple-Negative Breast Cancer
## Abstract
### Simple Summary
Current treatments for patients with metastatic triple negative breast cancer (TNBC) are generally ineffective. This manuscript shows for the first time that the survival of mice with metastatic TNBC can be markedly increased through dietary manipulation. Our study revealed that the survival of some mice with metastatic TNBC was increased by replacing their normal diet with artificial diets in which the levels of amino acids (AAs) are manipulated, and the levels of lipids are markedly reduced. The anticancer activity of this non-pharmacological strategy was higher than that of drugs currently used in the treatment of patients with metastatic TNBC. This anticancer strategy also increased the survival of mice with other types of metastatic cancers. Manipulation of AA and lipid levels with artificial diets may be a useful strategy to treat patients with metastatic TNBC and other types of disseminated cancer.
### Abstract
Patients with metastatic triple negative breast cancer (TNBC) need new therapies to improve the low survival rates achieved with standard treatments. In this work, we show for the first time that the survival of mice with metastatic TNBC can be markedly increased by replacing their normal diet with artificial diets in which the levels of amino acids (AAs) and lipids are strongly manipulated. After observing selective anticancer activity in vitro, we prepared five artificial diets and evaluated their anticancer activity in a challenging model of metastatic TNBC. The model was established by injecting 4T1 murine TNBC cells into the tail vein of immunocompetent BALB/cAnNRj mice. First-line drugs doxorubicin and capecitabine were also tested in this model. AA manipulation led to modest improvements in mice survival when the levels of lipids were normal. Reducing lipid levels to $1\%$ markedly improved the activity of several diets with different AA content. Some mice fed the artificial diets as monotherapy lived much longer than mice treated with doxorubicin and capecitabine. An artificial diet without 10 non-essential AAs, with reduced levels of essential AAs, and with $1\%$ lipids improved the survival not only of mice with TNBC but also of mice with other types of metastatic cancers.
## 1. Introduction
Breast cancer is the most common malignancy in women [1,2]. Triple negative breast cancer (TNBC) is a subtype of breast cancer defined by negative expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor-2. It represents approximately 15–$20\%$ of all breast cancer cases [3]. TNBCs generally have an aggressive behavior and treatment options are more limited than for other subtypes of breast cancer. The 5-year survival rate of women diagnosed with metastatic TNBC is approximately $12\%$ [2].
Pharmacotherapy is the main form of treatment for patients with metastatic TNBC. The preferred treatment options for patients with stage IV TNBC are the anthracyclines doxorubicin or liposomal doxorubicin, the antimetabolites capecitabine or gemcitabine, the microtubule inhibitors paclitaxel, vinorelbine or eribulin, and the antibody–drug conjugate sacituzumab govitecan [4]. Alternative first-line treatments can be used when certain biomarkers are detected. When BRCA mutations are identified, PARP inhibitors (olaparib) or platinum compounds (cisplatin or carboplatin) can be used [4,5,6]. For PD-L1 expressing TNBC, the preferred treatment option is pembrolizumab plus a chemotherapy drug [4,7]. Although these drugs can increase patient survival and palliate disease-related symptoms, they do not usually cure the disease. New therapies for patients with metastatic TNBC are therefore highly needed.
TNBC cells acquire metabolic alterations [8,9,10] that may be exploited to develop new therapies for patients with metastatic TNBC. Cancer cells reprogram their metabolism to fulfill their elevated energy demands, to produce the large amounts of building blocks required for biosynthesis and proliferation, and to survive under conditions of elevated oxidative stress [11]. Cancer cells commonly develop alterations in amino acid (AA) metabolism. For example, many cancer cells rely on external sources of non-essential AAs (NEAAs) to maintain their proliferative demands and redox homeostasis [11,12]. Targeting the altered AA metabolism of cancer cells using pharmacological and dietary strategies shows promising results against a wide variety of cancers [11,12]. In breast cancer, pharmacological strategies have been developed to target cystine (CySS) uptake [13,14], to disrupt glutamine (Gln) catabolism [15], to inhibit proline (Pro) catabolism [16,17], and to deplete plasma levels of asparagine (Asn), arginine (Arg) and cyst(e)ine (Cys). The enzymes L-asparaginase [18,19,20], ADI-PEG20 [21] and cyst(e)inase [22] have shown anticancer activity in murine breast cancer models, and ADI-PEG20 in combination with doxorubicin has recently been evaluated in a phase I clinical trial in patients with metastatic breast cancer and other metastatic tumors [23]. Dietary restriction of several AAs has also shown in vivo anticancer effects in TNBC models, including restrictions of methionine (Met) [24,25,26,27], Arg [28], Asn [19,20], and double restriction of serine (Ser) and glycine (Gly) [29].
Cancer cells develop changes in lipid metabolism that may also be exploited to develop new therapies for patients with metastatic TNBC. Dietary lipids provide high levels of fatty acids. Cancer cells use these fatty acids to fulfill their energy demands, to produce lipid bilayers for the new cells created during tumor growth, and to support many other processes involved in cancer survival, progression and metastasis [30,31]. For example, dietary lipids provide linoleic acid. This omega-6 essential fatty acid is a precursor of arachidonic acid, which cancer cells avidly consume to generate pro-inflammatory prostaglandins and leukotrienes that have crucial roles in many processes involved in cancer progression [32]. Some cancer cells are also known to overexpress CD36, a membrane protein used for the cellular uptake of fatty acids [33]. The overexpression of this membrane protein is correlated with a poor prognosis of breast cancer and other types of cancer, and its pharmacological inhibition decreases the metastatic potential in murine cancer models, including TNBC models [33]. Since lipid availability is crucial for cancer development, it is not surprising that high-fat diets accelerate cancer progression in mice [34,35,36,37,38,39,40,41,42,43,44], including mice with TNBC and other subtypes of breast cancer [45,46,47,48]. Clinical data have also shown that the levels of dietary lipids influence disease progression in women with breast cancer. Recurrence of early-stage resected breast cancer was higher in women with a high-fat diet compared to women with a low-fat diet [49]. A low-fat dietary pattern was also found to reduce mortality in women after breast cancer [50]. It is important to note that low-fat diets in these preclinical and clinical studies consisted of diets with 5–$10\%$ of lipids in their composition; the anticancer effects of diets with a percentage of lipids below $5\%$ is underexplored.
We have recently shown that artificial diets based on selective amino acid restriction induce marked anticancer activity in mice with colon and renal cancers [51,52]. In this article, after observing that an artificial media lacking 10 NEAAs induced selective anticancer activity in TNBC cells in vitro, we show that a diet lacking these 10 AAs induced anticancer activity in a challenging animal model of metastatic TNBC. The in vivo anticancer activity of this diet was markedly improved when the lipid levels were reduced from $14\%$ to $1\%$. Four artificial diets with selective AA restrictions and very low levels of lipids ($1\%$) induced anticancer activity in mice with metastatic TNBC. Mean survivals in mice fed these diets were higher than in mice treated with the standard therapies doxorubicin or capecitabine. One of the diets also induced anticancer activity in mice with other types of metastatic cancers. Although none of the animals used in this work were cured by any standard or experimental treatment, several mice treated with our artificial diets had very long survivals.
## 2.1. Drugs and Reagents
MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, A22310005) and SDS (sodium dodecyl sulfate, 1423631209) were obtained from Panreac Applichem (Darmstadt, Germany). 5-fluorouracil (5-FU) and resazurin were purchased from Sigma (Kawasaki, Japan). Cisplatin, choline bitartrate [450225000], tert-butylhydroquinone (TBHQ, 150822500) and casein (isolated from bovine milk, 276070010) were purchased from Thermo Scientific Acros Organics (Waltham, MA, USA). We also used doxorubicin (50 mg powder for solution, Farmiblastina, Pfizer (New York, NY, USA), 958314.9), capecitabine (500 mg/tablet, Sandoz, Basel, Switzerland), anti-PD-1 (anti-mouse PD-1 (CD279), clone RMP1-14, BE0146, Bioxcell, Lebanon, NH, USA), India Ink (Superblack India Ink, Speedball (Statesville, NC, USA), 33X089A), and sterile physiological serum for diluting injected drugs (Kin laboratory, Dos Hermanas, Spain, 160407.1). Mineral Mix (AIN-93M-MX, 960401) and Vitamin Mix (AIN Vitamin Mixture 76, 905454) were purchased from MP Biomedicals (Eschwege, Germany). Sucrose was obtained from a local market (MAS Supermarket, Seville, Spain). Cellulose and corn starch were purchased from Farmusal (local pharmacy, Granada, Spain). Extra virgin olive oil (marketable olive oil developed for Dia Supermarket, Spain, 112529), salmon oil (marketable oil developed for Pets Purest, England, B06WWFTRXM) and coconut oil (marketable oil developed for Mercadona Supermarket, Spain, 848000041937) were used as a fat source. Essential amino acid mix and L-glutamine (Gln) were obtained from Myprotein (Manchester, England).
L-alanine (Ala, A1688), L-arginine (Arg, A3675), L-asparagine-1-hydrate (Asn, A1668), L-aspartic acid (Asp, A3715), L-cysteine (Cys, A3694), L-cystine (CySS, A1703), L-glutamic acid (Glu, A1704), glycine (Gly, A3707), L-leucine (Leu, A1426), L-methionine (Met, A1340), L-proline (Pro, A1707), L-serine (Ser, A1708) and L-tyrosine (Tyr, A3437) were purchased from Panreac Applichem. Cell culture reagents were obtained from Biowest (Nuaillé, France) and Thermo Fisher Scientific, unless otherwise indicated.
## 2.2. Cell Culture
4T1 (murine triple negative breast cancer cells, CRL-2539), LL/2 (murine lung cancer cells, CRL-1642), CT26.WT (murine colorectal cancer cells, CRL-2638), B16-F10 (murine melanoma cells, CRL-6475) and MDA-MB-231 (human triple negative breast cancer cells, HTB-26) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). HaCaT (non-malignant human keratinocytes, 300493 [53]), BT-474 (human luminal B type breast cancer cells [ER+; PR+; Her-2+], 300131), T-47D (human luminal A type breast cancer cells [ER+; PR+; Her-2−], 300353), SK-BR-3 (human breast cancer cells; HER-2 positive, 300333), A549 (human non-small cell lung cancer cells, 300114), Calu-1 (human squamous lung cancer cells, 300141), MeWo (human melanoma cells; BRAF WT, 300285), NIH:OVCAR-3 (human ovarian cancer cells, 300307) and SK-OV-3 (human ovarian cancer cells, 330342) were purchased from the Cell Line Service (CLS, Hamburg, Germany). UACC-62 (human melanoma cells; BRAF mut) was obtained from the National Cancer Institute (Rockville, MD, USA). HT29 (human colorectal cancer cells) were generously provided by Dr. Helleday (Karolinska Institute, Sweden). ID8 Trp53−/− (murine ovarian cancer cells) were a gift from Dr. Iain A. McNeish (Institute of Cancer Sciences, University of Glasgow, UK [54]). 4T1, CT26.WT, T-47D, Calu-1, NIH:OVCAR-3 and UACC-62 were cultured in RPMI 1640. LL/2, B16-F10, MDA-MB-231, HaCaT, BT-474, SK-BR-3, A549, MeWo, SK-OV-3, HT29 and ID8 Trp53−/− were grown in Dulbecco’s modified Eagle’s medium (DMEM) high glucose medium. All media were supplemented with 100 U/mL penicillin, 100 μg/mL streptomycin and $10\%$ fetal bovine serum (FBS), except medium for ID8 Trp53−/−, which was supplemented with 0.11 g/L sodium pyruvate, $4\%$ FBS and $1\%$ insulin–transferrin–selenium. All cells were cultured in a humidified 37 °C incubator with $5\%$ CO2.
## 2.3. In Vitro Experiments
Exponentially growing cells were seeded in 96-well plates. After 24 h, the medium was removed and replaced by amino acid-manipulated media (see details below), by complete medium (controls), or by complete medium with several concentrations of an anticancer drug. The cells were visualized daily under a microscope and photographed (20× magnification) on the third and seventh days of treatment using a Huawei P9 lite *Leica camera* adapted to an inverted microscope. After the treatment period, cell viability was estimated with the MTT assay or the resazurin assay. The MTT is a colorimetric assay based on the capacity of viable cells to reduce yellow tetrazolium MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) into an insoluble and purple-colored formazan product. At 24 h after seeding, the cells were exposed to several concentrations of anticancer drugs or to artificial medium. After the treatment period, the medium was removed and 125 μL of MTT diluted in medium (1 mg/mL) was added to the wells. The plates were incubated for 2–4 h at 37 °C, $5\%$ CO2. Then, 80 μL $20\%$ SDS in 0.02 M HCl were added to the plates, which were incubated overnight at 37 °C. Finally, optical densities were measured at 540 nm on a multiwell plate spectrophotometer reader. The resazurin assay is a redox-based fluorometric/colorimetric technique based on the capability of viable cells to reduce blue reagent resazurin into the pink, fluorescent and soluble product resorufin. At 24 h after seeding, the cells were exposed to artificial medium for 7 days. The cells were then allowed to recover in their corresponding standard media for 3 days. After treatments and recovery period, medium was removed and 150 μL of resazurin solution (20 μg/mL in medium) was added to each well for 5–7 h (depending on the cell line). The optical densities of each well were measured at 540 nm and 620 nm on a multiwell plate spectrophotometer reader. In both assays, the results were expressed as percentages of cell viability in relation to untreated cells grown in their standard medium. Data for the artificial media were averaged from at least three independent experiments. The data for the anticancer drugs were averaged from two independent experiments and were expressed as the means ± standard error of the mean (SEM).
Artificial media lacking AAs were prepared as described previously [51,52]. Briefly, powdered DMEM medium without AAs (D9800-13; US Biological, Salem, MA, USA) was supplemented with sodium bicarbonate (Panreac, 141638.1211), glucose (Panreac, 141341.1211), FBS, penicillin/streptomycin and specific AAs (see Table 1).
## 2.4. Animals
Female BALB/cAnNRj mice and female C57BL/6JRj mice (10 weeks or older) were purchased from Janvier Labs® (Le Genest-Saint-Isle, France). To allow adequate acclimation, they were housed in our animal laboratory facilities for at least two weeks before starting the experiments. The animals were kept in standard conditions (12 h light/12 h dark cycle, 70–$75\%$ humidity, 24 °C, with ad libitum access to food and water). The mice were fed a standard diet (ssniff diet R/M-Z E/R/S; V1724-000, ssniff Spezialdiäten, Soest, Germany). All mice were 12 weeks or older at the beginning of the experiments.
The experiments were approved by the Animal Ethics Committee of the University of Seville (CEEA-US2018-$\frac{6}{2}$ and CEEA-US2019-20) and Junta de Andalucía ($\frac{15}{05}$/$\frac{2018}{090}$ and $\frac{13}{11}$/$\frac{2020}{131}$). They were carried out under the recommendations of the European Union on animal experimentation (Directive of the European Counsel $\frac{2010}{630}$/EU).
## 2.5. In Vivo Cancer Models
In all cancer models, murine cells (5th–7th passage) were cultured in 75-cm2 flask until approximately 60–$70\%$ confluence. Medium was removed and cells were washed twice with sterile PBS. The cells were then incubated with trypsin/EDTA solution for 1–3 min at 37 °C to allow the cells to have a rounded shape but without detaching. Next, the trypsin/EDTA solution was aspirated, cells were resuspended in 5 mL of sterile PBS and the cell suspension was pipetted up and down to break up any cell aggregate before adding $2.5\%$ FBS supplemented medium. Then, a working cell suspension (between 5 × 105–25 × 106 cells/mL depending on the cancer model) was prepared. The cell suspension was centrifuged (250 g) at room temperature for 5 min. The medium was then removed, and cells were resuspended in warm sterile filtered PBS. Cells were counted again to ensure that the cell suspension was at the correct density. Finally, a 1 mL syringe (insulin type with a 29-G × $\frac{1}{2}$″ needle) was filled with 0.2 mL of the working cell suspension, which was injected into the tail vein or into the peritoneal cavity of the mice. One day before initiating the treatments, mice were housed in individual cages to avoid cannibalism. Treatments started four, eight or twenty-one days (depending on the model) after injecting the cancer cells so that they had time to adapt to the new environment and proliferate before the beginning of the treatments. The aim of our research was not to prevent metastasis, but to find new treatments for patients with stablished metastatic cancers. Untreated animals (control group) continued to be fed with their standard diet (ssniff diet). Other groups of mice received drugs used in patients with the selected type of cancer. In the groups of mice treated with the artificial diets, treatment consisted of replacing their normal diet with one of the artificial diets. Treatments with the artificial diets lasted at least 4 weeks, unless otherwise specified or shown. The in vivo cancer models used in this work [54,55,56,57,58,59,60,61] are summarized in Table S1.
In the triple negative breast cancer model, female BALB/cAnNRj mice were inoculated with 105 4T1 cancer cells in the tail vein [55,56]. Treatments started 8 days after the injection of the cells. Doxorubicin and capecitabine were used as positive controls. Doxorubicin (0.5 mg/kg) was injected intraperitoneally once a week for 4 weeks. Capecitabine (450 mg/kg/day) was administered in the diet following a $\frac{7}{7}$ on/off schedule; the animals received 2 or 3 cycles depending on their state of health. To follow the recommendations of the Animal Ethics Committee, treatments were initially screened using 2–4 mice per group. The anticancer activity of the active treatments was then evaluated in independent experiments with a higher number of animals. To facilitate comparison between groups, mice receiving the same treatment were merged in the survival curves. Therefore, survival data of mice included in the control or positive control groups in the screening experiments were used for the preparation of the survival curves of several active treatments. All experiments included a control group (untreated mice) and a positive control group (doxorubicin or capecitabine).
The colon cancer model was established by injecting 105 CT26.WT cells in the peritoneal cavity (peritoneal dissemination model) or in the tail vein (lung metastasis model) of female BALB/cAnNRj mice [57,58]. In both models, treatments started 4 days after cancer cell inoculation. Capecitabine (450 mg/kg/day) was administered in the diet following a $\frac{7}{7}$ on/off schedule; the animals received 2 or 3 cycles depending on their state of health.
In the lung cancer model, 2 × 106 LL/2 cells were injected in the tail vein of female C57BL/6JRj mice [59,60]. Treatments started 7 days after the inoculation of the cancer cells. Anti-PD-1 was used as a positive control; it was administered intraperitoneally every 4 days for a total of 4 doses. In each dose, mice received 250 μg anti-PD-1 diluted in pH 7.0 buffer (InVivoPure, IP0070, Bioxcell).
The ovarian cancer model was established by injecting 5 × 106 ID8 Trp53−/− cells into the peritoneal cavity of female C57BL/6JRj mice [54]. Treatments started 21 days after the inoculation of the cancer cells. The positive control cisplatin (5 mg/kg) was administered intraperitoneally once a week for 4 weeks.
The melanoma cancer model was established by inoculating 106 murine B16-F10 melanoma cells in the tail vein of female C57BL/6JRj mice [61]. Treatments started 4 days after the injection of the cancer cells. Cisplatin was used as a positive control. Cisplatin (5 mg/kg) was administered intraperitoneally once a week for 4 weeks.
All animals were monitored daily, and body weights were recorded periodically (at least three times per week). Mice were sacrificed by cervical dislocation when signs of cancer progression were apparent; these signs (e.g., respiratory distress and reduced curiosity and mobility) indicated that survival for an additional 2 days was unlikely (Table S2). Necropsy was performed to verify the cause of death and to observe the extent of the disease. The presence of tumors was confirmed in all sacrificed mice, and similar tumor loads were observed unless otherwise specified or shown. Lungs were dyed with India ink and fixed with Fekete’s solution (100 mL of $70\%$ ethanol, 5 mL of $100\%$ glacial acetic acid and 10 mL of $4\%$ formaldehyde). With this technique, tumors show a white appearance and normal lung parenchyma appears black. In the melanoma model, lungs were not dyed with India ink because melanoma tumors have a natural black appearance.
## 2.6. Artificial Diet Preparation and Composition
All artificial diets (Table 2) were prepared in our laboratory. All solid ingredients were mixed until they formed a well-blended dry powder. The oil was then added to the mixture, and enough water was slowly added until a soft dough was formed. The dough was air dried for 2 h, manually pelleted, air-dried for an additional 24 h, and stored at room temperature until use. Fresh diets were prepared for each independent experiment.
Casein (bovine casein 27607, Acros Organics) constituted $6\%$ of the dry diets TB4 and TB5. The typical amount (g) of AAs in 100 g and 6 g (shown in brackets) of the casein used in the experiments is Gln + Glu: 21.7 (1.302), Leu: 9 (0.54), Met: 2.9 (0.174), Phe: 4.8 (0.288), His: 2.6 (0.156), Lys: 7.5 (0.45), Thr: 4.1 (0.246), Ile: 4.3 (0.258), Val: 5.3 (0.318), Trp: 1.2 (0.072), Cys/CySS: 0.7 (0.042), Arg: 3.4 (0.204), Gly: 1.7 (0.102), Ser: 5.7 (0.342), Tyr: 5.2 (0.312), Ala: 2.9 (0.174), Asp + Asn: 6.9 (0.414), Pro: 10.1 (0.606).
The dry diets contained $1\%$ Vitamin Mix (AIN Vitamin Mixture 76, MP Biomedical). A total of 100 g of the dry diets contained (mg) thiamine hydrochloride (0.6), riboflavin (0.6), pyridoxine hydrochloride (0.7), nicotinic acid [3], d-calcium pantothenate (1.6), folic acid (0.2), d-biotin (0.02), cyanocobalamin (0.001), retinyl palmitate premix (250,000 IU/g) (1.6), DL-a-tocopherol acetate (250 IU/g) [20], cholecalciferol (400,000 IU/g) (0.25), menaquinone (0.005), sucrose (972.9). The dry diets contained $3.5\%$ Mineral Mix (AIN-93M-MX, MP Biomedical). A total of 100 g of the dry diets contained $1.25\%$ calcium carbonate, $0.875\%$ monopotassium phosphate, $0.098\%$ potassium citrate, $0.259\%$ sodium chloride, $0.163\%$ potassium sulfate, $0.085\%$ magnesium oxide, $0.021\%$ ferric citrate, $0.0058\%$ zinc carbonate, $0.0022\%$ manganese carbonate, $0.0011\%$ copper carbonate, $0.000035\%$ potassium iodate, $0.000035\%$ sodium selenate, $0.000028\%$ ammonium paramolybdate-tetrahydrate, $0.0051\%$ sodium metasilicate-nonahydrate, 0.00095 chromium potassium sulfate-dodecahydrate, $0.0000595\%$ lithium chloride, $0.000284\%$ boric acid, $0.00022\%$ sodium fluoride, $0.00011\%$ nickel carbonate hydroxide, $0.000021\%$ ammonium meta-vanadate and $0.73\%$ sucrose. Tert-butylhydroquinone (150822500, Acros Organics) was used as an antioxidant for the TB2-TB5 diets, comprising $0.0008\%$ of the dry diet.
The Ssniff diet was used as a control diet (SM R/M-S E, 10 mm; V1724-000). This diet contains $21\%$ protein, $7\%$ fat, $4\%$ fiber, $6.2\%$ ash, $33.3\%$ starch and $4.6\%$ sugar.
## 2.7. Statistical Analysis
Results were expressed as mean ± standard error mean (SEM). Statistical analysis was performed with the GraphPad Prism version 7.0 software. Statistical analysis for the Kaplan–Meier survival curve was calculated using the Gehan–Breslow–Wilcoxon (GBW) test. A p value > 0.05 is not considered statistically significant and is not represented by any symbol. The p-value < 0.05 is considered statistically significant and is indicated with an asterisk (*), <0.01 with two asterisks (**) and <0.001 with three asterisks (***).
## 3.1. Amino Acid Restriction Induces Selective Anticancer Activity in Breast Cancer Cells In Vitro
Patients with metastatic TNBC need selective anticancer treatments, that is, treatments that can eliminate their cancer cells without significantly affecting their normal cells. We therefore evaluated our anticancer strategy in human TNBC cells (MDA-MB-231) versus human non-malignant cells (HaCaT). We also used murine TNBC cells (4T1) to detect potential experimental artifacts caused by species differences in sensitivity to treatments [62]. We selected a medium lacking 10 NEAAs (all NEAAs except Gln, M1), which previously showed selective cytotoxic activity against colon cancer cells and renal cancer cells [51,52]. This medium lacked all NEAAs except Gln, to force cancer cells to biosynthesize them. Gln was used as a source of amino groups for the biosynthesis of the other NEAAs. Since the anticancer activity of a restriction therapy lacking several components cannot be tested following the standard dose–response approach (e.g., IC50 values cannot be calculated), we used the following experimental approach [51,52]. The three cell lines were grown in the AA-deficient medium (M1) or in a complete medium (M0) for seven days. Cell morphology and density were visualized daily under a microscope, and representative photographs were taken on days 3 and 7. Cell viability was determined on day 7 with the MTT assay. Because doxorubicin and capecitabine, a prodrug of 5-fluorouracil (5-FU), are standard treatments for TNBC patients, we also tested doxorubicin and 5-FU in the same cell lines.
Figure 1 shows that our artificial medium lacking 10 NEAAs (M1) induced selective cytotoxicity against TNBC cells, especially after 7 days of treatment. Non-malignant cells (HaCaT) incubated with M1 proliferated slower than those incubated with M0, but they eventually saturated the wells after 7 days of exposure. Clear antiproliferative effects were observed in both human and murine TNBC cells (MDA-MB-231 and 4T1) incubated for 7 days with M1. Importantly, the standard anticancer drugs doxorubicin and 5-FU did not show selective cytotoxicity in this panel of cell lines. After three days of treatment, both drugs induced potent cytotoxic effects against the cancer cells, but also against the non-malignant cells (Figure 2). The IC50 values (mean ± SEM, µM) for 5-FU were 1.11 ± 0.74 in HaCaT cells, 16.71 ± 12.09 in MDA-MB-231 cells and 1.86 ± 1.03 in 4T1 cells. The IC50 values (mean ± SEM, µM) for doxorubicin were 0.14 ± 0.12 in HaCaT cells, 0.20 ± 0.15 in MDA-MB-231 cells and 3.16 ± 2.17 in 4T1 cells. Figure 1An artificial medium lacking 10 NEAAs induces selective cytotoxic activity in triple-negative breast cancer cells. Cells were grown in a complete medium (M0) or in a medium lacking 10 AAs (M1) for seven days. Cells were monitored by microscopic visualization and photographed on days 3 and 7. Representative photographs at 20× magnification are shown. Cell viability was estimated with the MTT assay and is shown at the bottom right of the photographs when it was less than $10\%$. The detailed compositions of M0 (a) and M1 (b) are shown in Table 1. Figure 2Cytotoxic effect of 5-fluorouracil (a) and doxorubicin (b) on triple-negative breast cancer cells and non-malignant cells. Cells were treated for 72 h, and cell viability was estimated with the MTT assay. Data show the mean ± SEM of at least 2 independent experiments.
Our medium lacking 10 NEAAs (M1) also induced selective cytotoxicity against other types of human breast cancer cells: BT-474 (luminal B type [ER+; PR+; Her-2+]), T-47D (luminal A type [ER+; PR+; Her-2-]) and SK-BR-3 (HER-2 positive) (Table S3 and Figure S1). These data indicate that the selective anticancer activity of our restriction therapy is not limited to TNBC cells.
## 3.2. Anticancer Activity of an Artificial Diet Lacking 10 NEAAs in Mice with Metastatic Triple-Negative Breast Cancer
Since the medium lacking 10 NEAAs (M1) induced selective anticancer activity in TNBC cells in vitro, we prepared an artificial diet lacking the same AAs (diet TB1) to test its anticancer activity in an in vivo model of metastatic TNBC. The model was established by injecting murine TNBC cells (4T1) into the tail vein of immunocompetent female BALB/cAnNRj mice. In this model, all untreated mice die several weeks after the inoculation of the cancer cells. Treatments began 8 days after the injection of the cancer cells. Mice were euthanized by cervical dislocation when signs of disease progression were apparent; these signs (e.g., respiratory distress and/or reduced mobility and curiosity) indicated that survival for an additional 48 h was unlikely. The postmortem examination confirmed the presence of tumors in all euthanized mice, mainly in the lungs. Doxorubicin, a standard treatment for patients with metastatic breast cancer, was used as a positive control. Table 3 and Figure 3 show the results from three independent experiments. Diet TB1 was well tolerated and modestly increased mice survival; mean survival of mice fed diet TB1 was approximately 5 days longer than control mice (Figure 3). Most mice fed our artificial diet lived longer than mice treated with doxorubicin. This is a challenging in vivo model in which standard therapies induce a low activity. Figure 4 shows lung photographs at the time of sacrifice of representative mice from each group. Untreated mice and mice treated with doxorubicin had a similar number of tumors with similar size. Mice fed diet TB1 generally had fewer tumors, but some of them were bigger. Possibly, diet TB1 inhibited the proliferation of some tumors, but others continued to grow until they caused respiratory distress in the animals.
## 3.3. An Artificial Diet without 10 NEAAs and with 1% Lipids Induces Anticancer Activity in Mice with Metastatic Triple-Negative Breast Cancer
We have recently reported that several artificial diets with low levels of lipids induced marked anticancer activity in mice with renal cell carcinoma and colon cancer. [ 51,52]. To evaluate the impact of reducing lipid levels on TNBC progression, mice inoculated with 4T1 cells were treated with diet TB2; this diet was prepared by reducing the lipid levels of diet TB1 from $14\%$ to $1\%$ (Table 2). Treatments began 8 days after the intravenous inoculation of the 4T1 cancer cells. Animals were treated with oral capecitabine (450 mg/kg/day), with diet TB2 (the normal diet was replaced by this diet for 28 days) or were left untreated (control group).
Results, shown in Table 4, Figure 5a and Figure S2, indicate that diet TB2 induced a marked anticancer activity in two of the seven mice; these two mice were sacrificed on days 61 and 84. Importantly, diet TB2 was well tolerated, and mice did not suffer significant weight losses despite the drastic reduction in lipid levels. Mice treated with capecitabine showed marked adverse effects (e.g., decreases in body weight and decreases in spontaneous motor activity) that reverted at the end of each treatment cycle (Figure 5b). In these experiments, capecitabine was completely inactive. These results indicate that lipid levels can markedly increase mice survival in a low percentage of animals with metastatic TNBC fed an artificial diet lacking NEAAs.
## 3.4. An Artificial Diet without 10 NEAAs, with Reduced Levels of EAAs, and with 1% Lipids (Diet TB3) Induces Anticancer Activity in Mice with Metastatic Triple-Negative Breast Cancer
Diets TB1 and TB2 lack 10 NEAAs; however, these diets contain high levels of all essential AAs (EAAs). Evidence suggests that restriction of some EAAs (e.g., methionine) induces anticancer activity in TNBC models [24,25,26,27]. We therefore sought to improve the anticancer activity of our artificial diets by reducing their levels of EAAs. In diet TB3, all EAAs except Leu were reduced by a factor of approximately 3.5 with respect to diets TB1 and TB2. Keeping high Leu levels may be important to prevent proteolysis [63,64]. Diet TB3 contained $1\%$ coconut oil instead of $1\%$ olive oil to reduce the levels of monounsaturated fatty acids (MUFA), which may protect cancer cells against ferroptotic cell death [65].
Treatments began 8 days after the tail vein injection of the 4T1 cancer cells. Animals were treated with diet TB3 (the normal diet was replaced by this diet for 6 weeks), with capecitabine (450 mg/kg/day, $\frac{7}{7}$ schedule, 3 cycles) or were left untreated (control group). Mean survivals were 26.1 ± 3.2 in untreated mice, 43.8 ± 16.2 in mice fed diet TB3 and 24.2 ± 1.7 in mice treated with capecitabine. Most mice treated with diet TB3 lived longer than untreated mice (Figure 6a). One of the mice treated with diet TB3 survived the initial 6-week treatment and, after coming back to a normal diet for 4 weeks, treatment was restarted for an additional 6-week period. This animal was sacrificed on day 124 with cancer-related symptoms (decreased spontaneous motor activity and accelerated breathing), and the autopsy confirmed the presence of several tumors in the lungs. Although diet TB3 was well tolerated, body weights decreased continuously during treatment. Capecitabine also induced significant weight losses that also reverted at the end of each treatment cycle (Figure 6b).
## 3.5. Diet TB3 Induces Anticancer Activity in Mice with Other Types of Metastatic Cancers
To test if the marked in vivo anticancer activity of diet TB3 was specific for BALB/cAnNRj mice inoculated with 4T1 cells, and to screen its therapeutic potential for other types of cancer, we tested the anticancer activity of diet TB3 in other metastatic cancer models. We first carried out several in vitro experiments and observed that our artificial medium M1 also induced selective cytotoxicity in lung cancer cells, colorectal cancer cells, ovarian cancer cells, and melanoma cells versus human non-malignant cells (Figure S3). Then, we evaluated diet TB3 in mice with several types of metastatic cancers: lung cancer (intravenous injection of LL/2 cancer cells in C57BL/6JRj mice), colon cancer (intraperitoneal or intravenous injection of CT26WT cancer cells in BALB/cAnNRj mice), ovarian cancer (intraperitoneal injection of ID8 Trp53−/− cancer cells in C57BL/6JRj mice) and melanoma (intravenous injection of B16-F10 cancer cells in C57BL/6JRj mice). We used 3–4 mice per group in each cancer model to follow the Animal Ethics Committee recommendations and limit the number of mice to a minimum. A total of 16 mice with different types of metastatic cancers were treated with diet TB3 in these experiments. Treatments started 4 days after the injection of the cancer cells in the two colon cancer models, in the lung cancer model and in the melanoma model. Treatments began 21 days after the injection of the cancer cells in the ovarian cancer model; this model progresses slower than the other cancer models and treatments can be initiated later. We used capecitabine, cisplatin or anti-PD-1 as positive controls.
Diet TB3 improved mice survival in all the cancer models (Table 5 and Figure 7a,c,e,g,i). In the lung cancer model, melanoma model and intraperitoneal colon cancer model, mice fed diet TB3 lived approximately 4 days longer than untreated mice. Although this survival improvement was moderate, the activity of the standard treatments was similar (melanoma) or worse (lung cancer and intraperitoneal colon cancer). All mice inoculated intravenously with the colon cancer cells and treated with diet TB3 lived longer than untreated mice; the survival improvement was 44.7 days. One of the mice with colon cancer survived the initial 6-week treatment. The mouse developed disease symptoms and diet TB3 was restarted on day 116. Despite receiving diet TB3, the disease finally advanced and the mouse was sacrificed on day 137. In the ovarian cancer model, all mice fed diet TB3 lived longer than untreated mice (mean survival improvements was 15.5 days). However, the positive control cisplatin was much better. Since cisplatin was administered intraperitoneally, it may exert a direct cytotoxic effect on the ovarian cancer cells (which were also inoculated in the peritoneal cavity); this may contribute to explaining the high activity of cisplatin in this cancer model.
A total of 23 mice with different types of metastatic cancers (including TNBC) were treated with diet TB3 (normal diet was replaced by this artificial diet). The total mean survival was 30.5 ± 2.2 days for untreated mice and 45.4 ± 6.4 days for mice treated with diet TB3 (Table 5). Figure 8 and Figure S4 show representative photographs of mice in all these cancer models; the aggressiveness of these models may explain why none of the animals were cured by any standard or experimental treatment. In all these models, the inoculation of the cancer cells led to the development of multiple tumors in the animals. In the lung metastasis models (Figure 8a,b and Figure S4a,b), necropsies showed numerous tumors in the lungs of most mice. The number of tumors was generally higher in animals with short survival times. Mice with longer survival times generally showed fewer but bigger tumors, which eventually compromised respiratory function. In the lung metastasis models, tumors outside the thoracic cavity were also observed in some mice with long survivals. In the peritoneal dissemination models (Figure 8c and Figure S4c), necropsies showed that animals with longer survival times generally had fewer but bigger tumors in the peritoneal cavity. Diet TB3 reduced mice body weight in all cancer models (Figure 7b,d,f,h,j). Figure 7Diet TB3 induces marked anticancer activity in mice with different types of metastatic cancer. Survival of mice left untreated (control), treated with diet TB3 (normal diet was replaced with this diet), or treated with intraperitoneal anti-PD-1 (250 µg/dose), oral capecitabine (450/mg/day) or intraperitoneal cisplatin (5 mg/kg). Survival (a,c,e,g,i) and body weights (b,d,f,h,j) of mice with metastatic cancers treated with diet TB3 or a standard anticancer drug. The p-value was calculated with the Gehan–Breslow–Wilcoxon test. See text for further details. Figure 8Representative photographs at the time of sacrifice of mice with different types of metastatic cancers. In these models, mice were treated with diet TB3 (normal diet was replaced by TB3 for 6 weeks), with a standard anticancer drug or did not receive any treatment (control, normal diet). In the models of TNBC (a) and colon cancer (b), the lungs were excised and stained with India ink (tumors show a white appearance and normal lung parenchyma appears black). In the ovarian cancer model, representative photographs of the peritoneal cavity are shown (c). The day of sacrifice is shown in brackets.
## 3.6. Diets TB4 and TB5 (Artificial Diets with 6% Casein and 1% Lipids) Induce Anticancer Activity in Mice with Metastatic Triple-Negative Breast Cancer
We next prepared diets TB4 and TB5 to continue exploring the role of manipulating AAs and lipids on the progression of mice with metastatic TNBC. These two diets contain low levels of the protein casein ($6\%$), low lipid levels ($1\%$ coconut oil in diet TB4 and $1\%$ salmon oil in diet TB5) and a $5\%$ Gln supplement. Diet TB5 also contains a $5\%$ Leu supplement (Table 2). We chose casein because this protein provides low levels of the sulfur-containing AAs Cys and Met. We have previously observed that controlling the levels of Cys, Met, Gln and Leu could change the activity of the artificial diets in mice with renal and colon cancers [51,52]. Because the balance between saturated and unsaturated fatty acids may play a role in the survival of cancer cells [66], we used coconut oil (rich in saturated fatty acids) or salmon oil (rich in unsaturated fatty acids). Treatments began 8 days after the inoculation of 4T1 cancer cells into the tail vein of immunocompetent BALB/c mice. Animals were left untreated (control group) or were treated with capecitabine (450 mg/kg/day), diet TB4 or diet TB5 (the normal diet was replaced with one of these diets for 6 weeks). Both diets induced anticancer activity in mice with metastatic TNBC (Figure 9a and Table 6). One mouse treated with diet TB4 had a very long survival; it was sacrificed on day 253 with cancer-related symptoms, and the autopsy revealed the presence of a metastatic tumor in the peritoneal cavity and a marked splenomegaly (Figure S5). This diet was well tolerated, and mice did not suffer significant weight loss (Figure 9b). Two mice fed diet TB5 survived the initial treatment and received additional treatment cycles (normal diet was replaced with TB5 diet) when cancer-related symptoms appeared. One of the mice developed advanced disease symptoms and was sacrificed on day 71; the autopsy confirmed the presence of a metastatic tumor in the lungs and several small tumors in the thorax. The other mouse was treated several times with TB5 and was eventually sacrificed on day 448 because of the appearance of persistent blood in the urine. The autopsy confirmed the presence of two metastatic tumors in the peritoneal cavity (Figure S5). Diet TB5 induced a marked weight loss that reverted after treatment. Several cycles of the standard anticancer drug capecitabine did not improve the survival of mice with metastatic TNBC. Our previous data revealed that diet TB5 (also denominated diet TC7) was ineffective in several mice with metastatic colon cancer [51].
## 4. Discussion
Most patients diagnosed with metastatic TNBC do not overcome the disease. The available pharmacological treatments can prolong patient survival and palliate disease-related symptoms, but they are rarely curative. The aim of this work was to evaluate the anticancer potential of artificial diets based on selective restriction of AAs in mice with metastatic TNBC.
In addition to acquiring DNA alterations [67], TNBC cells develop metabolic changes that may be exploited therapeutically [8,9,10]. Other research groups have previously shown that limiting the levels of specific AAs with AA-depleting enzymes or through dietary restriction induced in vivo anticancer effects in TNBC models [19,20,24,25,26,27,28,29]. However, none of these studies have shown major improvements in the survival of animals with metastatic TNBC. In this work, we took a different approach to exploit the altered metabolism of cancer cells and increase the efficacy of this therapeutic strategy. Instead of restricting the levels of a particular AA, we created massive changes in AA levels and ratios to generate challenging metabolic environments for cancer cells. As discussed previously [64], cancer cells have mutations and other DNA changes that provide them with a survival advantage under a standard physiological environment. However, these same DNA alterations may cause their death in a different environment, because the survival of cancer cells depends not only on the acquisition of beneficial DNA changes, but also on favorable environments for these DNA changes. Since all cancer cells have originated under environments in which the levels and ratios of the 20 proteinogenic AAs are relatively constant, changing these levels and ratios with artificial diets may create new and unfavorable metabolic environments for cancer cells. Under these new metabolic environments, the DNA aberrations of cancer cells may become a liability that leads to their selective death. Normal cells have a functional DNA and may therefore resist the temporal AA imbalances created with artificial diets [64].
To test the therapeutic potential of our anticancer strategy, we used an experimental approach focused on cancer patients’ needs [68,69]. Cancer patients need treatments that can eliminate their cancer cells without significantly affecting their normal cells. The existing anticancer drugs can kill cancer cells through a variety of mechanisms of action; however, they also kill normal cells at similar concentrations. This implies that cancer patients cannot receive the drug doses required to eliminate their cancer cells, because these doses would also eliminate their normal cells and would be lethal. Patients receive tolerable doses rather than effective doses, which are insufficient to cure the disease in most cases. To be therapeutically useful, an experimental treatment must be selective towards cancer cells, and its selectivity should be higher than that of the existing therapies. We therefore initiated our investigation by evaluating if our experimental treatment could kill TNBC cells without significantly affecting non-malignant cells. Then, we evaluated if its selectivity was higher than that of drugs used in patients with TNBC. We prepared an artificial medium lacking 10 AAs (M1 medium) and observed that its selective anticancer activity (Figure 1) was higher than that of doxorubicin and 5-FU (Figure 2). These two drugs actually lacked selectivity toward cancer cells, probably because the non-malignant cells used in the experiments have high proliferative rates, and chemotherapy drugs also target normal cells with high proliferative rates. M1 medium also induced selective anticancer activity against other types of human breast cancer cells (Figure S1).
Our in vitro experiments proved that TNBCs and other types of breast cancer cells can be selectively killed by manipulating AA levels. However, these results should be interpreted cautiously, because the metabolic environment of cells growing in vitro and in vivo is extremely different. For example, in our artificial medium, the concentration of 10 of the 20 proteinogenic AAs before adding FBS was $0\%$. These low concentrations cannot be achieved in the systemic circulation of patients, because the liver and muscles can provide AAs to ensure that their plasma levels are not so drastically reduced [64]. We therefore continued our investigation by using in vivo experiments.
Patients with metastatic TNBC need curative treatments or, at least, treatments that improve the survival rates achieved with the existing therapies. We therefore selected an animal model of metastatic TNBC to evaluate if our artificial diets were curative or, at least, better than the standard treatments [68]. We prepared a diet lacking the same 10 NEAAs as medium M1 (diet TB1) and treated the animals by replacing their normal diet with this artificial diet. Several independent experiments showed that mice with metastatic TNBC fed diet TB1 lived several days longer than untreated mice and mice treated with doxorubicin (Figure 3). However, the survival improvements achieved with diet TB1 were mild. The activity of this diet (also known as diet T1) was also mild in mice with renal cell carcinoma [52]. We then screened several diets with other AA combination (2–3 mice per group), without observing improvements in the survival of mice with TNBC (results not shown). However, when we reduced the lipid levels of diet TB1 from $14\%$ to $1\%$ to create diet TB2, a marked survival improvement was observed in two mice (they lived several weeks longer than untreated mice; Figure 5).
Diets TB1 and TB2 lacked 10 NEAAs but contained high levels of all EAAs. Since EAAs can facilitate tumor progression [70], we sought to improve the anticancer activity of our diets by reducing the levels of EAAs. In diet TB3, the levels of all EAAs except Leu were reduced, and lipid levels were kept at $1\%$ (Table 2). One mouse treated with diet TB3 had a very long survival; it was sacrificed 124 days after the inoculation of the cancer cells (Figure 6). To exclude the possibility that this high activity could have been artificially increased by our experimental model (4T1 cells inoculated in the tail vein of BALB/c mice), and to evaluate the therapeutic potential of this diet in mice with other types of cancer, we screened diet TB3 in several animal models of metastasis. The results revealed that diet TB3 prolonged mice survival in all the selected cancer models. The activity was moderate in the lung cancer model, melanoma model and intraperitoneal colon cancer model. In the intravenous colon cancer model, one mouse lived 137 days after the inoculation of the cancer cells. In the intraperitoneal ovarian cancer model, all four C57BL/6 mice fed diet TB3 lived approximately 2 weeks longer than untreated mice. In this model, treatments started 21 days after the inoculation of the ovarian cancer cells, which suggests that the cancer cells were fully established when the treatments started. These results suggest that the genetic background of the 4T1 cell line or the BALB/c mice are not artificially increasing the activity of our diets. They also show that the anticancer activity of our diets is not limited to a particular type of cancer. As shown in Table 5, a total of 23 mice with different types of metastatic cancers (including TNBC) were treated with diet TB3; the global mean survival was 30.5 ± 2.2 days for untreated mice and 45.4 ± 6.4 days for mice fed diet TB3 (Figure 7 and Table 5).
Our next approach to improve the activity of our diets was to reduce the levels of all AAs by using a low percentage of the protein casein. Proteins allow the sustained liberation and absorption of AAs. After observing that $6\%$ was the lowest percentage of casein required to avoid weight loss in mice (results not shown), we prepared two casein-based diets (TB4 and TB5). Both diets were supplemented with $6\%$ glutamine to keep nitrogen balance. Diet TB4 contained $1\%$ coconut oil, while diet TB5 contained $1\%$ salmon oil and a $5\%$ Leu supplement. Both diets improved the survival of mice with metastatic TNBC, while several cycles of the first-line anticancer drug capecitabine did not improve mice survival under our experimental conditions (Table 6 and Figure 9). Capecitabine actually had a negative effect on mice survival. Possibly, capecitabine did not induce anticancer activity under our experimental conditions, and drug toxicity slightly reduced mice survival. One mouse treated with diet TB4 lived 253 days and a mouse treated with diet TB5 stayed alive for more than one year (448 days).
Our results show for the first time that the survival of mice with metastatic TNBC can be markedly extended by replacing their normal food with an artificial diet. Our most active diets were obtained by manipulating the levels of many AAs simultaneously and by reducing lipid levels to $1\%$. Importantly, our results were observed in a challenging animal model of metastasis, in which doxorubicin and capecitabine (two first-line treatments for patients with metastatic TNBC) were virtually ineffective. One of our diets (TB3) also induced anticancer activity in mice with other types of metastatic cancers (Figure 7 and Figure 8 and Table 5), therefore suggesting that this anticancer strategy has therapeutic potential for different types of cancer. The clinical translatability of this therapeutic strategy would be straightforward; the normal diet of cancer patients would be temporarily replaced with an artificial diet. Currently, we are evaluating the safety and efficacy of an artificial diet with selective restriction of AAs ($6\%$ casein, $5\%$ Gln and $2.5\%$ Leu) and very low levels of lipids ($1\%$) as monotherapy in patients with different types of metastatic cancers.
It is important to note that our artificial diets induced a marked anticancer activity only in some mice with metastatic TNBC. Survival improvements in the rest of the mice were low or non-existent. This is a typical response pattern of immunotherapies, which suggests that our diets may stimulate adaptive immunity to control tumor growth in some mice. In mice with ovarian cancer, however, all mice responded similarly to diet TB3 and lived approximately 2 weeks longer than untreated mice (Figure 7g). It is also important to note that, unlike diets TB1, TB2 and TB4, diets TB3 and TB5 induced a marked weight loss in the animals, which may compromise the safety of these diets in cancer patients. The reason for such a high weight loss cannot be explained by changes in any particular dietary component (see Table 2). For example, the low lipid levels ($1\%$) of diets TB3 and TB5 cannot explain this weight loss, because diets TB2 and TB4 also contain the same percentage of lipids and did not markedly reduce the weight of the animals. Neither the use of casein versus AA mixtures, the amount of Leu, nor the type of lipid can explain the high weight loss observed in animals fed diets TB3 and TB5.
The precise mechanism of action of this therapeutic strategy is unknown and will be difficult to unravel. The first reason is that this anticancer strategy does not use any drug, which makes it challenging to measure interactions with potential cancer drug targets. In addition, because the metabolic environments of cells growing in vitro and in vivo are extremely different, any mechanistic insight obtained in vitro will be difficult to extrapolate to an in vivo situation. For example, in vitro experiments do not consider the fact that liver and muscle proteolysis supplies free AAs to buffer the lack of specific AAs. Proteomics and metabolomics analyses in tumor samples and healthy tissues can provide valuable information on the biological changes elicited by specific artificial diets. Comparing these changes in tumor samples and healthy tissues may help explain why this therapeutic strategy affects cancer cells without causing toxicity in healthy tissues. However, since this therapeutic strategy is based on changing the levels of many nutrients simultaneously, it will be difficult to link biological changes induced by these diets to variations in the levels of specific nutrients. Fortunately, the universally accepted regulatory requirement of benefit over risk does not include mechanism of action as a requisite for approval. In fact, the mechanism of action of numerous approved drugs continues to be unknown or poorly understood (e.g., general anesthetics, lithium, etc. [ 71,72]). In our opinion, the artificial diets probably create unfavorable metabolic environments for the proliferation and survival of cancer cells. As discussed previously, the DNA aberrations that provide cancer cells with a survival advantage under a normal metabolic environment may become a liability under the new metabolic environments created with our artificial diets. These unfavorable environments can be created with different diets, and these new environments may be toxic to cancer cells with different sets of mutations [64]. This would explain why diets with different compositions are active in a particular type of cancer (e.g., diets TB1-TB5 are active in TNBC), and why the same diet is active in different types of cancer (e.g., diet TB3 is active in TNBC, melanoma, colon cancer, lung cancer and ovarian cancer).
Several possible mechanisms may help to explain why diets with restrictions of AAs and lipids induce selective toxicity towards cancer cells. Unlike normal cells, cancer cells may have mutations and other DNA alterations in the metabolic pathways involved in the synthesis of NEAAs and may be unable to obtain sufficient levels if they are eliminated from the diet [64]. Cancer cells may also have a higher dependency on certain NEAAs such as Cys. Cancer cells are known to produce high levels of reactive oxygen species (ROS) such as hydrogen peroxide, and *Cys is* necessary to generate glutathione (Glu–Cys–Gly), which in turn is crucial for protecting cells from the cytotoxic effects of ROS. Since our diets lack or have very low levels of Cys, they may induce the accumulation of cytotoxic levels of ROS in cancer cells. In addition, cancer cells have higher proliferative demands than most normal cells and need higher levels of EAAs to produce new proteins for the dividing cancer cells. Diets with reduced levels of EAAs would restrict protein synthesis, cell division and tumor growth. As discussed in the Section 1. lipid restriction may also decrease the proliferative capacity of cancer cells by reducing the availability of fatty acids, which are needed to produce lipid membranes for the new cancer cells. Finally, normal cells have functional checkpoints and may move out of the cell cycle into a quiescent state under conditions of nutrient deprivation. Cancer cells, however, usually have mutations and other DNA alterations that may prevent them from arresting the cell cycle under unfavorable conditions. Entering the cell cycle under conditions of nutrient deprivation may cause their death [73].
Although our artificial diets improved the survival of some mice with metastatic TNBC, and were more effective than the anticancer drugs doxorubicin and capecitabine, all mice eventually died. Future research is needed to improve the efficacy of this non-pharmacological strategy. This work shows that the anticancer activity of diets based on AA manipulation is increased when the levels of lipids are drastically reduced. The anticancer activity of our diets may be further increased by manipulating other dietary constituents, such as vitamins and minerals. Eliminating specific micronutrients in a normal diet is complex because they are present in most foods. However, since our artificial diets can be prepared from scratch, any dietary component can be completely eliminated. Our preliminary results indicate that eliminating specific micronutrients from the artificial diets can increase their anticancer activity in mice with metastatic cancers. In addition, although our diets may be clinically useful as monotherapy, future research would be important to evaluate their anticancer activity in combination with the standard drugs used in cancer patients.
## 5. Conclusions
Current treatments for patients with metastatic TNBC are generally ineffective. Our study revealed that the survival of some mice with metastatic TNBC was markedly increased by replacing their normal diet with artificial diets in which the levels of AAs and lipids are strongly manipulated. The anticancer activity of this non-pharmacological strategy was higher than the activity of drugs currently used in the treatment of patients with metastatic TNBC. This anticancer strategy also increased the survival of mice with different types of metastatic cancers. Diets TB3 and TB5 induced a marked weight loss in the animals, which may compromise the safety of these diets in cancer patients. Manipulating AA and lipid levels with artificial diets may be a useful strategy to treat patients with metastatic disease, including patients with TNBC.
## 6. Patents
E. Guillén-Mancina, J.M Calderón-Montaño, J.J. Jiménez-Alonso, V. Jiménez-González, E. Burgos-Morón, and M. López-Lázaro are inventors of a patent related to this work licensed to AMINOVITA, S.L. and University of Seville.
## References
1. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A.. **Cancer Statistics**. *CA Cancer J. Clin.* (2022) **72** 7-33. DOI: 10.3322/caac.21708
2. **Surveillance, Epidemiology, and End Results Program-Breast Cancer Survival Statistics**. (2022)
3. Li X., Yang J., Peng L., Sahin A.A., Huo L., Ward K.C., O’Regan R., Torres M.A., Meisel J.L.. **Triple-Negative Breast Cancer Has Worse Overall Survival and Cause-Specific Survival than Non-Triple-Negative Breast Cancer**. *Breast Cancer Res. Treat.* (2017) **161** 279-287. DOI: 10.1007/s10549-016-4059-6
4. **NCCN Guidelines Version 4.2022 Breast Cancer**. (2022)
5. Tutt A.N.J., Garber J.E., Kaufman B., Viale G., Fumagalli D., Rastogi P., Gelber R.D., de Azambuja E., Fielding A., Balmaña J.. **Adjuvant Olaparib for Patients with BRCA1-or BRCA2-Mutated Breast Cancer**. *N. Engl. J. Med.* (2021) **384** 2394-2405. DOI: 10.1056/NEJMoa2105215
6. Gonzalez-Angulo A.M., Timms K.M., Liu S., Chen H., Litton J.K., Potter J., Lanchbury J.S., Stemke-Hale K., Hennessy B.T., Arun B.K.. **Incidence and Outcome of BRCA Mutations in Unselected Patients with Triple Receptor-Negative Breast Cancer**. *Clin. Cancer Res.* (2011) **17** 1082-1089. DOI: 10.1158/1078-0432.CCR-10-2560
7. Cortes J., Rugo H.S., Cescon D.W., Im S.-A., Yusof M.M., Gallardo C., Lipatov O., Barrios C.H., Perez-Garcia J., Iwata H.. **Pembrolizumab plus Chemotherapy in Advanced Triple-Negative Breast Cancer**. *N. Engl. J. Med.* (2022) **387** 217-226. DOI: 10.1056/NEJMoa2202809
8. Gong Y., Ji P., Yang Y.S., Xie S., Yu T.J., Xiao Y., Jin M.L., Ma D., Guo L.W., Pei Y.C.. **Metabolic-Pathway-Based Subtyping of Triple-Negative Breast Cancer Reveals Potential Therapeutic Targets**. *Cell Metab.* (2021) **33** 51-64.e9. DOI: 10.1016/j.cmet.2020.10.012
9. Wang Z., Jiang Q., Dong C.. **Metabolic Reprogramming in Triple-Negative Breast Cancer**. *Cancer Biol. Med.* (2020) **17** 44-59. DOI: 10.20892/j.issn.2095-3941.2019.0210
10. Sun X., Wang M., Wang M., Yu X., Guo J., Sun T., Li X., Yao L., Dong H., Xu Y.. **Metabolic Reprogramming in Triple-Negative Breast Cancer**. *Front. Oncol.* (2020) **10** 428. DOI: 10.3389/fonc.2020.00428
11. Martínez-Reyes I., Chandel N.S.. **Cancer Metabolism: Looking Forward**. *Nat. Rev. Cancer* (2021) **21** 669-680. DOI: 10.1038/s41568-021-00378-6
12. Tajan M., Vousden K.H.. **Dietary Approaches to Cancer Therapy**. *Cancer Cell* (2020) **37** 767-785. DOI: 10.1016/j.ccell.2020.04.005
13. Timmerman L.A., Holton T., Yuneva M., Louie R.J., Padró M., Daemen A., Hu M., Chan D.A., Ethier S.P., van’t Veer L.J.. **Glutamine Sensitivity Analysis Identifies the XCT Antiporter as a Common Triple-Negative Breast Tumor Therapeutic Target**. *Cancer Cell* (2013) **24** 450-465. DOI: 10.1016/j.ccr.2013.08.020
14. Lanzardo S., Conti L., Rooke R., Ruiu R., Accart N., Bolli E., Arigoni M., Macagno M., Barrera G., Pizzimenti S.. **Immunotargeting of Antigen XCT Attenuates Stem-like Cell Behavior and Metastatic Progression in Breast Cancer**. *Cancer Res.* (2016) **76** 62-72. DOI: 10.1158/0008-5472.CAN-15-1208
15. Gross M.I., Demo S.D., Dennison J.B., Chen L., Chernov-Rogan T., Goyal B., Janes J.R., Laidig G.J., Lewis E.R., Li J.. **Antitumor Activity of the Glutaminase Inhibitor CB-839 in Triple-Negative Breast Cancer**. *Mol. Cancer Ther.* (2014) **13** 890-901. DOI: 10.1158/1535-7163.MCT-13-0870
16. Scott G.K., Yau C., Becker B.C., Khateeb S., Mahoney S., Jensen M.B., Hann B., Cowen B.J., Pegan S.D., Benz C.C.. **Targeting Mitochondrial Proline Dehydrogenase with a Suicide Inhibitor to Exploit Synthetic Lethal Interactions with P53 Upregulation and Glutaminase Inhibition**. *Mol. Cancer Ther.* (2019) **18** 1374-1385. DOI: 10.1158/1535-7163.MCT-18-1323
17. Elia I., Broekaert D., Christen S., Boon R., Radaelli E., Orth M.F., Verfaillie C., Grünewald T.G.P., Fendt S.M.. **Proline Metabolism Supports Metastasis Formation and Could Be Inhibited to Selectively Target Metastasizing Cancer Cells**. *Nat. Commun.* (2017) **8** 15267. DOI: 10.1038/ncomms15267
18. Sun J., Nagel R., Zaal E.A., Ugalde A.P., Han R., Proost N., Song J., Pataskar A., Burylo A., Fu H.. **SLC 1A3 Contributes to L-asparaginase Resistance in Solid Tumors**. *EMBO J.* (2019) **38** e102147. DOI: 10.15252/embj.2019102147
19. Knott S.R.V., Wagenblast E., Khan S., Kim S.Y., Soto M., Wagner M., Turgeon M.O., Fish L., Erard N., Gable A.L.. **Asparagine Bioavailability Governs Metastasis in a Model of Breast Cancer**. *Nature* (2018) **554** 378-381. DOI: 10.1038/nature25465
20. Krall A.S., Mullen P.J., Surjono F., Momcilovic M., Schmid E.W., Halbrook C.J., Thambundit A., Mittelman S.D., Lyssiotis C.A., Shackelford D.B.. **Asparagine Couples Mitochondrial Respiration to ATF4 Activity and Tumor Growth**. *Cell Metab.* (2021) **33** 1013-1026.e6. DOI: 10.1016/j.cmet.2021.02.001
21. Qiu F., Chen Y.R., Liu X., Chu C.Y., Shen L.J., Xu J., Gaur S., Forman H.J., Zhang H., Zheng S.. **Cancer: Arginine Starvation Impairs Mitochondrial Respiratory Function in ASS1-Deficient Breast Cancer Cells**. *Sci. Signal.* (2014) **7** ra31. DOI: 10.1126/scisignal.2004761
22. Cramer S.L., Saha A., Liu J., Tadi S., Tiziani S., Yan W., Triplett K., Lamb C., Alters S.E., Rowlinson S.. **Systemic Depletion of L-Cyst(e)Ine with Cyst(e)Inase Increases Reactive Oxygen Species and Suppresses Tumor Growth**. *Nat. Med.* (2017) **23** 120-127. DOI: 10.1038/nm.4232
23. Yao S., Janku F., Koenig K., Tsimberidou A.M., Piha-Paul S.A., Shi N., Stewart J., Johnston A., Bomalaski J., Meric-Bernstam F.. **Phase 1 Trial of ADI-PEG 20 and Liposomal Doxorubicin in Patients with Metastatic Solid Tumors**. *Cancer Med.* (2022) **11** 340-347. DOI: 10.1002/cam4.4446
24. Strekalova E., Malin D., Good D.M., Cryns V.L.. **Methionine Deprivation Induces a Targetable Vulnerability in Triple-Negative Breast Cancer Cells by Enhancing TRAIL Receptor-2 Expression**. *Clin. Cancer Res.* (2015) **21** 2780-2791. DOI: 10.1158/1078-0432.CCR-14-2792
25. Strekalova E., Malin D., Rajanala H., Cryns V.L.. **Preclinical Breast Cancer Models to Investigate Metabolic Priming by Methionine Restriction**. *Methods Mol. Biol.* (2019) **1866** 61-73. DOI: 10.1007/978-1-4939-8796-2_6
26. Jeon H., Kim J.H., Lee E., Jang Y.J., Son J.E., Kwon J.Y., Lim T., Kim S., Park J.H.Y., Kim J.-E.. **Methionine Deprivation Suppresses Triple-Negative Breast Cancer Metastasis in Vitro and in Vivo**. *Oncotarget* (2016) **7** 67223-67234. DOI: 10.18632/oncotarget.11615
27. Hens J.R., Sinha I., Perodin F., Cooper T., Sinha R., Plummer J., Perrone C.E., Orentreich D.. **Methionine-Restricted Diet Inhibits Growth of MCF10AT1-Derived Mammary Tumors by Increasing Cell Cycle Inhibitors in Athymic Nude Mice**. *BMC Cancer* (2016) **16**. DOI: 10.1186/s12885-016-2367-1
28. Cheng C.-T.T., Qi Y., Wang Y.-C.C., Chi K.K., Chung Y., Ouyang C., Chen Y.-R.Y.-H.Y.H.R., Oh M.E., Sheng X., Tang Y.. **Arginine Starvation Kills Tumor Cells through Aspartate Exhaustion and Mitochondrial Dysfunction**. *Commun. Biol.* (2018) **1** 178. DOI: 10.1038/s42003-018-0178-4
29. Sullivan M.R., Mattaini K.R., Dennstedt E.A., Nguyen A.A., Sivanand S., Reilly M.F., Meeth K., Muir A., Darnell A.M., Bosenberg M.W.. **Increased Serine Synthesis Provides an Advantage for Tumors Arising in Tissues Where Serine Levels Are Limiting**. *Cell Metab.* (2019) **29** 1410-1421.e4. DOI: 10.1016/j.cmet.2019.02.015
30. Broadfield L.A., Pane A.A., Talebi A., Swinnen J.V., Fendt S.M.. **Lipid Metabolism in Cancer: New Perspectives and Emerging Mechanisms**. *Dev. Cell* (2021) **56** 1363-1393. DOI: 10.1016/j.devcel.2021.04.013
31. Hoy A.J., Nagarajan S.R., Butler L.M.. **Tumour Fatty Acid Metabolism in the Context of Therapy Resistance and Obesity**. *Nat. Rev. Cancer* (2021) **21** 753-766. DOI: 10.1038/s41568-021-00388-4
32. Wang D., Dubois R.N.. **Eicosanoids and Cancer**. *Nat. Rev. Cancer* (2010) **10** 181-193. DOI: 10.1038/nrc2809
33. Pascual G., Avgustinova A., Mejetta S., Martín M., Castellanos A., Attolini C.S.O., Berenguer A., Prats N., Toll A., Hueto J.A.. **Targeting Metastasis-Initiating Cells through the Fatty Acid Receptor CD36**. *Nature* (2017) **541** 41-45. DOI: 10.1038/nature20791
34. Incio J., Tam J., Rahbari N.N., Suboj P., McManus D.T., Chin S.M., Vardam T.D., Batista A., Babykutty S., Jung K.. **PlGF/VEGFR-1 Signaling Promotes Macrophage Polarization and Accelerated Tumor Progression in Obesity**. *Clin. Cancer Res.* (2016) **22** 2993-3004. DOI: 10.1158/1078-0432.CCR-15-1839
35. Incio J., Liu H., Suboj P., Chin S.M., Chen I.X., Pinter M., Ng M.R., Nia H.T., Grahovac J., Kao S.. **Obesity-Induced Inflammation and Desmoplasia Promote Pancreatic Cancer Progression and Resistance to Chemotherapy**. *Cancer Discov.* (2016) **6** 852-869. DOI: 10.1158/2159-8290.CD-15-1177
36. Philip B., Roland C.L., Daniluk J., Liu Y., Chatterjee D., Gomez S.B., Ji B., Huang H., Wang H., Fleming J.B.. **A High-Fat Diet Activates Oncogenic Kras and COX2 to Induce Development of Pancreatic Ductal Adenocarcinoma in Mice**. *Gastroenterology* (2013) **145** 1449-1458. DOI: 10.1053/j.gastro.2013.08.018
37. Malvi P., Chaube B., Singh S.V., Mohammad N., Pandey V., Vijayakumar M.V., Radhakrishnan R.M., Vanuopadath M., Nair S.S., Nair B.G.. **Weight Control Interventions Improve Therapeutic Efficacy of Dacarbazine in Melanoma by Reversing Obesity-Induced Drug Resistance**. *Cancer Metab.* (2016) **4** 21. DOI: 10.1186/s40170-016-0162-8
38. Xia S., Lin R., Jin L., Zhao L., Kang H.-B., Pan Y., Liu S., Qian G., Qian Z., Konstantakou E.. **Prevention of Dietary-Fat-Fueled Ketogenesis Attenuates BRAF V600E Tumor Growth**. *Cell Metab.* (2017) **25** 358-373. DOI: 10.1016/j.cmet.2016.12.010
39. Pandey V., Vijayakumar M.V., Ajay A.K., Malvi P., Bhat M.K.. **Diet-Induced Obesity Increases Melanoma Progression: Involvement of Cav-1 and FASN**. *Int. J. Cancer* (2012) **130** 497-508. DOI: 10.1002/ijc.26048
40. Labbé D.P., Zadra G., Yang M., Reyes J.M., Lin C.Y., Cacciatore S., Ebot E.M., Creech A.L., Giunchi F., Fiorentino M.. **High-Fat Diet Fuels Prostate Cancer Progression by Rewiring the Metabolome and Amplifying the MYC Program**. *Nat. Commun.* (2019) **10** 4358. DOI: 10.1038/s41467-019-12298-z
41. Tang F.-Y., Pai M.-H., Chiang E.-P.I.. **Consumption of High-Fat Diet Induces Tumor Progression and Epithelial–Mesenchymal Transition of Colorectal Cancer in a Mouse Xenograft Model**. *J. Nutr. Biochem.* (2012) **23** 1302-1313. DOI: 10.1016/j.jnutbio.2011.07.011
42. Ngo T.H., Barnard R.J., Anton T., Tran C., Elashoff D., Heber D., Freedland S.J., Aronson W.J.. **Effect of Isocaloric Low-Fat Diet on Prostate Cancer Xenograft Progression to Androgen Independence**. *Cancer Res.* (2004) **64** 1252-1254. DOI: 10.1158/0008-5472.CAN-03-3830
43. Ngo T.H., Barnard R.J., Cohen P., Freedland S., Tran C., DeGregorio F., Elshimali Y.I., Heber D., Aronson W.J.. **Effect of Isocaloric Low-Fat Diet on Human LAPC-4 Prostate Cancer Xenografts in Severe Combined Immunodeficient Mice and the Insulin-like Growth Factor Axis**. *Clin. Cancer Res.* (2003) **9** 2734-2743. PMID: 12855654
44. Kobayashi N., Barnard R.J., Said J., Hong-Gonzalez J., Corman D.M., Ku M., Doan N.B., Gui D., Elashoff D., Cohen P.. **Effect of Low-Fat Diet on Development of Prostate Cancer and Akt Phosphorylation in the Hi-Myc Transgenic Mouse Model**. *Cancer Res.* (2008) **68** 3066-3073. DOI: 10.1158/0008-5472.CAN-07-5616
45. Sundaram S., Yan L.. **High-Fat Diet Enhances Mammary Tumorigenesis and Pulmonary Metastasis and Alters Inflammatory and Angiogenic Profiles in MMTV-PyMT Mice**. *Anticancer Res.* (2016) **36** 6279-6287. DOI: 10.21873/anticanres.11223
46. Evangelista G.C.M., Salvador P.A., Soares S.M.A., Barros L.R.C., da Xavier F.H.C., Abdo L.M., Gualberto A.C.M., Macedo G.C., Clavijo-Salomon M.A., Gameiro J.. **4T1 Mammary Carcinoma Colonization of Metastatic Niches Is Accelerated by Obesity**. *Front. Oncol.* (2019) **9** 685. DOI: 10.3389/fonc.2019.00685
47. Bousquenaud M., Fico F., Solinas G., Rüegg C., Santamaria-Martínez A.. **Obesity Promotes the Expansion of Metastasis-Initiating Cells in Breast Cancer**. *Breast Cancer Res.* (2018) **20** 104. DOI: 10.1186/s13058-018-1029-4
48. Wu M., Chang C.. **High Fat Diet-Induced Breast Cancer Model in Rat**. *Bio-Protoc* (2016) **6** e1852. DOI: 10.21769/BioProtoc.1852
49. Chlebowski R.T., Aragaki A.K., Anderson G.L., Thomson C.A., Manson J.A.E., Simon M.S., Howard B.V., Rohan T.E., Snetselar L., Lane D.. **Low-Fat Dietary Pattern and Breast Cancer Mortality in the Women’s Health Initiative Randomized Controlled Trial**. *Proc. J. Clin. Oncol.* (2017) **35** 2919-2926. DOI: 10.1200/JCO.2016.72.0326
50. Chlebowski R.T., Blackburn G.L., Thomson C.A., Nixon D.W., Shapiro A., Hoy M.K., Goodman M.T., Giuliano A.E., Karanja N., McAndrew P.. **Dietary Fat Reduction and Breast Cancer Outcome: Interim Efficacy Results from the Women’s Intervention Nutrition Study**. *J. Natl. Cancer Inst.* (2006) **98** 1767-1776. DOI: 10.1093/jnci/djj494
51. Jiménez-Alonso J., Guillén-Mancina E., Calderón-Montaño J., Jiménez-González V., Díaz-Ortega P., Burgos-Morón E., López-Lázaro M.. **Artificial Diets Based on Selective Amino Acid Restriction versus Capecitabine in Mice with Metastatic Colon Cancer**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14163378
52. Calderón-Montaño J.M., Guillén-Mancina E., Jiménez-Alonso J.J., Jiménez-González V., Burgos-Morón E., Mate A., Pérez-Guerrero M.C., López-Lázaro M.. **Manipulation of Amino Acid Levels with Artificial Diets Induces a Marked Anticancer Activity in Mice with Renal Cell Carcinoma**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms232416132
53. Boukamp P., Petrussevska R.T., Breitkreutz D., Hornung J., Markham A., Fusenig N.E.. **Normal Keratinization in a Spontaneously Immortalized Aneuploid Human Keratinocyte Cell Line**. *J. Cell Biol.* (1988) **106** 761-771. DOI: 10.1083/jcb.106.3.761
54. Walton J., Blagih J., Ennis D., Leung E., Dowson S., Farquharson M., Tookman L.A., Orange C., Athineos D., Mason S.. **CRISPR/Cas9-Mediated Trp53 and Brca2 Knockout to Generate Improved Murine Models of Ovarian High-Grade Serous Carcinoma**. *Cancer Res.* (2016) **76** 6118-6129. DOI: 10.1158/0008-5472.CAN-16-1272
55. Aslakson C.J., Miller2 F.R.. **Selective Events in the Metastatic Process Defined by Analysis of the Sequential Dissemination of Subpopulations of a Mouse Mammary Tumor**. *Cancer Res.* (1992) **52** 1399-1405. PMID: 1540948
56. Liu Y., Wang L., Liu J., Xie X., Hu H., Luo F.. **Anticancer Effects of ACT001 via NF-ΚB Suppression in Murine Triple-Negative Breast Cancer Cell Line 4T1**. *Cancer Manag. Res.* (2020) **12** 5131. DOI: 10.2147/CMAR.S244748
57. Corbett T.H., Griswold Jr D.P., Roberts B.J., Peckham J.C., Schabel Jr F.M.. **Tumor Induction Relationships in Development of Transplantable Cancers of the Colon in Mice for Chemotherapy Assays, with a Note on Carcinogen Structure**. *Cancer Res.* (1975) **35** 2434-2439. PMID: 1149045
58. Wang L., Hu X., Xu Y., Liu Z.. **Arsenic Trioxide Inhibits Lung Metastasis of Mouse Colon Cancer via Reducing the Infiltration of Regulatory T Cells**. *Tumour Biol.* (2016) **37** 15165-15173. DOI: 10.1007/s13277-016-5377-3
59. Burikhanov R., Hebbar N., Noothi S.K., Shukla N., Sledziona J., Araujo N., Kudrimoti M., Wang Q.J., Watt D.S., Welch D.R.. **Chloroquine-Inducible Par-4 Secretion Is Essential for Tumor Cell Apoptosis and Inhibition of Metastasis**. *Cell Rep.* (2017) **18** 508-519. DOI: 10.1016/j.celrep.2016.12.051
60. Zhao M., Suetsugu A., Ma H., Zhang L., Liu F., Zhang Y., Tran B., Hoffman R.M.. **Efficacy against Lung Metastasis with a Tumor-Targeting Mutant of Salmonella Typhimurium in Immunocompetent Mice**. *Cell Cycle* (2012) **11** 187. DOI: 10.4161/cc.11.1.18667
61. Yan J., Wang Z.Y., Yang H.Z., Liu H.Z., Mi S., Lv X.X., Fu X.M., Yan H.M., Zhang X.W., Zhan Q.M.. **Timing Is Critical for an Effective Anti-Metastatic Immunotherapy: The Decisive Role of IFNγ/STAT1-Mediated Activation of Autophagy**. *PLoS ONE* (2011) **6**. DOI: 10.1371/journal.pone.0024705
62. Calderón-Montaño J.M., Burgos-Morón E., López-Lázaro M.. **The in Vivo Antitumor Activity of Cardiac Glycosides in Mice Xenografted with Human Cancer Cells Is Probably an Experimental Artifact**. *Oncogene* (2014) **33** 2947-2948. DOI: 10.1038/onc.2013.229
63. Efeyan A., Comb W.C., Sabatini D.M.. **Nutrient-Sensing Mechanisms and Pathways**. *Nature* (2015) **517** 302-310. DOI: 10.1038/nature14190
64. López-Lázaro M.. **Selective Amino Acid Restriction Therapy (SAART): A Non- Pharmacological Strategy against All Types of Cancer Cells**. *Oncoscience* (2015) **2** 857. DOI: 10.18632/oncoscience.258
65. Magtanong L., Ko P.J., To M., Cao J.Y., Forcina G.C., Tarangelo A., Ward C.C., Cho K., Patti G.J., Nomura D.K.. **Exogenous Monounsaturated Fatty Acids Promote a Ferroptosis-Resistant Cell State**. *Cell Chem. Biol.* (2019) **26** 420-432.e9. DOI: 10.1016/j.chembiol.2018.11.016
66. Zhao G., Tan Y., Cardenas H., Vayngart D., Wang Y., Huang H., Keathley R., Wei J.J., Ferreira C.R., Orsulic S.. **Ovarian Cancer Cell Fate Regulation by the Dynamics between Saturated and Unsaturated Fatty Acids**. *Proc. Natl. Acad. Sci. USA* (2022) **119** e2203480119. DOI: 10.1073/pnas.2203480119
67. Staaf J., Glodzik D., Bosch A., Vallon-Christersson J., Reuterswärd C., Häkkinen J., Degasperi A., Amarante T.D., Saal L.H., Hegardt C.. **Whole-Genome Sequencing of Triple-Negative Breast Cancers in a Population-Based Clinical Study**. *Nat. Med.* (2019) **25** 1526-1533. DOI: 10.1038/s41591-019-0582-4
68. López-Lázaro M.. **Two Preclinical Tests to Evaluate Anticancer Activity and to Help Validate Drug Candidates for Clinical Trials**. *Oncoscience* (2015) **2** 91-98. DOI: 10.18632/oncoscience.132
69. López-Lázaro M.. **A Simple and Reliable Approach for Assessing Anticancer Activity in Vitro**. *Curr. Med. Chem.* (2015) **22** 1324-1334. DOI: 10.2174/0929867322666150209150639
70. Lieu E.L., Nguyen T., Rhyne S., Kim J.. **Amino Acids in Cancer**. *Exp. Mol. Med.* (2020) **52** 15-30. DOI: 10.1038/s12276-020-0375-3
71. Medić B., Stojanović M., Stimec B.V., Divac N., Vujović K.S., Stojanović R., Čolović M., Krstić D., Prostran M.. **Lithium-Pharmacological and Toxicological Aspects: The Current State of the Art**. *Curr. Med. Chem.* (2018) **27** 337-351. DOI: 10.2174/0929867325666180904124733
72. Kissin I., Vlassakov K.V.. **Pharmacology of General Anesthetics: Quantitative History of Research Attractiveness**. *Anesth. Analg.* (2021) **132** 1486-1488. DOI: 10.1213/ANE.0000000000005441
73. Scott L., Lamb J., Smith S., Wheatley D.N.. **Single amino acid (arginine) deprivation: Rapid and selective death of cultured transformed and malignant cells**. *Br. J. Cancer* (2000) **83** 800-810. DOI: 10.1054/bjoc.2000.1353
|
---
title: 'Inhibitory Effect of Isopanduratin A on Adipogenesis: A Study of Possible
Mechanisms'
authors:
- Prapenpuksiri Rungsa
- Htoo Tint San
- Boonchoo Sritularak
- Chotima Böttcher
- Eakachai Prompetchara
- Chatchai Chaotham
- Kittisak Likhitwitayawuid
journal: Foods
year: 2023
pmcid: PMC10000982
doi: 10.3390/foods12051014
license: CC BY 4.0
---
# Inhibitory Effect of Isopanduratin A on Adipogenesis: A Study of Possible Mechanisms
## Abstract
The root of Boesenbergia rotunda, a culinary plant commonly known as fingerroot, has previously been reported to possess anti-obesity activity, with four flavonoids identified as active principles, including pinostrobin, panduratin A, cardamonin, and isopanduratin A. However, the molecular mechanisms underlying the antiadipogenic potential of isopanduratin A remain unknown. In this study, isopanduratin A at non-cytotoxic concentrations (1–10 μM) significantly suppressed lipid accumulation in murine (3T3-L1) and human (PCS-210-010) adipocytes in a dose-dependent manner. Downregulation of adipogenic effectors (FAS, PLIN1, LPL, and adiponectin) and adipogenic transcription factors (SREBP-1c, PPARγ, and C/EBPα) occurred in differentiated 3T3-L1 cells treated with varying concentrations of isopanduratin A. The compound deactivated the upstream regulatory signals of AKT/GSK3β and MAPKs (ERK, JNK, and p38) but stimulated the AMPK-ACC pathway. The inhibitory trend of isopanduratin A was also observed with the proliferation of 3T3-L1 cells. The compound also paused the passage of 3T3-L1 cells by inducing cell cycle arrest at the G0/G1 phase, supported by altered levels of cyclins D1 and D3 and CDK2. Impaired p-ERK/ERK signaling might be responsible for the delay in mitotic clonal expansion. These findings revealed that isopanduratin A is a strong adipogenic suppressor with multi-target mechanisms and contributes significantly to anti-obesogenic activity. These results suggest the potential of fingerroot as a functional food for weight control and obesity prevention.
## 1. Introduction
With the steady increase in the number of overweight and obese populations in recent years, obesity has been declared a pandemic disease by the World Health Organization (WHO) [1]. Obesity is the result of an energy imbalance, characterized by excessive fat accumulation in the body. This irregularity, though a non-communicable disorder, is closely associated with several metabolic conditions, such as hyperglycemia, hyperlipidemia, hypertension, cancer, and cardiovascular diseases, all of which have a high mortality rate and can cause a socioeconomic burden, particularly in countries where access to the healthcare system is limited [2].
Modulation of the excess mass of adipose tissues due to hyperplasia (excessive adipogenesis) and the hypertrophy of adipocytes is one of the reasonable strategies to regulate lipid homeostasis and obesity. Recently, inhibition of adipogenic differentiation and maturation has become a novel therapeutic approach to treating obesity [3]. Adipogenesis, a multistep process that converts undifferentiated preadipocytes into mature adipocytes, is modulated by a series of biochemical cascades that include coordinated changes in hormone sensitivity and gene expression, together with morphological alterations. Triggered by adipogenic stimulants, preadipocytes undergo mitotic clonal expansion (MCE) to re-enter the cell cycle. Concurrently, the upregulation of adipogenic regulating genes and adipogenic effector proteins leads to adipocyte differentiation and maturation [4,5,6,7].
Adipocyte differentiation and development are directed by lipogenesis-related transcription factors such as CCAAT/enhancer-binding protein alpha (C/EBPα), peroxisome proliferator-activated receptor gamma (PPARγ), sterol response element-binding protein-1c (SREBP-1c) [8,9], and the adenosine monophosphate-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) enzymes [10]. AMPK, a serine/threonine kinase, forms a heterotrimeric complex with one catalytic α subunit and two regulatory β and γ subunits [11]. Its roles in cellular lipid metabolism involve the synthesis and degradation of fatty acids. Another upstream regulatory molecule in adipocyte differentiation is protein kinase B (AKT), as its activation strongly links to the upregulation of SREBP-1c and cellular lipogenesis [12]. Subsequent phosphorylation of glycogen synthase kinase 3β (GSK3β) by AKT upregulates C/EBPα and promotes adipocyte maturation [13]. Additionally, mitogen-activated protein kinases (MAPKs), including c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and stress-activated protein kinase (p38), mediate adipogenesis [14]. Suppression of these signaling molecules efficiently inhibits adipocyte differentiation [15,16]. For example, inhibition of p38 function can hamper adipocyte differentiation by suppressing PPARγ transcription. Modulation of these biomolecules during adipocyte differentiation proved to be a promising strategy to limit cellular lipogenesis and adipocyte differentiation and maturation [17].
Recently, a growing body of evidence has revealed medicinal and culinary plants as a rich source of phytochemicals that exert their anti-obesity potential through multi-target mechanisms [18,19,20]. Boesenbergia rotunda (L.) Mansf., also known as *Boesenbergia pandurata* (Roxb.) Schltr., is commonly called fingerroot. The plant is found in the wild and is widely cultivated in South Asia and Southeast Asia [21,22]. Traditionally, people use its roots as food and flavoring agents. In Thailand, they are the main ingredient in shrimp soup, which is popularly consumed by lactating women to help improve their breast milk supply. Various medicinal values for fingerroot were reported, including anti-inflammatory, antimicrobial, antiviral [21,22,23,24], anti-obesity [25], anti-osteoporosis [26], and anticancer activities [27], as well as aphrodisiac and vasorelaxant effects [28]. The bioactive constituents were characterized as several subclasses of flavonoids [29,30].
In a recent study, the anti-obesity activity of fingerroot was demonstrated in mice on a high-fat diet [31]. Our previous phytochemical study of the roots of this plant revealed the presence of several flavonoids, along with a monoterpene alcohol and a styrylpyrone [32]. In a preliminary Oil Red O assay, we found that the flavonoids pinostrobin, panduratin A, isopanduratin A, and cardamonin were strong adipogenic inhibitors, which may be responsible for the anti-obesity activity of fingerroot (see Section 3.1). In our previous study, pinostrobin was shown to inhibit adipogenesis in murine 3T3-L1 preadipocytes by lowering the levels of lipid-metabolism-mediating proteins, such as C/EBPα, PPARγ, and SREBP-1c, and suppressing the signals of MAPKs (p38 and JNK) and AKT (AKT/GSK3β and AKT/AMPKα-ACC) [33]. The other flavonoids, i.e., panduratin A and cardamonin, were previously investigated for the molecular mechanisms underlying their anti-adipogenic effects in 3T3-L1 cells [25,34,35]. In this study, we report the inhibitory effects of isopanduratin A, another fingerroot flavonoid, on adipogenesis in mouse 3T3-L1 and human PCS-210-010 preadipocytes. The relevant molecular mechanisms are also elucidated and addressed.
## 2.1. Chemicals, Reagents, and Culture Media
Isopanduratin A and other phytochemicals were isolated and characterized from B. rotunda roots with a protocol described previously [32]. The purity of these phytochemicals was more than $98\%$ (by NMR). Dimethyl sulfoxide (DMSO), Oil Red O, crystal violet, isobutylmethylxanthine (IBMX), dexamethasone, isopropanol, RNase A, and skim milk powder were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ethanol, methanol, formaldehyde, and chloroform were ordered from Merck KgaA (Darmstadt, Germany). Dulbecco’s Modified Eagle Medium (DMEM), fetal bovine serum (FBS), penicillin/streptomycin solution, l-glutamine, and trypsin were bought from Gibco (Gaithersburg, MA, USA). Fibroblast basal medium (FBM) was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Insulin was ordered from Himedia (Mumbai, India). Bicinchoninic acid (BCA) protein assay kit, western chemiluminescent ECL substrate, and radio-immunoprecipitation assay (RIPA) buffer were acquired from Thermo-Fisher (Rockford, IL, USA). A protease inhibitor cocktail was obtained from Roche Applied Science (Indianapolis, IN, USA). Primary antibodies against β-actin (Cat. No. 4970; dilution 1:1000), Cyclin D1 (Cat. No. 2978; dilution 1:1000), Cyclin D3 (Cat. No. 2936; dilution 1:2000), CDK2 (Cat. No. 2546; dilution 1:1000), AKT (Cat. No. 4691; dilution 1:1000), p-AKT (Ser473) (Cat. No. 4060; dilution 1:2000), GSK3β (Cat. No. 12456; dilution 1:1000), p-GSK3β (Ser9) (Cat. No. 9322; dilution 1:1000), AMPKα (Cat. No. 5831; dilution 1:1000), p-AMPKα (Thr172) (Cat. No. 2535; dilution 1:1000), AMPKβ$\frac{1}{2}$ (Cat. No. 4150; dilution 1:1000), p-AMPKβ1 (Ser182) (Cat. No. 4186; dilution 1:1000), ACC (Cat. No. 3676; dilution 1:1000), p-ACC (Ser79) (Cat. No. 11818; dilution 1:1000), PPARγ (Cat. No. 2435; dilution 1:1000), C/EBPα (Cat. No. 8178; dilution 1:1000), FAS (Cat. No. 3180; dilution 1:1000), PLIN1 (Cat. No. 9349; dilution 1:1000), adiponectin (Cat. No. 2789; dilution 1:1000), ERK$\frac{1}{2}$ (Cat. No. 9102; dilution 1:1000), p-ERK$\frac{1}{2}$ (Thr202/Tyr204) (Cat. No. 4695; dilution 1:1000), JNK (Cat. No. 9252; dilution 1:1000), p-JNK (Thr183/Tyr185) (Cat. No. 9251; dilution 1:1000), p38 (Cat. No. 8690; dilution 1:1000), p-p38 (Thr180/Tyr182) (Cat. No. 4511; dilution 1:1000), and horseradish peroxidase (HRP)-linked secondary antibodies (Cat. No. 7074; dilution 1:2000) were purchased from Cell Signaling Technology (Danvers, MA, USA). Specific primary antibodies against SREBP-1c (Cat. No. PA1-337; dilution 1:1000) and LPL (Cat. No. PA5-85126; dilution 1:1000) were acquired from Invitrogen (Waltham, MA, USA).
## 2.2. Cell Culture and Adipocyte Differentiation
Human PCS-210-010 preadipocyte and mouse embryonic preadipocyte 3T3-L1 cells obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA) were, respectively, cultured in FBM and DMEM containing $10\%$ FBS, 100 units/mL of penicillin/streptomycin, and 2 mmol/L of l-glutamine under humidified conditions of $5\%$ CO2 at 37 °C. For a differentiation program to convert preadipocytes to adipocytes, preadipocytes growing as monolayers up to $90\%$ confluent for 2 days were exposed to a differentiation medium made of FBM or DMEM containing $10\%$ FBS, 0.5 mM IBMX, 1 μM dexamethasone, and 5 μg/mL insulin for 2 days. At this stage, various concentrations of isopanduratin A were added, while $0.5\%$ (v/v) DMSO was used as vehicle control. The differentiation medium was replaced with culture medium supplemented with 5 μg/mL of insulin. After further incubation for 2 days, cells were maintained in complete medium, which was changed every 2 days until lipid-droplet-containing adipocytes were observed under the microscope. Undifferentiated and differentiated cells were defined as negative control and positive control groups, respectively.
## 2.3. Cytotoxicity Assay
Following the recommended course of action, the cytotoxicity of isopanduratin A was evaluated using a crystal violet colorimetric assay [33]. Cells were seeded in a 96-well plate at a density of 1 × 104 cells/well and incubated under humidified $5\%$ CO2 at 37 °C overnight and then exposed for 48 h to isopanduratin A in a range of final concentrations (0–100 μM). A vehicle control ($0.5\%$ (v/v) DMSO) was also included. Dead detached cells were removed after washing twice with phosphate buffer saline (PBS; pH 7.4). The adherently viable cells were then stained with crystal violet solution ($0.05\%$ w/v) for 30 min at room temperature after being fixed with $10\%$ w/v formic aldehyde for 30 min. The assayed plate was washed twice with deionized water to remove any excess crystal violet solution and then left to dry overnight. The stained cells were treated with 100 μL of methanol prior to absorbance measurement (570 nm) with a microplate reader (Anthros, Durham, NC, USA). The percentage of cell viability was calculated using the absorbance value of each treatment relative to that of the vehicle control.
## 2.4. Cell Proliferation Assay and Cell Cycle Analysis
The ability of 3T3-L1 cells to proliferate in the presence of isopanduratin A at its non-cytotoxic doses for 24–72 h was investigated by crystal violet staining [33,36]. 3T3-L1 cells (3.5 × 103 cells/well in a 96-well plate) growing as a monolayer for 2 days were exposed to differentiation medium containing varying concentrations of isopanduratin A (0–10 μM) and incubated for 24, 48, and 72 h. A vehicle ($0.5\%$ (v/v) DMSO) was also included. At the end of each incubation period, the crystal violet staining assay was carried out as described previously, and the ability of cells to proliferate was calculated and reported as the percentage of cell proliferation in each treatment relative to that of the vehicle control measured at 24 h.
The impact of isopanduratin A on the passage of 3T3-L1 cells through the cell cycle was analyzed by flow cytometry. Cells seeded in a 6-well plate and at $90\%$ confluent of their growth were treated with non-cytotoxic doses of isopanduratin A for 18 h. Undifferentiated or differentiated control cells were established by exposure to $0.5\%$ (v/v) DMSO. Cells in each treatment and control were harvested by centrifugation for 5 min at 2500× g and 4 °C and then fixed overnight in 1 mL of ice-cold $70\%$ (v/v) ethanol at −20 °C. The fixed cells were washed with PBS (pH 7.4), stained with 50 μg/mL PI solution (400 μL) containing 5 μg/mL DNase-free RNase solution for 30 min at room temperature, and kept away from light. DNA content was analyzed by flow cytometry (EMD Millipore, Austin, TX, USA). The percentages of cells in the G0/G1, S, and G2/M phases were then calculated using the FlowJo V10 software trial version (Williamson Way, Ashland, OR, USA).
## 2.5. Assessment of Cellular Lipid Content
The impact of isopanduratin A, at varying non-toxic doses, on the formation of lipid droplets in 3T3-L1 and PCS-210-010 adipocytes was evaluated by the Oil Red O staining assay. Both adipocytic cells undergoing the differentiation program, as described previously, were fixed with $10\%$ formaldehyde for 30 min at room temperature, and then the fixed cells were stained with Oil Red O solution (at an Oil Red O:distilled water ratio of 6:4) for 1 h at room temperature. The stained cells were washed twice with $60\%$ (v/v) isopropanol and randomly photographed under an inverted light microscope (Nikon Ts2, Tokyo, Japan). Intracellular Oil Red O-stained lipid droplets were eluted using $100\%$ isopropanol, and their absorbance values at 500 nm wavelength were measured using a microplate reader (Anthros, Durham, NC, USA).
The effects of isopanduratin A at varying non-cytotoxic doses on cellular triglyceride and released glycerol levels were also determined, respectively, using triglyceride and glycerol assay kits (Sigma Aldrich, St. Louis, MO, USA), in accordance with the instructions of the manufacturer. Undifferentiated or differentiated cells treated with DMSO ($0.5\%$ v/v) functioned as controls for each experiment.
## 2.6. Western Blotting
The effects of isopanduratin A (0–10 μM) on the expression of proteins related to adipogenesis after 48 h of incubation were tracked by western blot analysis. Undifferentiated and differentiated 3T3-L1 cells treated with DMSO ($0.5\%$ v/v) functioned as controls. Cells were collected and lysed on ice in RIPA buffer supplemented with a protease inhibitor cocktail for 45 min. Cell lysates were quantified for protein concentration using the BCA assay and stored at −80 °C until further use. Equal protein samples (30 μg) were loaded to separate on $10\%$ SDS-PAGE and transferred onto a nitrocellulose membrane (BIO-RAD, Hercules, CA, USA). The membranes were blocked in $5\%$ skim milk for 1 h at room temperature and incubated overnight with primary antibodies at 4 °C. The membranes were then washed (7 min × 3 times) with Tris-buffered saline with $0.1\%$ Tween® 20 (TBST) before incubation with HRP-conjugated secondary antibody for 2 h at room temperature. The membranes were washed 3 times with TBST to remove excess antibodies and detected using western chemiluminescent ECL substrates. The protein expression level was calculated as the ratio of the band intensity of the target protein to that of β-actin—a housekeeping protein.
## 2.7. Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR)
The impact of isopanduratin A on the expression of some proteins involved in the differentiation of 3T3-L1 adipocytes was confirmed at the transcriptional level using the RT-qPCR technique. 3T3-L1 preadipocytes (5 × 104 cells/well in a 6-well plate) with up to $90\%$ confluent were treated with varying non-cytotoxic doses of isopanduratin A for 2 days in differentiation medium. Undifferentiated and differentiated 3T3-L1 cells treated with DMSO ($0.5\%$ (v/v) functioned as controls for this study. The medium was removed, and the cells were rinsed thrice with ice-cold PBS (pH 7.4) and extracted for their RNA using the PureLink™ RNA Mini Kit (Invitrogen, Carisbad, CA, USA). An equal amount (1 μg) of total RNA was reverse-transcribed to complementary DNA with a RevertAid first-strand cDNA synthesis kit (Thermo Scientific Pierce, Rockford, IL, USA). The Bio-Rad Luna Universal qPCR master mix (Hercules, CA, USA) was used in the assay reaction, while amplification was performed with the Bio-Rad CFX96 Touch real-time PCR detection system (Hercules, CA, USA), in accordance with the instructions of the manufacturer. The RT-qPCR primers (Table 1) and conditions were previously described elsewhere [36]. The expression level of each target gene was normalized with that of Gapdh—a housekeeping gene. Relative mRNA expression levels were analyzed using the 2−(ave.∆∆CT) method, where CT is the threshold cycle.
## 2.8. Statistical Analysis
All experiments were carried out in triplicate, and the results are expressed as mean ± standard deviation (SD). Statistical comparison of means by one-way analysis of variance (ANOVA) with Tukey’s post hoc test was performed using GraphPad Prism 8.0.2 software (San Diego, CA, USA). A p-value of <0.05 was considered statistically significant.
## 3.1. Effect of Isopanduratin A on Adipogenesis in 3T3-L1 Preadipocytes
In this study, murine 3T3-L1 preadipocyte cells, which can differentiate into mature adipocytes under appropriate conditions [4,37], were used. Initially, the toxicity of each test compound was evaluated at 5 μM by a crystal violet assay, as previously described [33]. At this concentration, pinostrobin [1], panduratin A [3], isopanduratin A [4], and cardamonin [6] were all non-toxic and showed a significant reduction in intracellular lipid content in the Oil Red O staining assay (Table 2), suggesting their anti-adipogenic potential. Isopanduratin A showed a drop in the percentage of stained cells to approximately $60\%$, compared to the vehicle control. The cytotoxic effect of isopanduratin A was then further assessed in a wider range of concentrations (0–100 μM). The highest non-toxic dose was found to be 10 μM, and the half-maximum inhibitory concentration was 28.63 ± 0.70 μM.
The dose-dependent effect of isopanduratin A on 3T3-L1 adipocyte differentiation was then further examined by measuring the accumulation of cellular lipid droplets stained with Oil Red O dye (Figure 1a). Figure 1b shows that isopanduratin A at 5 and 10 μM inhibited cell differentiation in a dose-dependent manner, as indicated by the lower percentage of stained lipid droplets. The intracellular triglyceride content in the cells exposed to 1–10 μM isopanduratin A for 48 h decreased significantly, compared to untreated control cells (Figure 1c), although a reduction in cellular lipid droplets by 1 μM isopanduratin A was not clearly observed. Similarly, isopanduratin A at 1–10 μM significantly increased the amount of extracellular glycerol released from differentiated cells (Figure 1d).
The expression of proteins related to lipid metabolism as markers of mature adipocytes was further investigated in differentiated cells. Elevated expression levels of FAS, LPL, PLIN, and adiponectin, which play an important role in lipogenesis, were clearly observed in cells cultured with differentiation medium for 8 days (Figure 2a). Intriguingly, 5–10 μM of isopanduratin A significantly suppressed the expression of PLIN (Figure 2c) and adiponectin (Figure 2e) in differentiated cells, while lower levels of FAS (Figure 2b) and LPL (Figure 2d) were observed at as low as 1 μM of isopanduratin A. These results demonstrated that isopanduratin A at non-cytotoxic doses could efficiently limit lipogenesis during cell differentiation.
## 3.2. Isopanduratin A Inhibits Mitotic Clonal Expansion during Adipogenesis
Preadipocytes undergo mitotic clonal expansion (MCE) during the early stage of adipogenesis. Before the beginning of cell differentiation, these growth-arrested preadipocytes usually undergo a few rounds of mitosis. Concurrent reentry into the cell cycle caused by MCE leads to an increased number of adipocytes [7]. MCE is mediated by the activation of cyclin-dependent kinase (CDK) and cyclin family proteins. Following MCE, activated C/EBPβ stimulates C/EBPα, which in turn causes PPARγ to begin transcription [36,38].
As presented in Figure 3a (see Figure S1), isopanduratin A (1–10 μM) significantly inhibited the proliferation of 3T3-L1 preadipocytes after incubation for 24, 48, and 72 h, compared to differentiated control cells at each time point. The effect of isopanduratin A on cell cycle progression during MCE was further determined. The number of cells at different stages of the cell cycle was assessed after culture in differentiation medium for 18 h in the presence or absence of 1–10 μM of isopanduratin A. The histograms obtained from flow cytometry reveal the entry into the S phase of the cell cycle in differentiated 3T3-L1 cells (Figure 3b). Surprisingly, isopanduratin A significantly hindered the progression of the cell cycle, as indicated by the higher number of cells in the G0/G1 phase, compared to the differentiated control group (Figure 3c).
Figure 4 shows that isopanduratin A markedly altered the expression of MCE-mediated proteins (cyclins D1 and D3 and CDK2) in differentiated 3T3-L1 cells after 18 h of incubation, as proven by western blot analysis. Cyclin D1 is known to be suppressed, while other cyclin proteins are upregulated, during the initial phase of adipogenesis [39]. Cyclin D1 inhibits adipogenesis by preventing the expression of C/EBPα [40]. In this study, a reduction in cyclin D1 levels was observed in differentiated 3T3-L1 cells, but this downregulation was effectively reversed by isopanduratin A (Figure 4a,b) (See Figure S2). Lower levels of CDK2 (Figure 4c) and cyclin D3 (Figure 4d) were found in cells treated with isopanduratin A (10 μM) in comparison with the differentiated control group. These observations indicate that isopanduratin A delayed cell passage in the cell cycle by modulating MCE-mediated protein expression.
## 3.3. Isopanduratin A Downregulates Adipogenic Transcription Factors
To further elucidate the molecular mechanisms underlying the suppressive effect of isopanduratin A on adipogenesis, the expression of various adipogenic transcription factors was determined at both the mRNA and protein expression levels. Preadipocyte 3T3-L1 cells were collected during the early differentiation stage after 48 h of incubation with or without differentiation medium with isopanduratin A at non-toxic concentrations. Upregulated levels of transcription factor mRNA, including PPARγ, SREBP-1C, and C/EBPα, were observed in cells cultured in differentiation medium for 48 h (Figure 5a) (see Figure S3). Nevertheless, isopanduratin A at 5 and 10 μM significantly decreased the levels of SREBP-1C and PPARγ mRNA, compared to those of the differentiated control cells. It should be noted that the decreased level of C/EBPα mRNA was observed only in the 3T3-L1 cells incubated with isopanduratin A at a high concentration (10 μM). Consistent with the mRNA levels detected by qRT-PCR, western blotting revealed lower expression levels of the SREBP-1C, PPARγ, and C/EBPα proteins in the differentiated 3T3-L1 cells cultured with 5–10 μM of isopanduratin A, compared to differentiated control groups (Figure 5b–d).
## 3.4. Upstream Signals from MAPKs Are Modulated by Isopanduratin A
Mitogen-activated protein kinases (MAPKs), including ERK, p38, and JNK, play an important role during adipogenesis, in which their regulating roles, such as cell proliferation and differentiation, are exerted [37]. Suppression of MAPK signaling molecules efficiently inhibits adipocyte development, and it has been demonstrated that altering these biomolecules during adipocyte differentiation is one of the promising strategies to slow adipogenesis and cellular lipid metabolism [16].
In the present investigation, a western blot analysis was performed to determine whether isopanduratin A modulates the signaling molecules in the MAPK pathway (Figure 6a). The decreased levels of p-JNK/JNK (Figure 6b) (see Figure S4) and p-ERK/ERK (Figure 6c) were clearly indicated in the 3T3-L1 cells cultured with differentiation medium containing 5–10 μM of isopanduratin A, compared to differentiated control cells. It is worth noting that isopanduratin A at a high concentration (10 μM) dramatically suppressed p-p38/p38 signaling (Figure 6d). Thus, these results indicated that isopanduratin A might attenuate adipogenesis by inhibiting the MAPK pathway.
## 3.5. Isopanduratin A Modulates the Crosstalk between AMPK-ACC and AKT/GSK3β Signals
Several reports suggest that AMP-activated protein kinase (AMPK) regulates the cellular energy balance by inhibiting lipogenesis and promoting lipolysis [41,42]. In this study, isopanduratin A affected AMPK signaling molecules, as illustrated by Western blot analysis (Figure 7a) (see Figures S5 and S6). The activation of the AMPK pathway by this compound was indicated by the highly elevated levels of p-ACC/ACC (Figure 7b), p-AMPKα/AMPKα (Figure 7c), and p-AMPKβ/AMPKβ (Figure 7f) in the differentiated 3T3-L1 cells cultured with 10 μM of isopanduratin A for 48 h.
Protein kinase B (AKT) is another upstream molecule that plays an important role in adipogenesis. Phosphorylated AKT (p-AKT) suppresses AMPK-ACC signals, resulting in the upregulation of adipogenic transcription factors and promotion of lipogenesis [43]. Additionally, p-GSK3β, which mediates the transcription of adipogenic transcription factors, is also modulated by p-AKT. The AKT/GSK3β cascade is required for the expression of C/EBPβ, C/EBPα, and PPARγ during cell differentiation [44]. Consistent with the elevated expression of AMPK-ACC signals and decreased levels of adipogenic transcription factors, the ratios of p-AKT/AKT (Figure 7d) and p-GSK3β/GSK3β (Figure 7e) were suppressed by isopanduratin A. These results suggest that isopanduratin A modulates the signaling pathways of AKT/GSK3β and AKT/AMPK-ACC to inhibit adipogenesis.
## 3.6. Isopanduratin A Suppresses Adipocyte Maturation in Human Preadipocytes
The antiadipogenic potential of isopanduratin A was further studied in primary human PCS-210-010 preadipocytes. The lipid contents were analyzed by Oil Red O staining (Figure 8a). Treatment with isopanduratin A at 1, 5, and 10 μM decreased the number of cellular lipid droplets by $93.51\%$, $71.75\%$, and $49.79\%$, respectively (Figure 8b). These results suggested that isopanduratin A suppresses adipogenesis in human preadipocytes in a dose-dependent manner.
## 4. Discussion
Obesity is associated with the onset of metabolic syndrome and various degenerative diseases that can cause various chronic health problems and often lead to premature death. During the recent COVID-19 pandemic, obesity increased the risk of hospitalization and admission to intensive care units [45]. The unusual expansion of adipose tissue, a characteristic feature of obesity, depends on adipocyte hypertrophy (an increase in cell size) and/or hyperplasia (an increase in cell number) [46]. It is commonly acknowledged that a long-term regulated lifestyle that involves reducing food intake and increasing physical activity can effectively lower body weight. However, these diet and lifestyle modifications are challenging for many overweight patients. Currently, nutrition intervention is highlighted as an alternative strategy to treat obesity [47].
In this study, in the Oil Red O staining assay, isopanduratin A at non-toxic concentrations reduced the number of mature, lipid-containing adipocytes in both mouse 3T3-L1 (Figure 1a,b) and human PCS-210-010 (Figure 8) preadipocyte models. These results indicate its anti-adipogenic activity. It should be noted that isopanduratin A at 1 μM could reduce cellular fat accumulation in human preadipocytes more than in murine preadipocytes. Lipid metabolism plays a crucial role in adipocyte differentiation, and its dysregulation is a critical factor in the development of obesity [48]. The decrease in intracellular triglyceride content and the elevated levels of released glycerol (Figure 1c,d) demonstrated the lipolytic effect of isopaduratin A.
The suppressive effect of isopanduratin A on 3T3-L1 adipogenesis is further evidenced by decreased expression levels of adipogenic effectors, including FAS, PLIN1, LPL, and adiponectin (Figure 2). These lipid-metabolism-modulating proteins are essential for maintaining cellular lipid homeostasis and are associated with various metabolic conditions such as hyperlipidemia, insulin resistance, atherosclerosis, and obesity [9,37,48,49,50,51,52]. Due to its ability to modulate cellular lipid accumulation and interact with these lipid metabolism proteins, isopanduratin A might be a potential nutraceutical candidate for the treatment of several metabolic diseases.
Mitotic clonal expansion (MCE) is the process in which the number of premature adipocytes increases as a result of cell cycle re-entry and the repeated cycles (two–three cycles) of cell proliferation at the early stage of adipogenesis [7]. Several natural compounds that possess an anti-adipogenic potential exhibit cell cycle arrest in differentiated preadipocytes [37,38,39]. As mentioned above, growth-arrested preadipocytes undergo MCE, which is mediated by the activation of cyclin/CDK complexes [7]. Interestingly, treatment with 1–10 μM of isopanduratin A showed a significant decrease in the percentage of cell proliferation, compared to the differentiation control cells (Figure 3a). Increased cyclin D1 expression in preadipocytes treated with isopanduratin A, with concomitant lowered levels of cyclin D3 and CDK2 (Figure 4), indicated cell cycle arrest in the G0/G1 phase. Similar effects on cyclin D1 levels were reported earlier for other natural polyphenols such as delphinidin and curcumin, both of which are strong anti-adipogenic compounds [53,54]. The increase in cyclin D1 levels may suggest that isopanduratin A also inhibits adipogenesis by activating the Wnt/β-catenin signaling pathway. Consistent with the change in the DNA content analyzed by flow cytometry, the accumulation of G0/G1 cells and the decrease in S phase cells occurred in differentiated preadipocytes cultured with isopanduratin A at 1–10 μM (Figure 3b,c). These results suggested that isopanduratin A inhibited the generation of mature adipocytes from preadipocytes by triggering cell cycle arrest.
After the MCE period, activation of C/EBPα triggers PPARγ transcription in association with the expression of adipogenesis-regulating proteins [36]. During adipocyte differentiation, transcription factors C/EBPα, PPARγ, and SREBP-1c cross-activate one another to exert their adipogenic functions [38,55]. Previous studies showed that C/EBPα controls the expression of SREBP-1c and that low C/EBPα levels lead to reduced PPARγ activity. In addition, gene expressions related to cellular lipid storage and insulin response are affected by C/EBPα [56,57]. Intriguingly, isopanduratin A suppressed adipogenesis in 3T3-L1 cells by downregulating these transcription factors at both the translation and transcription levels (Figure 5).
The expression of adipogenic transcription factors is also governed by the opposite correlation between the AKT/GSK3β and the AMPK-ACC pathways. As these two pathways critically mediate the upstream machinery of adipocyte differentiation [58], the regulation of proteins involved in these processes could be another mechanism for suppressing adipogenesis. It is plausible that AMPK and AKT are competitively phosphorylated by an energy balance sensor that controls several metabolic pathways [59]. The AKT/GSK3β cascade is vital for the expressions of C/EBPβ, C/EBPα, and PPARγ during cell differentiation [60].
Moreover, the AMPK pathway influences the expression of FAS and FABP4, which participate in lipogenesis at the late stage of adipogenesis [57]. In mouse and human mesenchymal cells, upregulated levels of C/EBPα, PPARγ, and SREBP-1c are caused by the downregulation of AMPK, which also affects the activation of ACC [55]. Activation of AMPK (p-AMPK), in association with ACC initiation, hampers triglyceride and fatty acid production by suppressing SREBP-1c and FAS during adipogenesis [47,59]. Therefore, the good correlation between the suppressive effects of isopanduratin A on adipogenic proteins (Figure 4 and Figure 5) and the downregulated levels of p-AKT and p-GSK3β as well as the upregulated levels of p-AMPK and p-ACC (Figure 7) suggests that the compound inhibits adipogenesis and lipogenesis in mature adipocytes through the AKT/AMPK-ACC pathway.
*In* general, extracellular stimuli can induce MAPK signaling, which, in turn, activates several intracellular responses through the phosphorylation of specific sites and components, including ERK, JNK, and p38. Studies showed that adipogenic transcription regulators are influenced by proteins in the MAPK family [61]. In this study, isopanduratin A decreased the phosphorylated forms of JNK, ERK, and p38 (Figure 6). Interestingly, isopanduratin A suppressed MAPK signaling concomitantly with a reduction in intracellular lipid accumulation (Figure 1). ERK phosphorylation is known to be essential for cell proliferation and cell cycle progression during the MCE process [62,63,64]. Isopanduratin A prevented MCE, in parallel with the downregulated levels of p-ERK/ERK (Figure 6c). On the other hand, in our previous report, pinostrobin did not suppress MCE, in agreement with its lack of activity on p-ERK/ERK [33]. Panduratin A and cardamonin, the other adipogenic suppressors obtained from fingerroot, have never been reported for MCE interference.
It is worth noting that the non-theoretical alteration of the upstream regulating molecules (p-AKT, p-GSK3β, p-AMPK, p-ACC, and p-ERK) observed in this study could be the result of late-stage detection. However, isopanduratin A indeed restricts these signaling pathways during adipogenesis. Although more in-depth investigations are needed, the overall results suggest that isopanduratin A suppresses adipogenesis through multi-target mechanisms.
## 5. Conclusions
Fingerroot (Bosenbergia rotunda) possesses pinostrobin, panduratin A, cardamonin, and idopanduratin A as adipogenic inhibitors. Isopanduratin A suppresses adipogenesis by modulating AKT/AMPK-ACC (AKT/GSK3β and AKT/AMPK-ACC) and MAPK (JNK/ERK/p38) signals that correspond to the downregulation of key adipogenic regulators (SREBP-1c, PPARγ, and C/EBPα) and adipogenic effectors (FAS, PLIN1, LPL, and adiponectin) (Figure 9). It is worth noting that isopanduratin A also inhibits MCE by preventing ERK phosphorylation at the early stage of adipogenesis. This property is absent in pinostrobin and has not yet been described for panduratin A or cardamonin. Taken together, our results shed light on the molecular mechanisms underlying the anti-adipogenic activity of isopanduratin A and provide further evidence for the potential use of fingerroot as a functional food against weight gain and obesity. Rigorous preclinical and clinical trials should be performed to establish this hypothesis. As a culinary plant, fingerroot might be consumed directly as a functional food or used as an ingredient in nutraceutical products for body weight control. However, the safety for long-term daily consumption, as well as the stability and bioavailability of the active principles, must be thoroughly investigated before any application can be realized.
## References
1. **Obesity and Overweight**
2. Hruby A., Hu F.B.. **The Epidemiology of Obesity: A big picture**. *Pharmacoeconomics* (2015.0) **33** 673-689. DOI: 10.1007/s40273-014-0243-x
3. Balusamy S.R., Veerappan K., Ranjan A., Kim Y.J., Chellappan D.K., Dua K., Lee J., Perumalsamy H.. *Phytomedicine* (2020.0) **66** 153129. DOI: 10.1016/j.phymed.2019.153129
4. Rosen E.D., MacDougald O.A.. **Adipocyte differentiation from the inside out**. *Nat. Rev. Mol. Cell Biol.* (2006.0) **7** 885-896. DOI: 10.1038/nrm2066
5. Moseti D., Regassa A., Kim W.K.. **Molecular Regulation of Adipogenesis and Potential Anti-Adipogenic Bioactive Molecules**. *Int. J. Mol. Sci.* (2016.0) **17**. DOI: 10.3390/ijms17010124
6. Guru A., Issac P.K., Velayutham M., Saraswathi N.T., Arshad A., Arockiaraj J.. **Molecular mechanism of down-regulating adipogenic transcription factors in 3T3-L1 adipocyte cells by bioactive anti-adipogenic compounds**. *Mol. Biol. Rep.* (2021.0) **48** 743-761. DOI: 10.1007/s11033-020-06036-8
7. Tang Q.Q., Otto T.C., Lane M.D.. **Mitotic clonal expansion: A synchronous process required for adipogenesis**. *Proc. Natl. Acad. Sci. USA* (2003.0) **100** 44-49. DOI: 10.1073/pnas.0137044100
8. Jakab J., Miškić B., Mikšić Š., Juranić B., Ćosić V., Schwarz D., Včev A.. **Adipogenesis as a Potential Anti-Obesity Target: A Review of Pharmacological Treatment and Natural Products**. *Diabetes Metab. Syndr. Obes. Targets Ther.* (2021.0) **14** 67-83. DOI: 10.2147/DMSO.S281186
9. Madsen L., Petersen R.K., Sørensen M.B., Jørgensen C., Hallenborg P., Pridal L., Fleckner J., Amri E.Z., Krieg P., Furstenberger G.. **Adipocyte differentiation of 3T3-L1 preadipocytes is dependent on lipoxygenase activity during the initial stages of the differentiation process**. *Biochem. J.* (2003.0) **375** 539-549. DOI: 10.1042/bj20030503
10. Ann J.Y., Eo H., Lim Y.. **Mulberry leaves (**. *Genes Nutr.* (2015.0) **10** 46. DOI: 10.1007/s12263-015-0495-x
11. Carling D.. **The AMP-activated protein kinase cascade—A unifying system for energy control**. *Trends Biochem. Sci.* (2004.0) **29** 18-24. DOI: 10.1016/j.tibs.2003.11.005
12. Porstmann T., Santos C.R., Griffiths B., Cully M., Wu M., Leevers S., Griffiths J.R., Chung Y.-L., Schulze A.. **SREBP activity is regulated by mTORC1 and contributes to Akt-dependent cell growth**. *Cell Metab.* (2008.0) **8** 224-236. DOI: 10.1016/j.cmet.2008.07.007
13. Ross S.E., Erickson R.L., Hemati N., MacDougald O.A.. **Glycogen synthase kinase 3 is an insulin-regulated C/EBPα kinase**. *Mol. Cell. Biol.* (1999.0) **19** 8433-8441. DOI: 10.1128/MCB.19.12.8433
14. Bost F., Aouadi M., Caron L., Binétruy B.. **The role of MAPKs in adipocyte differentiation and obesity**. *Biochimie* (2005.0) **87** 51-56. DOI: 10.1016/j.biochi.2004.10.018
15. Engelman J.A., Lisanti M.P., Scherer P.E.. **Specific inhibitors of p38 mitogen-activated protein kinase block 3T3-L1 adipogenesis**. *J. Biol. Chem.* (1998.0) **273** 32111-32120. DOI: 10.1074/jbc.273.48.32111
16. Ma X., Wang D., Zhao W., Xu L.. **Deciphering the roles of PPARγ in adipocytes via dynamic change of transcription complex**. *Front. Endocrinol.* (2018.0) **9** 473. DOI: 10.3389/fendo.2018.00473
17. Ando Y., Sato F., Fukunaga H., Iwasaki Y., Chiba Y., Tebakari M., Daigo Y., Kawashima J., Kamei J.. **Placental extract suppresses differentiation of 3T3-L1 preadipocytes to mature adipocytes via accelerated activation of p38 MAPK during the early phase of adipogenesis**. *Nutr. Metab.* (2019.0) **16** 32. DOI: 10.1186/s12986-019-0361-8
18. Munhoz A., Frode T.S.. **Isolated Compounds from Natural Products with Potential Antidiabetic Activity—A Systematic Review**. *Curr. Diabetes Rev.* (2018.0) **14** 36-106. DOI: 10.2174/1573399813666170505120621
19. Qi L.W., Liu E.H., Chu C., Peng Y.B., Cai H.X., Li P.. **Anti-diabetic agents from natural products—An update from 2004 to 2009**. *Curr. Top. Med. Chem.* (2010.0) **10** 434-457. DOI: 10.2174/156802610790980620
20. Fu C., Jiang Y., Guo J., Su Z.. **Natural Products with Anti-obesity Effects and Different Mechanisms of Action**. *J. Agric. Food Chem.* (2016.0) **64** 9571-9585. DOI: 10.1021/acs.jafc.6b04468
21. Eng-Chong T., Yean-Kee L., Chin-Fei C., Choon-Han H., Sher-Ming W., Li-Ping C.T., Gen-Teck F., Khalid N., Abd Rahman N., Karsani S.A.. *Evid.-Based Complement. Altern. Med. eCAM* (2012.0) **2012** 473637. DOI: 10.1155/2012/473637
22. Kanjanasirirat P., Suksatu A., Manopwisedjaroen S., Munyoo B., Tuchinda P., Jearawuttanakul K., Seemakhan S., Charoensutthivarakul S., Wongtrakoongate P., Rangkasenee N.. **High-content screening of Thai medicinal plants reveals**. *Sci. Rep.* (2020.0) **10** 19963. DOI: 10.1038/s41598-020-77003-3
23. Chahyadi A., Hartati R., Wirasutisna K.R.. *Procedia Chem.* (2014.0) **13** 13-37. DOI: 10.1016/j.proche.2014.12.003
24. Isa N., Abdelwahab S., Mohan S., Abdul A., Sukari M., Taha M., Syam S., Narrima P., Cheah S.C., Ahmad S.. **In vitro anti-inflammatory, cytotoxic and antioxidant activities of boesenbergin A, a chalcone isolated from**. *Braz. J. Med. Biol. Res.* (2012.0) **45** 524-530. DOI: 10.1590/S0100-879X2012007500022
25. Kim D.-Y., Kim M.-S., Sa B.-K., Kim M.-B., Hwang J.-K.. *Int. J. Mol. Sci.* (2012.0) **13** 994-1005. DOI: 10.3390/ijms13010994
26. Saah S., Siriwan D., Trisonthi P.. **Biological activities of**. *Food Biosci.* (2021.0) **41** 101011. DOI: 10.1016/j.fbio.2021.101011
27. Kirana C., Jones G.P., Record I.R., McIntoch G.H.. **Anticancer properties of panduratin A isolated from**. *J. Nat. Med.* (2007.0) **61** 131-137. DOI: 10.1007/s11418-006-0100-0
28. Ongwisespaiboon O., Jiraungkoorskul W.. **Fingerroot,**. *Pharmacogn. Rev.* (2017.0) **11** 27-30. DOI: 10.4103/phrev.phrev_50_16
29. Rozmer Z., Perjési P.. **Naturally occurring chalcones and their biological activities**. *Phytochem. Rev.* (2016.0) **15** 87-120. DOI: 10.1007/s11101-014-9387-8
30. Vergoten G., Bailly C.. **Interaction of panduratin A and derivatives with the SARS-CoV-2 main protease (m**. *J. Biomol. Struct. Dyn.* (2022.0) 1-11. DOI: 10.1080/07391102.2022.2112618
31. Myoung K., Ahn Y.T., Lee M.H., Park D., Ahn Y.M., Huh C.S.. **Fingerroot (**. *J. Korean Soc. Food Sci. Nutr.* (2013.0) **42** 26-32. DOI: 10.3746/jkfn.2013.42.1.026
32. Chatsumpun N., Sritularak B., Likhitwitayawuid K.. **New Biflavonoids with α-Glucosidase and Pancreatic Lipase Inhibitory Activities from**. *Molecules* (2017.0) **22**. DOI: 10.3390/molecules22111862
33. San H.T., Khine H., Sritularak B., Prompetchara E., Chaotham C., Che C.T., Likhitwitayawuid K.. **Pinostrobin: An Adipogenic Suppressor from Fingerroot (**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11193024
34. Kim D., Lee M.S., Jo K., Lee K.E., Hwang J.K.. **Therapeutic potential of panduratin A, LKB1-dependent AMP-activated protein kinase stimulator, with activation of PPARα/δ for the treatment of obesity**. *Diabetes Obes. Metab.* (2011.0) **13** 584-593. DOI: 10.1111/j.1463-1326.2011.01379.x
35. Seo Y.J., Jin H., Lee K., Song J.H., Chei S., Oh H.J., Oh J.H., Lee B.Y.. **Cardamonin suppresses lipogenesis by activating protein kinase A-mediated browning of 3T3-L1 cells**. *Phytomed. Int. J. Phytother. Phytopharm.* (2019.0) **65** 153064. DOI: 10.1016/j.phymed.2019.153064
36. Khine H.E.E., Sungthong R., Sritularak B., Prompetchara E., Chaotham C.. **Untapped Pharmaceutical Potential of 4,5,4′-Trihydroxy-3,3′-dimethoxybibenzyl for Regulating Obesity: A Cell-Based Study with a Focus on Terminal Differentiation in Adipogenesis**. *J. Nat. Prod.* (2022.0) **85** 1591-1602. DOI: 10.1021/acs.jnatprod.2c00213
37. Yu H.S., Kim W.J., Bae W.Y., Lee N.K., Paik H.D.. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12103037
38. Kim W.J., Yu H.S., Bae W.Y., Ko K.Y., Chang K.H., Lee N.K., Paik H.D.. *J. Food Biochem.* (2021.0) **45** e13896. DOI: 10.1111/jfbc.13896
39. Marcon B.H., Shigunov P., Spangenberg L., Pereira I.T., de Aguiar A.M., Amorín R., Rebelatto C.K., Correa A., Dallagiovanna B.. **Cell cycle genes are downregulated after adipogenic triggering in human adipose tissue-derived stem cells by regulation of mRNA abundance**. *Sci. Rep.* (2019.0) **9** 5611. DOI: 10.1038/s41598-019-42005-3
40. Fu M., Rao M., Bouras T., Wang C., Wu K., Zhang X., Li Z., Yao T.P., Pestell R.G.. **Cyclin D1 inhibits peroxisome proliferator-activated receptor gamma-mediated adipogenesis through histone deacetylase recruitment**. *J. Biol. Chem.* (2005.0) **280** 16934-16941. DOI: 10.1074/jbc.M500403200
41. Hardie D.G., Ross F.A., Hawley S.A.. **AMPK: A nutrient and energy sensor that maintains energy homeostasis**. *Nat. Rev. Mol. Cell Biol.* (2012.0) **13** 251-262. DOI: 10.1038/nrm3311
42. Ahmad B., Serpell C.J., Fong I.L., Wong E.H.. **Molecular mechanisms of adipogenesis: The anti-adipogenic role of AMP-activated protein kinase**. *Front. Mol. Biosci.* (2020.0) **7** 76. DOI: 10.3389/fmolb.2020.00076
43. Bengoechea-Alonso M.T., Ericsson J.. **A phosphorylation cascade controls the degradation of active SREBP1**. *J. Biol. Chem.* (2009.0) **284** 5885-5895. DOI: 10.1074/jbc.M807906200
44. Li Y., Xu S., Mihaylova M.M., Zheng B., Hou X., Jiang B., Park O., Luo Z., Lefai E., Shyy J.Y.J.. **AMPK phosphorylates and inhibits SREBP activity to attenuate hepatic steatosis and atherosclerosis in diet-induced insulin-resistant mice**. *Cell Metab.* (2011.0) **13** 376-388. DOI: 10.1016/j.cmet.2011.03.009
45. Lighter J., Phillips M., Hochman S., Sterling S., Johnson D., Francois F., Stachel A.. **Obesity in Patients Younger Than 60 Years Is a Risk Factor for COVID-19 Hospital Admission**. *Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am.* (2020.0) **71** 896-897. DOI: 10.1093/cid/ciaa415
46. Marín-Aguilar F., Pavillard L.E., Giampieri F., Bullón P., Cordero M.D.. **Adenosine Monophosphate (AMP)-Activated Protein Kinase: A New Target for Nutraceutical Compounds**. *Int. J. Mol. Sci.* (2017.0) **18**. DOI: 10.3390/ijms18020288
47. Saito M., Yoneshiro T., Matsushita M.. **Food Ingredients as Anti-Obesity Agents**. *Trends Endocrinol. Metab. TEM* (2015.0) **26** 585-587. DOI: 10.1016/j.tem.2015.08.009
48. Yao Y., Li X.B., Zhao W., Zeng Y.Y., Shen H., Xiang H., Xiao H.. **Anti-obesity effect of an isoflavone fatty acid ester on obese mice induced by high fat diet and its potential mechanism**. *Lipids Health Dis.* (2010.0) **9** 49. DOI: 10.1186/1476-511X-9-49
49. Yanai H., Yoshida H.. **Beneficial Effects of Adiponectin on Glucose and Lipid Metabolism and Atherosclerotic Progression: Mechanisms and Perspectives**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20051190
50. Walton R.G., Zhu B., Unal R., Spencer M., Sunkara M., Morris A.J., Charnigo R., Katz W.S., Daugherty A., Howatt D.A.. **Increasing adipocyte lipoprotein lipase improves glucose metabolism in high fat diet-induced obesity**. *J. Biol. Chem.* (2015.0) **290** 11547-11556. DOI: 10.1074/jbc.M114.628487
51. Jiang H., Pu Y., Li Z.H., Liu W., Deng Y., Liang R., Zhang X.M., Zuo H.D.. **Adiponectin, May Be a Potential Protective Factor for Obesity-Related Osteoarthritis**. *Diabetes Metab. Syndr. Obes. Targets Ther.* (2022.0) **15** 1305-1319. DOI: 10.2147/DMSO.S359330
52. Ranganathan G., Unal R., Pokrovskaya I., Yao-Borengasser A., Phanavanh B., Lecka-Czernik B., Rasouli N., Kern P.A.. **The lipogenic enzymes DGAT1, FAS, and LPL in adipose tissue: Effects of obesity, insulin resistance, and TZD treatment**. *J. Lipid Res.* (2006.0) **47** 2444-2450. DOI: 10.1194/jlr.M600248-JLR200
53. Rahman N., Jeon M., Kim Y.S.. **Delphinidin, a major anthocyanin, inhibits 3T3-L1 pre-adipocyte differentiation through activation of Wnt/β-catenin signaling**. *BioFactors* (2016.0) **42** 49-59. DOI: 10.1002/biof.1251
54. Ahn J., Lee H., Kim S., Ha T.. **Curcumin-induced suppression of adipogenic differentiation is accompanied by activation of Wnt/beta-catenin signaling**. *Am. J. Physiol. Cell Physiol.* (2010.0) **298** C1510-C1516. DOI: 10.1152/ajpcell.00369.2009
55. Choi D.H., Han J.H., Yu K.H., Hong M., Lee S.Y., Park K.H., Lee S.U., Kwon T.H.. **Antioxidant and Anti-Obesity Activities of**. *J. Microbiol. Biotechnol.* (2020.0) **30** 21-30. DOI: 10.4014/jmb.1910.10040
56. Payne V.A., Au W.S., Lowe C.E., Rahman S.M., Friedman J.E., O’Rahilly S., Rochford J.J.. **C/EBP transcription factors regulate SREBP1c gene expression during adipogenesis**. *Biochem. J.* (2010.0) **425** 215-224. DOI: 10.1042/BJ20091112
57. Ambele M.A., Dhanraj P., Giles R., Pepper M.S.. **Adipogenesis: A Complex Interplay of Multiple Molecular Determinants and Pathways**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21124283
58. Peng X.D., Xu P.Z., Chen M.L., Hahn-Windgassen A., Skeen J., Jacobs J., Sundararajan D., Chen W.S., Crawford S.E., Coleman K.G.. **Dwarfism, impaired skin development, skeletal muscle atrophy, delayed bone development, and impeded adipogenesis in mice lacking Akt1 and Akt2**. *Genes Dev.* (2003.0) **17** 1352-1365. DOI: 10.1101/gad.1089403
59. He Y., Li Y., Zhao T., Wang Y., Sun C.. **Ursolic acid inhibits adipogenesis in 3T3-L1 adipocytes through LKB1/AMPK pathway**. *PLoS ONE* (2013.0) **8**. DOI: 10.1371/journal.pone.0070135
60. Day E.A., Ford R.J., Steinberg G.R.. **AMPK as a Therapeutic Target for Treating Metabolic Diseases**. *Trends Endocrinol. Metab. TEM* (2017.0) **28** 545-560. DOI: 10.1016/j.tem.2017.05.004
61. Guo L., Li X., Huang J.X., Huang H.Y., Zhang Y.Y., Qian S.W., Zhu H., Zhang Y.D., Liu Y., Liu Y.. **Histone demethylase Kdm4b functions as a co-factor of C/EBPβ to promote mitotic clonal expansion during differentiation of 3T3-L1 preadipocytes**. *Cell Death Differ.* (2012.0) **19** 1917-1927. DOI: 10.1038/cdd.2012.75
62. Chang E., Kim C.Y.. **Natural Products and Obesity: A Focus on the Regulation of Mitotic Clonal Expansion during Adipogenesis**. *Molecules* (2019.0) **24**. DOI: 10.3390/molecules24061157
63. Prusty D., Park B.H., Davis K.E., Farmer S.R.. **Activation of MEK/ERK signaling promotes adipogenesis by enhancing peroxisome proliferator-activated receptor gamma (PPARgamma) and C/EBPalpha gene expression during the differentiation of 3T3-L1 preadipocytes**. *J. Biol. Chem.* (2002.0) **277** 46226-46232. DOI: 10.1074/jbc.M207776200
64. Belmonte N., Phillips B.W., Massiera F., Villageois P., Wdziekonski B., Saint-Marc P., Nichols J., Aubert J., Saeki K., Yuo A.. **Activation of extracellular signal-regulated kinases and CREB/ATF-1 mediate the expression of CCAAT/enhancer binding proteins beta and -delta in preadipocytes**. *Mol. Endocrinol.* (2001.0) **15** 2037-2049. DOI: 10.1210/mend.15.11.0721
|
---
title: Association of Alternative Markers of Carbohydrate Metabolism (Fructosamine
and 1,5-Anhydroglucitol) with Perioperative Characteristics and In-Hospital Complications
of Coronary Artery Bypass Grafting in Patients with Type 2 Diabetes Mellitus, Prediabetes,
and Normoglycemia
authors:
- Alexey N. Sumin
- Natalia A. Bezdenezhnykh
- Andrey V. Bezdenezhnykh
- Anastasiya A. Kuzmina
- Yuliya A. Dyleva
- Olga L. Barbarash
journal: Diagnostics
year: 2023
pmcid: PMC10000986
doi: 10.3390/diagnostics13050969
license: CC BY 4.0
---
# Association of Alternative Markers of Carbohydrate Metabolism (Fructosamine and 1,5-Anhydroglucitol) with Perioperative Characteristics and In-Hospital Complications of Coronary Artery Bypass Grafting in Patients with Type 2 Diabetes Mellitus, Prediabetes, and Normoglycemia
## Abstract
Patients with type 2 diabetes make up 25 to $40\%$ of those referred for coronary bypass surgery, and the impact of diabetes on the results of the operation is studied in various aspects. To assess the state of carbohydrate metabolism before any surgical interventions, including CABG, daily glycemic control, and the determination of glycated hemoglobin (HbA1c) is recommended. Glycated hemoglobin reflects the glucose concentration for the 3 months prior to the measurement, but alternative markers that reflect glycemic fluctuations over a shorter period of time may be useful in preoperative preparation. The aim of this study was to study the relationship between the concentration of alternative markers of carbohydrate metabolism (fructosamine and 1,5-anhydroglucitol) with patients’ clinical characteristics and the rate of hospital complications after coronary artery bypass grafting (CABG). Method. In the cohort of 383 patients, besides the routine examination, additional markers of carbohydrate metabolism were determined before and on days 7–8 after CABG: glycated hemoglobin (HbA1c), fructosamine, and 1,5-anhydroglucitol. We evaluated the dynamics of these parameters in groups of patients with diabetes mellitus, prediabetes, and normoglycemia, as well as the association of these parameters with clinical parameters. Additionally, we assessed the incidence of postoperative complications and factors associated with their development. Results. In all groups of patients (diabetes mellitus, prediabetes, normoglycemia), there was a significant decrease in the level of fructosamine on the 7th day after CABG compared with baseline (p1st–2nd point 0.030, 0.001, and 0.038 in groups 1, 2, and 3, respectively), whereas the level of 1,5-anhydroglucitol did not change significantly. The preoperative level of fructosamine was associated with the risk of surgery according to the EuroSCORE II scale ($$p \leq 0.002$$), as were the number of bypasses ($$p \leq 0.012$$), body mass index and overweightness ($p \leq 0.001$ in both cases), triglyceride ($p \leq 0.001$) and fibrinogen levels ($$p \leq 0.002$$), preoperative and postoperative glucose and HbA1c levels ($p \leq 0.001$ in all cases), left atrium size ($$p \leq 0.028$$), multiplicity of cardioplegia, cardiopulmonary bypass duration and aortic clamp time ($p \leq 0.001$ in all cases). The preoperative level of 1,5-anhydroglucitol showed an inverse correlation with fasting glucose and fructosamine levels before surgery ($p \leq 0.001$ in all cases), intima media thickness ($$p \leq 0.016$$), and a direct correlation with LV end-diastolic volume ($$p \leq 0.020$$). The combined endpoint (presence of significant perioperative complications + extended hospital stay after surgery >10 days) was identified in 291 patients. In binary logistic regression analysis patient age ($$p \leq 0.005$$) and fructosamine level ($$p \leq 0.022$$) were independently associated with the development of this composite endpoint (presence of significant perioperative complications + extended hospital stay after surgery >10 days). Conclusions: This study demonstrated that in patients after CABG there was the significant decrease in the level of fructosamine compared with baseline, whereas the level of 1,5-anhydroglucitol did not change. Preoperative fructosamine levels were one of the independent predictors of the combined endpoint. The prognostic value of preoperative assessment of alternative markers of carbohydrate metabolism in cardiac surgery deserves further study.
## 1. Introduction
Coronary artery bypass grafting (CABG) is the best method of myocardial revascularization for patients with diabetes mellitus (DM) and multivessel coronary disease [1]. Among patients undergoing CABG, the proportion of patients with DM is constantly growing and currently reaches $40\%$; the presence of DM increases the number of postoperative complications and worsens the long-term prognosis [2,3,4]. Therefore, the search for ways to reduce the negative impact of DM on the results of coronary bypass surgery continues, the optimal targets for carbohydrate metabolism are being studied, and methods of preoperative preparation and perioperative management of patients with DM are being improved [3,5].
Currently, to assess the state of carbohydrate metabolism before CABG, the determination of glycated hemoglobin (HbA1c) is recommended [5]. Glycated hemoglobin reflects the concentration of glucose throughout the life of an erythrocyte, i.e., 3 months prior to the measurement. During preoperative preparation, alternative markers may be useful, reflecting glycemic fluctuations over a shorter period [6]. Markers of carbohydrate metabolism such as fructosamine and 1,5-anhydroglucitol are deprived of these restrictions [7].
Fructosamines are called glycated blood serum proteins formed during the reaction of glucose mainly with albumin [8]. The half-life of serum proteins is less than the life of red blood cells. Therefore, unlike glycated hemoglobin, the level of fructosamine reflects the degree of permanent or transient increase in glucose levels not in 3 months but in 1–3 weeks prior to the study. Recently, publications have begun to appear on the relationship of this marker with cardiovascular prognosis [6,9]. Since fructosamine may provide a more accurate assessment of glycemic variability and short-term therapeutic efficacy than HbA1c [7], it may be useful in assessing the achievement of carbohydrate metabolism compensation in preparing patients with DM for coronary bypass surgery. However, until now, fructosamine has not been not practically used for this purpose and there are only a few studies on this topic [10].
1,5-Anhydroglucitol (1,5-AG) is a glucid molecule, and tubular reabsorption of 1,5-AG competes with glucose. In situations where the glucose concentration exceeds the renal threshold of approximately 180 mg/dL (10 mmol/L), glomerular glucose excretion increases, as does its tubular reabsorption. In this situation, 1,5-AG, normally filtered in the glomerulus, is not reabsorbed into the tubules, increasing its urinary excretion and decreasing plasma concentration. Therefore, the plasma concentration of 1,5-AG may be a marker of prior (1–2 weeks) exposure to hyperglycemia above the renal glucose threshold, reflecting postprandial hyperglycemia peaks [7]. 1,5-AG is a biomarker for acute hyperglycemia; in acute hyperglycemia, renal reabsorption is inhibited by glucose and 1,5-AG is excreted in the urine, whereas its serum level decreases rapidly. 1,5-AG reflects jumps in glucose levels from 1–3 days to 2 weeks [11]. In this regard, a low level of serum 1,5-AG may be a clinical marker of short-term glycemic disorders and low levels of 1,5-AG reflect severe plaque calcification in CAD [12] and may be a predictor of cardiovascular disease and mortality after acute coronary syndrome [13]. With planned PCI, low and exacerbated levels of 1,5-anhydroglucitol are associated with cardiovascular events [12,14]; however, this biomarker has not been studied in cardiac surgeries.
The aim of this study was to study the relationship between the concentration of alternative markers of carbohydrate metabolism (fructosamine and 1,5-anhydroglucitol) with patients’ clinical characteristics and the rate of hospital complications after CABG.
## 2.1. Study Population
This single-center, cross-sectional, observational study was conducted at the Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo. Consecutive patients who under-went elective CABG in the cardiovascular surgery department of the clinic from 22 March 2011 to 22 March 2012 were included. The study design is shown in Figure 1. In total, the study involved 732 consecutive patients who were planned for CABG. For 9 of them, the revascularisation tactics were revised to percutaneous intervention due to the comorbidity, and 15 patients were denied myocardial revascularization and were excluded from the study. Thus, CABG was performed in 708 patients included in the study. In 383 consecutive patients, fructosamine and 1,5-anhydroglucitol were determined. Upon admission to the hospital for preparation for CABG in all of these 383 patients, glycemic status was examined.
For patients with borderline fasting hyperglycemia according to the criteria of the World Health Organization/International Diabetes Federation (6.1–6.9 mmol/L (110–125 mg/dL) and without previously established diabetes mellitus, as well as patients with previously known prediabetes and in the absence of contraindications, an oral glucose tolerance test with 75 g of glucose was performed. If the results of several fasting studies or postprandial glycemia was sufficient to establish a diagnosis of diabetes, an oral glucose tolerance test was not performed. The diagnosis of type 2 diabetes mellitus and other disorders of carbohydrate metabolism (CMD) was established in accordance with current WHO criteria for the modern classification of diabetes mellitus and other glycemic disorders [15,16].
## 2.2. Data Collection
Baseline preoperative and perioperative indicators were obtained from the electronic database of the CABG registry and medical records. The data of anamnesis, laboratory examinations, echocardiography, coronary angiography, ultrasound and angiographic examination of the aorta, brachiocephalic and peripheral arterial pools, and the frequency of postoperative complications were analyzed. Confirmation of the presence and assessment of the prevalence of atherosclerotic lesions was carried out using color duplex scanning of the extracranial sections of the brachiocephalic arteries and arteries of the lower extremities (Aloka 5500 device). Not earlier than six months before CABG, patients underwent coronary angiography. Stenosis of the main coronary arteries, narrowing the lumen of the vessel by $70\%$ or more or the trunk of the left coronary artery by $50\%$ or more, was considered hemodynamically significant. A more detailed description of the materials and methods of this study, including criteria for the diagnosis of diabetes mellitus and other glycemic disorders, as well as preoperative examination and patient preparation and perioperative glycemic management, are described in a previously published article [17].
## 2.3. Evaluation of Indicators of Carbohydrate Metabolism
The concentration of glucose in venous blood plasma was assessed by the hexokinase method. The level of fructosamine was determined by the kinetic colorimetric method; the level of 1,5-anhydrogluccitol (1.5 AG) was determined by enzyme immunoassay. An increase in fructosamine corresponds to an increase in glucose levels, and an increase in 1,5-anhydroglucitol corresponds to a decrease in glycemia. Clear reference values for these markers have not been established. The level of glycated hemoglobin (HbA1c) of hemolyzed whole blood was determined by turbidimetric inhibitory immunoassay according to the NGSP (National Glycohemoglobin Standardization Program) standard and standardized according to the reference values adopted in the Diabetes Control and Complications Trial (DCCT). A level of HbA1c up to $6.0\%$ (42 mmol/mol) was considered normal.
## 2.4. Hospital Postoperative Complications
We took into account the following perioperative complications of CABG: the development of intra- and postoperative myocardial infarction; heart failure requiring prolonged inotropic support; paroxysms of atrial fibrillation; stroke; acute renal failure; multiple organ failure; respiratory complications (pneumonia, respiratory failure, hydrothorax); complications from the wound of the sternum (prolonged exudation, purulent complications, diastasis of the sternum, mediastinitis); bleeding and remediastinotomy for bleeding. An analysis of hospital mortality and its causes was also carried out. Two combined endpoints were used to analyze hospital treatment outcomes: [1] presence of significant perioperative complications + prolonged hospital stay after surgery (>10 days); and [2] presence of significant perioperative complications. All complications described above were considered significant, with the exception of the following: hydrothorax, pneumothorax, and hydropericardium not requiring puncture and wound complications that do not require secondary surgical treatment of the wound, antibiotic therapy, and an increase in the length of stay in the hospital.
## 2.5. Statistical Analyses
Statistical processing was carried out using standard software packages “STATISTICA 8.0” (Dell Software, Inc., Round Rock, TX, USA) and SPSS 17.0 (IBM, Armonk, NY, USA). The distribution of quantitative data was checked using the Shapiro–Wilk test. Because the distribution of all quantitative characteristics differed from normal, they were described using the median, indicating the upper and lower quartiles (25th and 75th percentiles). To compare three independent groups, the Kruskal–Wallis test was used, followed by analysis of intergroup differences using the Mann–Whitney method or the χ2 test. The Wilcoxon test was used to assess the perioperative dynamics of carbohydrate metabolism. For a small number of observations, the Fisher’s exact test with Yates correction was used. To solve the problem of multiple comparisons, the Bonferroni correction was used. Thus, taking into account the number of degrees of freedom, the critical level of significance p when comparing the three groups was taken equal to 0.017; in other cases-it was 0.05. The association of carbohydrate metabolism markers with perioperative characteristics was assessed using Spearman’s rank correlation. To identify factors independently associated with CABG in-hospital outcomes, we evaluated binary logistic regression (forward LR method) in two models: [1] presence of significant perioperative complications + extended hospital stay after surgery (>10 days); and [2] significant hospital complications. The model included factors such as glucose, type 2 diabetes mellitus, alternative markers of carbohydrate metabolism, overweightness or obesity, left atrial size, LV end-diastolic size, total perioperative parameters (duration of surgery, duration of CPB, number of shunts), preoperative heart rate at rest, and medical therapy, including hypoglycemic ones. Performance of carbohydrate metabolism preoperative parameters in discriminating the risk of the composite endpoint-1 development (significant perioperative complications + extended hospital stay after surgery) after CABG was evaluated through receiver operating characteristic curve analysis.
## 3. Results
A sample of 383 patients was divided into three groups depending on their glycemic status: Group 1—patients with DM 2 ($$n = 125$$), Group 2—patients with prediabetes ($$n = 67$$), and Group 3—patients without carbohydrate metabolism disorders ($$n = 191$$).
## 3.1. Initial Characteristics of Patients in the Groups of Diabetes Mellitus, Prediabetes, and Normoglycemia
Patients of the three groups did not differ in age, class of angina pectoris and heart failure, the prevalence of arterial hypertension, and the frequency of cardiovascular events in history (Table 1). There were significantly fewer men in the prediabetes and DM2 groups than in the normoglycemia group. The prediabetes and diabetes groups had a higher proportion of obese individuals compared with those without CMD (Table 1, Figure 2). Patients with diabetes had a higher incidence of carotid surgery and a lower incidence of smoking compared with the group without CMD (Table 1).
The median body mass index in patients with DM and prediabetes was significantly higher than the BMI of the group with normal glucose metabolism (Table 1). Patients did not differ in the main drug therapy before CABG, with the exception of hypoglycemic therapy, which was taken only by patients with DM (Table 1). In the DM group, $32.1\%$ of patients took oral antihyperglycemic drugs before hospitalization and $15.2\%$ received insulin. During hospitalization in preparation for CABG, $41.6\%$ of patients with DM received insulin therapy (Table 1). Preoperative EuroSCORE II risk scores showed the lowest risk score among prediabetic individuals compared with the other two groups (Table 1). At the same time, medians of hospitalization of the DM and prediabetes groups were similar—13.0 days for the normoglycemia group—12.0 days ($p \leq 0.001$).
Overweightness or obesity was very common in this cohort, occurring in $90.4\%$ of diabetic patients, $85.1\%$ of pre-diabetic patients, and $69.1\%$ of the normoglycemic group. It is noteworthy that the percentage of overweightness, obesity, and long-term hospitalization among patients with prediabetes was no more favorable than in patients with DM and worse than in patients with normoglycemia (Figure 1). A similar trend, which did not reach statistical significance, was found for three-vessel disease, stem coronary artery disease, stenosis of the carotid arteries, and arteries of the lower extremities (Figure 1).
All groups were comparable in the frequency of cardiopulmonary bypass, combined surgery, duration of CPB, duration of aortic clamping, and other main characteristics of the surgery (Table 2). In the analysis of routine preoperative laboratory parameters, the median triglycerides were highest in the DM group compared with the other two groups; the rest of the indicators were comparable (Table 2).
The size of the left atrium was significantly larger in the DM group compared with the normoglycemia group (Table 2); there were no significant differences for other indicators. The previously noted trend that the median values of the groups with prediabetes are very close to the values of the DM group was also observed for echocardiographic parameters: linear and volumetric LV dimensions, left atrial dimensions, LV myocardial mass, LV myocardial mass index, and LV ejection fraction (Table 2).
## 3.2. Perioperative Dynamics of Carbohydrate Metabolism Markers in the Groups of Diabetes Mellitus, Prediabetes, and Normoglycemia
Figure 3 graphically reflects the fluctuations in the values of carbohydrate metabolism markers, and Table 2 shows their numerical values and critical significance levels p, both when comparing groups with each other and when comparing marker levels before and after surgery. Preoperative indicators of fructosamine and glucose naturally increased from the group without HMD to the DM group, with statistical significance when comparing each of the three groups with each other (Table 3, Figure 3), whereas on days 7–8 the differences remained significant only when comparing both groups with the DM group (Table 3). Moreover, the differences between the prediabetes and normoglycemia groups (1 and 2) disappeared due to the fact that on days 7–8 the median glucose values in the normoglycemia group increased and approached those of the prediabetes group (Table 3, Figure 3). At the same time, the level of fructosamine, which is an integral indicator of glucose for 3–4 weeks, decreased in the prediabetes group to the values of the normoglycemia group. At the same time, in all three groups there was a decrease in the median values of fructosamine when comparing preoperative and postoperative levels (Table 3). We can explain this by perioperative fasting and significantly lower carbohydrate intake up to 7–8 days after CABG and the fact that fructosamine was dynamic enough to reflect this condition. The median glucose also decreased on days 7–8 in groups 1 and 2. However, this was not the case in the normoglycemia group, where it even slightly increased in group 3 and became closer to the median of the prediabetes group, as indicated above.
Preoperative HbA1c levels naturally increased from the group without CMD to the DM group with statistical significance when comparing each of the three groups with each other, both before surgery and on days 7–8 ($p \leq 0.001$ in all cases) (Table 3, Figure 3). At the same time, there were no significant differences between preoperative and postoperative HbA1c values in any group. This is probably due to a slower change in glycated hemoglobin, which reflects glycemia in the 3 months prior to the study.
Attention is drawn to the following feature observed for almost all studied markers of carbohydrate metabolism except for HbA1c (glucose, fructosamine, and 1,5-anhydroglucitol), which is clearly visible in Figure 3: small quartile ranges before surgery, their proximity to the median in the prediabetes and normoglycemia groups before surgery, and a significant increase in the quartile ranges on days 7–8 after CABG. We can explain this by the fact that at rest and in the absence of diabetes mellitus, a high stability of carbohydrate metabolism is known, which was demonstrated by groups 1 and 2 (the diabetes group before the operation already had a large range of all markers). However, the operational stress caused a large amplitude of fluctuations in carbohydrate metabolism from hyperglycemia to hypoglycemia, and on the 7–8th day in all three groups we observe a high scatter of indicators between the 25th and 75th percentiles.
## 3.3. Correlation of Fructosamine and 1.5 Anhydroglucitol Levels before and after Surgery with Perioperative Characteristics of Patients
Furthermore, the correlation of fructosamine and 1,5-anhydroglucitol, determined before and on days 7–8 after CABG, with perioperative characteristics of patients was tested (Table 4). There was a direct correlation of the preoperative level of fructosamine with a variety of clinical characteristics: risk assessment according to EuroSCORE II, off-pump CABG, the number of cardioplegia, the duration of cardiopulmonary bypass and aortic clamping and the total duration of the operation, the number of shunts and distal anastomoses, body mass index, overweightness and obesity, the presence of type 2 diabetes mellitus, and the number of hospitalization days after CABG (Table 4). Laboratory parameters also directly correlated with the level of preoperative fructosamine–glucose before CABG and on days 7–8 after surgery, fructosamine after surgery, triglycerides, and fibrinogen level (Table 4). According to the result of echocardiography, there was a direct correlation of fructosamine with the size of the left atrium and an inverse one with an indicator of diastolic function (Vf). Fructosamine, determined on days 7–8, was associated with the presence of diabetes mellitus, body mass index, overweightness or obesity, intraoperative blood loss, off-pump CABG, LV posterior wall thickness and LV myocardial mass, diastolic indices (Vf), preoperative levels of triglycerides, and glucose and glycated hemoglobin determined upon admission and 7–8 days after CABG.
The preoperative level of 1.5 anhydroglucitol showed an inverse correlation with fasting glucose and fructosamine levels before surgery and on days 7–8 after CABG, the presence of type 2 diabetes mellitus, and intima media thickness and a direct correlation with end-diastolic volume. The postoperative level of 1.5 anhydroglucitol (days 7–8) was inversely correlated with combined operations, the presence of diabetes, operations on a beating heart, overweightness or obesity, and preoperative and postoperative levels of glucose and fructosamine.
## 3.4. Complications after CABG in the Groups of Diabetes Mellitus, Prediabetes, and Normoglycemia
For the majority of hospital complications, the trend remained—the prediabetes group was no more favorable in terms of the number of complications than DM and there were no statistically significant differences in the number of complications (Figure 4). The lethal outcome was in $2.6\%$ of cases in the DM group and $1.8\%$ in the normoglycemia group; there were no deaths in the hospital in the prediabetes group ($$p \leq 0.872$$).
## 3.5. Factors Associated with the Development of Hospital Complications of CABG
We analyzed the incidence of combined endpoints. The first combined endpoint (presence of significant perioperative complications + extended hospital stay after surgery (>10 days)) was identified in 291 patients; 92 patients had no endpoint. Patients with a combined endpoint-1 were mostly women and were older; there was an association with DM, obesity, a higher preoperative EuroScore II risk, a longer CPB duration and the total duration of operations, the number of distal anastomosis, higher levels of glucose and fructosamine before surgery, an increase in the left atrium size, and the LV myocardium mass (Supplementary Table S1). In binary logistic regression analysis (Table 5), only patient age ($$p \leq 0.005$$) and fructosamine level ($$p \leq 0.022$$) were independently associated with the development of this composite endpoint-1. For this model, statistical significance was χ2[2] = 14.2, $$p \leq 0.001$$, the Nagelkerke R2 value was 0.11, and the model correctly classified $84.1\%$ of cases.
Patients with a composite endpoint-2 (significant hospital complications) were older and had higher LA dimensions, LV myocardial mass, presence of multifocal atherosclerosis, higher preoperative glucose levels, higher preoperative EuroScore risk, longer duration of CPB, and more shunts. In binary logistic regression analysis (Table 6), only patient age ($$p \leq 0.001$$), aortic occlusion time ($$p \leq 0.001$$), and number of shunts ($$p \leq 0.004$$) were independently associated with the development of this composite endpoint-2. For this model, statistical significance was χ2[2] = 15.9, $$p \leq 0.001$$, the Nagelkerke R2 value was 0.213, and the model correctly classified $77.2\%$ of cases.
The association of carbohydrate metabolism preoperative parameters in discriminating the risk of the composite endpoint-1 development (significant perioperative complications + extended hospital stay after surgery) after CABG is presented in Figure 5. As shown in Supplementary Table S2, the areas under the curves were maximal for fructosamine before surgery (0.629, $$p \leq 0.001$$). The areas under the curves of other indicators were smaller, which indicated insufficient distinguishing ability.
## 4. Discussion
The present study shows that alternative markers of carbohydrate metabolism, fructosamine and 1,5-anhydroglucitol, are associated with different clinical characteristics in patients undergoing coronary bypass surgery. According to the regression analysis, the level of fructosamine was associated with one of the combined endpoints (significant hospital complications and long hospital stay after CABG).
So far, only a few studies have evaluated the association of fructosamine with the presence of perioperative complications in cardiac surgery. Thus, in a study by Kowalczuk-Wieteska et al. [ 10], they concluded that the levels of glycated hemoglobin and fructosamine equally determine the risk of perioperative complications in cardiac surgery patients. However, when analyzing the results of this study, it turned out that the preoperative level of these markers was higher in the group of patients without postoperative complications (although the authors did not reveal statistically significant differences). In contrast to this study, we found an association of increased fructosamine levels with the number of perioperative complications. In this regard, our results are consistent with the results of the prognostic value of fructosamine in patients undergoing primary total joint arthroplasty. This study showed that patients with fructosamine levels ≥292 mmol/L had a significantly higher risk of infectious complications (OR 6.2, $$p \leq 0.009$$), readmission (OR 3.0, $$p \leq 0.03$$), and reoperation (OR 3.4, $$p \leq 0.02$$). At the same time, there was no predictive value of HbA1c levels of ≥$7\%$ [18]. In a more recent multicenter study, similar results were obtained in patients with knee replacement surgery [19]. Apparently, the study of fructosamine as a prognostic marker is also deserved in cardiac surgery.
Another marker of carbohydrate metabolism, 1,5-anhydroglucitol, has not yet been studied in cardiac surgery. In studies in patients with PCI, encouraging results were obtained in terms of its prognostic value. For example, Fujiwara et al. showed that low baseline values of 1,5-anhydroglucitol were associated with the development of adverse events in the prospective observation of patients after PCI. In a multivariate logistic analysis, low 1,5-AG values were independently associated with coronary revascularization or target vessel revascularization ($$p \leq 0.04$$ and $$p \leq 0.044$$, respectively) [20]. In another study by Takahashi et al. when observing patients for a year after PCI, it was noted that low and exacerbated levels of 1,5-anhydroglucitol were associated with cardiovascular events [14]. However, when analyzing the results obtained by the authors, it turned out that, according to the initial values, the groups with the presence and absence of subsequent complications of PCI did not differ; these differences appeared when assessing 1,5-anhydroglucitol in dynamics. In our opinion, it is still incorrect to interpret these data as a proven prognostic value of 1,5-anhydroglucitol in these patients. In addition, in patients after OCT-guided PCI, low 1,5-AG levels were not associated with the development of MACE, in contrast to the presence of diabetes mellitus [12]. In our study, we also did not reveal the prognostic value of 1,5-anhydroglucitol in patients with CABG, perhaps these data should be clarified in further studies in this direction.
What is the possible clinical significance of this study? A recent meta-analysis of 30 studies including 34,650 patients convincingly demonstrated that low glycated hemoglobin (<$5.5\%$) before cardiac surgery is associated with a significant reduction in perioperative complications (such as death, acute kidney injury, neurological, and infectious complications) [5]. These data overcome the existing concerns among clinicians about tight perioperative glycemic control [21], and there is a proposal to achieve the maximum possible reduction in the level of glycated hemoglobin before surgery, not only in patients with DM but in general in all patients before cardiac surgery [22]. However, due to the longer period required to achieve the optimal values of glycated hemoglobin (up to 3 months), markers of carbohydrate metabolism such as fructosamine deserve attention. Alternative markers of carbohydrate metabolism (fructosamine and 1,5-anhydroglucitol) are easy to determine and may be useful for preoperative preparation and prediction of surgical outcomes. Until now, their use has been limited due to the lack of convincing data on the possible predictive value of these biomarkers. We hope that our study will initiate a more intensive study of this issue.
## Study Limitation
The present study had several limitations that need to be taken into account when evaluating its results. First, we did not take into account the level of glycemic control, which could potentially affect the results of CABG. Second, in our study we did not investigate postoperative troponin levels, which could detect perioperative myocardial injury and allow a more accurate assessment of the contribution of fructosamine and 1,5-anhydroglucitol to the clinical outcomes of CABG. In addition, in the studied cohort of patients, we did not assess the level of glycated hemoglobin before surgery, which did not allow us to compare its prognostic value with the markers of carbohydrate metabolism that we studied. Also, to diagnose prediabetes in terms of fasting glucose and HbA1c, we used the WHO criteria, which are less strong than those of the American Diabetes Association [15,16,23].
## 5. Conclusions
This study demonstrated that in patients after CABG there was the significant decrease in the level of fructosamine compared with baseline, whereas the level of 1,5-anhydroglucitol did not change. Preoperative fructosamine levels were one of the independent predictors of the combined endpoint. The prognostic value of preoperative assessment of alternative markers of carbohydrate metabolism in cardiac surgery deserves further study.
## References
1. Neumann F.J., Sousa-Uva M., Ahlsson A., Alfonso F., Banning A.P., Benedetto U., Byrne R.A., Collet J.P., Falk V., Head S.J.. **2018 ESC/EACTS Guidelines on myocardial revascularization**. *Eur. Heart J.* (2019) **40** 87-165. DOI: 10.1093/eurheartj/ehy394
2. Santos K.A., Berto B., Sousa A.G., Costa F.A.. **Prognosis and Complications of Diabetic Patients Undergoing Isolated Coronary Artery Bypass Surgery**. *Braz. J. Cardiovasc. Surg.* (2016) **31** 7-14. DOI: 10.5935/1678-9741.20160002
3. Cosentino F., Grant P.J., Aboyans V., Bailey C.J., Ceriello A., Delgado V., Federici M., Filippatos G., Grobbee D.E., Hansen T.B.. **2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD**. *Eur. Heart J.* (2020) **41** 255-323. DOI: 10.1093/eurheartj/ehz486
4. Ivanov S.V., Sumin A.N.. **Current trends in routine myocardial revascularization**. *Complex Issues Cardiovasc. Dis.* (2021) **10** 25-35. DOI: 10.17802/2306-1278-2021-10-2-25-35
5. Corazzari C., Matteucci M., Kołodziejczak M., Kowalewski M., Formenti A.M., Giustina A., Beghi C., Barili F., Lorusso R.. **Impact of preoperative glycometabolic status on outcomes in cardiac surgery: Systematic review and meta-analysis**. *J. Thorac. Cardiovasc. Surg.* (2022) **164** 1950-1960.e10. DOI: 10.1016/j.jtcvs.2021.05.035
6. Selvin E., Rawlings A.M., Lutsey P.L., Maruthur N., Pankow J.S., Steffes M., Coresh J.. **Fructosamine and Glycated Albumin and the Risk of Cardiovascular Outcomes and Death**. *Circulation* (2015) **132** 269-277. DOI: 10.1161/CIRCULATIONAHA.115.015415
7. Bergman M., Abdul-Ghani M., DeFronzo R.A., Manco M., Sesti G., Fiorentino T.V., Ceriello A., Rhee M., Phillips L.S., Chung S.. **Review of methods for detecting glycemic disorders**. *Diabetes Res. Clin. Pract.* (2020) **165** 108233. DOI: 10.1016/j.diabres.2020.108233
8. Danese E., Montagnana M., Nouvenne A., Lippi G.. **Advantages and Pitfalls of Fructosamine and Glycated Albumin in the Diagnosis and Treatment of Diabetes**. *J. Diabetes Sci. Technol.* (2015) **9** 169-176. DOI: 10.1177/1932296814567227
9. Zaccardi F., Kurl S., Pitocco D., Ronkainen K., Laukkanen J.A.. **Serum fructosamine and risk of cardiovascular and all-cause mortality: A 24-year prospective population-based study**. *Nutr. Metab. Cardiovasc. Dis.* (2015) **25** 236-241. DOI: 10.1016/j.numecd.2014.09.007
10. Kowalczuk-Wieteska A.M., Wróbel M., Rokicka D., Szymborska-Kajanek A., Foremny J., Nadziakiewicz P., Zembala M., Strojek K.. **Determination of the value of glycated hemoglobin HbA1c and fructosamine in assessing the risk of perioperative complications after cardiac surgery in patients with type 2 diabetes**. *Kardiochirurgia Torakochirurgia Pol.* (2016) **13** 305-308. DOI: 10.5114/kitp.2016.64869
11. Migała M., Chałubińska-Fendler J., Zielińska M.. **1,5-Anhydroglucitol as a Marker of Acute Hyperglycemia in Cardiovascular Events**. *Rev. Diabet. Stud.* (2022) **18** 68-75. DOI: 10.1900/RDS.2022.18.68
12. Teng H.I., Chen H.Y., Tsai C.T., Huang W.C., Chen Y.Y., Hsueh C.H., Hau W.K., Lu T.M.. **The clinical impact of serum 1,5-anhydro-D-glucitol levels on coronary artery calcification and adverse outcomes assessed by coronary optical coherence tomography in diabetic patients**. *Front. Cardiovasc. Med.* (2022) **9** 997649. DOI: 10.3389/fcvm.2022.997649
13. Ouchi S., Shimada K., Miyazaki T., Takahashi S., Sugita Y., Shimizu M., Murata A., Kadoguchi T., Kato T., Aikawa T.. **Low 1,5-anhydroglucitol levels are associated with long-term cardiac mortality in acute coronary syndrome patients with hemoglobin A1c levels less than 7.0**. *Cardiovasc. Diabetol.* (2017) **16** 151. DOI: 10.1186/s12933-017-0636-1
14. Takahashi S., Shimada K., Miyauchi K., Miyazaki T., Sai E., Ogita M., Tsuboi S., Tamura H., Okazaki S., Shiozawa T.. **Low and exacerbated levels of 1,5-anhydroglucitol are associated with cardiovascular events in patients after first-time elective percutaneous coronary intervention**. *Cardiovasc. Diabetol.* (2016) **15** 145. DOI: 10.1186/s12933-016-0459-5
15. 15.
World Health Organization
Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF ConsultationWorld Health OrganizationGeneva, Switzerland2006150. *Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation* (2006) 1-50
16. 16.
International Diabetes Federation
IDF Diabetes Atlas10th ed.International Diabetes FederationBrussels, Belgium2021. *IDF Diabetes Atlas* (2021)
17. Sumin A.N., Bezdenezhnykh N.A., Bezdenezhnykh A.V., Osokina A.V., Kuz’mina A.A., Tsepokina A.V., Barbarash O.L.. **Screening for Glucose Metabolism Disorders, Assessment the Disse Insulin Resistance Index and Hospital Prognosis of Coronary Artery Bypass Surgery**. *J. Pers. Med.* (2021) **11**. DOI: 10.3390/jpm11080802
18. Shohat N., Tarabichi M., Tischler E.H., Jabbour S., Parvizi J.. **Serum Fructosamine: A Simple and Inexpensive Test for Assessing Preoperative Glycemic Control**. *J. Bone Joint Surg. Am.* (2017) **99** 1900-1907. DOI: 10.2106/JBJS.17.00075
19. Shohat N., Tarabichi M., Tan T.L., Goswami K., Kheir M., Malkani A.L., Shah R.P., Schwarzkopf R., Parvizi J.. **2019 John Insall Award: Fructosamine is a better glycaemic marker compared with glycated haemoglobin (HbA1C) in predicting adverse outcomes following total knee arthroplasty: A prospective multicentre study**. *Bone Joint J.* (2019) **101-B (Supple. C)** 3-9. DOI: 10.1302/0301-620X.101B7.BJJ-2018-1418.R1
20. Fujiwara T., Yoshida M., Akashi N., Yamada H., Tsukui T., Nakamura T., Sakakura K., Wada H., Arao K., Katayama T.. **Lower 1,5-anhydroglucitol is associated with adverse clinical events after percutaneous coronary intervention**. *Heart Vessel.* (2016) **31** 855-862. DOI: 10.1007/s00380-015-0682-0
21. Bennett S.R., Alayesh Y.M., Algarni A.M., Alotaibi O.D., Aladnani A.A., Fernandez J.A., Bennett M.R.. **Effect of Acute Stress Glycemic Control and Long-Term Glycemic Control on the Incidence of Post-Operative Infection in Diabetics Undergoing Cardiac Surgery**. *Cureus* (2021) **13** e14031. DOI: 10.7759/cureus.14031
22. Ferraris V.A.. **Commentary: Breaking the perioperative glucose control barrier is like breaking the sound barrier-it takes a team!**. *J. Thorac. Cardiovasc. Surg.* (2022) **164** 1961-1962. DOI: 10.1016/j.jtcvs.2021.06.004
23. **2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2022**. *Diabetes Care* (2022) **45** S17-S38. DOI: 10.2337/dc22-S002
|
---
title: Factors Associated with Impaired Resistive Reserve Ratio and Microvascular
Resistance Reserve
authors:
- Tatsuro Yamazaki
- Yuichi Saito
- Daichi Yamashita
- Hideki Kitahara
- Yoshio Kobayashi
journal: Diagnostics
year: 2023
pmcid: PMC10000988
doi: 10.3390/diagnostics13050950
license: CC BY 4.0
---
# Factors Associated with Impaired Resistive Reserve Ratio and Microvascular Resistance Reserve
## Abstract
Coronary microvascular dysfunction (CMD) is described as an important subset of ischemia with no obstructive coronary artery disease. Resistive reserve ratio (RRR) and microvascular resistance reserve (MRR) have been proposed as novel physiological indices evaluating coronary microvascular dilation function. The aim of this study was to explore factors associated with impaired RRR and MRR. Coronary physiological indices were invasively evaluated in the left anterior descending coronary artery using the thermodilution method in patients suspected of CMD. CMD was defined as a coronary flow reserve <2.0 and/or index of microcirculatory resistance ≥25. Of 117 patients, 26 ($24.1\%$) had CMD. RRR (3.1 ± 1.9 vs. 6.2 ± 3.2, $p \leq 0.001$) and MRR (3.4 ± 1.9 vs. 6.9 ± 3.5, $p \leq 0.001$) were lower in the CMD group. In the receiver operating characteristic curve analysis, RRR (area under the curve 0.84, $p \leq 0.001$) and MRR (area under the curve 0.85, $p \leq 0.001$) were both predictive of the presence of CMD. In the multivariable analysis, previous myocardial infarction, lower hemoglobin, higher brain natriuretic peptide levels, and intracoronary nicorandil were identified as factors associated with lower RRR and MRR. In conclusion, the presence of previous myocardial infarction, anemia, and heart failure was associated with impaired coronary microvascular dilation function. RRR and MRR may be useful to identify patients with CMD.
## 1. Introduction
The traditional understanding is that epicardial coronary artery disease (CAD) plays a major role in ischemic heart disease, although previous registry data showed that only less than one-half of patients suspected of angina had significant lesions in epicardial coronary arteries [1,2]. In this context, ischemia with no obstructive CAD (INOCA) has been increasingly recognized as a major etiology of ischemic heart disease [3,4], in which coronary microvascular dysfunction (CMD) and vasospastic angina are described as important subsets of INOCA in the expert consensus document [5]. Since CMD reportedly deteriorates a patient’s quality of life and prognosis [3,6], accurate identification and diagnosis are clinically relevant. Coronary flow reserve (CFR), which is the ratio of hyperemic to resting blood flow, represents coronary blood flow capacity including epicardial coronary arteries and microvasculature to accommodate an increasing demand for oxygen at excise or stress [7]. Since reduced CFR indicates the presence of CMD when no significant epicardial CAD exists, the recent European and American guidelines recommend the measurement of CFR in patients suspected of INOCA [8,9]. CFR relies on resting flow for the calculation, and, thus, hemodynamic perturbation including a change in heart rate, blood pressure, and left ventricular contractility affects CFR value [10]. Recently, resistive reserve ratio (RRR) and microvascular resistance reserve (MRR) have been proposed as novel physiological indices to represent coronary microvascular dilation function [11,12]. Given that these indices take into account the information on coronary pressure as well as flow [11,12], RRR and MRR may better estimate coronary microvascular function as compared with CFR. Indeed, previous reports showed that RRR was superior to CFR in predicting future cardiovascular events in patients with CAD [13,14]. However, data are scarce on factors related to RRR and MRR. The aim of the present study was to explore factors associated with impaired RRR and MRR.
## 2.1. Study Population
This was a retrospective, single-center study at Chiba University Hospital. Between July 2020 and June 2022, a wire-based coronary physiological assessment was conducted on 117 patients who were suspected of having CMD due to their chest pain with no apparent epicardial CAD. The invasive physiological assessment was performed in the LAD. Patients with a physiological assessment in a nonelective setting (i.e., acute coronary syndrome) ($$n = 5$$) and missing data ($$n = 4$$) were excluded. In addition, patients with angiographically significant epicardial CAD (percentage of diameter stenosis on visual assessment >$50\%$) in the LAD were also excluded. Thus, a total of 108 patients were included in the present analysis. This study was done in accordance with the Declaration of Helsinki. The ethics committee of the Chiba University Graduate School of Medicine approved this study (Approval number: M10348, date: 27 July 2022). Informed consent was obtained in the form of opt-out.
## 2.2. Invasive Coronary Physiological Assessment
The invasive diagnostic procedure is schematized in Figure 1. After the administration of intracoronary isosorbide dinitrate, a coronary angiography was performed per local standard practice [15,16]. In the present study, wire-based invasive coronary physiological indices were measured by the bolus-saline injection thermodilution method using a 6 Fr guiding catheter with no side holes [17,18]. After equalization, the pressure sensor guidewire (PressureWire X, Abbot Vascular, Santa Clara, USA) was advanced into the distal third of the LAD, and 3 milliliters of room-temperature saline were injected into the LAD at 3 times, automatically calculating mean transit time (Tmn) with a dedicated system (CoroFlow system, Coroventis Research, Uppsala, Sweden). Simultaneously, mean aortic pressure (Pa) and distal coronary pressure (Pd) were measured. Maximum hyperemia was induced by intracoronary administration of papaverine (12 mg) or nicorandil (2 mg) [16,19]. All indices of coronary pressure and flow (i.e., Tmn) were measured at resting and hyperemic conditions.
Multiple coronary physiological indices were evaluated in this study as follows: the ratio of Pd to Pa (resting Pd/Pa), fractional flow reserve (FFR), baseline resting index (BRI), index of microcirculatory resistance (IMR), CFR, RRR, and MRR, all of which were calculated using Pa, Pd, and Tmn at rest and hyperemia. FFR was defined as Pd/Pa at hyperemia. BRI and IMR, both of which represent a coronary microvascular tone, were defined as Pd multiplied by Tmn at resting and hyperemic conditions, respectively [13,14,20,21]. CFR was defined as resting Tmn divided by hyperemic Tmn. RRR, the ratio of microvascular tone at rest to that at hyperemia was defined as follows: RRR = BRI/IMR = (resting Pd × resting Tmn)/(hyperemic Pd × hyperemic Tmn) = CFR × (resting Pd/hyperemic Pd) [13,14]. In the present study, MRR was calculated by using indices obtained by a bolus-saline thermodilution method, rather than measured by absolute coronary blood flow using a continuous-saline thermodilution method. The definition of MRR was as follows: MRR = CFR × (resting Pa/hyperemic Pd) = (CFR/FFR) × (resting Pa/hyperemic Pa) = RRR × (resting Pa/resting Pd) [12,22]. The cut-off values for abnormal FFR, IMR, and CFR were determined as ≤0.80, ≥25, and <2.0, respectively [8,9]. In the present study, patients with abnormal CFR and/or IMR (i.e., CFR < 2.0 and/or IMR ≥ 25) were defined as having CMD [5,8,9].
## 2.3. Endpoints and Statistical Analysis
The primary interest of this study was to explore factors associated with impaired (i.e., lower) RRR and MRR. All statistical analyses were performed using JMP pro version 16.0 (SAS Institute Inc., Cary, CA, USA). Continuous variables were expressed as mean ± standard deviation and compared with the Student t-test. Categorical variables were expressed as frequency (%) and assessed with Fisher’s exact test. The normal distribution was visually evaluated with histograms. Due to the skewed distribution, a log transformation was performed to assess the level of brain natriuretic peptide (BNP). The receiver operating characteristic (ROC) curve analyses were performed to assess the best cut-off value of RRR and MRR for predicting CMD. Univariable and multivariable linear regression analyses were performed to explore factors related to coronary physiological indices. In the regression models, we included variables reportedly affecting coronary physiological statuses such as age, sex, body mass index, diabetes, hypertension, previous myocardial infarction (MI), renal function assessed with estimated glomerular filtration rate, anemia evaluated with a hemoglobin level, heart failure estimated by log-transformed BNP, and hyperemic agent (i.e., intracoronary papaverine versus nicorandil) [23,24,25,26,27,28,29,30,31,32]. The results of the regression analysis are displayed in a heat map. As a sensitivity analysis, the univariable and multivariable linear regression analyses were performed after excluding cases in which intracoronary nicorandil was used to achieve maximum hyperemia. A value of $p \leq 0.05$ was considered statistically significant. No corrections for multiple comparisons were performed.
## 3. Results
Of the 108 patients, 26 ($24.1\%$) had CMD (Table 1). Baseline characteristics between patients with and without CMD are summarized in Table 1. Patients with CMD were more likely to be women, while other characteristics were similar between the two groups (Table 1).
Coronary physiological findings are shown in Table 2. To archive maximum hyperemia, intracoronary papaverine, and nicorandil were used in $62.0\%$ and $38.0\%$, respectively. The use of nicorandil was more frequent in women than in men ($79.3\%$ vs. $22.8\%$, $p \leq 0.001$). FFR, BRI, and IMR were significantly higher and CFR, RRR, and MRR were lower in patients with CMD than those without (Table 2).
The ROC curve analyses showed that RRR and MRR were both predictive of the presence of CMD (Figure 2). With the best cut-off value, the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of RRR ≤ 3.4 and MRR ≤ 3.7 for CMD were 0.77, 0.84, 0.61, 0.92, and 0.82, and 0.77, 0.87, 0.65, 0.92, and 0.84, respectively.
In the univariable analysis, female gender, the presence of previous MI, a lower hemoglobin level, higher log-transformed BNP, and intracoronary nicorandil as a hyperemic agent were significantly associated with lower RRR and MRR (Figure 3).
Multivariable analysis indicated previous MI, a lower hemoglobin level, higher log-transformed BNP, and intracoronary nicorandil as predictors of lower RRR and MRR (Figure 4).
When excluding cases in which intracoronary nicorandil was used to achieve maximum hyperemia (Table 3 and Table 4), the overall results were similar (Figure 5, Figure 6 and Figure 7).
## 4. Discussion
The present study demonstrated that among patients suspected of CMD, approximately one quarter had invasively assessed CFR <2.0 and/or IMR ≥25. Patients with CMD had a lower CFR, RRR, and MRR than those without. Multivariable analysis identified previous MI, anemia, and heart failure as factors associated with impaired RRR and MRR. To our knowledge, this is the first report exploring predictors of the novel indices, RRR and MRR, for evaluating coronary microvascular dilation function.
## 4.1. RRR and MRR
Recently, INOCA has been of clinical interest, in which CMD is the main subset [5]. Since invasive identification and subsequent medical therapy were shown to improve the quality of life in patients with INOCA [33,34], an accurate diagnosis is clinically relevant. Although CFR (<2.0) and IMR (≥25) are suggested to define INOCA in the guidelines [8,9], whether the two indices can accurately identify patients with CMD remains uncertain. CFR is affected by hemodynamic perturbation such as a change in heart rate, blood pressure, and left ventricular contractility [10], and IMR is influenced by the amount of myocardium subtended to the location of the pressure-temperature sensor [35]. To overcome the limitations of CFR and IMR measurement, recently emerged RRR and MRR may be useful for evaluating coronary microvascular dilation function. While previous studies showed that only one physiologic index, including FFR, CFR, or IMR, was unable to fully discriminate patients at higher risks of clinical events, RRR is an integrated physiologic index of both coronary flow and pressure, potentially resulting in better risk stratification in CMD [13,14]. In fact, a previous single-center study ($$n = 1692$$) showed that RRR (mean value 2.88) was useful to stratify risks for all-cause mortality in patients with angina or ischemia and nonobstructive CAD, with the best cut-off value of 2.62 [14]. Another patient-level pooled cohort in Korea, Japan, and Spain demonstrated that lower RRR was associated with worse clinical outcomes in a stepwise manner and that even in patients with preserved FFR (>0.80) and CFR (>2.0), lower RRR (<3.5) was related to an increased risk of patient-oriented composite outcomes during the long-term follow-up [13]. The cut-off (median) value for predicting outcomes suggested in the pooled data (i.e., 3.5) was in line with that for the presence of CMD in the present study (i.e., 3.4), although RRR in the present study was numerically higher than that of previous studies [13,14]. In the prior pooled data, >$30\%$ of patients had CFR <2.0 [13], whereas approximately $10\%$ did in the present study, suggesting that our study cohort represented relatively preserved coronary microvascular function.
MRR was originally developed as the index measured by absolute coronary blood flow with a continuous-saline thermodilution method using a dedicated microcatheter [36]. MRR is conceptually specific for microcirculation and independent of myocardial mass [12]. Although MRR was calculated by using indices obtained with a bolus-saline thermodilution method in the present study, it has the potential to avoid influence with epicardial CAD and the amount of myocardium [12]. The suggested cut-off value of MRR for the presence of CMD in this study (i.e., 3.7) was slightly higher than that of RRR, which may be reasonable due to the calculation formula (i.e., MRR = RRR × [resting Pa/resting Pd]) [12,22]. Given that CFR, RRR, and MRR were all significantly lower in patients with CMD than those without, multiple physiological assessments can aid in identifying patients with CMD. Further studies are needed to elucidate the cut-off values of RRR and MRR and whether the novel indices are superior to conventional invasive indices such as CFR and IMR in estimating coronary microvascular function.
## 4.2. Factors Associated with RRR and MRR
It is conceivable that CMD, greater resting coronary blood flow, or both result in impaired microvascular dilation response (i.e., RRR and MRR) [13,14], which are reportedly associated with several clinical and procedural factors. For instance, FFR was preserved while IMR, CFR, RRR, and MRR were more impaired in women than in men in the univariable analysis in the present study, probably due to the longer hyperemic Tmn (slower coronary blood flow) (Figure 3). However, previous studies showed that women had lower CFR, with a shorter resting Tmn (faster coronary blood flow) [37,38]. The longer hyperemic Tmn in women may be confounded with the higher likelihood of nicorandil use as a hyperemic agent. Indeed, when excluding cases in which maximum hyperemia was achieved by intracoronary nicorandil, the female gender was no longer associated with lower CFR, RRR, and MRR in both univariable and multivariable analyses (Figure 6 and Figure 7). Women are likely to have impaired CFR, though the underlying mechanisms remain unclear. Additionally, a previous study in which prognostic implications of RRR were evaluated in patients with INOCA showed that the rate of women was higher in patients with reduced RRR (<2.62) than in their counterparts [14]. In the multivariable adjustment with hemoglobin and BNP levels, female gender was no longer a significant factor associated with CFR, RRR, and MRR in the present study, suggesting that anemia may play a role in a higher likelihood of CMD in women. Apart from gender differences, several patient characteristics such as older age and the presence of diabetes are known to be associated with impaired CFR [24,39]. A recent retrospective study showed that MRR was significantly lower in diabetic patients with suspected angina and nonobstructive CAD than those without diabetes [31], and diabetes was also reportedly associated with lower RRR [13,14]. Although the present study did not show the direct relation of diabetes to CFR, the multivariable analysis indicated that patients with diabetes had nonsignificantly lower RRR and MRR.
In this study, a multivariable analysis identified previous MI, anemia, and heart failure as factors associated with impaired RRR and MRR. In a recent prospective study in which invasive measures of coronary microvascular function such as CFR and IMR were repeatedly evaluated in patients undergoing primary percutaneous coronary artery intervention for ST-segment-elevation MI, IMR remained high (i.e., 25.6 ± 17.8) at one month after the index event [40]. Patients with a history of MI are likely to have coronary arteriosclerosis and impaired microvascular function [41], probably resulting in lower RRR and MRR. The increased resting and impaired hyperemic coronary blood flow in patients with anemia and heart failure were reported in previous investigations, as shown in the present study [42,43,44], supported by the fact that lower hemoglobin and higher BNP levels were associated with a shorter resting Tmn and BRI in the univariable models (Figure 3). It is conceivable that the increased resting coronary blood flow reflected a patient condition where hyperemic status, at least partially, was achieved even at rest, preventing “additional” maximum hyperemia by intracoronary administration of papaverine and nicorandil. Although intracoronary nicorandil is safe and effective to induce hyperemia [19], an achievable hyperemic effect by intracoronary papaverine may be greater as compared with nicorandil [32], leading to the significant influence of different hyperemic agents (i.e., papaverine vs. nicorandil) on RRR and MRR. In previous reports, a hyperemic effect of intracoronary papaverine is induced earlier and lasts longer than that of nicorandil [45,46]. However, when excluding cases in which nicorandil was used for inducing maximum hyperemia, the overall results were similar. Thus, we believe that the presence of previous MI, anemia, and heart failure may be significant predictors of impaired RRR and MRR. To estimate whether a patient has CMD in clinical practice, these factors may be taken into account.
## 4.3. Study Limitations
There were some limitations in the present study. This was a retrospective, single-center, observational study, and the sample size was modest. The number of patients included in this study may be acceptable to perform the multivariable analyses [47], however, a larger sample size would be preferred. Although the present study included patients suspected of CMD, only one quarter had CFR <2.0 and/or IMR ≥25. Noninvasive stress tests to evaluate myocardial ischemia were not performed in a uniform manner and thus, the data were not available. Different hyperemic agents, such as intracoronary papaverine, nicorandil, intravenous adenosine, and adenosine triphosphate reportedly have different characteristics in safety, efficacy, and availability in real-world clinical practice. The decision of physiological measurement and the selection of hyperemic agent were left to the operator′s discretion. Even though the sensitivity analysis confirmed similar results between the entire study population and cases in which intracoronary papaverine was used to achieve maximum hyperemia, a selection bias is possible. In this study, we estimated MRR by using a bolus-saline thermodilution method rather than using a continuous-saline thermodilution method as done in previous reports [12,22].
## 5. Conclusions
Coronary microvascular dilation function assessed with RRR and MRR was impaired in patients with CMD, both of which may help estimate coronary microvascular function. The presence of previous MI, anemia, and heart failure were identified as factors associated with lower RRR and MRR. The clinical usefulness of RRR and MRR beyond conventional physiological indices such as CFR and IMR deserves further investigation.
## References
1. Patel M.R., Peterson E.D., Dai D., Brennan J.M., Redberg R.F., Anderson H.V., Brindis R.G., Douglas P.S.. **Low diagnostic yield of elective coronary angiography**. *N. Engl. J. Med.* (2010) **362** 886-895. DOI: 10.1056/NEJMoa0907272
2. Reeh J., Therming C.B., Heitmann M., Højberg S., Sørum C., Bech J., Husum D., Dominguez H., Sehestedt T., Hermann T.. **Prediction of obstructive coronary artery disease and prognosis in patients with suspected stable angina**. *Eur. Heart J.* (2019) **40** 1426-1435. DOI: 10.1093/eurheartj/ehy806
3. Beltrame J.F., Tavella R., Jones D., Zeitz C.. **Management of ischaemia with non-obstructive coronary arteries (INOCA)**. *BMJ.* (2021) **375** e060602. DOI: 10.1136/bmj-2021-060602
4. Ford T.J., Ong P., Sechtem U., Beltrame J., Camici P.G., Crea F., Kaski J.C., Bairey Merz C.N., Pepine C.J., Shimokawa H.. **Assessment of Vascular Dysfunction in Patients Without Obstructive Coronary Artery Disease: Why, How, and When**. *JACC Cardiovasc. Interv.* (2020) **13** 1847-1864. DOI: 10.1016/j.jcin.2020.05.052
5. Kunadian V., Chieffo A., Camici P.G., Berry C., Escaned J., Maas A.H.E.M., Prescott E., Karam N., Appelman Y., Fraccaro C.. **An EAPCI Expert Consensus Document on Ischaemia with Non-Obstructive Coronary Arteries in Collaboration with European Society of Cardiology Working Group on Coronary Pathophysiology & Microcirculation Endorsed by Coronary Vasomotor Disorders International Study Group**. *EuroIntervention* (2021) **16** 1049-1069. PMID: 32624456
6. Schumann C.L., Mathew R.C., Dean J.L., Yang Y., Balfour P.C., Shaw P.W., Robinson A.A., Salerno M., Kramer C.M., Bourque J.M.. **Functional and Economic Impact of INOCA and Influence of Coronary Microvascular Dysfunction**. *JACC Cardiovasc. Imaging* (2021) **14** 1369-1379. DOI: 10.1016/j.jcmg.2021.01.041
7. Gould K.L., Lipscomb K., Hamilton G.W.. **Physiologic basis for assessing critical coronary stenosis. Instantaneous flow response and regional distribution during coronary hyperemia as measures of coronary flow reserve**. *Am. J. Cardiol.* (1974) **33** 87-94. DOI: 10.1016/0002-9149(74)90743-7
8. Knuuti J., Wijns W., Saraste A., Capodanno D., Barbato E., Funck-Brentano C., Prescott E., Storey R.F., Deaton C., Cuisset T.. **2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes**. *Eur. Heart J.* (2020) **41** 407-477. DOI: 10.1093/eurheartj/ehz425
9. Gulati M., Levy P.D., Mukherjee D., Amsterdam E., Bhatt D.L., Birtcher K.K., Blankstein R., Boyd J., Bullock-Palmer R.P., Conejo T.. **2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines**. *Circulation* (2021) **144** e368-e454
10. De Bruyne B., Bartunek J., Sys S.U., Pijls N.H., Heyndrickx G.R., Wijns W.. **Simultaneous coronary pressure and flow velocity measurements in humans. Feasibility, reproducibility, and hemodynamic dependence of coronary flow velocity reserve, hyperemic flow versus pressure slope index, and fractional flow reserve**. *Circulation* (1996) **94** 1842-1849. DOI: 10.1161/01.CIR.94.8.1842
11. Layland J., Carrick D., McEntegart M., Ahmed N., Payne A., McClure J., Sood A., McGeoch R., MacIsaac A., Whitbourn R.. **Vasodilatory capacity of the coronary microcirculation is preserved in selected patients with non-ST-segment-elevation myocardial infarction**. *Circ. Cardiovasc. Interv.* (2013) **6** 231-236. DOI: 10.1161/CIRCINTERVENTIONS.112.000180
12. De Bruyne B., Pijls N.H.J., Gallinoro E., Candreva A., Fournier S., Keulards D.C.J., Sonck J., Van’t Veer M., Barbato E., Bartunek J.. **Microvascular Resistance Reserve for Assessment of Coronary Microvascular Function: JACC Technology Corner**. *J. Am. Coll. Cardiol.* (2021) **78** 1541-1549. DOI: 10.1016/j.jacc.2021.08.017
13. Lee S.H., Lee J.M., Park J., Choi K.H., Hwang D., Doh J.H., Nam C.W., Shin E.S., Hoshino M., Murai T.. **Prognostic Implications of Resistive Reserve Ratio in Patients With Coronary Artery Disease**. *J. Am. Heart Assoc.* (2020) **9** e015846. DOI: 10.1161/JAHA.119.015846
14. Toya T., Ahmad A., Corban M.T., Özcan I., Sara J.D., Sebaali F., Escaned J., Lerman L.O., Lerman A.. **Risk Stratification of Patients With NonObstructive Coronary Artery Disease Using Resistive Reserve Ratio**. *J. Am. Heart Assoc.* (2021) **10** e020464. DOI: 10.1161/JAHA.120.020464
15. Sawano M., Yamaji K., Kohsaka S., Inohara T., Numasawa Y., Ando H., Iida O., Shinke T., Ishii H., Amano T.. **Contemporary use and trends in percutaneous coronary intervention in Japan: An outline of the J-PCI registry**. *Cardiovasc. Interv. Ther.* (2020) **35** 218-226. DOI: 10.1007/s12928-020-00669-z
16. Kawase Y., Matsuo H., Kuramitsu S., Shiono Y., Akasaka T., Tanaka N., Amano T., Kozuma K., Nakamura M., Yokoi H.. **Clinical use of physiological lesion assessment using pressure guidewires: An expert consensus document of the Japanese association of cardiovascular intervention and therapeutics-update 2022**. *Cardiovasc. Interv. Ther.* (2022) **37** 425-439. DOI: 10.1007/s12928-022-00863-1
17. Yamazaki T., Nishi T., Saito Y., Tateishi K., Kato K., Kitahara H., Kobayashi Y.. **Discrepancy between plaque vulnerability and functional severity of angiographically intermediate coronary artery lesions**. *Cardiovasc. Interv. Ther.* (2022) **37** 691-698. DOI: 10.1007/s12928-022-00851-5
18. Murasato Y., Kinoshita Y., Shite J., Hikichi Y., Nam C.W., Koo B.K.. **Difference in basic concept of coronary bifurcation intervention between Korea and Japan. Insight from questionnaire in experts of Korean and Japanese bifurcation clubs**. *Cardiovasc. Interv. Ther.* (2022) **37** 89-100. DOI: 10.1007/s12928-020-00742-7
19. Jang H.J., Koo B.K., Lee H.S., Park J.B., Kim J.H., Seo M.K., Yang H.M., Park K.W., Nam C.W., Doh J.H.. **Safety and efficacy of a novel hyperaemic agent, intracoronary nicorandil, for invasive physiological assessments in the cardiac catheterization laboratory**. *Eur. Heart J.* (2013) **34** 2055-2062. DOI: 10.1093/eurheartj/eht040
20. Nishi T., Kitahara H., Saito Y., Nishi T., Nakayama T., Fujimoto Y., Matsumiya G., Kobayashi Y.. **Invasive assessment of microvascular function in patients with valvular heart disease**. *Coron. Artery Dis.* (2018) **29** 223-229. DOI: 10.1097/MCA.0000000000000594
21. Saito Y., Kitahara H., Nishi T., Fujimoto Y., Kobayashi Y.. **Decreased resting coronary flow and impaired endothelial function in patients with vasospastic angina**. *Coron. Artery Dis.* (2019) **30** 291-296. DOI: 10.1097/MCA.0000000000000721
22. Saito Y., Nishi T., Kato K., Kitahara H., Kobayashi Y.. **Resistive reserve ratio and microvascular resistance reserve in patients with coronary vasospastic angina**. *Heart Vessels* (2022) **37** 1489-1495. DOI: 10.1007/s00380-022-02051-w
23. Crea F., Camici P.G., Bairey Merz C.N.. **Coronary microvascular dysfunction: An update**. *Eur. Heart J.* (2014) **35** 1101-1111. DOI: 10.1093/eurheartj/eht513
24. Nahser P.J., Brown R.E., Oskarsson H., Winniford M.D., Rossen J.D.. **Maximal coronary flow reserve and metabolic coronary vasodilation in patients with diabetes mellitus**. *Circulation* (1995) **91** 635-640. DOI: 10.1161/01.CIR.91.3.635
25. Wessel T.R., Arant C.B., McGorray S.P., Sharaf B.L., Reis S.E., Kerensky R.A., von Mering G.O., Smith K.M., Pauly D.F., Handberg E.M.. **Coronary microvascular reactivity is only partially predicted by atherosclerosis risk factors or coronary artery disease in women evaluated for suspected ischemia: Results from the NHLBI Women’s Ischemia Syndrome Evaluation (WISE)**. *Clin. Cardiol.* (2007) **30** 69-74. DOI: 10.1002/clc.19
26. Selthofer-Relatić K., Bošnjak I., Kibel A.. **Obesity Related Coronary Microvascular Dysfunction: From Basic to Clinical Practice**. *Cardiol. Res. Pract.* (2016) **2016** 8173816. DOI: 10.1155/2016/8173816
27. Antony I., Nitenberg A., Foult J.M., Aptecar E.. **Coronary vasodilator reserve in untreated and treated hypertensive patients with and without left ventricular hypertrophy**. *J. Am. Coll. Cardiol.* (1993) **22** 514-520. DOI: 10.1016/0735-1097(93)90058-9
28. Konijnenberg L.S.F., Damman P., Duncker D.J., Kloner R.A., Nijveldt R., van Geuns R.M., Berry C., Riksen N.P., Escaned J., van Royen N.. **Pathophysiology and diagnosis of coronary microvascular dysfunction in ST-elevation myocardial infarction**. *Cardiovasc. Res.* (2020) **116** 787-805. DOI: 10.1093/cvr/cvz301
29. Östlund-Papadogeorgos N., Ekenbäck C., Jokhaji F., Mir-Akbari H., Witt N., Jernberg T., Wallén H., Linder R., Törnerud M., Samad B.A.. **Blood haemoglobin, renal insufficiency, fractional flow reserve and plasma NT-proBNP is associated with index of microcirculatory resistance in chronic coronary syndrome**. *Int. J. Cardiol.* (2020) **317** 1-6. DOI: 10.1016/j.ijcard.2020.05.037
30. Neishi Y., Akasaka T., Tsukiji M., Kume T., Wada N., Watanabe N., Kawamoto T., Kaji S., Yoshida K.. **Reduced coronary flow reserve in patients with congestive heart failure assessed by transthoracic Doppler echocardiography**. *J. Am. Soc. Echocardiogr.* (2005) **18** 15-19. DOI: 10.1016/j.echo.2004.08.007
31. Gallinoro E., Paolisso P., Candreva A., Bermpeis K., Fabbricatore D., Esposito G., Bertolone D., Fernandez Peregrina E., Munhoz D., Mileva N.. **Microvascular Dysfunction in Patients With Type II Diabetes Mellitus: Invasive Assessment of Absolute Coronary Blood Flow and Microvascular Resistance Reserve**. *Front. Cardiovasc. Med.* (2021) **8** 765071. DOI: 10.3389/fcvm.2021.765071
32. Matsumoto H., Mikuri M., Masaki R., Tanaka H., Ogura K., Arai T., Sakai R., Oishi Y., Okada N., Shinke T.. **Feasibility of intracoronary nicorandil for inducing hyperemia on fractional flow reserve measurement: Comparison with intracoronary papaverine**. *Int. J. Cardiol.* (2020) **314** 1-6. DOI: 10.1016/j.ijcard.2020.05.013
33. Ford T.J., Stanley B., Good R., Rocchiccioli P., McEntegart M., Watkins S., Eteiba H., Shaukat A., Lindsay M., Robertson K.. **Stratified Medical Therapy Using Invasive Coronary Function Testing in Angina: The CorMicA Trial**. *J. Am. Coll. Cardiol.* (2018) **72** 2841-2855. DOI: 10.1016/j.jacc.2018.09.006
34. Ford T.J., Stanley B., Sidik N., Good R., Rocchiccioli P., McEntegart M., Watkins S., Eteiba H., Shaukat A., Lindsay M.. **1-Year Outcomes of Angina Management Guided by Invasive Coronary Function Testing (CorMicA)**. *JACC Cardiovasc. Interv.* (2020) **13** 33-45. DOI: 10.1016/j.jcin.2019.11.001
35. Echavarría-Pinto M., van de Hoef T.P., Nijjer S., Gonzalo N., Nombela-Franco L., Ibañez B., Sen S., Petraco R., Jimenez-Quevedo P., Nuñez-Gil I.J.. **Influence of the amount of myocardium subtended to a coronary stenosis on the index of microcirculatory resistance. Implications for the invasive assessment of microcirculatory function in ischaemic heart disease**. *EuroIntervention* (2017) **13** 944-952. DOI: 10.4244/EIJ-D-16-00525
36. van ‘t Veer M., Adjedj J., Wijnbergen I., Tóth G.G., Rutten M.C., Barbato E., van Nunen L.X., Pijls N.H., De Bruyne B.. **Novel monorail infusion catheter for volumetric coronary blood flow measurement in humans: In vitro validation**. *EuroIntervention* (2016) **12** 701-707. DOI: 10.4244/EIJV12I6A114
37. Kobayashi Y., Fearon W.F., Honda Y., Tanaka S., Pargaonkar V., Fitzgerald P.J., Lee D.P., Stefanick M., Yeung A.C., Tremmel J.A.. **Effect of Sex Differences on Invasive Measures of Coronary Microvascular Dysfunction in Patients With Angina in the Absence of Obstructive Coronary Artery Disease**. *JACC Cardiovasc. Interv.* (2015) **8** 1433-1441. DOI: 10.1016/j.jcin.2015.03.045
38. Chung J.H., Lee K.E., Lee J.M., Her A.Y., Kim C.H., Choi K.H., Song Y.B., Hahn J.Y., Kim H.Y., Choi J.H.. **Effect of Sex Difference of Coronary Microvascular Dysfunction on Long-Term Outcomes in Deferred Lesions**. *JACC Cardiovasc. Interv.* (2020) **13** 1669-1679. DOI: 10.1016/j.jcin.2020.04.002
39. Stegehuis V.E., Wijntjens G.W.M., Bax M., Meuwissen M., Chamuleau S.A.J., Voskuil M., Koch K.T., Di Mario C., Vrints C., Haude M.. **Impact of clinical and haemodynamic factors on coronary flow reserve and invasive coronary flow capacity in non-obstructed coronary arteries: A patient-level pooled analysis of the DEBATE and ILIAS studies**. *EuroIntervention* (2021) **16** e1503-e1510. DOI: 10.4244/EIJ-D-19-00774
40. Demirkiran A., Robbers L.F.H.J., van der Hoeven N.W., Everaars H., Hopman L.H.G.A., Janssens G.N., Berkhof H.J., Lemkes J.S., van de Bovenkamp A.A., van Leeuwen M.A.H.. **The Dynamic Relationship Between Invasive Microvascular Function and Microvascular Injury Indicators, and Their Association With Left Ventricular Function and Infarct Size at 1-Month After Reperfused ST-Segment-Elevation Myocardial Infarction**. *Circ. Cardiovasc. Interv.* (2022) **15** 892-902. DOI: 10.1161/CIRCINTERVENTIONS.122.012081
41. Lee J.M., Layland J., Jung J.H., Lee H.J., Echavarria-Pinto M., Watkins S., Yong A.S., Doh J.H., Nam C.W., Shin E.S.. **Integrated physiologic assessment of ischemic heart disease in real-world practice using index of microcirculatory resistance and fractional flow reserve: Insights from the International Index of Microcirculatory Resistance Registry**. *Circ. Cardiovasc. Interv.* (2015) **8** e002857. DOI: 10.1161/CIRCINTERVENTIONS.115.002857
42. Scheel K.W., Brody D.A., Ingram L.A., Keller F.. **Effects of chronic anemia on the coronary and coronary collateral vasculature in dogs**. *Circ. Res.* (1976) **38** 553-559. DOI: 10.1161/01.RES.38.6.553
43. Hoffman J.I., Spaan J.A.. **Pressure-flow relations in coronary circulation**. *Physiol Rev.* (1990) **70** 331-390. DOI: 10.1152/physrev.1990.70.2.331
44. Heusch G.. **Coronary blood flow in heart failure: Cause, consequence and bystander**. *Basic Res. Cardiol.* (2022) **117** 1. DOI: 10.1007/s00395-022-00909-8
45. Lee J.M., Kato D., Oi M., Toyofuku M., Takashima H., Waseda K., Amano T., Kurita A., Ishihara H., Lim W.H.. **Safety and efficacy of intracoronary nicorandil as hyperaemic agent for invasive physiological assessment: A patient-level pooled analysis**. *EuroIntervention* (2016) **12** e208-e215. DOI: 10.4244/EIJV12I2A34
46. Mizukami T., Sonck J., Gallinoro E., Kodeboina M., Canvedra A., Nagumo S., Bartunek J., Wyffels E., Vanderheyden M., Shinke T.. **Duration of Hyperemia With Intracoronary Administration of Papaverine**. *J. Am. Heart Assoc.* (2021) **10** e018562. DOI: 10.1161/JAHA.120.018562
47. Austin P.C., Steyerberg E.W.. **The number of subjects per variable required in linear regression analyses**. *J. Clin. Epidemiol.* (2015) **68** 627-636. DOI: 10.1016/j.jclinepi.2014.12.014
|
---
title: 'The Impact of the Medical Insurance System on the Health of Older Adults in
Urban China: Analysis Based on Three-Period Panel Data'
authors:
- Hongfeng Zhang
- Peng Cheng
- Lu Huang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10000990
doi: 10.3390/ijerph20053817
license: CC BY 4.0
---
# The Impact of the Medical Insurance System on the Health of Older Adults in Urban China: Analysis Based on Three-Period Panel Data
## Abstract
The impact of the medical insurance system (MIS) on the health of older adults is a key element of research in the field of social security. Because China’s MIS consists of different types of insurance, and the benefits and levels of coverage received by participating in different medical insurance vary, different medical insurance may have a differential impact on the health of older adults. This has rarely been studied before. In this paper, the panel data of the third phase of the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2013, 2015 and 2018 were used to investigate the impact of participation in social medical insurance (SMI) and commercial medical insurance (CMI) on the health of urban older adults and its mechanism relationship. The study found that SMI had a positive impact on the mental health of older adults, but only in the eastern region. Participation in CMI was positively correlated with the health of older adults, but this association was relatively small and was only observed in the sample of older adults aged 75 years and above. In addition, future life security plays an important role in the process of improving the health of older adults through medical insurance. Both research hypothesis 1 and research hypothesis 2 were verified. The results of this paper show that the evidence of the positive effect of medical insurance on the health of older adults in urban areas proposed by scholars is not convincing enough. Therefore, the medical insurance scheme should be reformed, focusing not only on coverage, but on enhancing the benefits and level of insurance, so as to enhance its positive impact on the health of older adults.
## 1. Introduction
Health is an inevitable requirement for the promotion of all-round human development and is a common aspiration of people all over the world. The 2021 data from the 7th National Census shows that China’s population aged 60 and above is 260 million, an increase of $5.4\%$ over the 6th National Census data. According to a survey conducted by the China Health and Wellness Commission, the percentage of older adults suffering from chronic diseases is as high as $75\%$. It follows that the number and health status of older adults have changed. In order to reduce the risk to the health of older adults due to disease, the establishment of a comprehensive medical insurance system (MIS) has become an important policy challenge for countries around the world. By the end of 2021, the number of people participating in social medical insurance (SMI) in China has exceeded 1.3 billion, with the participation rate stable at over $95\%$. The closer the participation rate of SMI is to universal coverage, which has a catalytic effect on reducing the psychological burden of patients and increasing the utilization of medical services [1,2], is also crucial to economic and social development [3]. However, the relationship between SMI coverage and health is more controversial, with some studies suggesting that participation in SMI is beneficial to health improvement [4,5] and others showing that expansion of SMI coverage does not necessarily improve health [6,7].
China has continued to improve the multi-level medical security system with basic medical insurance as the main body, supplemented by other various forms of supplementary insurance and commercial medical insurance (CMI), to gradually improve the level of protection and service capacity of commercial insurance, and provide systematic health services for the people. The total income of CMI premiums increased from 2.8 billion yuan in 2000 to 880.36 billion yuan in 2021, an increase of nearly 320 times. This fully reflects the importance that the Chinese government attaches to CMI and shows that CMI is of great value in meeting the multi-level health needs of the people and improving their health. As an important supplement to basic medical insurance, CMI can effectively play the role of improving social welfare and relieving the pressure of medical treatment [8]. CMI has been verified by scholars at home and abroad for reducing the risk of illness among older adults and improving the health of the insured group [9,10,11].
There are disparities between SMI and CMI groups in terms of coverage benefits, levels and medical services [12], which has led to health variability among different medical insurance groups. Existing studies have explored many factors influencing the health of older adults, but examining the role of medical insurance on the health of older adults lacks sufficient attention, especially the health status of different types of insurance groups, which has not received sufficient attention. This is an important issue in the transformation of China’s MIS reform from a focus on coverage improvement to diversity and equity.
Scholars have performed a great deal of analysis on the impact of the MIS on the health of older adults, but more often than not, the impact of a particular medical insurance on the health of older adults has been studied separately. Meanwhile, the evaluation of the health of older adults is mostly based on the ability of daily activities and cognitive ability, and the measurement index is relatively single. The health status of older adults needs to be measured comprehensively from multiple aspects. In view of this, this study analyzes the impact of the MIS on the health of older adults in urban areas based on the China Health and Retirement Longitudinal Study (CHARLS) three-period panel data, and further investigates the heterogeneity and transmission mechanism of this impact. Based on this, the marginal contribution of this paper is mainly reflected in the following three aspects. First, consider the impact of different types of medical insurance on the health of older adults in urban areas. This paper discusses the health status of older adults in urban areas who participate in SMI and CMI, and analyzes how different types of medical insurance affect the health status of older adults in urban areas, breaking through the limitations of existing studies which only focus on the coverage of medical insurance. Second, consider the path of medical insurance. Medical insurance can effectively play the role of spiritual security, and participation in medical insurance can improve residents’ sense of security and future life security. Therefore, this paper analyzes the internal mechanism of future life security in medical insurance affecting the health of older adults in urban areas. Thirdly, the influence of demographic characteristics and regional characteristics is considered comprehensively. This paper analyzes the age and regional heterogeneity of the influence of medical insurance on the health of older adults in urban areas, which is helpful to grasp the practical operation of medical insurance and deepen the internal mechanism of medical insurance. By comparing the effects of SMI and CMI on the health of older adults in urban areas, it can provide a theoretical basis for the adjustment and improvement of medical insurance policies.
## 2.1. Definition Study of Health
Health is a state in which a person is in good physical, mental, and social condition. In 1989, the World Health Organization proposed that health is not only the absence of disease in the body, but also a state of mental health and good social adaptation, in which individuals can perform their daily tasks positively and effectively and maintain a good state of mind and character. Some scholars believe that health is not only the health of people’s psychological world, and the study of health should be directed to people’s overall situation [13]. Other scholars point out that health is a good state of social life in which individuals can actively engage in social participation, fully develop their psychological potential, and have good self-perception [14,15]. With the increase of aging and the rise of the disease rate in the older adults group, the health status of older adults presents different characteristics [16]. Scholars have defined the health status of older adults mainly in three aspects: physical health, mental health and self-health [7,13].
## 2.2. Impact of the MIS on the Health of Older Adults
In order to improve the health level of older adults and ensure that they enjoy equalized medical and health services, governments around the world have introduced many policy measures, and the Chinese government has also continued to establish and improve the MIS and medical service system with Chinese characteristics, which is of great significance in improving the health status of older adults and providing health services throughout their life cycle.
Different MIS are a manifestation of the interaction of economic and social factors, and the selection of an appropriate MIS has an important impact on the health status of older adults. This paper mainly analyzes the impact and mechanism of different types of MIS on the health of older adults. SMI is enforced by the government through legislation to establish an SMI fund, and workers are given appropriate subsidies or reimbursement when they fall ill [17]. It has been argued that participation in SMI can improve the financial access to health care and the accessibility of health care services for older adults [18,19,20], which somehow increases the likelihood of maintaining good health in older adults groups. This is because SMI shares the financial risk of the sick group and guarantees the utilization of medical and health services. CMI is a voluntary insurance for units and individuals, and the insurance premiums paid are operated by insurance agencies, from which they can receive a certain amount of medical expenses in case of major diseases [21]. Related studies have shown that cognitive ability, mental health, and self-health of older adults who participate in CMI are improved to a greater extent [22,23,24,25], and will improve the health of participants through the channels of increased financial accessibility to medical care, reduced precautionary savings, and improved lifestyles. It has also been noted that medical insurance does not positively affect the health status of older adults [26,27]. It has been demonstrated in different ways that the MIS has a significant effect on the health of older adults, but there are many factors that influence the health of older adults, such as the type of disease, economic income, and living environment, and the health-promoting effect of medical insurance may be reduced or even disappear [28,29]. The health role of medical insurance may or may not be present [30,31]. Therefore, more in-depth research is needed to determine whether medical insurance is effective in improving the health of older adults. Based on the above analysis, the following research hypothesis is proposed in this paper.
## 2.3. The Role of Mediating: Future Life Security
The health of older adults is multidimensional and influenced by a variety of factors and pathways. Medical insurance functions as a financial accessibility and economic risk sharing for medical care, which to some extent reduces the economic risk of the sick group and enhances the utilization of medical services for the insured individuals [2]. Thus, it can be seen that medical insurance plays a significant role in material security and economic security. Medical insurance has a significant impact effect on the health of older adults, and there may be other important effects in addition to the health effects from medical service utilization that have been widely validated. The group enrolled in medical insurance has better self-soothing ability [32], which facilitates the function of spiritual security and subjective comfort and enhances their sense of security for their future life.
As an effective protection mechanism for patients to cope with health risks, medical insurance plays an important role in enhancing the sense of security and confidence in the future life of the insured group [33]. Some scholars argue that individuals who participate in medical insurance have a greater sense of well-being and security and a higher level of health compared to individuals who do not participate in medical insurance [34]. It has also been noted that the group with medical insurance has a good sense of security and health, and they have a higher happiness index in life [35,36]. In case of illness, medical insurance can reimburse medical expenses, which reduces the psychological pressure of the participants, and medical insurance can also provide price subsidies for the participants, which reduces the possibility of “not getting medical treatment” and increases the participants’ hope for their future life. However, there are differences in the medical resources available to individuals with different MIS, and these medical resources are an important factor in future life security. Therefore, it is reasonable to suggest that MIS can influence the health status of older adults by affecting their future life security. In view of the above analysis, the following research hypothesis is proposed in this paper.
Finally, based on the literature analysis and research hypotheses, this paper proposes a corresponding analytical framework for medical insurance reform and health improvement of urban older adults in China (as shown in Figure 1).
## 3.1. Data
This paper uses data from the CHARLS organized and implemented by the China Social Science Survey Center of Peking University in 2013, 2015 and 2018, which used a scientific sampling method to select 150 counties in proportion to their probability, and each county then randomly selected three villages or communities, and each village or community then randomly selected people over 45 years old as the main respondents, collecting about 20,000 valid. The sample size is wide and representative. The data provide basic information, family information, and work status of older adults, including their insurance status and health status. In this paper, we mainly select respondents who are urban residents and are sixty and above for statistical analysis, excluding samples below sixty. During the study, we excluded the samples with missing key variables in the data, but it did not affect the validity of statistical inference, and finally collated 2403 valid samples.
## 3.2.1. Dependent Variable
The dependent variable is the health of older adults in urban areas. In this paper, the health status of older adults was measured in several dimensions, mainly including physical health, mental health and self-health of older adults. In terms of physical health, it is mainly based on the Activities of Daily Living (ADL) scale, which measures six items in the questionnaire: dressing, eating, bathing, toileting, transferring out of bed, and doing household activities, and the respondents’ scores are processed in reverse order, and the range of values is 6 to 24, with higher scores indicating better physical health. In terms of mental health, the questionnaire was used to determine the frequency of 10 situations in the last week, including “feeling depressed, struggling to do anything, not sleeping well, feeling lonely, worrying about small things, feeling unable to continue living, having difficulty concentrating, feeling happy, etc. “ The options “don’t know” and “refused to answer” were eliminated from the questionnaire. The scores of negative emotions were reversed, 1 to 4 for most of the time, sometimes or half of the time, not too much, rarely or not at all, and 1 to 4 for positive emotion, respectively, according to the questionnaire. In terms of self-assessment of health, the questionnaire was based on the question “How do you think your health is?” Respondents’ scores were processed in reverse order, and the options were assigned as 1 = very poor, 2 = poor, 3 = fair, 4 = good, and 5 = very good, with higher scores indicating better self-health.
## 3.2.2. Independent Variable
The independent variable is the MIS. The MIS includes SMI and CMI. SMI is measured according to the questionnaire “Are you currently participating in urban workers’ medical insurance, urban and rural residents’ medical insurance”, as long as the respondent participates in one of the medical insurances, he/she is considered to have SMI and is assigned a value of 1, otherwise, he/she is assigned a value of 0. CMI is measured by the questionnaire “Do you participate in CMI purchased by your organization or individually”, and respondents are considered to have CMI as long as they participate in one of the CMI and are assigned a value of 1, otherwise they are assigned a value of 0. Since respondents can only participate in one of the MIS, therefore, the two variables add up to 1.
## 3.2.3. Control Variables
This paper controls for individual characteristics of older adults and variables that may affect individual health, which include three main categories. The first category: demographic characteristics variables. They mainly include gender, age, education level, and marital status. “ Married living with spouse” and “married, but not living with spouse temporarily because of work and other reasons” were combined into “normal marriage” and assigned a value of 1. “ Separated”, “divorced”, “widowed”, “never married” are combined as “The second category: socio-economic characteristics variables. It mainly consists of the number of children and pension insurance. The number of children was based on the questionnaire “How many children do you have?” Pension insurance is measured according to the questionnaire: “Are you currently participating in/receiving urban workers’ pension insurance, supplementary pension insurance or urban and rural residents’ pension insurance? Respondents are considered to have pension insurance as long as they participate in one of the pension insurances, and are assigned a value of 1. Otherwise, they are assigned a value of 0. The third category: health characteristics variables. The influence of chronic diseases and health behaviors is mainly examined. Chronic diseases were measured according to the questionnaire “Have any doctors ever told you that you have chronic diseases such as hypertension, diabetes and kidney disease?” was measured. If the respondent suffers from one of the chronic diseases, it will be set as a chronic disease patient, with a value of 1, otherwise it will be 0. Health behavior was measured based on the questionnaire “Do you ride a bicycle, play tai chi, walk, play sports, exercise, walk, etc. every week? was measured. Respondents were assigned a value of 1 if they answered yes, and 0 otherwise.
## 3.2.4. Mediating Variable
The mediating variable is future life security. In addition to analyzing the direct impact of the MIS on the health of older adults, this paper also examines whether the MIS affects the health of older adults through other channels. The security of future life is measured by the questionnaire “Have you been hopeful about the future in the past week”. The mediating variable was a continuous variable. Figure 2 shows the mechanism of the effect of medical insurance on the health of older adults in urban areas in China.
## 3.3.1. OLS Model
Since the dependent variables in this paper are continuous variables, a multiple linear regression model is used to estimate the effect of the MIS on the health of older adults. The model is expressed in the following equation. [ 1]Hi=α+βXi+γZi+εi In the formula, Hi represents the health status of older adults (physical health, mental health, and self-health), Xi represents the medical insurance status of older adults (SMI, CMI), and Zi represents the control variables (demographic characteristics, socioeconomic characteristics, and health characteristics); α, β, and γ represent the parameters to be estimated; and εi represents the random disturbance terms, including elements that are not considered in this study but affect the health of older adults.
## 3.3.2. Mediating Effect Model
According to the previous theoretical analysis, the main mechanism of the MIS’s influence on the health of older adults is future life security. In order to test the role of future life security in the MIS’s influence on the health of older adults, this study uses the mediating effect model to test it. The model was set up with the following equation. [ 2]Hi=α+βXi+γZi+εi [3]Mi=φ0+aXi+γZi+εi [4]Hi=φ1+cβXi+bMi+δZi+σi Equation [2] represents the total effect of the MIS on the health of older adults; Equation [3] represents the effect of the MIS on the mediating variables; and Equation [4] represents the effect of the MIS on the health of older adults after adding the mediating variables. a, b, and c are significant, indicating that there is a partial mediating effect; a and b are significant but c is not, indicating that there is a full mediating effect; and when at least one of a and b insignificant and c significant, then a Sobel test is required and if significant, then there is a mediating effect.
## 4.1.1. Basic Characteristics of Older Adults
According to the sample data of this study, the mean values of physical health, mental health and self-health of the urban older adults are 22.97, 33.61 and 3.13, respectively, which shows that there is still more room for improving the health level of urban older adults in all dimensions. The proportions of urban older adults with SMI and CMI were $89.19\%$ and $35.15\%$, respectively. The average age of the study sample was 70.28 years old, and the mean value of education level was 1.89, mainly concentrated in primary and junior high school education, and the proportion of females in the sample was $52.71\%$. The proportion of the sample with normal marriages was $79.01\%$, which indicates the relatively harmonious marriages of urban older adults. The proportion of urban older adults with chronic diseases is $27.62\%$, which shows that urban older adults are less likely to suffer from chronic diseases. The proportion of urban older adults who have pension insurance is $75.69\%$. The proportion of urban elderly who have health behaviors is $91.18\%$. For details, see Table 1.
## 4.1.2. Inequality in Medical Insurance Types and the Health of Older Adults
Separate measurement of different dimensions of the health of urban older adults allows us to clearly see the physical health, mental health, and self-health status of urban older adults who participate in different types of medical insurance. Figure 3 and Figure 4 provides an overview of the health status of the total sample and sub-sample participant groups.
Overall, the mean physical health, mental health, and self-health values of urban older adults participating in CMI were higher than those of urban older adults participating in SMI, by 0.06, 0.04, and 0.02 points, respectively. By year: in 2013, the health status of urban older adults who participated in CMI and SMI was basically the same as the total sample; in 2015, the health status of the sample data of those who participated in SMI and CMI was basically the same, with the same physical health and self-health scores; in 2018, the mental health and self-health of urban older adults who participated in CMI were higher than those who participated in SMI, with 1.06 and 0.43 points higher, respectively. The above analysis reflects that the health level of the urban older adults participating in CMI is higher than that of those participating in SMI, the high coverage rate of basic medical insurance does not represent the good health status of the urban older adults.
## 4.2. Regression Analysis of the MIS on the Health of Older Adults
Table 2 shows the results of the multiple linear regression of medical insurance in China. To determine the effects of different types of medical insurance on the health status of older adults, model 2-1 classified medical insurance into two types: SMI and CMI. The results showed that the health status of older adults with SMI and CMI did not improve significantly compared to those without medical insurance. However, the extent of the positive effect of CMI on the health of older adults was greater compared to the extent of the effect of SMI on the health of older adults.
Model 2-2 was based on model 2-1 by adding control variables such as demographic characteristics, socioeconomic characteristics and health behavior characteristics. The results from the medical insurance regression model 2-2 showed that when other control variables were added, the effects of SMI and CMI on the health of older adults remained largely consistent with model 2-1, no significant effects were produced. H1b was tested.
Chronic variables had a significant negative effect on different dimensions of health of older adults. The possibility of poorer health status is higher in older adults with chronic diseases, which is because having chronic diseases such as hypertension and chronic bronchitis affects the daily life and organism activities of older adults, which in turn negatively affects their physical health; at the same time, older adults are more likely to judge the severity of the disease by subjectively felt symptoms, which often increases the negative psychological implication of older adults, leading to self-health and negative evaluations of mental health. Health behavior variables had a significant positive effect on different dimensions of health status of older adults, older adults with health behaviors such as walking, exercise, and sports had better health status. This may be due to the following reasons: health behaviors can reduce loneliness and depression of older adults, prevent the decline of self-care ability and instrumental daily living ability in older adults, increase life satisfaction, and thus improve their physical health and mental health; older adults can participate in health behaviors, their own subjective perception of relatively better health, and a relatively high evaluation of self-health. The number of children has a significant negative effect on the physical and mental health of older adults. On the one hand, it is because older adults’ physical health is adversely affected by taking care of a large number of children in their younger years, and the large number of children increases the intensity and frequency of intergenerational care, and intergenerational care in old age further damages their physical health, which in turn has a negative impact on the physical health of older adults; on the other hand, it is because a large number of children will increase family chores, and older adults worry about their children’s work, family, health, etc., they will have various psychological disorders and worries, which will affect their mental health. The marriage variable has a significant positive effect on the mental health of older adults, probably because a harmonious marriage is conducive to the maintenance of a good lifestyle and a positive attitude toward life, which is conducive to spiritual cultivation and soul enhancement, and thus can have a positive effect on the mental health of older adults.
## 4.3. Robustness Tests
In this paper, from the perspective of replacing the econometric model and sample data, the robustness test of the underlying regression results is conducted by using a panel fixed effects model and new sample data. The results of the analysis using the panel fixed effects model and new sample data are shown in Table 3. From Table 3, it can be seen that the coefficients of the independent variables, SMI and CMI, are not significant, indicating that medical insurance does not promote the health of older adults in urban China, and the results of the base regression are verified. For the other control variables, the positive effects of health behaviors passed the significance test, and the results of the underlying regression were verified.
## 4.4.1. Regional Heterogeneity Test
Considering that the eastern, central and western regions of China are quite different in terms of economic development level, infrastructure construction, social welfare treatment, etc. Therefore, in reference to existing research [37], according to the division method of China National Bureau of Statistics, this paper will divide the province (district, city) where the sample data is located into three sub sample data regions, namely, the east, the central, and the west, to investigate the impact of the MIS in different regions on the health of urban older adults. It can be seen from the estimation results in Table 4 that the estimation results in the western and central regions are basically consistent with the basic regression results in Table 2. CMI and SMI have no significant impact on the health of older adults at the statistical level, while CMI in the eastern region has no significant impact on the health of urban older adults at the statistical level. Only SMI has a significant impact on the mental health of older adults at the statistical level. H1a was tested.
## 4.4.2. Age Heterogeneity Test
Groups with different demographic characteristics have different health status, and the number of medical services used by different groups will be affected by age factors. Therefore, in this paper, the sample data will be divided into two sub-sample data of older adults between age 60 and 74 and older adults aged 75 and above according to the age division criteria, and the impact of medical insurance on the health of urban older adults in different age groups will be examined separately. From the estimation results in Table 5, it can be seen that the estimated results of older adults aged 60 and 74 are basically consistent with the results of the base regression in Table 2, which indicates that both CMI and SMI have no statistically significant effects on the health of the old adults at the statistical level. In contrast, participation in CMI has a statistically significant effect on the health of older adults aged 75 and above at the statistical level. H1a was tested again.
## 4.5. Further Study: Mediating Effects
The heterogeneity analysis above verified that CMI has a significant effect on the health of older adults aged 75 and above, and SMI has a significant effect on the mental health of older adults living in eastern China. This paper next verifies the mediators of the effect of CMI on the health of older adults aged 75 and above and the mediators of the effect of SMI on the mental health of older adults living in eastern China. We used the three-step regression method to estimate the mediating effect of future life security and control for covariates.
## 4.5.1. Regression Results of Mediating Effects in the Effect of CMI on the Health of Older Adults Aged 75 and Above
The results of the mediating regression of future life security in the effect of CMI on the health of older adults are shown in Table 6. The coefficients of the mediating variable future life security all passed the test at the $5\%$ statistical level, indicating that CMI effectively improves the health status of older adults by significantly enhancing their future life security. Specifically, models 6-1, 6-4, and 6-7 are the main effects of CMI on the health of older adults, and the other models in Table 6 are tests of the mediating effects of future life security. The main effects regression results show that the coefficient of CMI is positive at the $10\%$ significance level, indicating that CMI significantly enhances the health of older adults aged 75 and above. The results of the mediating effect regression showed that future life security played a positive mediating role in the improvement of health of older adults aged 75 and above by CMI, and the proportion of the mediating effect to the total effect was $13.594\%$, $31.959\%$, and $28.654\%$, respectively. Therefore, H2 was verified.
## 4.5.2. Regression Results of Mediating Effects in the Effect of SMI on the Mental Health of Older Adults Living in Eastern China
The mediating effect model was constructed to empirically test the mechanism of the role of future life security in SMI on the mental health of older adults living in eastern China, and the regression results are shown in Table 7. Model 7-1 shows the main effect of SMI on the mental health of older adults living in eastern China, and Model 7-2 and Model 7-3 show the mediating effect of future life security. The main effect regression results show is that the coefficient of SMI is positive at the $5\%$ level of significance, indicating that SMI significantly enhances the mental health of older adults living in eastern China. The regression results of the mediating effect showed that future life security played a positive mediating role in SMI to enhance the mental health of older adults living in eastern China, and the proportion of the mediating effect to the total effect was $19.583\%$. Therefore, SMI can effectively improve the health of older adults living in eastern China by increasing their sense of future life security. H2 was again verified.
## 5. Discussion
We investigated the relationship between different MIS and the health of older adults in urban China. Our longitudinal regression analysis based on phase III panel data shows that SMI has a positive impact on the mental health of older adults, but only in specific areas. Participation in CMI is positively related to the health of older adults, but this association is relatively small, which is only observed in older adults aged 75 and above. This shows that the coverage rate of medical insurance is not positively related to the health of urban older adult groups, and the current plan of full coverage of medical insurance has research limitations. These research results are basically consistent with those of Chinese scholar Yu Da chuan [33,38]. As for the relationship between medical insurance and the health of older adults, our results are in sharp contrast with those of Cheng Ling guo [39], who reported more positive results using Chinese Longitudinal Healthy Longevity Survey (CLHLS) data. However, our research is different from theirs. We used the data of the three longitudinal surveys from 2013 to 2018, while they only used the data of two periods (2005–2008). As for the relationship between China’s medical insurance and the health of older adults, most previous studies have reported that the groups participating in medical insurance have good health conditions. These studies use data earlier than our study (2013–2018) (such as 2005–2011), which shows that the impact of medical insurance on the health of older adults should not only focus on short-term effects.
We examined the mediating role of future life security. The results show that the MIS can have a positive and indirect impact on the health of urban older adults groups by increasing the future life safety of the insured groups. As we all know, with the growth of age, the health status of older adults will inevitably decline, and the probability of illness will also increase, which hinders the possibility of older adults to enjoy a happy old age. In order to mitigate the negative impact and consequences of the disease, they often obtain a health risk protection mechanism by participating in medical insurance, reduce the concern of the insured group about the economic burden of the disease, and enhance their sense of security in future life, so as to maintain a good health level. Although some scholars pointed out that medical insurance has little effect on health, the improvement of the health level of older adults in China still requires medical insurance to play a role [40].
In this study, control variables such as health behavior and chronic diseases have significant effects on the health of urban older adults. The urban older adults with healthy behaviors were more likely to be in good health [41,42,43], because their physique was improved and their mood was relaxed through exercise, walking and other healthy behaviors, which is conducive to improving the health of older adults. In addition, urban older adults without chronic diseases are more likely to be in good health. Datta et al. found that older adults without chronic diseases had good health in all aspects [44].
According to the results of this study, the Chinese government should increase medical insurance benefits to effectively provide medical resources for urban older adult groups, especially urban low-income groups. The government should also reform the MIS. Against the background that medical insurance is close to full coverage, it should reduce the medical insurance costs of urban residents, and cover a wider range of outpatient patients and more types of diseases.
## 6. Conclusions
Through the above research, the following conclusions are drawn: firstly, the promotion effect of CMI on the health of older adults is greater than that of SMI. As shown in Figure 3, the health level of older adults participating in CMI is higher than that of older adults participating in SMI; secondly, SMI has a positive impact on the mental health of older adults, but only in certain areas. CMI was positively correlated with the health of older adults, but this association was relatively small and was only observed in the sample of older adults aged 75 years and above; thirdly, medical insurance is greatly influenced by future life security in improving the health level of older adults in urban areas. Medicare improves the health of older adults by increasing their future security. The policy implications of the above conclusions are as follows: first, the MIS plays a certain role in promoting the health of older adults in urban areas, and older adults participating in medical insurance play a very important role in the medical security system. SMI can meet the basic medical needs of the urban older adults, CMI can effectively protect the personalized medical service needs of older adults, properly handle the relationship between SMI and CMI, can better play the role of the MIS. Second, we should not blindly pursue the coverage of SMI while ignoring the development of CMI. The government needs to continuously enrich and improve the supply of CMI products, encourage CMI companies to develop insurance types that are compatible with SMI and suitable for urban older adults groups, and pay attention to the medical service needs of urban older adults groups. Thirdly, medical insurance needs to take targeted measures according to the characteristics of the group if it wants to serve older adults better. We will comprehensively improve the level of medical service coverage for different groups, and make the content and level of medical insurance coverage more complementary and cohesive.
There are limitations to the study. Firstly, this paper focuses only on the health status of older adults aged sixty and above and the impact of medical insurance on their health, but not on the people below sixty. Although older adults are somewhat representative, they are not representative of all groups because of the heterogeneity of health status among different groups and the different impact of medical insurance. Future studies should expand the study to include more individuals. Secondly, we cannot account for the uninsured older adults. We estimate that some older adults who are not covered by medicare have health problems that are not diagnosed in a timely manner. This suggests that we may be underestimating medicare’s contribution to older adults’ health. If so, the effect of medical insurance may be much bigger than we estimate. Despite the limitations of the study, we believe that this study makes full use of vertical panel data and provides new insights for understanding the relationship between MIS and health. We also look forward to China’s experience in reforming the MIS, providing valuable lessons for countries around the world seeking to reform social insurance plans.
## References
1. Skipper N.. **On the demand for prescription drugs: Heterogeneity in price responses**. *Health Econ.* (2013) **22** 857-869. DOI: 10.1002/hec.2864
2. Huang F., Gan L.. **The impacts of China’s urban employee basic medical insurance on healthcare expenditures and health outcomes**. *Health Econ.* (2017) **26** 149-163. DOI: 10.1002/hec.3281
3. Yan F., Dong L., Wang B., Ge J., Wang B.. **Using risk meshing to improve three-dimensional risk assessment of chemical industry**. *Process Saf. Environ.* (2022) **168** 1166-1178. DOI: 10.1016/j.psep.2022.10.078
4. Liubao G.U., Huihui F., Jian J.I.N.. **Effects of medical insurance on the health status and life satisfaction of the elderly**. *Iran. Public Health* (2017) **46** 1193
5. Yuan B., Li J., Liang W.. **The interaction of delayed retirement initiative and the multilevel social health insurance system on physical health of older people in China**. *Int. J. Health Plan. Manag.* (2022) **37** 452-464. DOI: 10.1002/hpm.3352
6. Luo C.l.. **Urban Citizens’ Health Differences and Medical Expenditure Behaviors in China**. *Financ. Econ.* (2008) **10** 63-75. DOI: 10.16538/j.cnki.jfe.2008.10.003
7. Ma X., Oshio T.. **The impact of social insurance on health among middle-aged and older adults in rural China: A longitudinal study using a three-wave nationwide survey**. *BMC Public Health* (2020) **20**. DOI: 10.1186/s12889-020-09945-2
8. Wang M.J., Zhu M.L.. **Effects of Commercial Health Insurance to Consumption and the Structure of Consumption—Based on rational expectation and an analysis of household asset structure**. *Insur. Stud.* (2015) **6** 19-31. DOI: 10.13497/j.cnki.is.2015.06.003
9. Dunlop D.D., Song J., Lyons J.S., Manheim L.M., Chang R.W.. **Racial/ethnic differences in rates of depression among preretirement adults**. *Am. Public Health* (2003) **93** 1945-1952. DOI: 10.2105/AJPH.93.11.1945
10. Wilper A.P., Woolhandler S., Lasser K.E., Mccormick D., Bor D.H., Himmelstein D.U.. **Health insurance and mortality in US adults**. *Am. Public Health* (2009) **99** 2289-2295. DOI: 10.2105/AJPH.2008.157685
11. Zhou D.S., Dang S.Q.. **Effect of commercial health insurance on health of residents: Empirical evidence from CGSS data**. *Chin. Health Policy* (2021) **14** 8-15. DOI: 10.3969/j.issn.1674-2982.2021.08.002
12. Lv S.J., Sun J.. **Urban and Rural Basic Medical Insurance and Social Adaptability of Middle-aged and Older Adults**. *Shanghai Econ.* (2021) **10** 24-37. DOI: 10.19626/j.cnki.cn31-1163/f.2021.10.004
13. Chen Y., Qin X.. **The Impact of Extreme Temperature Shocks on the Health Status of the Elderly in China**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph192315729
14. Takagi D., Kondo K., Kawachi I.. **Social participation and mental health: Moderating effects of gender, social role and rurality**. *BMC Public Health* (2013) **13**. DOI: 10.1186/1471-2458-13-701
15. Zhao L., Wu L.. **The Association between Social Participation and Loneliness of the Chinese Older Adults over Time—The Mediating Effect of Social Support**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph19020815
16. Banerjee S.. **Determinants of rural-urban differential in healthcare utilization among the elderly population in India**. *BMC Public Health* (2021) **21**. DOI: 10.1186/s12889-021-10773-1
17. Li B.Y.. **Establish a social insurance system with Chinese characteristics**. *Qiu Shi* (1995) **12** 20-24
18. Daysal N.M.. **Does uninsurance affect the health outcomes of the insured? Evidence from heart attack patients in California**. *Health Econ.* (2012) **31** 545-563. DOI: 10.1016/j.jhealeco.2012.04.004
19. Brinda E.M., Attermann J., Gerdtham U.G., Enemark U.. **Socio-economic inequalities in health and health service use among older adults in India: Results from the WHO Study on Global AGEing and adult health survey**. *Public Health* (2016) **141** 32-41. DOI: 10.1016/j.puhe.2016.08.005
20. Zhou J.. **Basic Medical Insurance: Robbing the Rich to Aid the Poor, or Robbing the Poor to Aid the Rich?**. *Financ. Econ. Res.* (2019) **34** 147-160
21. Wang X.T.. **On how commercial medical insurance can serve the national basic medical insurance system**. *Inq. Econ. Issues* (2002) **2** 73-77
22. Dor A., Sudano J., Baker D.W.. **The effect of private insurance on the health of older, working age adults: Evidence from the health and retirement study**. *Health Serv. Res.* (2006) **41** 759-787. DOI: 10.1111/j.1475-6773.2006.00513.x
23. DeRigne L.A., Porterfield S., Metz S.. **The influence of health insurance on parent’s reports of children’s unmet mental health needs**. *Matern. Child Health J.* (2009) **13** 176-186. DOI: 10.1007/s10995-008-0346-0
24. Simon K., Soni A., Cawley J.. **The impact of health insurance on preventive care and health behaviors: Evidence from the first two years of the ACA Medicaid expansions**. *Policy Anal. Manag.* (2017) **36** 390-417. DOI: 10.1002/pam.21972
25. Fan H.L., Liu S.C., Chen L.. **Does Commercial Health Insurance Enhance Public Health? —An Empirical Analysis Based on Chinese Micro Data**. *Insur. Stud.* (2019) **3** 116-127. DOI: 10.13497/j.cnki.is.2019.03.009
26. Fu H.Q., Yuan D., Lei X.Y.. **Health Status and Ex Ante Moral Hazard of Health Insurance: An Empirical Investigation on China’s New Rural Cooperative Medical Scheme**. *China Econ.* (2017) **16** 599-620. DOI: 10.13821/j.cnki.ceq.2017.01.07
27. Parchman M.L., Palazzo L., Austin B.T., Blasi P., Henrikson N.B., Gundersen G., Ganos E.. **Taking action to address medical overuse: Common challenges and facilitators**. *Am. Med.* (2020) **133** 567-572. DOI: 10.1016/j.amjmed.2020.01.001
28. 28.
WHO
The Determinants of HealthWorld Health OrganizationGeneva, Switzerland2011. *The Determinants of Health* (2011)
29. Celhay P., Martinez S., Munoz M., Perez M., Perez Cuevas R.. **Long-term effects of public health insurance on the health of children in Mexico: A retrospective study**. *Lancet Glob. Health* (2019) **7** e1448-e1457. DOI: 10.1016/S2214-109X(19)30326-2
30. Fisher E.S.. **Medical care—Is more always better?**. *N. Engl. J. Med.* (2003) **349** 1665-1667. DOI: 10.1056/NEJMe038149
31. Finkelstein A., Taubman S., Wright B., Bernstein M., Gruber J., Newhouse J.P.. **The Oregon health insurance experiment: Evidence from the first year**. *Q. J. Econ.* (2012) **127** 1057-1106. DOI: 10.1093/qje/qjs020
32. He W., Shen S.G.. **Participation Behavior and Benefit Attribution of Medical Insurance for Informal Employees**. *Financ. Trade Econ.* (2020) **41** 36-48. DOI: 10.19795/j.cnki.cn11-1166/f.20200313.005
33. Yu D.C., Ding J.D.. **The Effect of Social Medical Insurance on Elderly Health—Based on the Counterfactual Estimation of the Propensity Score Matching**. *Huazhong Univ. Sci. Technol.* (2016) **30** 107-115. DOI: 10.19648/j.cnki.jhustss1980.2016.02.014
34. Huang J.W.. **Subjective Well-being and Generational Differences of Migrants**. *South China Agric. Univ.* (2015) **14** 122-133. DOI: 10.7671/j.issn.1672-0202.2015.02.014
35. Cutler D.M., Zeckhauser R.J.. *The Anatomy of Health Insurance. Handbook of Health Economics* (2000) **Volume 1** 563-643. DOI: 10.1016/S1574-0064(00)80170-5
36. Sang L.. **Research on the Impact of Social Medical Insurance on Residents’ Happiness and its Internal Mechanism**. *Soc. Secur. Stud.* (2018) **6** 31-45
37. Zhang H., Huang L., Zhu Y., He X.. **Does low-carbon city construction improve total factor productivity? Evidence from a quasi-natural experiment in China**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph182211974
38. Luo H., Ren X., Li J., Wu K., Wang Y., Chen Q., Li N.. **Association between obesity status and successful aging among older people in China: Evidence from CHARLS**. *BMC Public Health* (2020) **20**. DOI: 10.1186/s12889-020-08899-9
39. Cheng L.G., Zhang Y.. **The New Rural Cooperative Medical Scheme: Financial Protection or Health Improvement?**. *Econ. Res.* (2012) **47** 120-133
40. Hu H.W., Liu G.E.. **Impact of Urban Resident Basic Medical Insurance on National Health: Effect Evaluation and Evidence of Mechanism**. *South China Econ.* (2012) **10** 186-199
41. Wang L.H., Zhang Y.. **Research on the Elderly’s Tai Chi Exercise Behavior and Its Effect on Health**. *Sport. Res.* (2020) **34** 79-85. DOI: 10.15877/j.cnki.nsic.20201229.001
42. Srivastava S., KJ V.J., Dristhi D., Muhammad T.. **Interaction of physical activity on the association of obesity-related measures with multimorbidity among older adults: A population-based cross-sectional study in India**. *BMJ Open* (2021) **11** e050245. DOI: 10.1136/bmjopen-2021-050245
43. Liu X., Shen P.L., Tsai Y.S.. **Dance intervention effects on physical function in healthy older adults: A systematic review and meta-analysis**. *Aging Clin. Exp. Res.* (2021) **33** 253-263. DOI: 10.1007/s40520-019-01440-y
44. Datta B.K., Husain M.J., Husain M.M., Kostova D.. **Noncommunicable disease-attributable medical expenditures, household financial stress and impoverishment in Bangladesh**. *SSM-Popul. Health* (2018) **6** 252-258. DOI: 10.1016/j.ssmph.2018.10.001
|
---
title: Impact on Glycemic Variation Caused by a Change in the Dietary Intake Sequence
authors:
- Alexis Alonso-Bastida
- Manuel Adam-Medina
- Dolores-Azucena Salazar-Piña
- Ricardo-Fabricio Escobar-Jiménez
- María-Socorro Parra-Cabrera
- Marisol Cervantes-Bobadilla
journal: Foods
year: 2023
pmcid: PMC10000994
doi: 10.3390/foods12051055
license: CC BY 4.0
---
# Impact on Glycemic Variation Caused by a Change in the Dietary Intake Sequence
## Abstract
This work presents an analysis of the effect on glycemic variation caused by modifying the macronutrient intake sequence in a person without a diagnosis of diabetes. In this work, three types of nutritional studies were developed: [1] glucose variation under conditions of daily intake (food mixture); [2] glucose variation under conditions of daily intake modifying the macronutrient intake sequence; [3] glucose variation after a modification in the diet and macronutrient intake sequence. The focus of this research is to obtain preliminary results on the effectiveness of a nutritional intervention based on the modification of the sequence of macronutrient intake in a healthy person during 14-day periods. The results obtained corroborate the positive effect on the glucose of consuming vegetables, fiber, or proteins before carbohydrates, decreasing the peaks in the postprandial glucose curves (vegetables: 113–117 mg/dL; proteins: 107–112 mg/dL; carbohydrates: 115–125 mg/dL) and reducing the average levels of blood glucose concentrations (vegetables: 87–95 mg/dL; proteins: 82–99 mg/dL; carbohydrates: 90–98 mg/dL). The present work demonstrates the preliminary potential of the sequence in the macronutrient intake for the generation of alternatives of prevention and solution of chronic degenerative diseases, improving the management of glucose in the organism and permeating in the reduction of weight and the state of health of the individuals.
## 1. Introduction
Consumption of food in human beings is an activity that provides the nutrients necessary for the adequate performance of the organism and prevents various diseases such as diabetes and cardiovascular diseases [1]. Consumption of any food generates elevations in glucose levels, defined as “glucose curves,” because of a gradual elevation in blood glucose levels that is subsequently attenuated due to the homeostatic processes of glucose in the organism throughout time [2]. This period is called the postprandial glucose stage, which lasts 4–5 h for each meal taken [3]. Information on the magnitude, fluctuations, and different characteristics of glucose curves (peaks, plateaus, rise and decay times) is defined as “glycemic variability” [4], which has taken on great relevance for the generation of actions toward the development of nutritional interventions.
Prolonged postprandial glucose episodes and their high frequency generate one of the main risk factors for developing Type 2 Diabetes Mellitus (T2DM) [5,6] since the average blood sugar level throughout the day is above the basal glucose levels. T2DM is a multifactorial disease occurring in the adult population [7], where the main characteristic is the presence of elevated blood glucose levels throughout the day (episodes of hyperglycemia) [8]. High glucose levels are mainly related to insulin resistance, a condition in which the different cells of the organism cannot assimilate the insulin hormone adequately. This condition leads to an increase in insulin secretion by the pancreas, which in turn facilitates the presence of hyperglycemia in the organism due to the low absorption of insulin by the cells [9]. This set of diseases is usually the product of carrying out many habits detrimental to health throughout the person’s life before being diagnosed with T2DM [10].
T2DM is a chronic degenerative disease that leads to a degradation in the quality of life and facilitates the presence of cardiovascular complications [11] and renal, ocular, and liver diseases [12], these being a small part of the set of conditions related to T2DM. It should be noted that because of the disease control actions by the health sector, there is an added problem [13,14], ranging from the viewpoint of proper care for the community [15] to the economic aspect where the investment required to satisfy this need is growing every year [16,17]. Therefore, several alternatives have been developed to prevent the condition, some of them focused on informing society about how a healthy diet and regular physical activity reduce the chances of developing T2DM [18].
Diet is the main factor in the increase in glucose levels, so it is essential to control how we consume food. Several studies have addressed this problem, thus generating alternative solutions to reduce the impact of postprandial glucose, such as Dahl et al. [ 19], who demonstrated how semaglutide has beneficial effects on the reduction of postprandial glucose, triglycerides, glucagon, and gastric emptying in people with T2DM. Rayner et al. [ 20] similarly demonstrate the effects of lixisenatide in reducing gastric emptying by promoting postprandial glucose dynamics. Vlachos et al. [ 21] present an in-depth review of the subject, concluding that reducing carbohydrates in conjunction with a higher fiber intake positively affects postprandial glucose reduction.
How different macronutrients are ingested affects the glycemic variability of organisms, modifying the time to glucose elevation, the glucose curve magnitude, and the glucose decay time [22,23]. Sun et al. [ 24] developed a study of 16 healthy people in which the effect of the proper order for macronutrient intake on glycemic variation was evaluated. The study showed that consuming vegetables followed by proteins and concluding with carbohydrates is an effective strategy to reduce postprandial glucose and prevent the generation of T2DM. Kubota et al. [ 25] corroborate that the correct order of the food sequence reduces the episodes of postprandial hyperglycemia, improving weight loss and metabolic function.
Considering the alternatives presented in the various sources of information and considering that those related works generate an analysis on the modification of the sequence of macronutrient intake concerning tests of about 4 to 5 h of glucose monitoring, the main objective of this work is to obtain preliminary results on the effectiveness of a nutritional intervention based on the modification of the order of macronutrient intake in a healthy person. The particularity of this work is to generate continuous glucose measurements during three periods of 14 days in an individual for whom three types of nutritional interventions were developed. In this way, difficult-to-access information is obtained on the behavior of glucose curves derived from the proposed nutritional intervention. Considering the scope of the present work, this research is a first approximation for the development on a larger scale of nutritional interventions focused on the modification of the macronutrient sequence that allows the reduction of postprandial glucose levels in healthy people. This way, the necessary conditions are obtained to carry out this experimentation on a larger scale. The results of this research will allow the generation of information for the development of alternative solutions in the generation of metabolic and chronic degenerative diseases.
## 2.1. Quasiexperimental Study Design
The study methodology consists of the steps described below:Generation of data: A series of body measurements, an indirect calorimetry test, and the development of a food reminder were developed in the participant to have an approximation of the nutritional status of the participant and to be able to propose the type of interventions in the sequence of macronutrient intake to follow so that there is no decompensation in the current type of food intake. Implantation of the continuous glucose monitoring sensor: In each test, a new interstitial glucose sensor was implanted to generate data on glucose dynamics. Daily diet (Test 1): Subsequently, Test 1 was developed, where glucose measurements were generated and focused on describing the variation of glucose levels in the face of the study subject’s daily diet (mixture of macronutrients without having any order in food consumption).Regular diet with ordered consumption of macronutrients (Test 2): Test 2 has the objective of obtaining the glucose dynamics when the sequence in the order of macronutrient consumption is modified without generating any change in the participant’s regular diet. Assisted diet with ordered consumption of macronutrients (Test 3): This test consists of generating measurements of glucose variation in the face of a modification in the participant’s daily diet considering the change in the sequence of macronutrient intake. Statistical analysis: Once the three different tests were generated, a statistical analysis of the results obtained was generated, which was the study’s core. In this analysis, the impact of the sequence in macronutrient consumption was quantified and contrasted concerning the postprandial glucose curves generated in each dietary intake. For this purpose, the proportions of macronutrients consumed per intake were used and related to glucose concentrations, magnitudes of the postprandial glucose peaks, and times in which postprandial glucose stabilizes.
## 2.2. Ethics of Research
In the research developed, the health and integrity of the participant were not put at risk in any way, being an observational experiment. Each of the procedures developed was evaluated and authorized by the Ethics Committee of the Faculty of Medicine of the Autonomous University of the State of Morelos (CONBIOETICA-17-CEI-003-201-81112). It should be emphasized that the participant was informed of the procedures to be developed, and once informed and in agreement with the guidelines to be developed, the participant signed the letter of informed consent.
## 2.3. Instrumentation
The instrumentation used in this study consists of an interstitial continuous glucose monitoring (CGCM) system (Freestyle libre, Abbott®, Chicago, IL, USA) for the acquisition of glucose measurements, an indirect calorimetry system (KORR Medical®, West Valley City, UT, USA) to obtain the participant’s daily energy consumption, a bioimpedance scale (BC-545 Segmental, Tanita® brand, Arlington Heights, IL, USA) and a stadiometer for the participant’s body detection, and a food intake and physical activity diary for macronutrient counting and physical activity intensity.
There are no conflicts of interest in this research. There is no relationship between the suppliers of the instrumentation used in the experimentation. The instruments were purchased with funding from CONACYT (project number 320155) and TecNM (project numbers 14002.22P and 14003.22P).
## 2.4. Subject of Study
The proposed study has great difficulty in its development in population studies due to the strict discipline required to carry out the dietary sequence in the required order, the filling of the food intake and physical activity diaries, and the glucose monitoring. Therefore, this study was developed on a pilot basis in a physically active healthy person (without a diagnosis of diabetes or any chronic degenerative disease) considered by the standards of a healthy person proposed by the World Health Organization (WHO) [26]. The participant is a 26-year-old male with a height of 1.78 m and a daily energy intake of 2810 calories (246 calories from physical activity, 591 calories derived from the participant’s daily activities and lifestyle, and 1973 calories from energy consumed at rest). Body measurements were taken at the beginning of each test, described in Table 1, where weight, body mass index, abdominal circumference, and percentages of muscle, fat, and visceral fat are considered.
To have certainty in the information, the participant received instructions to correctly fill out the food intake and physical activity recording instruments (both devices are standardized forms). In addition, the participant was instructed to record foods that were not consumed or added to the tools. This was combined with a 24-h reminder of the food consumed, carried out by trained personnel. Regarding continuous glucose monitoring, due to the conditions of the measuring instrument, the patient was instructed to take periodic manual measurements throughout the day, avoiding more than four hours between sizes (except for the participant’s sleeping hours). The correct storage of glucose readings was corroborated with the report generated by the Abbott® platform. For more information on the monitoring system, we recommend consulting [27].
## 2.5. Food Sequence
Considering the three tests developed, in the case of Tests 2 and 3, the participant was assigned a sequence in the intake of macronutrients, sectioned according to the type of intake developed (breakfast, snack 1, lunch, dinner, and snack 2) and repeating the type of food and its quantity for 4 consecutive days exchanging the order in the consumption of macronutrients. ( *This is* because the monotony of the food makes it difficult for participants to adhere to the needs of the experiment.) The sequence is presented regarding the symbology of the proposed macronutrients starting with the symbol on the left side and ending with the symbol on the right side (VF-CH-P-FT = 1. Vegetables and Fiber, 2. Carbohydrates, 3. Proteins of animal origin, 4. Fats). Table 2 presents the sequence used for each day of intake, denoting macronutrients as follows: P: Proteins of animal origin; CH: Carbohydrates; VF: Vegetables and Fiber; D: D: Dairy; FT: Fats; FR: Fruits.
## 2.6. Proportions of Macronutrients Ingested
Each food ingested by the participant was analyzed concerning its composition in carbohydrates, lipids, and proteins, obtaining for each macronutrient the weight (grams) and energetic quantity (calories) contained in the food. In addition, the percentage of energy provided by each macronutrient in each of the intakes analyzed was calculated. Table 3 presents the mean and standard deviations of the composition of each macronutrient analyzed in each intake developed for each of the three types of diets analyzed.
## 2.7. Diet for Each Test Developed
Throughout the experimentation, several menus were used to ensure proper adherence to the study by the participant. In the case of Tests 2 and 3, each of the menus was appropriately developed in such a way that the proportions of macronutrients ingested were analogous. For the reader to have a clear idea of the menu composition, the following is an example for each of the menus developed in each test.
## 2.8. Glucose Curve
The analysis of glycemic variability considers information on the dynamics of the glucose curves produced at each dietary intake. Figure 1 shows a contrast between the glucose measurement (left graph) and the respective magnitude of the curve generated after a meal (right graph). The beginning of the curve is the moment when food intake is generated, followed by the absorption of macronutrients by the organism, followed by a pronounced elevation in glucose levels until reaching the maximum peak, from where a decrease in glucose begins because of the homeostatic regulation process generated by the organism, thus generating abrupt changes in glucose derived from the effect of insulin secretion. Once the decline is complete, glucose stabilizes, thus attenuating the postprandial glucose curve generated. The magnitude of the glucose elevation (right graph) is calculated by subtracting the initial measurement from the glucose curve analyzed in each of the measurements over the time of the curve, thus having a magnitude of 0 mg/dL at the beginning, which over time can have positive or negative glucose concentration values due to the different types of absorption of the macronutrients ingested.
## 3.1. Glucose Measurement
In each test, 14 days of glucose measurements were generated. The result of the glucose variation in each test is presented in Figure 2, positioning in the upper part the glucose dynamics according to a daily diet (Test 1), in the central part the dynamics according to a daily diet modifying the sequence in the macronutrient intake (Test 2), and finally, in the lower part the glucose variation according to a modification in the diet and sequence of macronutrient intake (Test 3). The difference between tests is clear according to each of the glucose curves, being greater in Test 1 since there is no fixed schedule for food intake, contrary to what happened in Test 3, where the time in the glucose curves is constant. Consequently, the behavior of glucose is more homogeneous.
Quantitatively, the average variation between each of the tests is described in Table 4, where the glucose average data, the glucose management indicator (based on that proposed by Leelarathna et al. [ 28]), and the glucose coefficient of variation (considering that submitted by Rodbard [29]) are presented. These data were calculated for total glucose measurements in each test performed over the 14 days of size. The results demonstrate how a higher glucose variation coefficient correlates with lower glucose concentrations and a glucose management indicator. This phenomenon is visible when comparing Test 1 results with those in Test 3.
Five types of intakes were generated for each day throughout the tests. The postprandial glucose average of each intake developed throughout every test is presented in Table 5, where the postprandial glucose concentrations are lower at breakfast (ranging between 83–89 mg/dL) due to starting from a condition close to the basal level, contrary to dinner where the glucose ranges between 95–100 mg/dL because the glucose curve starts from a higher level since the time gaps between each intake avoid the homeostasis to reach a basal level after a postprandial period.
## 3.2. Food Sequence Modification Effect on Glucose
There are marked differences between the intakes analyzed in each test. Therefore, this work explored the magnitudes, elevation, and stabilization times in the different glucose curves developed in each intake. For evaluating the impact of the sequence of macronutrients in the dietary intake, each of the glucose curves was grouped among three different types of patterns:Carbohydrate intake at the beginning;Vegetable and fiber intake at the beginning;Animal protein intake at the beginning.
The results of this analysis are presented in Table 6, highlighting the following aspects: (a) *Higher maximum* glucose peaks occur when carbohydrates are consumed first. ( b) The consumption of vegetables and fiber or proteins generates lower glucose average levels in contrast to an early consumption of carbohydrates. ( c) Early consumption of carbohydrates generates shorter periods of elevation and stabilization in the glucose curves in contrast to an early consumption of proteins or vegetables, resulting in higher glucose average levels.
As a complement to Table 6, Figure 3 illustrates the dynamics of the glucose curves when the first macronutrient ingested is carbohydrates. It shows three graphs corresponding to the three main intakes (breakfast, lunch, and dinner). Each chart has two types of colors, red (referring to the results obtained in Test 2) and blue (Test 3), representative of the dynamics of the intake developed throughout the experiment, thus illustrating the postprandial glucose during a period of 5 h of measurement. Considering the numerical results presented, the postprandial glucose dynamics show lower elevations at breakfast (maximum glucose peaks below 130 mg/dL) than at lunch, where glucose peaks reach values close to 150 mg/dL, and dinner with top mounts above 150 mg/dL.
## 4. Discussion
Recently, the concept regarding the sequence and frequency of macronutrient intake has gained strength due to the benefits it generates for the organism [30]. Paoli et al. [ 31] proposed an example of this, where the modification of the frequency of intakes generates benefits in the reduction of intestinal inflammation, improving autophagy, and stress resistance. Henry et al. [ 32] allude to how consuming vegetables before carbohydrates is a strategy capable of optimizing glycemic control and positively influencing postprandial glucose. On the other hand, King et al. [ 33] describe how consuming a small dose of whey protein before a macronutrient meal mix stimulates insulin generation and improves postprandial glucose in people with T2DM.
Considering the current need for the generation of information and alternative solutions for the prevention of T2DM, this work developed the necessary experimentation to determine the effects of the sequence of macronutrient intake on glucose reduction after the adoption of dietary regimens that promote the decrease in glucose levels evaluated during periods of 14 days. The results presented in a preliminary way the effectiveness of the adoption of nutritional regimens focused on the anticipated consumption of vegetables, fiber, or protein for the reduction and good condition of glucose levels. The best results came from an early intake of vegetables and fiber with an average glucose of 87–95 mg/dL and peak and stabilization times starting at 2.34 and 2.96 h, respectively. In this way, pronounced postprandial glucose episodes are avoided, and the shape of the glucose curves is flattened, in contrast to early carbohydrate intake, which has peak values of 130–150 mg/dL.
The development of the experimentation presents significant difficulty for the participant and the researcher who carries it out, because a dietary plan must be designed for each participant, thus promoting adherence to the menu and guaranteeing the correct performance of the experiment. The gradual variety of the menus is of utmost importance since it favors the participant’s comfort and decreases the probability of desertion during the experimentation. The participant’s correct development of the experiment must be corroborated with the filling of food diaries and continuous glucose measurements. In the case of sample scaling, it is advisable to consider those mentioned above, thus favoring the conditions for correct development in the experimentation. For population studies, two experimental periods with a duration of 14 days per period should be developed. In the first one, the glycemic variation is evaluated under an assisted diet, and in the second one, under a similar diet, but in this case varying the sequence in which the different types of macronutrients are ingested. It is recommended that there be a 14-day rest period between each of the tests. Otherwise, adherence to experimentation is challenging in the second experiment stage.
The complexity of this type of research illustrates the difficulties that exist for participants in adhering to a dietary regimen [34,35]. Consequently, the primary concentration of this type of research is limited to evaluating a single glucose curve in a population of healthy individuals, as is the case of Sun et al. [ 24] with a population of 16 healthy individuals. Alternatively, research is limited to evaluating people with gestational diabetes mellitus (GDM), as proposed in Yong et al. [ 36], where, like what is presented in this work, glycemic variation is analyzed in 10 women with GDM, exchanging the sequence of macronutrients and measuring glucose with a GCM. The results of this work agree with Sun et al. [ 24] and Yong et al. [ 36], where an early consumption of vegetables, fiber, or protein reduces postprandial glucose. Taking these works as a reference point, both use only one feeding plan due to a shorter duration of the experimentation, contrary to the case presented in this work, where the time of investigation makes it necessary to change the feeding plan periodically. Classifying the macronutrients consumed in these studies is similar to the method used in this research, where food is classified according to the predominant macronutrient in its composition.
The particularity of this work is focused on four specific points:Development of an analytical study on the effect of food sequence on postprandial glucose curves and the impact on glucose levels throughout the research period, with statistical analysis being a fundamental part of generating the results obtained;Experimentation time of 42 days divided into three different tests;Periodic change in meal plans to achieve patient adherence to experimentation;Contrast between three different conditions of glycemic variation derived from the tests proposed (the basis for experimentation on a more significant number of population).
The experimentation developed is a pilot test that serves as a reference to evaluate the feasibility of carrying it out in a larger population under specific conditions of degradation in glucose homeostasis. Although the measures that must be taken to develop the experimentation are extensive, the benefits gained from it are significant. These can be included in the wide range of nutritional alternatives that can be proposed for the management and prevention of diabetes. One of the main benefits is the possibility of attending to the problem without generating an extra economic cost derived from its treatment. The present work opens a window of opportunities for developing several topics focused on managing and preventing metabolic diseases from a nutritional point of view.
## 5. Conclusions
In this work, three types of nutritional studies were developed to analyze the effect of managing the order of macronutrient intake. The results are consistent with the literature, indicating that early consumption of vegetables, fiber, or protein reduces the size of the postprandial glucose curves, thus decreasing blood glucose levels and improving glucose homeostasis in the organism. Considering that the work is a pilot test, based on the results obtained and the recommendations proposed to carry out the experimentation, it is feasible to develop it on a larger sample scale. This research is a potential milestone for the generation of knowledge focused on improving glucose homeostasis in different treatments for diabetes. This work generates the possibility of creating alternatives for the prevention and control of type 2 diabetes based on changes in the dietary sequence and in conjunction with pharmacological treatment (in the case of diabetes) that does not generate an extra economic expense for the health sectors and the people treated in them.
## References
1. **Healthy Diet**. (2019)
2. Tschritter O., Fritsche A., Shirkavand F., Machicao F., Häring H., Stumvoll M.. **Assessing the Shape of the Glucose Curve During an Oral Glucose Tolerance Test**. *Diabetes Care* (2003) **26** 1026-1033. DOI: 10.2337/diacare.26.4.1026
3. Soeters M.R.. **Food intake sequence modulates postprandial glycemia**. *Clin. Nutr.* (2020) **39** 2335-2336. DOI: 10.1016/j.clnu.2020.06.009
4. Nusca A., Tuccinardi D., Albano M., Cavallaro C., Ricottini E., Manfrini S., Pozzilli P., Di Sciascio G.. **Glycemic variability in the development of cardiovascular complications in diabetes**. *Diabetes Metab. Res. Rev.* (2018) **34** e3047. DOI: 10.1002/dmrr.3047
5. Ruijgrok C., Blaak E.E., Egli L., Dussort P., Vinoy S., Rauh S.P., Beulens J.W., Robertson M.D., Alssema M.. **Reducing postprandial glucose in dietary intervention studies and the magnitude of the effect on diabetes-related risk factors: A systematic review and meta-analysis**. *Eur. J. Nutr.* (2021) **60** 259-273. DOI: 10.1007/s00394-020-02240-1
6. Monnier L., Colette C., Owens D.. **Glucose variability and diabetes complications: Risk factor or biomarker? Can we disentangle the “Gordian Knot”?**. *Diabetes Metab.* (2021) **47** 101225. DOI: 10.1016/j.diabet.2021.101225
7. Bellary S., Kyrou I., Brown J.E., Bailey C.J.. **Type 2 diabetes mellitus in older adults: Clinical considerations and management**. *Nat. Rev. Endocrinol.* (2021) **17** 534-548. DOI: 10.1038/s41574-021-00512-2
8. Andreadi A., Bellia A., Di Daniele N., Meloni M., Lauro R., Della-Morte D., Lauro D.. **The molecular link between oxidative stress, insulin resistance, and type 2 diabetes: A target for new therapies against cardiovascular diseases**. *Curr. Opin. Pharmacol.* (2021) **62** 85-96. DOI: 10.1016/j.coph.2021.11.010
9. Andreadi A., Muscoli S., Tajmir R., Meloni M., Minasi A., Muscoli C., Ilari S., Mollace V., Della Morte D., Bellia A.. **Insulin Resistance and Acne: The Role of Metformin as Alternative Therapy in Men**. *Pharmaceuticals* (2023) **16**. DOI: 10.3390/ph16010027
10. Martín-Peláez S., Fito M., Castaner O.. **Mediterranean Diet Effects on Type 2 Diabetes Prevention, Disease Progression, and Related Mechanisms. A Review**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12082236
11. Pedreanez A., Mosquera J., Munoz N., Robalino J., Tene D.. **Diabetes, heart damage, and angiotensin II. What is the relationship link between them? A minireview**. *Endocr. Regul.* (2022) **56** 55-65. DOI: 10.2478/enr-2022-0007
12. Daneshzad E., Larijani B., Azadbakht L.. **Diet quality indices and cardiovascular diseases risk factors among diabetic women**. *J. Sci. Food Agric.* (2019) **99** 5926-5933. DOI: 10.1002/jsfa.9867
13. Juan J., Yang H.. **Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17249517
14. Alonso-Bastida A., Adam-Medina M., Posada-Gómez R., Salazar-Piña D.A., Osorio-Gordillo G.-L., Vela-Valdés L.G.. **Dyn amic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph19020716
15. Das A.K., Saboo B., Maheshwari A., Nair V.M., Banerjee S., Jayakumar C., Benny P.V., Prasobh P.S., Mohan A.R., Potty V.S.. **Health care delivery model in India with relevance to diabetes care**. *Heliyon* (2022) **8** e10904. DOI: 10.1016/j.heliyon.2022.e10904
16. Erzse A., Stacey N., Chola L., Tugendhaft A., Freeman M., Hofman K.. **The direct medical cost of type 2 diabetes mellitus in South Africa: A cost of illness study**. *Glob. Health Action* (2019) **12** 1636611. DOI: 10.1080/16549716.2019.1636611
17. Standl E., Khunti K., Hansen T.B., Schnell O.. **The global epidemics of diabetes in the 21st century: Current situation and perspectives**. *Eur. J. Prev. Cardiol.* (2019) **26** 7-14. DOI: 10.1177/2047487319881021
18. Man A.W.C., Li H., Xia N.. **Impact of Lifestyles (Diet and Exercise) on Vascular Health: Oxidative Stress and Endothelial Function**. *Oxidative Med. Cell. Longev.* (2020) **2020** 1496462. DOI: 10.1155/2020/1496462
19. Dahl K., Brooks A., Almazedi F., Hoff S.T., Boschini C., Bækdal T.A.. **Oral semaglutide improves postprandial glucose and lipid metabolism, and delays gastric emptying, in subjects with type 2 diabetes**. *Diabetes Obes. Metab.* (2021) **23** 1594-1603. DOI: 10.1111/dom.14373
20. Rayner C.K., Watson L.E., Phillips L.K., Lange K., Bound M.J., Grivell J., Wu T., Jones K.L., Horowitz M., Ferrannini E.. **Effects of Sustained Treatment With Lixisenatide on Gastric Emptying and Postprandial Glucose Metabolism in Type 2 Diabetes: A Randomized Controlled Trial**. *Diabetes Care* (2020) **43** 1813-1821. DOI: 10.2337/dc20-0190
21. Vlachos D., Malisova S., Lindberg F.A., Karaniki G.. **Glycemic Index (GI) or Glycemic Load (GL) and Dietary Interventions for Optimizing Postprandial Hyperglycemia in Patients with T2 Diabetes: A Review**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12061561
22. Yabe D., Kuwata H., Fujiwara Y., Sakaguchi M., Moyama S., Makabe N., Murotani K., Asano H., Ito S., Mishima H.. **Dietary instructions focusing on meal-sequence and nutritional balance for prediabetes subjects: An exploratory, cluster-randomized, prospective, open-label, clinical trial**. *J. Diabetes Complicat.* (2019) **33** 107450. DOI: 10.1016/j.jdiacomp.2019.107450
23. Shapira N.. **The Metabolic Concept of Meal Sequence vs. Satiety: Glycemic and Oxidative Responses with Reference to Inflammation Risk, Protective Principles and Mediterranean Diet**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11102373
24. Sun L., Goh H.J., Govindharajulu P., Leow M.K.-S., Henry C.J.. **Postprandial glucose, insulin and incretin responses differ by test meal macronutrient ingestion sequence (PATTERN study)**. *Clin. Nutr.* (2020) **39** 950-957. DOI: 10.1016/j.clnu.2019.04.001
25. Kubota S., Liu Y., Iizuka K., Kuwata H., Seino Y., Yabe D.. **A Review of Recent Findings on Meal Sequence: An Attractive Dietary Approach to Prevention and Management of Type 2 Diabetes**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12092502
26. Bull F.C., Al-Ansari S.S., Biddle S., Borodulin K., Buman M.P., Cardon G., Carty C., Chaput J.-P., Chastin S., Chou R.. **World Health Organization 2020 guidelines on physical activity and sedentary behaviour**. *Br. J. Sports Med.* (2020) **54** 1451-1462. DOI: 10.1136/bjsports-2020-102955
27. Blum A.. **Freestyle Libre Glucose Monitoring System**. *Clin. Diabetes* (2018) **36** 203-204. DOI: 10.2337/cd17-0130
28. Leelarathna L., Beck R.W., Bergenstal R.M., Thabit H., Hovorka R.. **Glucose Management Indicator (GMI): Insights and Validation Using Guardian 3 and Navigator 2 Sensor Data**. *Diabetes Care* (2019) **42** e60-e61. DOI: 10.2337/dc18-2479
29. **Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control**. *Diabetes Technol. Ther.* (2009) **11** S55-S67. DOI: 10.1089/dia.2008.0132
30. Xiao Q., Garaulet M., Scheer F.A.. **Meal timing and obesity: Interactions with macronutrient intake and chronotype**. *Int. J. Obes.* (2019) **43** 1701-1711. DOI: 10.1038/s41366-018-0284-x
31. Paoli A., Tinsley G., Bianco A., Moro T.. **The Influence of Meal Frequency and Timing on Health in Humans: The Role of Fasting**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11040719
32. Henry C.J., Kaur B., Quek R.Y.C.. **Chrononutrition in the management of diabetes**. *Nutr. Diabetes* (2020) **10** 6. DOI: 10.1038/s41387-020-0109-6
33. King D.G., Walker M., Campbell M.D., Breen L., Stevenson E.J., West D.J.. **A small dose of whey protein co-ingested with mixed-macronutrient breakfast and lunch meals improves postprandial glycemia and suppresses appetite in men with type 2 diabetes: A randomized controlled trial**. *Am. J. Clin. Nutr.* (2018) **107** 550-557. DOI: 10.1093/ajcn/nqy019
34. Juárez-Ramírez C., Théodore F.L., Villalobos A., Allen-Leigh B., Jiménez-Corona A., Nigenda G., Lewis S.. **The importance of the cultural dimension of food in understanding the lack of adherence to diet regimens among Mayan people with diabetes**. *Public Health Nutr.* (2019) **22** 3238-3249. DOI: 10.1017/S1368980019001940
35. Mendes R., Martins S., Fernandes L.. **Adherence to Medication, Physical Activity and Diet in Older Adults With Diabetes: Its Association With Cognition, Anxiety and Depression**. *J. Clin. Med. Res.* (2019) **11** 583-592. DOI: 10.14740/jocmr3894
36. Yong G., Jing Q., Yao Q., Yang K., Ye X.. **Changing Meal Sequence Affects Glucose Excursions in Gestational Diabetes Mellitus**. *J. Diabetes Res.* (2022) **2022** 7083106. DOI: 10.1155/2022/7083106
|
---
title: 'The Usage and Trustworthiness of Various Health Information Sources in the
United Arab Emirates: An Online National Cross-Sectional Survey'
authors:
- Mariam A. Almaazmi
- Kamel A. Samara
- Mohammed Jarai
- Hussain Majeed
- Hiba J. Barqawi
journal: Healthcare
year: 2023
pmcid: PMC10001002
doi: 10.3390/healthcare11050663
license: CC BY 4.0
---
# The Usage and Trustworthiness of Various Health Information Sources in the United Arab Emirates: An Online National Cross-Sectional Survey
## Abstract
Background: The increase in the quality and availability of health information as well as the accessibility of Internet-based sources, has driven growing demand for online health information. Information preferences are influenced by many factors, including information needs, intentions, trustworthiness, and socioeconomic variables. Hence, understanding the interplay of these factors helps stakeholders provide current and relevant health information sources to assist consumers in assessing their healthcare options and making informed medical decisions. Aims: To assess the different sources of health information sought by the UAE population and to investigate the level of trustworthiness of each source. Methods: The study adopted a descriptive online cross-sectional design. A self-administered questionnaire was used to collect data from UAE residents aged 18 years or above between July 2021 and September 2021. Health information sources, their trustworthiness, and health-oriented beliefs were explored through univariate, bivariate, and multivariate analysis in Python. Results: A total of 1083 responses were collected, out of which 683 ($63\%$) were females. Doctors were the first source of health information ($67.41\%$) before COVID-19, whereas websites were the first source ($67.22\%$) during the pandemic. Other sources, such as pharmacists, social media, and friends and family, were not prioritized as primary sources. Overall, doctors had a high trustworthiness of $82.73\%$, followed by pharmacists with a high trustworthiness of $59.8\%$. The Internet had a partial trustworthiness of $58.4\%$. Social media and friends and family had a low trustworthiness of $32.78\%$ and $23.73\%$, respectively. Age, marital status, occupation, and degree obtained were all significant predictors of Internet usage for health information. Conclusions: The population in the UAE commonly obtains health information from doctors who have been shown to have the highest trustworthiness; this is despite it not being the most common source used.
## 1. Introduction
The massive expansion of the Internet and social media, as well as its ease and wide accessibility, has led to a rise in health information-seeking behaviors. Despite a wide range of sources, accessing reliable health information remains challenging and elusive, with untrusted and uncredible sources potentially harming individuals’ health [1]. Therefore, researchers and clinicians aim to understand individuals’ health information patterns to better engage and promote successful behaviors [2]. Such behaviors include tackling health-threatening situations, making health-impacting decisions, and prioritizing preventive health habits.
Sources of health information can be categorized as Internet-based, entertainment-oriented, and information-oriented. Internet-based resources comprise broadly reaching mass data, such as blogs, websites, and social media, while entertainment-oriented include TV and podcasts [3]. Comparatively, information-oriented resources include healthcare providers or printed materials such as newspapers and brochures [4]. A global review study has shown that more than half of the public uses the Internet as a source of health information [5].
Health information research also includes evaluating a multitude of determinants for each resource. For example, the trustworthiness of a health information resource heavily determines its usage frequency and value, which in turn depends on various sociodemographic features [6]. Other research focuses on the different motives behind health information searching, which include symptom troubleshooting, searching before or after a clinical visit, or obtaining information for others [7,8]. For instance, individuals with long-standing diseases need to make decisions regarding their health; therefore, such patients tend to search more for information from multiple resources to make such decisions [3].
There is a paucity of research in the Gulf region on health information sources, with most focusing on the type of resource being used, with wide variation among the results being reported. A study conducted in Saudi Arabia showed that $87.6\%$ of the participants relied specifically on doctors as their primary source of health information, whereas the Internet was not commonly used as a primary or secondary source [9]. However, a study targeting students in the Sultanate of Oman showed that the Internet and family members are more commonly utilized sources of health information compared to doctors and other experts [10]. These results align with those of a Kuwaiti university study that showed $92.6\%$ of university students using the Internet as a health information source [11].
However, for the United Arab Emirates (UAE), there are no published results regarding primary or secondary general sources of health information. However, a study conducted by Figueiras regarding COVID-19 information resources exclusively found that only $20\%$ would consult a physician [12]. Hence, understanding the different sources of health information used and the level of trust by the population in the UAE is necessary for helping individuals make informed medical decisions and evaluating healthcare options. Therefore, the aims of this study were to (a) evaluate the different information sources used by the population in the United Arab Emirates and their trustworthiness, (b) the impact of COVID-19 on the health information sources, and (c) explore the Internet as a health information source.
## 2.1. Study Design and Target Population
A cross-sectional study was conducted between July 2021 and September 2021 to determine the sources of health information used by the population in the UAE. The eligibility criteria included (a) adults above the age of 18 years and (b) the ability to communicate in English and/or Arabic. Individuals younger than 18 years old and those who do not communicate in English or Arabic were excluded. This study and its protocols were reviewed and approved by the Research Ethics Committee of the University of Sharjah (REC-21-06-09-04S).
## 2.2. Questionnaire Development
A self-administered questionnaire was developed based on a review of the current literature on the topic [9,13,14,15,16,17]. It was developed in English and Arabic in Google Forms and was distributed online using different social media platforms. The questionnaire was initially developed in English, and translation was performed by two of the authors, who are fluent in both languages. It consisted of three sections, the first evaluating the demographic data and assessing their health status (presence of chronic diseases, frequency of health seeking, and the subjective rating of their health). It also made use of the well-established Single Item Literacy Screener (SILS). The second section investigated the different sources used before and after the COVID-19 pandemic, the frequency of usage, and the level of trust associated with each source. The third section evaluated the effect of those sources on the participants’ knowledge and health-related decision making. The questionnaire was pilot-tested several times, and provided feedback was assessed and incorporated where appropriate. To ensure the data had no missing variables, the questions were structured as “required” in Google Forms such that the participants could not move to the next question before answering the previous one.
## 2.3. Sampling and Data Collection
Sample size calculation is an essential part of any study to ensure adequate power. In this study, it was calculated using the well-established Cochran’s sample size formula, which is widely used, as can be seen in similar studies by the authors [18]. It states that for some standard normal variate z1−α2 (calculated from the confidence interval), standard deviation SD, and error d, the sample size s can be calculated as follows:s=z1−α22×SD2d2 Given the lack of any studies on the topic before, SD takes a value of 0.5 [19]. With a confidence level of $95\%$ and a margin error of $5\%$, the estimated sample size in this study was calculated to be 385. This was increased to 440, assuming a $20\%$ non-response bias. Given the non-probabilistic sampling technique used for recruitment, a total sample of 1000 was aimed for. The questionnaire was distributed through several online platforms such as e-mail, social media, and WhatsApp. A participant information sheet was presented before starting the questionnaire, and the agreement to fill out the questionnaire indicated consent to join the study. Additionally, no identifying data were collected to ensure participants’ anonymity.
## 2.4. Data Entry and Analysis
Data was exported from Google Forms to CSV format and processed in python using Matplotlib-v3.3.4, pandas-v1.2.4, and statsmodels-v0.12.2. Since all questions were required, there were no missing values. For univariate analysis, categorical variables were evaluated using percentages. Age was categorized into four groups, attempting to obtain meaningful groups (below 18; above 40) while ensuring that each group has a significant number of members to assist with statistical testing, as discussed later. Likert scale questions (ranked from 1 to 5), specifically the ones dealing with Internet frequency usage and health rating, were binned into three groups taking the middle score [3] as average. Hence, any score below the middle score was considered to be below average, while any score above was taken to be above average.
No outliers were detected. All demographic variables, health insurance status, health literacy, comorbidities, and health-oriented variables were used as predictors of Internet usage and knowledge source trustworthiness. Outcomes of interest were recoded into binary variables (frequency of Internet usage, doctor trustworthiness, social media trustworthiness, and Internet trustworthiness). This recoding was performed by combining the average and below-average groups into one and renaming it accordingly. This has the advantage of identifying factors associated with above-average trustworthiness and Internet frequency usage. Bivariate analyses were conducted to identify significant predictors using chi-squared tests. The significant predictors were then fed into a bivariate logistic regression model, which was evaluated using a log-likelihood ratio test. The cut-off for significance was a p-value less than 0.05.
## 3.1. Demographics
A total of 1,083 responses were collected. A total of $63.07\%$ of the participants were females, and $50.32\%$ were between 19 and 29 years old. A third were UAE nationals, and nearly half were other Arab nationalities. Nearly $60\%$ were residents of Sharjah and other northern emirates. A total of $39.06\%$ of the respondents were students, and of those, $75.24\%$ were students of health-related majors. As for occupation, $10.34\%$ of all respondents were in the healthcare field. Of the total sample [1083], $72.85\%$ have health insurance, and $84.30\%$ have no long-term medical condition. Almost two-thirds had a normal reading ability of health literacy, which was assessed as the ease of understanding health information independently. More details regarding demographics can be found in Table 1.
## 3.2.1. Usage of Health Information Sources
When asked about their sources of health information before COVID-19, participants reported doctors as the most common source at $67.41\%$, followed closely by websites ($62.51\%$) and social media ($51.15\%$). As for websites and social media, examples such as World Health Organization, local government websites, and local electronic newspapers were used in the questionnaire to attempt to unify the perception of the participants about what is meant by websites is accurate. However, during the COVID-19 pandemic, websites and blogs became the most used source of health information, with $67.22\%$ using it. Social media also increased to $63.99\%$, while doctors dropped to $59.19\%$. Figure 1 shows the frequencies for all health information sources surveyed.
The Internet was used mostly to learn about symptoms and diagnoses ($79.22\%$), as well as to gain more information about COVID-19 ($52.72\%$). Other uses of the Internet included subjects exploring treatment options ($44.41\%$), gaining more information after a doctor’s visit ($44.32\%$), researching self-treatment methods ($37.12\%$), modifying health and lifestyle behaviors ($28.53\%$), choosing a healthcare provider ($27.89\%$), and deciding if a doctor visit is needed ($26.87\%$). The main websites used were search engines ($64.17\%$), international health agencies ($48.29\%$), and local government websites ($46.26\%$).
The determinants of Internet usage were explored through bivariate and multivariate analyses. Health orientation ($p \leq 0.0005$), occupation ($p \leq 0.0005$), marital status ($$p \leq 0.00083$$), health literacy ($$p \leq 0.036$$), long-term medical conditions ($$p \leq 0.042$$), place of residence ($$p \leq 0.047$$), and age ($$p \leq 0.049$$) were shown to be significant predictors and fed into a logistic regression model. All predictors except long-term medical conditions and health literacy remained significant. People who are older than 30 years (30–39 years; $$p \leq 0.036$$, OR = 2.092 ($95\%$ CI: 1.051–4.162) and >40 years, $$p \leq 0.021$$, OR = 2.260 ($95\%$ CI: 1.131–4.513)) were more likely to use the Internet more frequently. On the other hand, married ($$p \leq 0.003$$, OR = 0.464 ($95\%$ CI: 0.280–0.769)), non-healthcare ($$p \leq 0.034$$, OR = 0.567 ($95\%$ CI: 0.335–0.960)), students of other non-health-related majors ($$p \leq 0.035$$, OR = 0.525 ($95\%$ CI: 0.289–0.957)), and unemployed individuals ($$p \leq 0.003$$, OR = 0.386 ($95\%$ CI: 0.206–0.725)) were less likely to use the Internet. Results from the binary logistic regression model can be found in Table 2.
## 3.2.2. Trustworthiness of Health Information Sources
Doctors were the most trustworthy source, with $82.73\%$ stating them to be of high trustworthiness. Interestingly, while websites and blogs were the most common health information source, only $30.93\%$ found them to be highly trustworthy. Overall, the least highly trustable health information source was social media, at $10\%$. Figure 2 shows the trustworthiness of the health information sources surveyed. With regard to doctors, social media, and the Internet, additional bivariate and multivariate analyses were conducted to explore the factors correlated with higher levels of trustworthiness.
With regards to doctor trustworthiness, health beliefs ($p \leq 0.0005$), marital status ($p \leq 0.0005$), health orientation ($$p \leq 0.025$$), occupation ($$p \leq 0.010$$), health consciousness ($$p \leq 0.012$$), long-term medical conditions ($$p \leq 0.025$$), and age ($$p \leq 0.027$$) were significant predictors at the bivariate level. Results of the multivariate regression showed that married individuals ($$p \leq 0.009$$, OR = 0.450 ($95\%$ CI: 0.248–0.820)) were less likely to trust doctors, while students of health-related majors ($$p \leq 0.047$$, OR = 1.876 ($95\%$ CI: 1.007–3.494)) were more likely to trust doctors, with all other variables being insignificant. Results from the binary logistic regression model can be found in Supplementary Table S1.
As for social media, age ($p \leq 0.0005$), nationality ($p \leq 0.0005$), sex ($$p \leq 0.006$$), occupation ($$p \leq 0.006$$), health beliefs ($$p \leq 0.020$$), and marital status ($$p \leq 0.030$$) all were found to be significant predictors of trustworthiness. However, at the multivariate level, health beliefs, marital status, and occupation were all shown to be insignificant. Hence, overall, individuals younger than 40 years of age (19–29 years; $p \leq 0.0005$, OR = 0.161 ($95\%$ CI: 0.085–0.305) and 30–39 years; $$p \leq 0.026$$, OR = 0.333 ($95\%$ CI: 0.126–0.876)), and non-Emirati Arabs ($$p \leq 0.002$$, OR = 0.448 ($95\%$ CI: 0.267–0.749)) were all less likely to trust social media. Results from the binary logistic regression model can be found in Supplementary Table S2.
Finally, the bivariate analysis showed occupation ($p \leq 0.0005$), sex ($p \leq 0.0005$), health literacy ($$p \leq 0.003$$), nationality ($$p \leq 0.020$$), and age ($$p \leq 0.020$$) to be significant in predicting Internet trustworthiness. When fed into the logistic regression model, only nationality was found to be insignificant. From the rest, only individuals with the normal reading ability of health literacy ($$p \leq 0.018$$, OR = 1.443 ($95\%$ CI: 1.066–1.952)) were more likely to trust the Internet. In contrast, individuals between 19 and 29 years of age ($$p \leq 0.026$$, OR = 0.584 (($95\%$ CI: 0.364–0.937)), females ($$p \leq 0.023$$, OR = 0.691 ($95\%$ CI: 0.503–0.950)), students of non-health-related majors ($$p \leq 0.002$$, OR = 0.385 ($95\%$ CI: 0.212–0.699)), and unemployed individuals ($$p \leq 0.033$$, OR = 0.496 ($95\%$ CI: 0.261–0.945)) were less trusting of the Internet as a health information source. Results from the binary logistic regression model can be found in Supplementary Table S3.
## 4. Discussion
This study aimed to explore the different health information sources used by the population in the United Arab Emirates and to evaluate their trust in them. Before the COVID-19 pandemic, doctors were the most common source, followed closely by websites and social media. However, during the COVID-19 pandemic, the Internet rose to the first place and became the most common source of health information. However, doctors overall were still regarded as the most trustworthy source, with the Internet being considered partially trustworthy by the majority of participants. Age, sex, and occupation were all statistically significant predictors for the pattern of health information seeking and the perceived trustworthiness of each source.
There was a difference in the pattern of resource preference before and after the COVID-19 pandemic, which was also reported by another research conducted in the UAE during the pandemic. The researchers reported that while websites and social media platforms were the most used sources of health information, they were not the most trusted [12]. The increase in the use of the Internet as a source of information seeking during the pandemic could be explained due to the decreased accessibility to physically consult health workers and increased health anxiety [20]. This could also explain this study’s findings since the UAE did restrict access to non-emergency health services during the pandemic.
Although searching for more information regarding COVID-19 was a common reason behind Internet usage, it did not rank first in our findings. The most common reason was to learn about diseases’ symptoms and diagnoses. This presents a different picture compared with global studies where the Internet was mostly used complementarily after a doctor’s consultation [21]. Despite a fair percentage of participants ($44\%$) supplementing their information from the Internet after a doctor’s visit, it was not the most common purpose of use; in fact, an equivalently large percentage ($37\%$) reported searching for self-treatment methods over the Internet. When it comes to specific websites used over the Internet, the most used websites were search engines; interestingly, other studies in the Gulf region (Saudi Arabia and Qatar) reported similar results [13,22]. However, even while being the most used health information source over the Internet, both studies showed search engines to be not particularly well-trusted. While not explored in this study, trusted websites include those of personal doctors, medical universities, and federal medical organizations [16].
Overall, our results demonstrated that a vast majority of the participants ($82.70\%$) regard doctors as the most credible source, whereas only a third of them ranked the Internet to be of high trustworthiness. This also matches a previous study where doctors ranked first in trustworthiness, followed by pharmacists [9]. Furthermore, our results showed that more than half the participants ($56.23\%$) regard social media as partially trustworthy, in line with results from Saudi Arabia, where similar percentages distrusted the various social media sources [9]. Sbaffi and Rowley looked at the factors impacting the credibility of health information on social media platforms. Such factors included the authority of the author, the level of expertise in the field, and the objectivity of the posted information [6]. Finally, more than half of the participants partially trusted friends and family as a health information source, with another quarter reporting it being of low trustworthiness, making it the second least trustworthy source on the list.
Overall, trustworthiness and determinants of Internet usage are functions of multiple sociodemographic factors. One of the variables that influence the trust of individuals in specific health resources is age; older people tend to have less trust in any resources that are not healthcare providers [23]. Moreover, we found that older people are more likely to have less trust in social media as a health information source overall. In comparison, young people tend to prioritize readily available resources, probably due to their increased information needs, which cover social, physical, cognitive, and sexual self-development processes [24].
Preference for sources also differs among males and females as well; not only do females prefer consulting more than one source, but they also tend to search for information more than males. Studies conducted in Kuwait and Egypt showed a significant association between sex and utilizing the world wide web as a health information source, where females were more likely to seek health information compared to males [11,25]. As demonstrated by carpenter et al., one of the largest sex differences was that females tend to use medication package inserts as an information source more than males [15]. In this study, we found females to be less trustworthy of both the Internet and social media. Finally, level of education is another factor influencing health information behavior; the younger and the more educated an individual is, the keener they are to use diverse sources when searching for health information [22,26,27].
## Limitations
Every study has limitations that may affect the generalizability of the results; hence, a careful review of this study’s limitations follows. The participants may not be representative of the U.A.E.’s overall population due to the convenience sampling used. Moreover, no stratification was used to attempt to achieve specific percentages for the emirates, nationality, or occupations. For example, the proportion of the specific nationalities in the sample is not consistent with the actual proportion in the general population (where locals usually account for around $10\%$ of the total population). However, care was taken during sampling to be inclusive and attempt to target all sectors of the community, and each group ended up having sufficient members for statistical analysis.
In addition, the sample consisted of a lower percentage of the older age groups, which may lead to bias. Given that older people may suffer from more long-term conditions and may need increased healthcare, this may affect the results and reveal different patterns of trustworthiness. Therefore, future studies could collect similar data from a larger sample and attempt to include older individuals. However, information access patterns by younger demographics are still relevant, given their unique healthcare challenges, as discussed above. No information was collected regarding the trustworthiness and frequencies of the websites being used by the participants. Similarly, no information regarding socioeconomic status (or an equivalent proxy) was collected. It is worth noting, however, that even then, the analysis above revealed several relations with the collected demographics. Finally, since this is a cross-sectional study, future prospective studies could be conducted to assess whether individuals consistently use the same sources of health information and the reasons behind it. Such studies can also evaluate other parameters of health information sources, such as accuracy and reliability. They can also attempt to address some of the limitations discussed here.
## 5. Conclusions
This research aimed to explore health information sources being utilized by the population in the UAE and the trustworthiness of each. While doctors used to be the most common health information source, the pandemic influenced health information-seeking patterns by prioritizing online sources such as social media and the Internet significantly increased. However, participants still recognized doctors as the most trusted source by the population in contrast to social media and friends and family, which were the least trusted sources. Finally, bivariate and multivariate analyses revealed a complicated interplay between source usage, source trustworthiness, and sociodemographic factors, most in line with global and regional studies.
## References
1. Alduraywish S.A., Altamimi L.A., Aldhuwayhi R.A., AlZamil L.R., Alzeghayer L.Y., Alsaleh F.S., Aldakheel F.M., Tharkar S.. **Sources of Health Information and Their Impacts on Medical Knowledge Perception Among the Saudi Arabian Population: Cross-Sectional Study**. *J. Med. Internet Res.* (2020) **22** e14414. DOI: 10.2196/14414
2. AlGhamdi K.M., Moussa N.A.. **Internet use by the public to search for health-related information**. *Int. J. Med. Inform.* (2012) **81** 363-373. DOI: 10.1016/j.ijmedinf.2011.12.004
3. Ashkanani H., Asery R., Bokubar F., AlAli N., Mubarak S., Buabbas A., Almajran A.. **Web-Based Health Information Seeking Among Students at Kuwait University: Cross-Sectional Survey Study**. *JMIR Form. Res.* (2019) **3** e14327. DOI: 10.2196/14327
4. Baheiraei A., Khoori E., Foroushani A.R., Ahmadi F., Ybarra M.L.. **What sources do adolescents turn to for information about their health concerns?**. *Int. J. Adolesc. Med. Health* (2014) **26** 61-68. DOI: 10.1515/ijamh-2012-0112
5. Carpenter D.M., DeVellis R.F., Hogan S.L., Fisher E.B., DeVellis B.M., Jordan J.M.. **Use and Perceived Credibility of Medication Information Sources for Patients with a Rare Illness: Differences by Gender**. *J. Health Commun.* (2011) **16** 629-642. DOI: 10.1080/10810730.2011.551995
6. Charan J., Biswas T.. **How to Calculate Sample Size for Different Study Designs in Medical Research?**. *Indian J. Psychol. Med.* (2013) **35** 121-126. DOI: 10.4103/0253-7176.116232
7. Chen Y., Li C., Liang J., Tsai C.. **Health information obtained from the internet and changes in medical decision making: Questionnaire development and cross-sectional survey [serial online]**. *J. Med. Internet Res.* (2018) **20** e47. DOI: 10.2196/jmir.9370
8. Choudhury S.M., Arora T., Alebbi S., Ahmed L., Aden A., Omar O., Taheri S.. **How Do Qataris Source Health Information?**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0166250
9. Clarke M.A., Moore J.L., Steege L.M., Koopman R.J., Belden J.L., Canfield S.M., Meadows S.E., Elliott S.G., Kim M.S.. **Health information needs, sources, and barriers of primary care patients to achieve patient-centered care: A literature review**. *Health Inform. J.* (2016) **22** 992-1016. DOI: 10.1177/1460458215602939
10. Dutta-Bergman M.J.. **Health attitudes, health cognitions, and health behaviors among Internet health information seekers: Population-based survey(Electronic Version)**. *J. Med. Internet Res.* (2004) **6** e15. DOI: 10.2196/jmir.6.2.e15
11. Figueiras M.J., Ghorayeb J., Coutinho M.V.C., Marôco J., Thomas J.. **Levels of Trust in Information Sources as a Predictor of Protective Health Behaviors During COVID-19 Pandemic: A UAE Cross-Sectional Study**. *Front. Psychol.* (2021) **12** 633550. DOI: 10.3389/fpsyg.2021.633550
12. Fiksdal A.S., Kumbamu A., Jadhav A.S., Cocos C., Nelsen L.A., Pathak J., McCormick J.B.. **Evaluating the process of online health information searching: A qualitative approach to exploring consumer perspectives**. *J. Med. Internet Res.* (2014) **16** e224. DOI: 10.2196/jmir.3341
13. Ghweeba M., Lindenmeyer A., Shishi S., Abbas M., Waheed A., Amer S.. **What Predicts Online Health Information-Seeking Behavior Among Egyptian Adults? A Cross-Sectional Study**. *J. Med. Internet Res.* (2017) **19** e216. DOI: 10.2196/jmir.6855
14. Greyson D.. **Health information practices of young parents**. *J. Doc.* (2017) **73** 778-802. DOI: 10.1108/JD-07-2016-0089
15. Jacobs W., Amuta A.O., Jeon K.C.. **Health information seeking in the digital age: An analysis of health information seeking behavior among US adults**. *Cogent Soc. Sci.* (2017) **3** 1302785. DOI: 10.1080/23311886.2017.1302785
16. Jamal A., Khan S.A., AlHumud A., Al-Duhyyim A., Alrashed M., Bin Shabr F., Alteraif A., Almuziri A., Househ M., Qureshi R.. **Association of Online Health Information–Seeking Behavior and Self-Care Activities Among Type 2 Diabetic Patients in Saudi Arabia**. *J. Med. Internet Res.* (2015) **17** e196. DOI: 10.2196/jmir.4312
17. Kontos E., Blake K.D., Chou W.S., Prestin A.. **Predictors of eHealth Usage: Insights on The Digital Divide From the Health Information National Trends Survey 2012**. *J. Med. Internet Res.* (2014) **16** e172. DOI: 10.2196/jmir.3117
18. Mills A., Todorova N.. **An Integrated Perspective on Factors Influencing Online Health-Information Seeking Behaviours**. (2016)
19. Powell J., Inglis N., Ronnie J., Large S.. **The characteristics and motivations of online health information seekers: Cross-sectional survey and qualitative interview study**. *J. Med. Internet Res.* (2011) **13** e20. DOI: 10.2196/jmir.1600
20. Ramsey I., Corsini N., Peters M.D.J., Eckert M.. **A rapid review of consumer health information needs and preferences**. *Patient Educ. Couns.* (2017) **100** 1634-1642. DOI: 10.1016/j.pec.2017.04.005
21. Samara K.A., Barqawi H.J., Aboelsoud B.H., AlZaabi M.A., Alraddawi F.T., Mannaa A.A.. **Hepatitis A virus knowledge and immunization attitudes and practices in the United Arab Emirates community**. *Sci. Rep.* (2021) **11** 2651. DOI: 10.1038/s41598-020-80089-4
22. Sbaffi L., Rowley J.. **Trust and credibility in web-based health information: A review and agenda for future research**. *J. Med. Internet Res.* (2017) **19** e218. DOI: 10.2196/jmir.7579
23. Simou E.. **Health information sources: Trust and satisfaction**. *Int. J. Healthc.* (2015) **2** 38-43. DOI: 10.5430/ijh.v2n1p38
24. Sultan K., Riju Joshua V., Misra U.. **Health Information Seeking Behavior of College Students in the Sultanate of Oman**. *KUST Med. J.* (2017) **9** 8-14
25. Williams S.L., Ames K., Lawson C.. **Preferences and trust in traditional and non-traditional sources of health information—A study of middle to older aged Australian adults**. *J. Commun. Healthc.* (2019) **12** 134-142. DOI: 10.1080/17538068.2019.1642050
26. Zhao X., Fan J., Basnyat I., Hu B.. **Online Health Information Seeking Using “#COVID-19 Patient Seeking Help” on Weibo in Wuhan, China: Descriptive Study**. *J. Med. Internet Res.* (2020) **22** e22910. DOI: 10.2196/22910
27. Zhao Y., Zhang J.. **Consumer health information seeking in social media: A literature review**. *Health Inf. Libr. J.* (2017) **34** 268-283. DOI: 10.1111/hir.12192
|
---
title: Upregulation of TLR4-Dependent ATP Production Is Critical for Glaesserella
parasuis LPS-Mediated Inflammation
authors:
- Fei Liu
- Yidan Gao
- Jian Jiao
- Yuyu Zhang
- Jianda Li
- Luogang Ding
- Lin Zhang
- Zhi Chen
- Xiangbin Song
- Guiwen Yang
- Jiang Yu
- Jiaqiang Wu
journal: Cells
year: 2023
pmcid: PMC10001010
doi: 10.3390/cells12050751
license: CC BY 4.0
---
# Upregulation of TLR4-Dependent ATP Production Is Critical for Glaesserella parasuis LPS-Mediated Inflammation
## Abstract
Glaesserella parasuis (G. parasuis), an important pathogenic bacterium, cause Glässer’s disease, and has resulted in tremendous economic losses to the global swine industry. G. parasuis infection causes typical acute systemic inflammation. However, the molecular details of how the host modulates the acute inflammatory response induced by G. parasuis are largely unknown. In this study, we found that G. parasuis LZ and LPS both enhanced the mortality of PAM cells, and at the same time, the level of ATP was enhanced. LPS treatment significantly increased the expressions of IL-1β, P2X7R, NLRP3, NF-κB, p-NF-κB, and GSDMD, leading to pyroptosis. Furthermore, these proteins’ expression was enhanced following extracellular ATP further stimulation. When reduced the production of P2X7R, NF-κB-NLRP3-GSDMS inflammasome signaling pathway was inhibited, and the mortality of cells was reduced. MCC950 treatment repressed the formation of inflammasome and reduced mortality. Further exploration found that the knockdown of TLR4 significantly reduced ATP content and cell mortality, and inhibited the expression of p-NF-κB and NLRP3. These findings suggested upregulation of TLR4-dependent ATP production is critical for G. parasuis LPS-mediated inflammation, provided new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis, and offered a fresh perspective on therapeutic strategies.
## 1. Introduction
Glaesserella (Haemophilus) parasuis (G. parasuis), a gram-negative bacterial species, is the etiologic agent of pigs Glässer’s disease which is characterized by fibrinous polyserositis, polyarthritis and meningitis in pigs [1,2]. In addition, it can be a contributor to swine respiratory disease and is found as a commensal bacterium in the nasal cavity of healthy swine [3]. Recently, G. parasuis has become one of the major causes of nursery morbidity and mortality in swine herds, resulting in significant economic losses in the pig industry [4]. So far, 15 serovars of G. parasuis have been identified, but >$20\%$ of isolates have not been isolated yet [5,6]. The serovar is thought to be an important virulence marker in G. parasuis [7]. G. parasuis serovars 4, 5, and 13 are the current epidemic strains in China, according to epidemiological studies, with serovar 5 of the organism being considered to be highly virulent and serovar 4 to be moderately virulent. [ 8,9]. Therefore, managing infection brought on by G. parasuis is essential since it is one of the most significant bacterial respiratory infections in pigs. Porcine alveolar macrophages (PAMs) are regarded as a crucial line of defense against G. parasuis infection in outbreaks of Glässer’s disease [10]. PAMs release pro-inflammatory and anti-inflammatory cytokines and chemokines to draw leucocytes to the infection site after recognizing the cell structures on the surface of the bacterium, phagocytosing, and lysing it [11,12,13]. However, the factors responsible for systemic infection and inflammatory responses of G. parasuis have not yet been fully clarified. Thus, the discovery of novel regulatory factors of G. parasuis-induced inflammatory responses may be an alternative strategy for the prevention and control of Glasser’s disease in swine production systems.
Because of sickness, aging, or damage, many cells die at this certain point. Defects can impair cell development and ultimately result in a number of illnesses, such as autoimmune disorders, cancer, or infections [14]. Recently, the field of cell death has rapidly advanced, and multiple cell death pathways have been discovered, including apoptosis, necroptosis, pyroptosis, ferroptosis, and autophagy-dependent cell death. Studies have shown that a large number of effectors of cell death can regulate activation of the NOD-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome, and NLRP3 inflammasome activation can lead to cell death [15,16]. At the moment, it is widely acknowledged that ligands for Toll-like receptors (TLRs), cytokine receptors (such as the IL-1 receptor and the TNF-α receptor), or NLRs can cause the activation of the transcription factor NF-κB and boost the production of NLRP3 and pro-IL-1β [17,18]. Lipopolysaccharide (LPS) is the most abundant component within the cell wall of Gram-negative bacteria, playing a vital role in the way bacteria interact with the environment and the host. LPS can lead to an acute inflammatory response toward pathogens [19,20]. Toll-like receptor 4 (TLR4), acting as a receptor for LPS, has a pivotal role in the regulation of immune responses to infection [21]. The binding of LPS to TLR4 leads to the activation of NF-κB which plays a crucial role in regulating the transcription of genes related to innate immunity and inflammation responses in the lungs and in monocytes [22].
Trimeric, non-selective cation channels P2X receptors are triggered by extracellular ATP. Because it plays a role in the pathways of apoptosis, inflammation, and tumor growth, the P2X7 receptor subtype is a therapeutic target [23,24]. Acute immobilization stress has been shown to activate P2X7 receptors in a significant quantity of extracellular ATP, which in turn activates NLRP3 and causes the production of inflammatory cytokines [25]. The P2X7R also activates intracellular pathways unrelated to the inflammasomes but frequently associated with them in order to increase inflammation. The activation of NF-κB, a transcription factor that regulates the production of various inflammatory genes such as TNFα, COX-2, and IL-1β, is perhaps one of the best characterized [26,27].
In this research, we explore the role of the ATP/P2X7 receptor axis on G. parasuis-induced Glässer’s disease, and the contribution of NLRP3 inflammasome to this pathological process. To further investigate the underlying causative processes of Glässer’s disease, we also explored the effects of various antagonist, agonists, and pathway inhibitors on P2X7 expression and activation. Collectively, these findings could provide a novel viewpoint on treatment options for Glässer’s disease.
## 2.1. Bacterial Strain and Cell Culture
G. parasuis serovar 5 stain LZ was isolated in our lab. Bacteria were grown on Trypticase Soy Agar and in Trypticase Soy Broth, respectively (TSA and TSB; OXOID), at 37 °C with the addition of $0.01\%$ nicotinamide adenine dinucleotide (NAD) and $5\%$ (v/v) inactivated bovine serum.
The RPMI1640 medium (Solarbio, Beijing, China) containing $10\%$ fetal bovine serum (FBS) (10091148, Gibco, New Zealand) and $1\%$ pen/strep solution (Solarbio, China) was used to maintain porcine alveolar macrophages (PAM) 3D$\frac{4}{2}$ cells (ATCC: CRL-2845) at 37 °C in a $5\%$ CO2 incubator.
## 2.2. Cell Viability Assay
To determine cell viability, the Cell Counting Kit-8 (CCK-8) assay (Beyotime, Shanghai, China) was used. Briefly, in a 96-well plate, PAM cells were planted and either received G. parasuis LZ/LPS treatment or not. After 24 h, 10 μL of CCK-8 solution was added to each well and incubated at 37°C for 2 h. The absorbance at a wavelength of 450 nm was read using a microplate reader (SpectraMax® M5, Molecular Devices, San Jose, CA, USA).
## 2.3. LPS Extraction and Quantification
LPS component of G. parasuis LZ was extracted using a Lipopolysaccharide Isolation Kit (Sigma, MAK339, St. Louis, MO, USA). LPS concentrations were determined with Pierce LAL Chromogenic Endotoxin Quantitation Kit (Thermo Fisher Scientific, New York, NY, USA) following the manufacturer’s instructions. In the RPMI1640 medium, LPS was diluted to a storage concentration of 1 mg/mL.
## 2.4. EdU (5-ethynyl-2′-deoxyuridine) Incorporation Assay
The BeyoClickTM EdU Cell Prolifer-ation Kit with Alexa Fluor 555 (Beyotime Biotechnology, Haimen, China) was used to conduct cell proliferation tests in accordance with the manufacturer’s recommendations. PAM cells were treated, then incubated with 10 μm EdU for 2 h at 37 °C. Then cells were subjected to $4\%$ para-formaldehyde fixation and $0.5\%$ Triton X-100 permeabilization steps at room temperature. After the fixatives were removed, $2\%$ BSA in PBS was used to wash the cells. PAM cells were stained with DAPI and treated in Click Additive Solution while being shielded from light. In the following step, a Leica SP8 confocal microscope was used to capture the fluorescence images of the EdU inclusion samples.
## 2.5. ATP Assays
The ATP levels of infected PAM cells were detected by an Enhanced ATP Assay Kit (S0027, Beyotime Biotechnology, Shanghai, China) based on the manufacturer’s instructions. Total ATP levels of PAM cells were quantified by firefly luciferase detection using a luminometer (Tecan Infinite 200pro) and calculated the ATP concentrations (nmol/μg) were based on ATP standard curve.
## 2.6. Enzyme-Linked Immunosorbent Assay (ELISA)
The samples in the medium during cell culture were collected at 4 °C and then added to a 96-well ELISA plate. To measure releases of inflammation-related cytokines from the cells, IL-1β Porcine ELISA Kit (ESIL1B, Invitrogen, Carlsbad, CA, USA) was performed according to the instructions. The absorption value at 450 nm was read by a microplate reader (SpectraMax® M5, Molecular Devices).
## 2.7. RNA Isolation and cDNA Synthesis
24 h after cells were treated with G. parasuis LZ, total RNA was extracted using the TRIzol (Life Technologies, Grand Island, NY, USA) technique. After re-suspending whole RNA pellets in RNase-free water, RNA was measured using $\frac{260}{280}$ UV spectrophotometry. Next, potentially contaminated DNA was removed by treating the samples with DNase I (Life Technologies). Then, in a 20 μL reaction mixture, 1 μg of total RNA from each sample was reverse transcribed using a ReverTra Ace qPCR RT Kit (TOYOBO, Osaka, Japan) to produce first-strand cDNA. The cDNA was then placed in a freezer before being used.
## 2.8. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR)
qRT-PCR was performed to measure mRNA expression with the following primers (IL-1β-F: TCTGCCCTGTACCCCAACTG, IL-1β-R: CCCAGGAAGACGGGATTT; β-actin-F: TCTGGCACCACACCTTCT, β-actin-R: GATCTGGGTCATCTTCTCAC). qRT-PCR was performed with SYBR® Green Real-time PCR Master Mix (TOYOBO, Osaka, Japan). cDNA synthesized in 2.7 was used in this chapter. The following cycling circumstances existed: after a denaturation stage at 95 °C for 30 min, 40 cycles of conventional PCR are performed. Melting curve analysis was used to determine the amplified products’ specificity. The 2−ΔΔCt technique was used for quantification. The expression of β-actin mRNA, which was consistent across all samples, was used to standardize gene expression values.
## 2.9. Western Blot
By lysing the cells with ice-cold RIPA buffer supplemented with a protease inhibitor cocktail, total cellular protein lysates were produced (Merck Millipore, Darmstadt, Germany). Following BCA protein quantification, samples were run through SDS-PAGE and then transferred to PVDF membranes. Membranes were incubated with the primary antibodies for an overnight period at 4 °C and with the secondary antibodies for an hour at room temperature following blocking with $5\%$ skim milk. Then, the membrane was visualized with enhanced chemiluminescence and quantified by densitometry. All proteins were normalized to the level of β-actin. The main antibodies were mouse anti-β-actin antibodies and those against NF-κB, p-NF-κB, GSDMD, NLRP3, IL-1β, caspase1, and P2X7 receptor from Cell Signaling Technology in the United States. The secondary antibodies were goat anti-rabbit and goat anti-mouse antibodies (Beyotime, China). Image J software was used to quantify the gray values of protein bands.
## 2.10. Immunofluorescence and Imaging Analysis
PAM were plated on a laser confocal Petri dish. Following the desired treatments, cells were fixed with $4\%$ paraformaldehyde for 10 min and permeabilized with $0.25\%$ Triton X-100 at room temperature for 15 min. Cells were blocked with $5\%$ goat serum for 50 min at room temperature before being incubated with primary NF-κB antibodies (1:200) overnight at 4 °C. The cells were stained with secondary antibodies (1:400) for 1 h after being washed with PBS. All dishes were mounted after being DAPI stained to identify nuclei. All slides were then mounted with ProLongTM Gold Anti-fade mountant. A Leica SP8 confocal microscope was used to capture the immunofluorescence images.
## 2.11. Plasmids and Transfection
Plasmids, negative control (sense UUCUCCGAACGUGUCACGUTT, antisense ACGUGACACGUUCGGAGAATT) and TLR4-siRNA (sense CAG-GAAUCCUGGUCUAUAATT, antisense UUAUAGACCAGGAUUCCUGTT), are were synthesized by Sangon (China). LipofectamineTM 3000 (Invitrogen, Carlsbad, CA, USA), transfections were carried out in accordance with the manufacturer’s instructions. In a nutshell, PAM cells were plated in six wells and transfected with 1 mg of plasmid when they were 30–$50\%$ confluent. After 24 h of incubation, cells were treated with LPS for further expression.
## 2.12. Plasmids and Transfection
Statistical Analysis: The reported results were statistically evaluated using the paired Student’s t-test method and comparisons between more than two groups were obtained using ANOVA. The reported values are expressed as mean standard errors (SEM). The graphs were plotted using GraphPad Prism version 7.0 (GraphPad Software, La Jolla, CA, USA). Asterisks were used to denote significant values (* $p \leq 0.05$ and ** $p \leq 0.001$), whereas ns values ($p \leq 0.05$) were used to denote non-significant values. Each experiment included at least three replicates.
## 3.1. G. parasuis LPS Enhanced the Mortality and the ATP Level of PAM Cells
We first examined the effect of G. parasuis on the viability of PAM cells. PAM cells were treated with G. parasuis LZ at MOI = 10 for 8 h. Compared with the mock group, the viability of PAM cells in the G. parasuis LZ group was lower (** $p \leq 0.01$) (Figure 1A). As well, the LPS of G. parasuis LZ also resulted in the cell viability decreases when compared with the mock group (** $p \leq 0.01$) (Figure 1B). To further investigate the effect of G. parasuis LZ and LPS on PAM proliferation, EdU staining was utilized. Results of the EdU staining showed that red fluorescence which represents proliferating PAM cells is significantly inhibited by G. parasuis LZ and LPS compared with the mock group (** $p \leq 0.01$) (Figure 1C,D). Extracellular ATP causes the cell membrane to become permeable and induces changes within the cell that could lead to apoptosis [27]. We test the level of extracellular ATP, and found that G. parasuis LZ and LPS significantly enhanced ATP levels (** $p \leq 0.01$) (Figure 1E,F). These results suggested that LPS-enhanced mortality may have a relationship with elevated extracellular ATP levels, and LPS may play a key role in the pathogenesis of G. parasuis.
## 3.2. ATP-Induced Pyroptosis and Activated P2X7R Pathway
Although most of the ATP is located intracellularly, it is released into the extracellular space under specific conditions, where it is a relevant signaling molecule. It activates P2X7 and increases inflammatory cytokine levels [28]. So we hypothesized that LPS could induce cellular inflammation by releasing ATP. In order to test it, we regulated the concentration of extracellular ATP in different ways, then observed the effect on IL-1β. The expression of IL-1β in the ATP-added group was higher than G. parasuis LZ only group (** $p \leq 0.01$) (Figure 2A). Nigericin (similar to ATP) also enhanced the expression of IL-1β. While apyrase (a highly active ATP-diphosphohydrolase) reduced the enhanced IL-1β level (** $p \leq 0.01$) (Figure 2A). As well, in Figure 2B, similar results were shown. We also test the mRNA level of IL-1β, and the results were consistent with Figure 2B. As shown in Figure 2D, LPS accelerated the expressions of P2X7R and NLRP3, and Nigericin further increase the expressions (** $p \leq 0.01$). We also tested the expressions of NF-κB and p-NF-κB, and found that NF-κB was activated by LPS (** $p \leq 0.01$), and Nigericin enhanced the expression (* $p \leq 0.05$). These results revealed that LPS-induced release of ATP-activated inflammation.
Physiological roles for GSDMD in both pyroptosis and IL-1β release during inflammasome signaling have been extensively characterized in macrophages and other mononuclear leukocytes. Assembly of N-GSDMD pores in the plasma membrane markedly increases its permeability to macromolecules, metabolites, ions, and major osmolytes, resulting in the rapid collapse of cellular integrity to facilitate pyroptosis [29]. As well, in this study, LPS treatment prominently increased the expression of N-GSDMD (** $p \leq 0.01$) (Figure 2D), Nigericin further increased the expression of N-GSDMD (* $p \leq 0.05$) which meant that pyroptosis was activated. All these results suggested that ATP-induced pyroptosis was through ATP/P2X7R pathway.
## 3.3. LPS-Induced Pyroptosis through Activated P2X7R Pathway
To further explore the relationship between P2X7R and pyroptosis, we used 10 μM A740003 (P2X Receptor Antagonist) to treat PAM cells. First, we tested the expression of P2X7R, and found that LPS-enhanced P2X7R was inhabited by A740003. This result meant A740003 worked very well (* $p \leq 0.05$) (Figure 3A). Then the expression of NLRP3 was observed, A740003 also reduced NLRP3 level significantly (* $p \leq 0.05$) (Figure 3A), P2X7R was involved in LPS-induced pyroptosis. As well, A740003 inhibited the expression of NF-κB and p-NF-κB compared with cells infected with the LPS group (** $p \leq 0.01$), meaning that NF-κB may be downstream of P2X7R in this study. When treated with A740003, the level of N-GSDMD was reduced compared with LPS-only group (* $p \leq 0.05$) (Figure 3B). As well, the level of f IL-1β showed the same result (Figure 3C). We tested A740003 influence on the PAM cells’ survival rate, and found that LPS increases the mortality of PAM cells, when treated with A740003, the mortality decreased (* $p \leq 0.05$). According to the results of immunofluorescence, NF-κB p65 expression was elevated and more protein entered into the nucleus. These results indicated that the P2X7R pathway plays a central role in the pathogenesis of G. parasuis.
## 3.4. NLRP3 Was Involved in the Formation of Inflammation
To better verify the role of the formation of inflammation in cell death, MCC950 (a potent and specific inhibitor of the NLRP3 inflammasome) was utilized in this study. First, we treated cells with different concentrations of MCC950, then observed the expression of NLRP3. Compared with the LPS group, MCC950 markedly reduced the expression of NLRP3 in a concentration-dependent manner (** $p \leq 0.01$) (Figure 4A). We also detected the expression of caspase 1, showing the same rule (Figure 4A). Subsequently, we tested the level of GSDMD. Compared with the LPS group, MCC950 could significantly reduce the expression of GSDMD (** $p \leq 0.01$) (Figure 4B). Then we tested the content of IL-1β in the culture medium by ELISA, and found that MCC950 also significantly reduced the secretion of IL-1β (** $p \leq 0.01$) (Figure 4C). Finally, the cell survival rate was measured by CCK8, and data showed MCC950 could significantly reduce the cell mortality rate that was increased by LPS (** $p \leq 0.01$). These results suggested that the formation of inflammasome bodies plays a key role in G. parasuis infection.
## 3.5. LPS Induced Inflammation in a TLR4-Dependent Manner
Toll-like receptor 4 (TLR4), acting as a receptor for LPS, has a pivotal role in the regulation of immune responses to infection [21]. The binding of LPS to TLR4 leads to the activation of NF-κB which plays a crucial role in regulating the transcription of genes related to innate immunity and inflammation responses in the lungs and in monocytes [22]. To prove that TLR4 plays an important role in G. parasuis infection, we used miRNA silencing technology to verify it. First, we tested the silence efficiency of siRNA and found that the siRNA significantly reduced the mRNA level of TLR4 (** $p \leq 0.01$), meaning that this siRNA worked well (Figure 5A). Then we observed the effect of TLR4 on ATP levels. Compared with the negative control group, we found that after silencing TLR4, ATP level decreased significantly (** $p \leq 0.01$) (Figure 5B). In addition, silencing TLR4 significantly restored cell death caused by LPS (** $p \leq 0.01$) (Figure 5C). Then, we detected the influence of TLR4 on the downstream inflammatory pathway, and found that the expressions of p-NF-κB and NLRP3 decreased, and TLR4 knockout decreased the activation of the NLRP3 inflammasome (** $p \leq 0.01$) (Figure 5D). These data evidently suggest that LPS induced inflammation in a TLR4-dependent manner.
## 4. Discussion
G. parasuis is the source of Glässer’s disease, which can lead to acute septicemia in non-immune high-health status pigs of all ages and cause instances of arthritis, fibrinous polyserositis, severe pneumonia, and meningitis in piglets worldwide [30]. In this research, we explored the role of the ATP/P2X7 receptor axis on G. parasuis-induced Glässer’s disease, and the contribution of NLRP3 inflammasome to this pathological process.
Bacterial lipopolysaccharides (LPS) are the major outer surface membrane components present in almost all Gram-negative bacteria and act as extremely strong stimulators of innate or natural immunity in diverse eukaryotic species ranging from insects to humans [31,32]. No matter the kind of bacteria involved or the infection location, bacterial adaptation alterations, such as modification of LPS production and structure, are a common motif in infections [33,34]. Generally speaking, these modifications cause the immune system to evade detection, persistent inflammation, and enhanced antimicrobial resistance [35]. LPS derived from *Escherichia coli* (E. coli) is a well-characterized inducer of inflammatory response in vivo that activates cytokine expression via NF-κB and MAPK signaling pathway in a TLR4-dependent manner [36]. According to studies, pseudomonas aeruginosa (P. aeruginosa) LPS changes appear to be a key element in this pathogen’s ability to adapt to chronic infection. Over the duration of the chronic P. aeruginosa infection, decreased LPS immunostimulatory potential helps the immune system avoid detection and survive [37]. It has been reported that anti-LPS antibodies can protect against mortality caused by hematogenous *Haemophilus influenzae* type b meningitis infections in infant rats [38]. In this study, we found that G. parasuis LZ induced cells death and severe inflammation in PAM cells (Figure 1A and Figure 3A), and LPS derived from G. parasuis LZ treatment group also has similar phenomena, these suggested that G. parasuis LPS plays a key role in host-pathogen interactions with the innate immune system.
Pyroptosis is an inflammatory form of cell death that is brought on by certain inflammasomes [39,40]. This kind of cell death causes the cleavage of gasdermin D (GSDMD) and the activation of dormant cytokines like IL-18 and IL-1β. Cell enlargement, lysis of the plasma membrane, fragmentation of the chromatin, and release of the pro-inflammatory substances inside the cell are all effects of pyroptosis [41]. The conventional inflammasome pathway, a noncanonical inflammasome pathway, and a newly discovered pathway are the pathways that cause pyroptosis [42,43]. Caspase-11 may selectively attach to the lipid A of intracellular LPS, which causes it to oligomerize, engage its proteolytic activity, and cleave the GSDMD to create a large number of holes in the cell membrane, ultimately causing membrane lysis and pyroptosis [44]. As well, the extracellular LPS stimulation of neutrophils can also activate the TLR4-P38-Cx43 pathway to autocrine ATP extracellularly [45]. The extracellular ATP could gather NLRP3 inflammasomes and subsequently activate the pro-caspase 1 through the P2X7 pathway, resulting in pyroptosis [46]. In this study, we found that G. parasuis LZ LPS induced cell death and promoted the increase of ATP content, thus activating the P2X7 pathway, promoting the development of IL-1β, and cleavage of GSDMD, leading to pyroptosis. This is consistent with the canonical inflammasome pathway. Luo et al. have reported that G. parasuis induces an inflammatory response in PAM cells through the activation of the NLRP3 inflammasome signaling pathway [30], which is consistent with our result.
G. parasuis, an opportunistic pathogen of the lower respiratory tract of pigs, is also associated with pneumonia and is involved in the porcine respiratory disease complex [47]. Secondary G. parasuis infection enhances highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) infection-mediated inflammatory responses [48]. The polarization of LPS-stimulated PAMs toward M1 PAMs greatly reduces PRRSV replication [49], mainly because LPS reduced the level of CD163 expression to inhibit PRRSV infection via TLR4-NF-κB pathway [30]. In this study G. parasuis LPS activated inflammatory responses through TLR4-NF-κB pathway, and combined with the above reference, we got the hypothesis that G. parasuis infection can significantly inhibit PRRSV replication through downregulation of CD163 expression via TLR4-NF-κB pathway. However, this hypothesis needs further verification.
In conclusion, G. parasuis induced PAM cell damage mainly through included pro-inflammatory and pro-pyroptosis events. The NLRP3 inflammasome in PAM cells plays a crucial role in G. parasuis-induced cells death and both TLR4- and P2X7R-dependent pathways are alternative signaling pathways required for NLRP3 inflammasome activation during the development of G. parasuis-induced Glässer’s disease. This work provides new insights into the molecular pathways underlying the inflammatory response induced by G. parasuis and a new perspective to inform the targeted treatment of G. parasuis-induced Glässer’s disease.
## References
1. Oliveira S., Pijoan C.. *Vet. Microbiol.* (2004) **99** 1-12. DOI: 10.1016/j.vetmic.2003.12.001
2. Ni H.B., Gong Q.L., Zhao Q., Li X.Y., Zhang X.X.. **Prevalence of**. *Prev. Vet. Med.* (2020) **182** 105083. DOI: 10.1016/j.prevetmed.2020.105083
3. Zhang P., Zhang C., Aragon V., Zhou X., Zou M., Wu C., Shen Z.. **Investigation of**. *Vet. Microbiol.* (2019) **231** 40-44. DOI: 10.1016/j.vetmic.2019.02.034
4. Matiaskova K., Kavanova L., Kulich P., Gebauer J., Nedbalcova K., Kudlackova H., Tesarik R., Faldyna M.. **The Role of Antibodies Against the Crude Capsular Extract in the Immune Response of Porcine Alveolar Macrophages to in Vitro Infection of Various Serovars of**. *Front. Immunol.* (2021) **12** 635097. DOI: 10.3389/fimmu.2021.635097
5. Galofre-Mila N., Correa-Fiz F., Lacouture S., Gottschalk M., Strutzberg-Minder K., Bensaid A., Pina-Pedrero S., Aragon V.. **A robust PCR for the differentiation of potential virulent strains of**. *BMC Vet. Res.* (2017) **13**. DOI: 10.1186/s12917-017-1041-4
6. Fu S., Guo J., Li R., Qiu Y., Ye C., Liu Y., Wu Z., Guo L., Hou Y., Hu C.A.. **Transcriptional Profiling of Host Cell Responses to Virulent**. *Int. J. Mol. Sci.* (2018) **19**. DOI: 10.3390/ijms19051320
7. Kielstein P., Rapp-Gabrielson V.J.. **Designation of 15 serovars of**. *J. Clin. Microbiol.* (1992) **30** 862-865. DOI: 10.1128/jcm.30.4.862-865.1992
8. Fu S., Yin R., Zuo S., Liu J., Zhang Y., Guo L., Qiu Y., Ye C., Liu Y., Wu Z.. **The effects of baicalin on piglets challenged with Glaesserella parasuis**. *Vet. Res.* (2020) **51** 102. DOI: 10.1186/s13567-020-00826-5
9. Correa-Fiz F., Galofre-Mila N., Costa-Hurtado M., Aragon V.. **Identification of a surface epitope specific of virulent strains of**. *Vet. Microbiol.* (2017) **198** 116-120. DOI: 10.1016/j.vetmic.2016.12.015
10. Olvera A., Ballester M., Nofrarias M., Sibila M., Aragon V.. **Differences in phagocytosis susceptibility in**. *Vet. Res.* (2009) **40** 24. DOI: 10.1051/vetres/2009007
11. Wang Y., Liu C., Fang Y., Liu X., Li W., Liu S., Liu Y., Liu Y., Charreyre C., Audonnet J.C.. **Transcription analysis on response of porcine alveolar macrophages to**. *BMC Genom.* (2012) **13**. DOI: 10.1186/1471-2164-13-68
12. He R., Hua K., Zhang S., Wan Y., Gong H., Ma B., Luo R., Zhou R., Jin H.. **COX-2 mediated crosstalk between Wnt/beta-catenin and the NF-kappaB signaling pathway during inflammatory responses induced by**. *Dev. Comp. Immunol.* (2020) **105** 103588. DOI: 10.1016/j.dci.2019.103588
13. Macedo N., Rovira A., Torremorell M.. *Vet. Res.* (2015) **46** 128. DOI: 10.1186/s13567-015-0263-3
14. Crowley L.C., Marfell B.J., Scott A.P., Boughaba J.A., Chojnowski G., Christensen M.E., Waterhouse N.J.. **Dead Cert: Measuring Cell Death**. *Cold Spring Harb. Protoc.* (2016) **2016** pdb-top070318. DOI: 10.1101/pdb.top070318
15. Gaidt M.M., Hornung V.. **The NLRP3 Inflammasome Renders Cell Death Pro-inflammatory**. *J. Mol. Biol.* (2018) **430** 133-141. DOI: 10.1016/j.jmb.2017.11.013
16. Zheng M., Williams E.P., Malireddi R.K.S., Karki R., Banoth B., Burton A., Webby R., Channappanavar R., Jonsson C.B., Kanneganti T.D.. **Impaired NLRP3 inflammasome activation/pyroptosis leads to robust inflammatory cell death via caspase-8/RIPK3 during coronavirus infection**. *J. Biol. Chem.* (2020) **295** 14040-14052. DOI: 10.1074/jbc.RA120.015036
17. Xing Y., Yao X., Li H., Xue G., Guo Q., Yang G., An L., Zhang Y., Meng G.. **Cutting Edge: TRAF6 Mediates TLR/IL-1R Signaling-Induced Nontranscriptional Priming of the NLRP3 Inflammasome**. *J. Immunol.* (2017) **199** 1561-1566. DOI: 10.4049/jimmunol.1700175
18. Lin K.M., Hu W., Troutman T.D., Jennings M., Brewer T., Li X., Nanda S., Cohen P., Thomas J.A., Pasare C.. **IRAK-1 bypasses priming and directly links TLRs to rapid NLRP3 inflammasome activation**. *Proc. Natl. Acad. Sci. USA* (2014) **111** 775-780. DOI: 10.1073/pnas.1320294111
19. Gram A., Kowalewski M.P.. **Molecular Mechanisms of Lipopolysaccharide (LPS) Induced Inflammation in an Immortalized Ovine Luteal Endothelial Cell Line (OLENDO)**. *Vet. Sci.* (2022) **9**. DOI: 10.3390/vetsci9030099
20. Haziak K., Herman A.P., Wojtulewicz K., Pawlina B., Paczesna K., Bochenek J., Tomaszewska-Zaremba D.. **Effect of CD14/TLR4 antagonist on GnRH/LH secretion in ewe during central inflammation induced by intracerebroventricular administration of LPS**. *J. Anim. Sci. Biotechnol.* (2018) **9** 52. DOI: 10.1186/s40104-018-0267-8
21. Ciesielska A., Matyjek M., Kwiatkowska K.. **TLR4 and CD14 trafficking and its influence on LPS-induced pro-inflammatory signaling**. *Cell. Mol. Life Sci.* (2021) **78** 1233-1261. DOI: 10.1007/s00018-020-03656-y
22. Meyers A.K., Zhu X.. **The NLRP3 Inflammasome: Metabolic Regulation and Contribution to Inflammaging**. *Cells* (2020) **9**. DOI: 10.3390/cells9081808
23. Di Virgilio F., Dal Ben D., Sarti A.C., Giuliani A.L., Falzoni S.. **The P2X7 Receptor in Infection and Inflammation**. *Immunity* (2017) **47** 15-31. DOI: 10.1016/j.immuni.2017.06.020
24. Cai X., Yao Y., Teng F., Li Y., Wu L., Yan W., Lin N.. **The role of P2X7 receptor in infection and metabolism: Based on inflammation and immunity**. *Int. Immunopharmacol.* (2021) **101** 108297. DOI: 10.1016/j.intimp.2021.108297
25. Wang D., Wang H., Gao H., Zhang H., Zhang H., Wang Q., Sun Z.. **P2X7 receptor mediates NLRP3 inflammasome activation in depression and diabetes**. *Cell Biosci.* (2020) **10** 28. DOI: 10.1186/s13578-020-00388-1
26. Genetos D.C., Karin N.J., Geist D.J., Donahue H.J., Duncan R.L.. **Purinergic signaling is required for fluid shear stress-induced NF-kappaB translocation in osteoblasts**. *Exp. Cell Res.* (2011) **317** 737-744. DOI: 10.1016/j.yexcr.2011.01.007
27. Adinolfi E., Giuliani A.L., De Marchi E., Pegoraro A., Orioli E., Di Virgilio F.. **The P2X7 receptor: A main player in inflammation**. *Biochem. Pharmacol.* (2018) **151** 234-244. DOI: 10.1016/j.bcp.2017.12.021
28. Dosch M., Gerber J., Jebbawi F., Beldi G.. **Mechanisms of ATP Release by Inflammatory Cells**. *Int. J. Mol. Sci.* (2018) **19**. DOI: 10.3390/ijms19041222
29. Karmakar M., Minns M., Greenberg E.N., Diaz-Aponte J., Pestonjamasp K., Johnson J.L., Rathkey J.K., Abbott D.W., Wang K., Shao F.. **N-GSDMD trafficking to neutrophil organelles facilitates IL-1beta release independently of plasma membrane pores and pyroptosis**. *Nat. Commun.* (2020) **11** 2212. DOI: 10.1038/s41467-020-16043-9
30. Luo X., Chang X., Zhou H., Lin H., Fan H.. *Vet. Microbiol.* (2021) **256** 109057. DOI: 10.1016/j.vetmic.2021.109057
31. Whitfield C., Trent M.S.. **Biosynthesis and export of bacterial lipopolysaccharides**. *Annu. Rev. Biochem.* (2014) **83** 99-128. DOI: 10.1146/annurev-biochem-060713-035600
32. Alexander C., Rietschel E.T.. **Bacterial lipopolysaccharides and innate immunity**. *J. Endotoxin Res.* (2001) **7** 167-202. DOI: 10.1179/096805101101532675
33. Maldonado R.F., Sa-Correia I., Valvano M.A.. **Lipopolysaccharide modification in Gram-negative bacteria during chronic infection**. *FEMS Microbiol. Rev.* (2016) **40** 480-493. DOI: 10.1093/femsre/fuw007
34. Rathinam V.A.K., Zhao Y., Shao F.. **Innate immunity to intracellular LPS**. *Nat. Immunol.* (2019) **20** 527-533. DOI: 10.1038/s41590-019-0368-3
35. Ciofu O., Tolker-Nielsen T., Jensen P.O., Wang H., Hoiby N.. **Antimicrobial resistance, respiratory tract infections and role of biofilms in lung infections in cystic fibrosis patients**. *Adv. Drug Deliv. Rev.* (2015) **85** 7-23. DOI: 10.1016/j.addr.2014.11.017
36. Zhao C., Yang X., Su E.M., Huang Y., Li L., Matthay M.A., Su X.. **Signals of vagal circuits engaging with AKT1 in alpha7 nAChR(+) CD11b(+) cells lessen**. *Cell Discov.* (2017) **3** 17009. DOI: 10.1038/celldisc.2017.9
37. Oberhardt M.A., Goldberg J.B., Hogardt M., Papin J.A.. **Metabolic network analysis of**. *J. Bacteriol.* (2010) **192** 5534-5548. DOI: 10.1128/JB.00900-10
38. Borrelli S., Diab A., Lindberg A., Svanborg C.. **Monoclonal anti-LPS inner core antibodies protect against experimental hematogenous**. *Microb. Pathog.* (2000) **28** 1-8. DOI: 10.1006/mpat.1999.0318
39. Shi J., Gao W., Shao F.. **Pyroptosis: Gasdermin-Mediated Programmed Necrotic Cell Death**. *Trends Biochem. Sci.* (2017) **42** 245-254. DOI: 10.1016/j.tibs.2016.10.004
40. Fernandes-Alnemri T., Wu J., Yu J.W., Datta P., Miller B., Jankowski W., Rosenberg S., Zhang J., Alnemri E.S.. **The pyroptosome: A supramolecular assembly of ASC dimers mediating inflammatory cell death via caspase-1 activation**. *Cell Death Differ.* (2007) **14** 1590-1604. DOI: 10.1038/sj.cdd.4402194
41. Fang Y., Tian S., Pan Y., Li W., Wang Q., Tang Y., Yu T., Wu X., Shi Y., Ma P.. **Pyroptosis: A new frontier in cancer**. *Biomed. Pharmacother.* (2020) **121** 109595. DOI: 10.1016/j.biopha.2019.109595
42. Jorgensen I., Miao E.A.. **Pyroptotic cell death defends against intracellular pathogens**. *Immunol. Rev.* (2015) **265** 130-142. DOI: 10.1111/imr.12287
43. Xu Y.J., Zheng L., Hu Y.W., Wang Q.. **Pyroptosis and its relationship to atherosclerosis**. *Clin. Chim. Acta* (2018) **476** 28-37. DOI: 10.1016/j.cca.2017.11.005
44. Chen X., He W.T., Hu L., Li J., Fang Y., Wang X., Xu X., Wang Z., Huang K., Han J.. **Pyroptosis is driven by non-selective gasdermin-D pore and its morphology is different from MLKL channel-mediated necroptosis**. *Cell Res.* (2016) **26** 1007-1020. DOI: 10.1038/cr.2016.100
45. Wang X., Qin W., Xu X., Xiong Y., Zhang Y., Zhang H., Sun B.. **Endotoxin-induced autocrine ATP signaling inhibits neutrophil chemotaxis through enhancing myosin light chain phosphorylation**. *Proc. Natl. Acad. Sci.USA* (2017) **114** 4483-4488. DOI: 10.1073/pnas.1616752114
46. Derangere V., Chevriaux A., Courtaut F., Bruchard M., Berger H., Chalmin F., Causse S.Z., Limagne E., Vegran F., Ladoire S.. **Liver X receptor beta activation induces pyroptosis of human and murine colon cancer cells**. *Cell Death Differ.* (2014) **21** 1914-1924. DOI: 10.1038/cdd.2014.117
47. Schuwerk L., Hoeltig D., Waldmann K.H., Strutzberg-Minder K., Valentin-Weigand P., Rohde J.. **Serotyping and pathotyping of**. *Vet. Res.* (2020) **51** 137. DOI: 10.1186/s13567-020-00862-1
48. Li J., Wang S., Li C., Wang C., Liu Y., Wang G., He X., Hu L., Liu Y., Cui M.. **Secondary**. *Vet. Microbiol.* (2017) **204** 35-42. DOI: 10.1016/j.vetmic.2017.03.035
49. Wang L., Hu S., Liu Q., Li Y., Xu L., Zhang Z., Cai X., He X.. **Porcine alveolar macrophage polarization is involved in inhibition of porcine reproductive and respiratory syndrome virus (PRRSV) replication**. *J. Vet. Med. Sci.* (2017) **79** 1906-1915. DOI: 10.1292/jvms.17-0258
|
---
title: The Characteristics of Sentinel Lymph Node Biopsy in Cutaneous Melanoma and
the Particularities for Elderly Patients—Experience of a Single Clinic
authors:
- Florin Bobircă
- Tiberiu Tebeică
- Adela Pumnea
- Dan Dumitrescu
- Cristina Alexandru
- Laura Banciu
- Ionela Loredana Popa
- Anca Bobircă
- Mihaela Leventer
- Traian Pătrașcu
journal: Diagnostics
year: 2023
pmcid: PMC10001011
doi: 10.3390/diagnostics13050926
license: CC BY 4.0
---
# The Characteristics of Sentinel Lymph Node Biopsy in Cutaneous Melanoma and the Particularities for Elderly Patients—Experience of a Single Clinic
## Abstract
Background: *Melanoma is* a malignant tumor that determines approximately $80\%$ of deaths as skin cancer-related. The sentinel lymph node (SLN) represents the first filter of tumor cells toward systemic dissemination. The primary objective was to outline the surgical specifics of the sentinel lymph node biopsy (SLNB) technique, correlate the location of the lymph node with the radiotracer load, and identify the characteristics of older patients. Methods: *In this* prospective study, 122 cases of malignant melanoma needing SLNB technique were included, between June 2019 and November 2022, resulting in 162 lymph nodes removed. Results: Patients’ mean age was 54.3 ± 14.4 years old, the prevalence of 70 years and older being $20.5\%$. The rate of positive SLN was $24.6\%$, with a single drainage in $68.9\%$ of cases. The frequency of seroma was $14.8\%$, while reintervention $1.6\%$. The inguinal nodes had the highest preoperative radiotracer load ($$p \leq 0.015$$). Patients 70 years old or older had significantly more advanced-stage melanoma ($68.0\%$ vs. $45.4\%$, $$p \leq 0.044$$, OR = 2.56) and a higher rate of positive SLN ($40.0\%$ vs. $20.6\%$, $$p \leq 0.045$$,OR = 2.57). Melanoma of the head and neck was more common among older individuals ($32.0\%$ vs. $9.3\%$, $$p \leq 0.007$$,OR = 4.60). Conclusions: The SLNB has a low rate of surgical complications and the positivity of SLN is not related to radiotracer load. Elderly patients are at risk for head and neck melanoma, have more advanced stages, a higher SLN positivity, and a greater rate of surgical complications.
## 1. Introduction
Melanoma is a malignant tumor originating in the melanocytic cells of the skin, and less frequently in the melanocytes of the eyes, mucous membranes, and meninges [1]. Although it represents approximately $1\%$ of all skin tumors, melanoma determines approximately $80\%$ of deaths caused by skin cancer [2]. According to European Cancer Information System, it is estimated that cutaneous melanoma represents $4\%$ of all new cancer diagnosed in 2020 (excluding non-melanoma skin tumors) and $1.3\%$ of all deaths caused by cancer [3].
Considering the increasing trend in the incidence of melanoma at all ages, in the next decade, the implementation of effective methods of prevention and early detection of the disease will be mandatory. Furthermore, it is well known that, not just for melanoma, but for all types of cancer, early initiation of therapy in advanced patients minimizes mortality and morbidity [4,5]. Disease staging, according to the eighth edition of the American Joint Committee on Cancer, followed by appropriate management are important factors influencing the survival rate of melanoma patients [6,7].
In the metastasis process, the primary site affected by cutaneous melanoma is represented by the regional lymph nodes, their evaluation to identify macrometastases detected clinically/imaging or micrometastases by sentinel lymph node biopsy (SLNB) is the most important prognostic factor in the early stages of the disease [8].
The sentinel lymph node (SLN) represents the first drainage station of the primary tumor, therefore, the first filter of tumor cells toward systemic dissemination [9]. In the past, regional lymph node dissection (Figure 1) was used to confirm the presence of local lymph node metastases, burdened by long-term and short-term complications. Based on the model of lymphatic extension of tumor cells, in the 1990s, Morton et al. developed the sentinel node biopsy, a less invasive technique for evaluating the condition of local lymph nodes. The sentinel lymph node biopsy has evolved into a staging technique for patients without clinically detected node metastases or through imaging. In this manner, unnecessary nodal dissections, burdened by high morbidity, are avoided, especially for those patients with multiple comorbidities such as diabetes mellitus and cardiovascular disease [1,10]. As a result, SLNB has been approved as a technique and included in the TNM (tumor, node, metastasis) staging classification for cutaneous melanoma [11].
According to the ASCO-SSO (American Society of Clinical Oncology—Society of Surgical Oncology) guideline for SLNB in melanoma, and ESMO (European Society for Medical Oncology), sentinel lymph node biopsy is recommended for patients in stage IB/II of the disease with the following criteria: Thin melanomas: 0.8 to 1.0 mm Breslow thickness with or without ulceration or <0.8 mm Breslow thickness with ulceration (T1b stage) after a thorough discussion with the patient of the potential benefits and risk of harms associated with the procedure (should be considered).
Melanomas with intermediate thickness: Breslow thickness of >1.0 to 4.0 mm (T2/T3 stages) (should be recommended).
Thick melanomas: 4.0 mm in Breslow thickness (T4 stage) after a thorough discussion with the patient of the potential benefits and risk of harms associated with the procedure (should be considered) [7].
The sentinel lymph node biopsy is necessary when it is impossible to acquire the tumor thickness after a superficial biopsy or after the lesion has undergone previous cryotherapy or electrodesiccation [12].
Over time, sentinel lymph node biopsy, initially associated with complete lymph node dissection, has been studied and applied in the case of skin and mucous tumors of the head and neck. The biopsy of the sentinel node of melanomas in these cases was controversial due to the complex architecture of the lymphatics at the head and neck level. The main concern was the multitude of lymph nodes that drain the primary tumor from the head and neck, causing less predictable lymphatic drainage, thus a change in the accuracy of SLNB [13]. Currently, this can be corrected by using imaging techniques (Figure 2) such as cross-sectional X-ray computed tomography (CT) and single photon emission computed tomography (SPECT) to accurately detect the radiotraced lymph node [14]. Another disadvantage of this topography is the presence of numerous vital vascular structures and cranial nerves which may compromise the procedure’s safety [13].
Another important issue regarding the particularities of SLNB is related to the patient’s age, as it is known that melanoma is frequently diagnosed in the elderly. In the age group 70 and older, the rate of positive SLN is higher than in the rest of the population, head, and neck site of the melanoma is the region most frequently affected, and the tumors are usually in advanced stages (T3/T4). Only after the positivity of the SLN, did the oncologists take into account the immune therapy, considered escaped therapy, and graft multiple possible complications [15,16].
The elderly should be the main focus of secondary melanoma prevention, that is, early diagnosis and screening to reduce mortality. Older people are more likely to develop and die from melanoma. The elderly may also have fewer treatment options because they may be less able to endure drug side effects, are more likely to have drug interactions, or may be excluded from clinical trials due to age eligibility requirements [17].
Sentinel lymph node biopsy represents the standard of care in the management of early stage melanoma patients. According to NCCN, depending on the subclinical micrometastatic disease in the SLN, 5–$40\%$ of patients who perform SLNB will be upstaged to pathologic stage III. Considering these, the patient must be informed about future management options, including imaging tests, adjuvant therapy, clinical trial enrollment, the requirement for a complete lymph node dissection (CLND), and routine follow-up [18,19,20,21,22].
The main endpoint of this study was to monitor the surgical particularities of the sentinel lymph node technique among melanoma patients. Secondary endpoints included the correlation of the lymph node’s location with the radiotracer load, with positivity, and the identification of significant differences related to the diagnosis of melanoma at an advanced age (70 years and older).
## 2. Materials and Methods
We conducted a longitudinal, prospective cohort study that involved the follow-up of 122 cases of malignant melanoma with an indication for the sentinel lymph node technique who underwent surgery in a private dermatology clinic in Bucharest (Dr. Leventer Centre) between June 2019 and November 2022, resulting in a total of 162 lymph nodes.
Inclusion criteria were patients older than 18 years old, diagnosis of melanoma with Breslow index >= 0.8 mm, lack of lymph node(s) or organ metastatic involvement (clinical and imaging), and patients within <= 6 weeks of diagnosis by excisional biopsy.
Exclusion criteria were as follows: patients < 18 years old, Breslow index < 0.8 mm, contraindication of surgery as a result of the pre-anesthetic evaluation, palpable peripheral lymph node(s), organ metastases (MRI or CT scan) and patients that had no detection of sentinel lymph nodes on lymphoscintigraphy. All patients gave written informed consent and ethical approval was obtained.
Surgery was performed within an interval of up to 6 h from lymphoscintigraphy in all patients.
The surgical strategy needed an interdisciplinary operative team, which included, in addition to the oncological surgeon, the plastic and the buco-maxillo-facial surgeon, as determined by the location of the melanoma, according to Breslow, the outcome of the preoperative lymphoscintigraphy, the indication connected to the oncological safety margins (1 or 2 cm) and the patient’s request. All patients received the combination strategy of detecting the sentinel lymph node with Tc99 and vital dye injection (methylene blue).
Demographic data were registered using a questionnaire fulfilled by all patients. Monitored variables were the following: location of melanomas, and lymph node(s) surgically removed, positivity of the lymph node(s) (positive or negative, IHC testing), BRAF gene mutation, Breslow index, tumor stage, tumor location, serum S100 protein testing, interval (days) between diagnostic excisional biopsy and performing the sentinel lymph node technique, issues related to lymphoscintigraphy (number of draining lymphatic basins, number of sentinel nodes, radioactive load at melanoma’s scaring site, but also preoperative radiotracer load and intraoperative and ex vivo sentinel lymph node radiotracer load). Additionally, data regarding the presence of personal cardio-vascular history, use of anticoagulant or antiaggregant therapy, oncological history, type of anesthesia, pre- and postoperative prophylactic antibiotic therapy, duration of surgery—node + excision with safety margins (cm), reinterventions, intraoperative complications, appearance of seromas, duration (days) until wound healing were monitored.
## Sentinel Lymph Node Surgical Technique
The sentinel lymph node biopsy is a minimally invasive surgical technique that aims to detect and remove the first lymph node(s), from the lymphatic basin(s) that drains the melanoma area. Prior to the re-excision with oncological margins of the scar, SLNB is completed within 4–6 weeks after the excisional biopsy [18].
A radiotracer, 0.5–1.0 mCi radio-colloidal Technetium Tc-99m, is injected intradermally in 4–5 spots surrounding the scar, at a maximum distance of 1 cm from this, preoperatively, on the day of the intervention or 24 h before. The nuclear medicine department performs lymphoscintigraphy as the following step to identify lymphatic drainage. Additional images obtained using single photon emission computed tomography (SPECT) may be necessary for certain anatomical regions, such as the cephalic extremity or the pelvic region, to identify the sentinel lymph node [23,24]. Methylene blue can be injected intradermally at the level of the scar to increase the method’s sensitivity. The gamma probe detects radioactivity and blue staining serves to identify the sentinel node properly. After identifying the sentinel node’s high radioactivity regions with a gamma probe, small incisions are made oriented in the direction of the nodes’ further dissection. The sentinel node is located, excised, and the gamma probe is used to analyze the radioactivity ex vivo. Lymph node exploration is continued to identify the presence of other drainage stations by identifying nodes with radioactivity greater than $10\%$ of the initially identified node [25].
Statistical analyses were performed using SPSS Statistics version 20. Nominal variables were reported as frequency and percentage, while comparison was performed using chi-squared test and Fisher’s test. Continuous data were presented as mean ± standard deviation (SD) and median (minimum–maximum), respectively, while differences between groups were established using Mann–Whitney U test and Kruskal–Wallis test. Odds ratio and $95\%$ confidence interval were calculated using binary logistic regression. A p-value < 0.05 was statistically significant for all tests.
## 3. Results
The total number of patients who underwent surgery was 122, with a total of 162 lymph nodes removed. *The* general characteristics of patients enrolled are illustrated in Table 1.
The patients’ mean age was 54.3 ± 14.4 years old (y.o), ranging from 21 to 84 y.o, with a prevalence of 70 years old and older of $20.5\%$ ($$n = 25$$). The majority of patients had just one lymph node removed (84, or $68.9\%$), while two lymph nodes were for $29.5\%$ of patients ($$n = 36$$) and three for $1.6\%$ of the cohort. The frequency of positive SLN patients was $24.6\%$, there were three patients with two positive lymph nodes.
In terms of gender, the rate of men was $50.8\%$ ($$n = 62$$). Furthermore, $61.6\%$ of women were at menopause ($$n = 37$$ from 60).
The most common region of melanomas was the thorax (39 patients—$32.0\%$), followed by lower limbs (27 patients—$22.1\%$), upper limbs (21 patients—$17.2\%$), abdomen (18 patients—$14.8\%$), and head and neck areas (17 patients—$13.9\%$).
According to NCCN criteria for tumor staging, it was found that most melanomas were classified as PT1b-30 ($24.6\%$), followed by pT2a-26 ($21.3\%$), pT4b-24 ($19.7\%$), pT3b-20 ($16.4\%$), pT3a-10($8.2\%$), pT4a-7($5.7\%$), pT2b-4 ($3.3\%$), and pT1a-1($0.8\%$), respectively. Therefore, advanced tumor stages pT3 and pT4 accounted for half of the biopsied melanomas.
The Breslow index had a mean of 3.04 ± 2.8 and it was used to determine the oncological safety margins, which were as follows: 1 cm for 35 patients, 1.5 cm for 3 patients ($2.5\%$), and 2 cm for the vast majority of the population ($68.9\%$), respectively.
The period from the tumor biopsy to the excisional surgery with sentinel lymph node removal had a mean of 30.3 ± 5.1 days.
The BRAF gene tested positive in 22 of the 31 examined cases, but for 2 individuals no data regarding BRAF gene screening was registered. Serum protein S100 should be checked particularly in patients with pT3 or pT4 tumor stages. Serum protein S100 was tested in 8 of 122 individuals in our sample and it was only positive in 1—a stage pT4b patient with a positive BRAF gene. Only 5 of the 122 patients had a history of cancer, and the mean body mass index was 24.1 ± 2.7 kg/m2.
General anesthesia was necessary for the majority of the surgeries ($75.4\%$), however, local anesthesia was helpful in 30 cases ($25.6\%$).
Despite the fact that a third had cardiovascular diseases ($31.96\%$), the complications of general anesthesia during surgery were only seen in three patients, including two with cardiac rhythm abnormalities and one with desaturation. Six patients ($4.9\%$) with a cardiovascular history required switching to fractionated heparin therapy and two of them also required reintervention for bleeding. As for intraoperative surgical complications, we specifically emphasize three cases with small arterial injuries that required extra hemostasis, as well as one case of nerve damage that had no neurological consequences. Thus, a total of seven cases ($5.7\%$) had intraoperative complications, the surgery’s duration for those individuals being slightly longer, but no significant difference was registered when compared with the rest of the sample studied (130.0 ± 32.3 vs. 124.7 ± 27.8, $$p \leq 0.821$$).
The entire cohort had a mean of the procedure’s duration of 125.0 ± 27.9 min. Patients with more than one lymph node removed had a statistically significant longer surgery compared to those having only one lymph node (153.9 ± 28.4 vs. 111.9 ± 14.9, $p \leq 0.001$). The tumor’s location was another variable that affected how long the procedure took; head and neck melanomas required a more complex and time-consuming procedure (145.9 ± 28.8 vs. 121.6 ± 26.5 min, $p \leq 0.001$).
No patient had postoperative infectious complications, and almost all patients (119–$97.54\%$) received prophylactic antibiotic therapy preoperatively (single dose), but also postoperatively (mean duration 3.8 ± 1.4 days). It should be noted that the three patients who did not receive antibiotic therapy had a history of drug polyallergies.
The duration of wound healing at the melanoma site had a mean of 12.5 ± 1.9 days, ranging from 10 to 21 days.
In terms of long-term surgical complications, in this study, we found that the prevalence of seroma was $14.8\%$. Comparing head and neck melanomas with melanomas in other regions, this complication was only observed in cases involving lymph node removal from the axillary and inguinal basins, not for cervical areas ($$p \leq 0.073$$). Percutaneous puncture with ultrasound guidance was performed for all patients with seroma (3–4 repeated punctures).
In contrast to other regions, patients with head and neck melanoma were older (61.6 ± 16.9 vs. 53.1 ± 13.6 y.o, $$p \leq 0.022$$) and had a greater Breslow index (4.0 ± 3.9 vs. 2.8 ± 2.5, $$p \leq 0.257$$), but a lower rate of positive SLN ($17.6\%$ vs. $25.7\%$, $$p \leq 0.474$$).
Characteristics of lymph nodes are shown in Table 2. Out of the 162 removed nodes, the prevalence of positive sentinel lymph node was $20.4\%$. The lymph distribution was as follows: 83 in the axillary region, 40 in the inguinal region, and 39 in the cervical region. There were 4 positive sentinel nodes from cervical region, 19 from axillary, and 10 from the inguinal area. At the site of the node excision, the axillary area exhibited the longest mean time for scarring, 7.73 ± 0.5 days.
Regarding lymphoscintigraphy, the detected radioactivity was compared depending on the location of the lymph nodes. Radioactivity was registered at the melanoma’s scar site, but also before surgery, during surgery, and ex vivo for the excised lymph node(s). Patients with higher levels of reactivity at the melanoma site, as well as during surgery and ex vivo had their reactive nodes excised from the inguinal regions, ex vivo registration being notably different from the cervical and axillar region. Preparatory nodes’ radioactivity varied depending on the area, with inguinal nodes having a significantly higher median of radioactivity than the rest, $$p \leq 0.015.$$ There were no statistically significant results when the node radioactivity reported was compared between positive and negative SLN.
**Table 2**
| Unnamed: 0 | N = 162 | Cervical N = 39 | Axillar N = 83 | Inguinal N = 40 | p-Value |
| --- | --- | --- | --- | --- | --- |
| Positive SLN | 33, 20.4% | 4, 10.3% | 19, 22.9% | 10, 25.0% | 0.191 |
| Scar (melanoma) | 10000.0 | 17700.0 | 18500.0 | 18000.00 | 0.521 |
| Median (min,max) | (1900–30,000) | (1900–30,000) | (2200–30,000) | (3000–30,000) | 0.521 |
| Preoperative | 655.0 | 760.0 | 550.0 | 783.0 | 0.015 |
| Median (min,max) | (20–18,000) | (90–18,000) | (20–2700) | (46–2800) | 0.015 |
| During surgery | 1335.0 | 1200.0 | 1200.0 | 1700.0 | 0.184 |
| Median (min,max) | (60–12,000) | (220–7500) | (60–12,000) | (120–7050) | 0.184 |
| Ex vivo | 1500.0 | 1000.0 | 1500.0 | 1600 | 0.03 |
| Median (min,max) | (50–9100) | (130–7600) | (50–9100) | (140–6900) | 0.03 |
| Ganglion site healing period (days) mean ± SD | 7.6 ± 0.6 | 7.4 ± 0.6 | 7.73 ± 0.5 | 7.6 ± 0.6 | 0.014 |
In our study, there were 25 patients 70 years old or older; the comparison between this subgroup with the rest of our cohort is shown in Table 3. Advanced stages of melanoma were more frequent among older patients, $68.0\%$ vs. $45.4\%$, $$p \leq 0.044$$, OR = 2.56 (1.00–6.49). A positive sentinel lymph node was found in $40.0\%$ of cases among 70 y.o or older patients, while only $20.6\%$ in younger patients, with a borderline statistical difference, $$p \leq 0.045$$, OR = 2.57 (1.00–6.56).
Another noteworthy observation is that melanoma of the head and neck is more common among individuals aged 70 y.o and older, with a frequency of $32.0\%$ vs. $9.3\%$, a statistically significant difference, $$p \leq 0.007$$, OR = 4.60 (1.55–13.61). There was no significant difference in the number of lymph nodes surgically removed following lymphoscintigraphy, with the majority of patients in both subgroups having only one lymph node removed ($68.0\%$ vs. $69.1\%$).
Older subjects had more cardiovascular diseases, such as hypertension, atrial fibrillation, and a history of myocardial infarction ($68.0\%$ vs. $22.7\%$, p =< 0.001). Additionally, out of six patients, five required switching anticoagulant therapy ($$p \leq 0.001$$). The only two cases of reintervention due to hemorrhage are in older patients, who both had a history of atrial fibrillation and needed anticoagulant switching.
Regarding the intraoperative complications, from a total of seven cases, five were from patients 70 y.o or older, $$p \leq 0.004$$, OR = 11.87 (2.15–65.60). Older individuals in this cohort required longer surgeries, with a mean of 131.6 ± 31.4 min compared with younger ones, 123.30 ± 26.9 min ($$p \leq 0.082$$).
Patients 70 y.o or older were more likely to develop post-operative seromas ($24.0\%$ vs. $12.4\%$, $$p \leq 0.202$$), and needed significantly more time to heal ($$p \leq 0.004$$, OR = 1.38 (1.08–1.75))
## 4. Discussion
According to our data, this is the first Romanian complex study on the use of the sentinel lymph node technique in malignant melanoma, as a key factor of diagnosis for the disease stage, which brings additional information about patients diagnosed with cutaneous melanoma. There is no national data to indicate the prevalence of melanoma in our country and the use of the sentinel ganglion technique in daily practice is subliminal, but still increasing compared to previous years. The study published in 2019, by the Dutch team, rigorously highlights, along with the prevalence of melanoma at the national level, the upward trend (statistically validated) of increasing the use of the sentinel ganglion technique among patients with primary melanoma [26].
The average age of the patients enrolled in our study was 54.3 ± 14.4 years, and the proportion of male patients was $48.8\%$, results being absolutely superimposed with the data from a Spanish study published in 2014 on a comparable population in terms of number of patients, in which the average age was 55.6 ± 15 years, and the percentage of male patients was $50.8\%$ [27].
Regarding the location of the melanoma in our study, the thorax was where the melanoma was most frequently identified, followed by lower limbs, upper limbs, and head and neck site (46,$8\%$, $22.1\%$, $17.2\%$, $13.9\%$, respectively). In a Spanish study, the distribution was similar regarding the trunk and upper limbs and completely different regarding the head and neck region, registering a much lower frequency of $2.4\%$ (three patients). The majority of patients had one lymph node removed ($67.3\%$), two nodes for $29.5\%$ of patients and three nodes for $1.6\%$, while in the Spanish group, the authors identified single drainage for $78.4\%$ versus $21.6\%$ multiple drainages [27]. According to another study, a Bulgarian one, it was found that approximately $70\%$ of melanoma cases had a single sentinel lymph node excised, and for the rest, two or more lymph nodes were removed [28].
The positivity rate in the group from our clinic was $24.6\%$. The data published in 2016 by the work team led by Leiter showed that in a larger German cohort, the sentinel lymph node positivity rate was very similar to ours, $23\%$, the population being followed for a longer period, approximately 9 years (2006–2014) [29].
Regarding the Breslow index, in our study the average value was 3.04 ± 2.8, while in a study analyzing the characteristics of 1663 patients with melanoma, the Breslow index had an average value of 1.34 ± 2.24, the significant difference being probably due to the large number of patients, but also to the early diagnosis of melanoma in the Austrian study [30].
The safety margins are in accordance with the international recommendations, respectively, 1 or 2 cm, in line with the Breslow index, and the closure of the resulting integumentary defects was performed by the plastic surgeon [31,32].
The sentinel lymph node technique is grafted by minimal postoperative complications versus total lymphadenectomy which is associated with high morbidity, both perioperatively and after, as it is shown by the team led by Leiter in a study published in 2016. Adverse events included lymphoedema, lymph fistula, seroma, and infection [29].
The data resulting from our study highlight a limited number of postoperative complications, $1.6\%$ reinterventions for bleeding, and $14.8\%$ of patients who developed seromas. None of the seromas required surgical drainage, all were evacuated percutaneously under ultrasound guidance. In comparison, in a study published in 2019, postoperative complications were found in a number of 39 patients, respectively, $9.5\%$, and were represented by: wound infection in 24 ($5.9\%$), seroma and lymphorrhea in 15 (3.7 %), wound dehiscence in 7 ($1.7\%$), lymphocele in 6 ($1.5\%$), and others in 3 ($0.7\%$) [33].
On the other hand, in our paper, during surgery, there were a number of seven cases that had complications related to bleeding or nerve damage and to anesthesia (rhythm disorders, desaturations). The duration of the surgery in this study was on average 125.0 ± 27.9 min, it includes both the detection and excision of the sentinel lymph node, as well as the excision with safety margins and also the coverage of the remaining skin defect. Furthermore, the rate of postoperative complications was not correlated with the duration of the surgery, an issue also confirmed by another research [33].
When dividing the cohort by the age limit—70 years and older—, $20.5\%$ belonged to the elderly group. Using the same age cutoff, a French paper identified $30\%$ of the population as elderly [15].
Statistically, it was demonstrated that the head and neck region was the most likely area for melanoma in patients above the age of 70 (by comparing with the features of individuals under the age of 70). Additionally, this population is at risk for developing advanced cancer stages; in our study, more than half of elderly patients had stage pT3 or pT4 melanoma. Our findings are consistent with the French study (1621 patients), which was conducted more than 15 years ago and on a much bigger scale and revealed that $36.7\%$ of senior people had advanced stages of the disease and that the head and neck region was the site where it occurred most frequently ($29.4\%$, $p \leq 0.001$) [15]. Additionally, an American study, published in 2013 and addressed to elderly patients and their particularities, showed that patients over 70 years old ($25.5\%$), diagnosed with melanoma, are the ones with a more advanced stage of the disease ($$p \leq 0.001$$) and higher Breslow index ($$p \leq 0.010$$) [34].
Moreover, the rate of positive sentinel nodes in this paper among 70 y.o or older was significantly higher than for younger patients ($40.0\%$), a result in line with a more recent study presented at the American Dermatology Conference in 2022, which demonstrated that the elderly population has a higher degree of sentinel node positivity $29.3\%$ versus $18.3\%$ among those younger, in their case the age cut-off being 75 years old [35].
As is well established, our analyses also showed that older patients have more frequent cardiovascular comorbidity ($31.9\%$), as well as requiring re-interventions for postoperative complications more often. As a result, this group also included the majority of patients who received preoperative anticoagulation. In the study conducted by Fleming et al., cardiovascular pathology was more frequent in the elderly group ($85\%$), but there are no data described in relation to anticoagulant therapy or postoperative complications [34]. The data from the Danish registry showed a lower rate of comorbidities associated (predominantly cardiovascular) of $19\%$ with just half of these accounting for only one underlying pathology [36].
In this paper, the group of older patients had a significantly longer period of wound healing. Additionally, in elderly patients, the BRAF gene was detected in six cases of a total of nine patients 70 y.o or older who were tested.
Regarding primary head and neck melanoma, the frequency in the studied population was $13.9\%$, while data from Serbia released in 2020 indicated a percentage of $14.99\%$ for this location. Despite the Serbian study’s longer study period of 10 years (2005–2015) compared to our study’s shorter term of three and a half years, these numbers were comparable [37]. In a recent study on a bigger cohort, the frequency of melanoma of the head and neck was similar to ours, $12\%$, but the overall positivity of SLNB was less frequent in comparison to our results, only $6.7\%$ [38].
Considering that the head and neck group was more frequent in our population [39] than the results from other countries, we set out to identify the particularities of those individuals. In this subgroup, patients were older, the Breslow index had a greater value; meanwhile, the positivity of sentinel lymph note was lower ($17.6\%$ vs. $25.7\%$), when compared to the rest of the cohort. The study published in 2023 by the Italian team, aiming to strictly follow the characteristics of patients with melanoma of the head and neck and indication for SLNB, had a total number of 93 cases in the period 2015–2021 with an average age of 58 years (50–70) and demonstrated a positivity rate of $19.35\%$ with a Breslow index (2.2 mm; 1.8–5.0 mm), higher than the negative ones (1.8 mm; 1.1–3.0 mm) [40]. In another study, conducted by Quaglio et al., the rate of positive SLN was just slightly different from our results ($25.0\%$) [41]. In this study, the authors also analyzed the potential predictivity of micro–macro–metastatic pattern, Breslow index, and ulceration, and the results showed that there is a higher likelihood of a non-sentinel lymph node for all of these factors.
In comparison, our results revealed similar age tendency and frequency of positive SLN, but a greater average of Breslow index for patients with head and neck melanoma, the variations could be attributed to the different numbers of subjects examined.
As for lymphoscintigraphy, measured radioactivity in our study showed that for the inguinal basin, the radioactivity captured by lymph nodes was higher than the other regions, data in accordance with one study, which shows that the detection after the radioactive load on lymphoscintigraphy is the highest in inguinal–femoral and axillary lymphatic basins and low in cervical region [28,42]. Meanwhile, there is no noticeable association between positive SLN and radioactivity in our study. Despite all that, the literature’s data are insufficient regarding the research on the relation between melanoma [43] and lymphoscintigraphic investigation of the sentinel lymph node [12,44], therefore, it might be worthwhile to perform a more thorough analysis in this regard.
The first most significant limitation of this study is that it was conducted in a single center. As a second limitation is the number of patients enrolled, SLNB technique with lymphoscintigraphy being a procedure available only in a few medical centers in Romania. Third, only a low number of patients had the BRAF gene tested, and no gene subtypes were examined; this information might have some bearing on the connection with SLN positivity.
## 5. Conclusions
The average age at diagnosis of malignant melanoma was 54.3 ± 14.4 years, with advanced stages of the disease (>$50\%$ stage III, IV), an average Breslow index of 3.04 ± 2.8, and a sentinel lymph node positivity rate of $24.6\%$.
Preoperative nodes’ radioactivity varied depending on the lymphatic drainage basin, with the inguinal nodes having the highest load ($$p \leq 0.015$$). There was no statistically significant association between the radiotracer load and the positivity of the lymph node.
The elective region of melanoma localization for patients 70 years or older is head and neck ($$p \leq 0.007$$), advanced stages are more frequently identified ($$p \leq 0.044$$), the positivity rate of SLN is higher ($$p \leq 0.045$$), and complications during surgery are more common ($$p \leq 0.004$$).
## References
1. Garbe C., Amaral T., Peris K., Hauschild A., Arenberger P., Bastholt L., Bataille V., Del Marmol V., Dréno B., Fargnoli M.C.. **European consensus-based interdisciplinary guideline for melanoma. Part 1: Diagnostics: Update 2022**. *Eur. J. Cancer* (2022.0) **170** 236-255. DOI: 10.1016/j.ejca.2022.03.008
2. Saginala K., Barsouk A., Aluru J.S., Rawla P., Barsouk A.. **Epidemiology of Melanoma**. *Med. Sci.* (2021.0) **9**. DOI: 10.3390/medsci9040063
3. **European Cancer Information System**
4. Whiteman D.C., Green A.C., Olsen C.M.. **The Growing Burden of Invasive Melanoma: Projections of Incidence Rates and Numbers of New Cases in Six Susceptible Populations through 2031**. *J. Investig. Dermatol.* (2016.0) **136** 1161-1171. DOI: 10.1016/j.jid.2016.01.035
5. Georgescu M.T., Patrascu T., Serbanescu L.G., Anghel R.M., Gales L.N., Georgescu F.T., Mitrica R.I., Georgescu D.E.. **When Should We Expect Curative Results of Neoadjuvant Treatment in Locally Advanced Rectal Cancer Patients?**. *Chirurgia* (2021.0) **116** 16. DOI: 10.21614/chirurgia.116.1.16
6. Bae Y.C., Jeong D.K., Kim K.H., Nam K.W., Kim G.W., Kim H.S., Nam S.B., Bae S.H.. **Adequacy of sentinel lymph node biopsy in malignant melanoma of the trunk and extremities: Clinical observations regarding prognosis**. *Arch. Plast. Surg.* (2020.0) **47** 42-48. DOI: 10.5999/aps.2019.00934
7. Michielin O., Van Akkooi A.C.J., Ascierto P.A., Dummer R., Keilholz U.. **Cutaneous melanoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up**. *Ann. Oncol.* (2019.0) **30** 1884-1901. DOI: 10.1093/annonc/mdz411
8. Cascinelli N., Belli F., Santinami M., Fait V., Testori A., Ruka W., Cavaliere R., Mozzillo N., Rossi C.R., MacKie R.M.. **Sentinel Lymph Node Biopsy in Cutaneous Melanoma**. *Clin. Nucl. Med.* (2016.0) **41** e498-e507. PMID: 27749418
9. Dogan N.U., Dogan S., Favero G., Köhler C., Dursun P.. **The Basics of Sentinel Lymph Node Biopsy: Anatomical and Patho-physiological Considerations and Clinical Aspects**. *J. Oncol.* (2019.0) **2019** 1-10. DOI: 10.1155/2019/3415630
10. Georgescu D.E., Patrascu T., Georgescu T.F., Tulin A., Mosoia L., Bacalbasa N., Stiru O., Georgescu M.-T.. **Diabetes Mellitus as a Prognostic Factor for Locally Advanced Rectal Cancer**. *In Vivo* (2021.0) **35** 2495-2501. DOI: 10.21873/invivo.12530
11. Keung E.Z., Gershenwald J.E.. **The eighth edition American Joint Committee on Cancer (AJCC) melanoma staging system: Implications for melanoma treatment and care**. *Expert Rev. Anticancer. Ther.* (2018.0) **18** 775-784. DOI: 10.1080/14737140.2018.1489246
12. Bluemel C., Herrmann K., Giammarile F., Nieweg O.E., Dubreuil J., Testori A., Audisio R.A., Zoras O., Lassmann M., Chakera A.H.. **EANM practice guidelines for lym-phoscintigraphy and sentinel lymph node biopsy in melanoma**. *Eur. J. Nucl. Med. Mol. Imaging* (2015.0) **42** 1750-1766. DOI: 10.1007/s00259-015-3135-1
13. Wu V.F., Malloy K.M.. **Sentinel Node Biopsy for Head and Neck Cutaneous Melanoma**. *Otolaryngol. Clin. N. Am.* (2021.0) **54** 281-294. DOI: 10.1016/j.otc.2020.11.004
14. Seim N.B., Wright C.L., Agrawal A.. **Contemporary use of sentinel lymph node biopsy in the head and neck**. *World J. Otorhinolaryngol. Head Neck Surg.* (2016.0) **2** 117-125. DOI: 10.1016/j.wjorl.2016.05.008
15. Ciocan D., Barbe C., Aubin F., Granel-Brocard F., Lipsker D., Velten M., Dalac S., Truchetet F., Michel C., Mitschler A.. **Distinctive Features of Melanoma and Its Man-agement in Elderly Patients**. *JAMA Dermatol.* (2013.0) **149** 1150. DOI: 10.1001/jamadermatol.2013.706
16. Bobircă A., Bobircă F., Ancuta I., Florescu A., Pădureanu V., Florescu D., Pădureanu R., Florescu A., Mușetescu A.. **Rheumatic Immune-Related Adverse Events—A Consequence of Immune Checkpoint Inhibitor Therapy**. *Biology* (2021.0) **10**. DOI: 10.3390/biology10060561
17. Lange J.R., Kang S., Balch C.M.. **Melanoma in the Older Patient: Measuring Frailty as an Index of Survival**. *Ann. Surg. Oncol.* (2011.0) **18** 3531-3532. DOI: 10.1245/s10434-011-2015-6
18. **NCCN Guidelines for Patients Melanoma**
19. Kachare S.D., Brinkley J., Wong J.H., Vohra N.A., Zervos E.E., Fitzgerald T.L.. **The Influence of Sentinel Lymph Node Biopsy on Survival for Intermediate-Thickness Melanoma**. *Ann. Surg. Oncol.* (2014.0) **21** 3377-3385. DOI: 10.1245/s10434-014-3954-5
20. Nakamura Y.. **The Role and Necessity of Sentinel Lymph Node Biopsy for Invasive Melanoma**. *Front. Med.* (2019.0) **6** 231. DOI: 10.3389/fmed.2019.00231
21. Faries M.B., Thompson J.F., Cochran A.J., Andtbacka R.H., Mozzillo N., Zager J.S., Jahkola T., Bowles T.L., Testori A., Beitsch P.D.. **Completion Dissection or Observation for Sentinel-Node Metastasis in Melanoma**. *N. Engl. J. Med.* (2017.0) **376** 2211-2222. DOI: 10.1056/NEJMoa1613210
22. Masoud S.J., Perone J.A., Farrow N.E., Mosca P.J., Tyler D.S., Beasley G.M.. **Sentinel Lymph Node Biopsy and Completion Lymph Node Dissection for Melanoma**. *Curr. Treat Options Oncol.* (2018.0) **19** 55. DOI: 10.1007/s11864-018-0575-4
23. Stoffels I., Boy C., Pöppel T., Kuhn J., Klötgen K., Dissemond J., Schadendorf D., Klode J.. **Association Between Sentinel Lymph Node Excision With or Without Preoperative SPECT/CT and Metastatic Node Detection and Disease-Free Survival in Melanoma**. *JAMA* (2012.0) **308** 1007-1014. DOI: 10.1001/2012.jama.11030
24. van den Berg N.S., Brouwer O.R., Schaafsma B.E., Mathéron H.M., Klop W.M.C., Balm A.J., van Tinteren H., Nieweg O.E., van Leeuwen F.W., Valdés Olmos R.A.. **Multimodal Surgical Guidance during Sentinel Node Biopsy for Melanoma: Combined Gamma Tracing and Fluorescence Imaging of the Sentinel Node through Use of the Hybrid Tracer Indocyanine Green– 99m Tc-Nanocolloid**. *Radiology* (2015.0) **275** 521-529. DOI: 10.1148/radiol.14140322
25. Carlson G.W., Murray D.R., Thourani V., Hestley A., Cohen C.. **The definition of the sentinel lymph node in melanoma based on radioactive counts**. *Ann. Surg. Oncol.* (2002.0) **9** 929-933. DOI: 10.1007/BF02557533
26. El Sharouni M.A., Witkamp A.J., Sigurdsson V., van Diest P.J.. **Trends in Sentinel Lymph Node Biopsy Enactment for Cutaneous Melanoma**. *Ann. Surg. Oncol.* (2019.0) **26** 1494-1502. DOI: 10.1245/s10434-019-07204-2
27. Soliveres E.S., Marín A.G., Miralles M.D., Riera C.N., Gomis A.C., Gordon M.M., Leal M.Á.A., García S.G.. **Sentinel Node Biopsy for Melanoma. Analysis of our Experience (125 Patients)**. *Cirugía Española* (2014.0) **92** 609-614. DOI: 10.1016/j.cireng.2013.08.004
28. Bagaria S.P., Faries M.B., Morton D.L.. **Sentinel node biopsy in melanoma: Technical considerations of the procedure as per-formed at the john wayne cancer institute**. *J. Surg. Oncol.* (2010.0) **101** 669-676. DOI: 10.1002/jso.21581
29. Leiter U., Stadler R., Mauch C., Hohenberger W., Brockmeyer N., Berking C., Sunderkötter C., Kaatz M., Schulte K.W., Lehmann P.. **Complete lymph node dissection versus no dissection in patients with sentinel lymph node biopsy positive melanoma (DeCOG-SLT): A multicentre, randomised, phase 3 trial**. *Lancet Oncol.* (2016.0) **17** 757-767. DOI: 10.1016/S1470-2045(16)00141-8
30. Ambrosini-Spaltro A., Cappello T.D., Deluca J., Carriere C., Mazzoleni G., Eisendle K.. **Melanoma incidence and Breslow tumour thickness development in the central Alpine region of South Tyrol from 1998 to 2012: A population-based study**. *J. Eur. Acad. Dermatol. Venereol.* (2015.0) **29** 243-248. DOI: 10.1111/jdv.12501
31. Melmar T.. **Melanoma Margins Trial Investigating 1cm v 2cm Wide Excision Margins for Primary Cutaneous Melanoma (MelMarT)**
32. Moncrieff M.D., Gyorki D., Saw R., Spillane A.J., Peach H., Oudit D., Geh J., Dziewulski P., Wilson E., Matteucci P.. **1 Versus 2-cm Excision Margins for pT2-pT4 Primary Cutaneous Melanoma (MelMarT): A Feasibility Study**. *Ann. Surg. Oncol.* (2018.0) **25** 2541-2549. DOI: 10.1245/s10434-018-6470-1
33. Solari N., Bertoglio S., Boscaneanu A., Minuto M., Reina S., Palombo D., Bruzzi P., Cafiero F.. **Sentinel lymph node biopsy in patients with malignant melanoma: Analysis of post-operative complications**. *ANZ J. Surg.* (2019.0) **89** 1041-1044. DOI: 10.1111/ans.15358
34. Fleming N.H., Tian J., De Miera E.V.-S., Gold H., Darvishian F., Pavlick A.C., Berman R., Shapiro R.L., Polsky D., Osman I.. **Impact of Age on the Management of Primary Melanoma Patients**. *Oncology* (2013.0) **85** 173-181. DOI: 10.1159/000351499
35. Jimenez P.R., Reymundo A., Delgado Y.. **33609 Factors associated with sentinel lymph node status in elderly melanoma pa-tients: A real practice cohort**. *J. Am. Acad. Dermatol.* (2022.0) **87** AB60. DOI: 10.1016/j.jaad.2022.06.273
36. Grann A.F., Frøslev T., Olesen A.B., Schmidt H., Lash T.L.. **The impact of comorbidity and stage on prognosis of Danish melanoma patients, 1987–2009: A registry-based cohort study**. *Br. J. Cancer* (2013.0) **109** 265-271. DOI: 10.1038/bjc.2013.246
37. Višnjić A., Kovačević P., Veličkov A., Stojanović M., Mladenović S.. **Head and neck cutaneous melanoma: 5-year survival analysis in a Serbian university center**. *World J. Surg. Oncol.* (2020.0) **18** 1-8. DOI: 10.1186/s12957-020-02091-4
38. Tejera-Vaquerizo A., Ribero S., Puig S., Boada A., Paradela S., Moreno-Ramírez D., Cañueto J., de Unamuno B., Brinca A., Descalzo-Gallego M.A.. **Survival analysis and sentinel lymph node status in thin cutaneous melanoma: A multicenter observational study**. *Cancer Med.* (2019.0) **8** 4235-4244. DOI: 10.1002/cam4.2358
39. Stanciu A., Zamfir-Chiru-Anton A., Stanciu M., Pantea-Stoian A., Nitipir C., Gheorghe D.. **Serum melatonin is inversely asso-ciated with matrix metalloproteinase 9 in oral squamous cell carcinoma**. *Oncol. Lett.* (2020.0) **13** 3011-3020
40. Rubatto M., Picciotto F., Moirano G., Fruttero E., Caliendo V., Borriello S., Sciamarrelli N., Fava P., Senetta R., Lesca A.. **Sentinel Lymph Node Biopsy in Malignant Melanoma of the Head and Neck: A Single Center Experience**. *J. Clin. Med.* (2023.0) **12**. DOI: 10.3390/jcm12020553
41. Quaglino P., Ribero S., Osella-Abate S., Macrì L., Grassi M., Caliendo V., Asioli S., Sapino A., Macripò G., Savoia P.. **Clinico-pathologic features of primary melanoma and sentinel lymph node predictive for non-sentinel lymph node involvement and overall survival in melanoma patients: A single centre observational cohort study**. *Surg. Oncol.* (2011.0) **20** 259-264. DOI: 10.1016/j.suronc.2010.11.001
42. Gherghe M., Bordea C., Blidaru A.. **Clinical significance of the lymphoscintigraphy in the evaluation of non-axillary sentinel lymph node localization in breast cancer**. *Chirurgia* (2015.0) **110** 26-32. PMID: 25800312
43. Voinea S., Sandru A., Gherghe M., Blidaru A.. **Peculiarities of lymphatic drainage in cutaneous malignant melanoma: Clinical experience in 75 cases**. *Chirurgia* (2014.0) **109** 26-33. PMID: 24524467
44. Mathelin C., Salvador S., Huss D., Guyonnet J.-L.. **Precise Localization of Sentinel Lymph Nodes and Estimation of Their Depth Using a Prototype Intraoperative Mini -Camera in Patients with Breast Cancer**. *J. Nucl. Med.* (2007.0) **48** 623-629. DOI: 10.2967/jnumed.106.036574
|
---
title: Antioxidant Activities and Cytotoxicity of the Regulated Calcium Oxalate Crystals
on HK-2 Cells of Polysaccharides from Gracilaria lemaneiformis with Different Molecular
Weights
authors:
- Jing-Hong Liu
- Yu-Yun Zheng
- Jian-Ming Ouyang
journal: Foods
year: 2023
pmcid: PMC10001015
doi: 10.3390/foods12051031
license: CC BY 4.0
---
# Antioxidant Activities and Cytotoxicity of the Regulated Calcium Oxalate Crystals on HK-2 Cells of Polysaccharides from Gracilaria lemaneiformis with Different Molecular Weights
## Abstract
The antioxidant activities of seven degraded products (GLPs) with different molecular weights (Mw) of polysaccharides from *Gracilaria lemaneiformis* were compared. The Mw of GLP1–GLP7 were 106, 49.6, 10.5, 6.14, 5.06, 3.71 and 2.42 kDa, respectively. The results show that GLP2 with Mw = 49.6 kDa had the strongest scavenging capacity for hydroxyl radical, DPPH radical, ABTS radical and reducing power. When Mw < 49.6 kDa, the antioxidant activity of GLPs increased with the increase in Mw, but when Mw increased to 106 kDa, their antioxidant activity decreased. However, the ability of GLPs to chelate Fe2+ ions increased with the decrease in polysaccharide Mw, which was attributed to the fact that the polysaccharide active groups (–OSO3– and –COOH) were easier to expose, and the steric hindrance was smaller when GLPs chelated with Fe2+. The effects of GLP1, GLP3, GLP5 and GLP7 on the crystal growth of calcium oxalate (CaOx) were studied using XRD, FT-IR, Zeta potential and thermogravimetric analysis. Four kinds of GLPs could inhibit the growth of calcium oxalate monohydrate (COM) and induce the formation of calcium oxalate dihydrate (COD) in varying degrees. With the decrease in Mw of GLPs, the percentage of COD increased. GLPs increased the absolute value of the Zeta potential on the crystal surface and reduced the aggregation between crystals. Cell experiments showed that the toxicity of CaOx crystal regulated by GLPs to HK-2 cells was reduced, and the cytotoxicity of CaOx crystal regulated by GLP7 with the smallest Mw was the smallest, which was consistent with the highest SOD activity, the lowest ROS and MDA levels, the lowest OPN expression level and the lowest cell necrosis rate. These results suggest that GLPs, especially GLP7, may be a potential drug for the prevention and treatment of kidney stones.
## 1. Introduction
Kidney stone is a disease that seriously endangers people’s health. The prevalence of kidney stones in adults in *China is* $5.8\%$ ($6.5\%$ in men and $5.1\%$ in women) [1], and about $9\%$ of people around the world are affected by kidney stones [2]. Kidney stones have not only a high prevalence, but also a high recurrence rate. Kidney stones can be roughly divided into six types by composition: calcium oxalate (CaOx), calcium phosphate, uric acid (urate), magnesium ammonium phosphate, cystine and purine stones, among which CaOx (including calcium oxalate monohydrate (COM) and calcium oxalate dihydrate (COD)) accounts for about 80–$84\%$ [3]. No matter what type of kidney stones, their formation is a complex multi-step process, including urine supersaturation, crystal nucleation, growth and aggregation [4].
COM crystals are more common than COD crystals in clinical stones, occurring about twice as often as COD crystals. Generally, COM crystals are mostlylarge aggregates with sharp edges and corners, which are highly toxic to renal epithelial cells, while COD crystals are numerous small-sized round blunt particles with fewer aggregates that are relatively less toxic to cells. Our previous study [5] showed that COM crystals exhibited higher cytotoxicity than COD crystals at the same size and crystal concentration, resulting in more LDH release, higher ROS level and lower mitochondrial membrane potential.
Gracilariales is a kind of macroalgae with more than 300 species, of which 160 species have been accepted by taxonomy [6]. Gracilaria lemaneiformis grows in tropical and subtropical waters around the world. It has strong adaptability to the environment, fast growth and rich resources. Gracilaria lemaneiformis polysaccharide (GLP) is the main component of Gracilaria lemaneiformis. Its structure is generally composed of alternating β-1,3 and α-1,4 D and L galactose residues [7]. GLP is an acidic polysaccharide rich in sulfate, with antioxidant, antiviral, anticoagulant and anti-tumor effects [8,9,10].
Natural polysaccharides are usually heavy in molecular weight (Mw). It is difficult for cells to enter through the body barrier, affecting their biological activity [11,12,13]. Degradation of polysaccharide to appropriate Mw can optimize their biological activity. Liu et al. [ 11] obtained two products, GLPL1 and GLPL2, with Mw of 5.2 kDa and 15.4 kDa by degrading original *Ganoderma lucidum* polysaccharides (GLPP). At a concentration of 10 mg/mL, the antioxidant activity of GLPL1 was significantly higher than that of GLPL2, and the scavenging rates of GLPL1 and GLPL2 on hydroxyl radicals (·OH) were $78.3\%$ and $53.6\%$, respectively. Sun et al. [ 12] obtained three polysaccharides, EPS-1, EPS-2 and EPS-3, with Mw of 256.2 kDa, 60.7 kDa and 6.55 kDa, by degrading P. purpurea polysaccharides with Mw of 2918 kDa using the microwave method. When the polysaccharide concentration was 2.0 mg/mL, the DPPH free radical scavenging rate was EPS-3 ($83.8\%$) > EPS-2 ($78.3\%$) > EPS-1 ($38.7\%$). That is, the polysaccharide with low Mw has higher antioxidant activity. Liao et al. [ 7] studied the hypoglycemic and antioxidant effects of polysaccharides extracted from *Gracilaria lemaneiformis* (GLP, 121.89 kDa) and their degradation products (GLP1, 57.02 kDa; GLP2, 14.29 kDa) in alloxan-induced diabetic mice. At the same dose, GLP1 showed better efficacy than GLP and GLP2. This suggests that it is not that the lower the Mw of a polysaccharide, the better its bioactivity.
In the previous study [14], we degraded the original *Gracilaria lemaneiformis* polysaccharides (GLP0) with Mw = 622 kDa and obtained seven degraded polysaccharides: GLP1, GLP2, GLP3, GLP4, GLP5, GLP6 and GLP7, with Mw of 106, 49.6, 10.5, 6.14, 5.06, 3.71 and 2.42 kDa, respectively. We also characterized their structures. Before and after degradation, the content of the characteristic group –OSO3– in the polysaccharides did not change much ($13.26\%$ ± $0.20\%$). GLPs have the ability to repair human renal proximal tubular epithelial cells (HK-2) damaged by oxalic acid, and the repair ability is closely related to polysaccharide Mw. GLP2, with Mw = 49.6 kDa, had the strongest repair ability, and too large or too small Mw led to a decrease in polysaccharide repair ability.
This paper further studied the differences in the antioxidant activity of these polysaccharides, as well as the regulation effects of these polysaccharides on the growth of CaOx crystals and the cytotoxicity difference of the regulated CaOx crystals, hoping to provide enlightenment as to finding the best activity of polysaccharides for the prevention and treatment of kidney stones.
## 2.1. Reagents and Instruments
Gracilaria lemaneiformis polysaccharide (GLP0) was provided by Beijing Newprobe Bioscience & Technology Co., Ltd. (Beijing, China). 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH), 2,2′-biazobis (3-ethylbenzothiazolin-6-sulfonic acid) diammonium salt (ABTS), phenazine (Ferrozine), ascorbic acid (Vc), ethylenediaminetetraacetic acid (EDTA), ferrous sulfate (FeSO4), phosphate buffer solution (PBS), potassium persulfate (K2S2O8), potassium ferricyanide (K3[Fe(CN)6]) and o-diazepine were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Analytically pure conventional reagents such as trichloroacetic acid (CCl3COOH), $30\%$ hydrogen peroxide (H2O2), sodium oxalate (Na2Ox) and calcium chloride (CaCl2) were purchased from Guangzhou Chemical Reagent Factory (Guangzhou, China). The experimental water was secondary distilled water.
Human proximal tubular epithelial cells (HK-2) were purchased from Shanghai Cell Bank, Chinese Academy of Sciences. The total superoxide dismutase (SOD) assay kit and malondialdehyde (MDA) assay kit were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). OPN antibody and goat anti-mouse IgG-FITC were purchased from Wuhan Boster Biological Technology Co., Ltd. (Wuhan, China). Cell Counting Kit-8 (CCK-8 kit), reactive oxygen species detection kit and 4′,6-diamidine-2-phenylindole (DAPI) dye were purchased from Shanghai Beyotime Bio-Tech Co., Ltd. (Shanghai, China). Hoechst 33342/PI double staining kit was purchased from Beijing Solarbio Technology Co., Ltd. (Beijing, China). Penicillin–streptomycin solution (100×), trypsin, fetal bovine serum (FBS) and DMEM/F12 medium (Gibco, Grand Island, NY, USA). Cell culture plates and confocal dishes were purchased from Wuxi NEST Biotechnology Co., Ltd. (Wuxi, China).
The Instruments used in this paper were as following: UV-Vis-NIR spectrophotometer (Cary 5000, Agilent, Santa Clara, CA, USA), X-ray powder diffractometer (D/MAX2400, Rigaku, Tokyo, Japan), X-L environmental scanning electron microscope (ESEM, Philips, Eindhoven, The Netherlands), nanoparticle size-Zeta potential analyzer (Zatasizer 300HS, Malvern, Worcestershire, UK), thermogravimetric analyzer (TGA/DSC 3+, Mettler Toledo, Zurich, Switzerland), inverted fluorescence microscope (DMRA2, Leica, Wetzlar, Germany), enzymometer (Safire2, Tecan, Männedorf, Switzerland), laser scanning confocal microscope (LSM 800, Zeiss, Jena, Germany).
## 2.2. Degradation of GLP0
According to reference [14], GLP0 with Mw = 622 kDa was degraded by $30\%$ H2O2 to obtain seven degraded polysaccharides with Mw of 106, 49.6, 10.5, 6.14, 5.06, 3.71 and 2.42 kDa, respectively, named GLP1, GLP2, GLP3, GLP4, GLP5, GLP6 and GLP7. Their structures were characterized by FT-IR, 1H NMR and 13C NMR.
## 2.3.1. Scavenging Hydroxyl Radicals (·OH) of GLPs
The H2O2/Fe2+ system was used to test the scavenging ability of polysaccharides on ·OH in vitro [15]. That is, FeSO4 solution (2.5 mmol/L, 1 mL) and phenanthroline solution (2.5 mmol/L, 1 mL) were added into test tubes, and then PBS with pH = 7.4 (20 mmol/L, 1 mL), H2O2 (20 mmol/L, 1 mL) and polysaccharide solution with different concentrations (0.15–3 mg/mL, 1 mL) were added successively. After mixing evenly, the temperature was kept at 37 °C for 90 min, and then the absorbance was measured at 536 nm by a UV-Vis-NIR spectrophotometer, which was repeated three times to obtain the average absorbance (A3). In the undamaged group, the volume was supplemented with distilled water without sugar solution and hydrogen peroxide, and the absorbance was A2. In the damaged group, the volume was supplemented by distilled water without sugar solution, and the absorbance was A1. Vc was used as the positive control. OH scavenging rate% = (A3 − A1)/(A2 − A1) × 100[1]
## 2.3.2. Scavenging DPPH Free Radicals of GLPs
The experiment was slightly modified according to reference [16]. An amount of 1 mL of DPPH solution (0.4 mmol/L) dissolved in absolute ethanol was mixed with 3 mL of polysaccharide solution in the test tube, and the final concentrations of polysaccharide were 0.15–3.0 mg/mL, respectively. After mixing, they were kept away from light for 30 min at 25 °C, and then the absorbance was detected at 517 nm, with Vc as positive control. DPPH scavenging rate% = [1 − (A2 − A1)/A0] × 100[2] Among them, A2 is the absorbance of the mixture of DPPH solution and polysaccharide solution, A1 is the absorbance of the mixture of blank solvent (anhydrous ethanol) and polysaccharide solution, and A0 is the absorbance of the mixture of DPPH solution and anhydrous ethanol.
## 2.3.3. Scavenging ABTS Free Radicals of GLPs
Referring to reference [17], ABTS solution (7 mmol/L) and K2S2O8 solution (2.45 mmol/L) were reacted at room temperature in the dark for 12–16 h at a volume ratio of 1:1. Then, 3 mL of mixed solution was reacted with 1 mL of polysaccharide solution (0.15–3 mg/mL) at 25 °C for 6 min, and the absorbance was measured at 734 nm. ABTS scavenging rate% = [1 − (A1 − A2)/A0] × 100[3] A0 is the control group without polysaccharide solution; A1 is the experimental group with polysaccharide solution; and A2 is the background group without ABST solution, that is, the absorbance A2 of the polysaccharide solution here is 0.
## 2.3.4. Reducing Power of GLPs
The experiment was slightly modified according to reference [18]. The Prussian blue method was used to compare the reducing power of GLPs. An amount of 2.5 mL of polysaccharide solution at different concentrations (0.15–3.0 mg/mL) was added to the test tube, followed by PBS (2.5 mL) with pH = 6.6 and $1\%$ K4[Fe(CN)6] (2.5 mL). It was then incubated at 50 °C for 30 min. After cooling to room temperature, $10\%$ CCl3COOH (2.5 mL) was added and centrifuged at 3000 r/min for 10 min. An amount of 2.5 mL of supernatant was added to 5 mL of distilled water and 0.5 mL of $0.1\%$ FeCl3·6H2O solution. After standing for 10 min, the absorbance was measured at 700 nm, and Vc was the positive control.
## 2.3.5. Iron chelating ability of GLPs
According to reference [19], the experiment was slightly modified. First, 1.0 mL of polysaccharide solution with different concentrations (0.15–3 mg/mL) was added to the test tube, followed by ferrous chloride solution (2.0 mmol/L, 0.05 mL), phenazine solution (5.0 mmol/L, 0.2 mL) and water (2.25 mL). After 10 min of mixing, Fe2+ ions could form a stable chelate with phenazine, and their characteristic absorption at 562 nm could be detected using ultraviolet spectrophotometry. After adding polysaccharide solution, the content of Fe2+ in the solution decreased and the absorbance decreased due to the chelating of polysaccharide with Fe2+. The control group was treated with water instead of ferrous chloride solution, the blank group was treated with water instead of polysaccharide solution and EDTA was used as positive control. Fe2+ chelating ability% = [A0 − (A1 − A2)]/A0 × 100[4] A1 is the absorbance of polysaccharide solution group, A2 is the absorbance of the control group and A0 is the absorbance of the blank group.
## 2.4.1. Crystal Growth
CaCl2 solution (22 mmol/L, 40 mL) was added to the beaker, and GLPs with a final concentration of 1.0 g/L were added. Then, 8 mL of distilled water was added and stirred at 37 °C for 5 min, and Na2Ox (22 mmol/L, 40 mL) was added to the above system. After reacting at 37 °C for 10 min, it was kept for 2 h and centrifuged. The bottom CaOx precipitate was washed with distilled water and anhydrous ethanol successively, then finally dried in a vacuum-drying oven.
## 2.4.2. XRD Characterization of Crystals
According to the XRD spectrum, the relative contents of COM and COD in CaOx were calculated using the K value method. The percentage of COD is:COD%=ICODICOM+ICOD×100 where ICOM is the intensity of the main diffraction peak (1¯01) plane of COM and ICOD is the intensity of the main diffraction peak [200] plane of COD.
## 2.4.3. SEM Characterization of Crystals
First, 1 mg of CaOx crystal was ultrasonically dispersed in 10 mL of anhydrous ethanol. The crystal was spotted on the quartz substrate after low-power ultrasound for 3 min and dried in vacuum at 50 °C. The size and morphology of the crystal were observed using SEM after gold spraying.
## 2.4.4. Zeta Potential of Crystals
An amount of 10 mg of CaOx crystals was dispersed in 30 mL of distilled water, and the Zeta potential value was measured using the Zeta potential analyzer after 10 min of ultrasound.
## 2.4.5. Thermogravimetric Analysis of Crystals
A nitrogen gas flow was used, and the test temperature was 30–900 °C with a temperature rise rate of 10 °C/min.
## 2.5.1. Cell Culture
HK-2 cells were cultured with DMEM/F12 medium containing $10\%$ FBS and $1\%$ penicillin–streptomycin at 37 °C, $5\%$ CO2 and saturated humidity. When the cells reached 80–$90\%$ confluence, 1 mL of $0.25\%$ trypsin-EDTA digestive solution was added, and the cells were cultured in an incubator at 37 °C for 3–5 min. The degree of cell digestion was observed under a microscope. During moderate digestion, DMEM/F12 medium containing $10\%$ FBS was added to terminate the digestion and gently blown to form a cell suspension. The cell suspension was inoculated into the corresponding culture dish to make the cells grow for subsequent experiments.
## 2.5.2. CCK-8 Assay for the Toxicity of Different Crystals on HK-2 Cells
HK-2 cells were seeded in 96-well plates at 1.0 × 105 cells/mL, 100 μL/well, and incubated in a $5\%$ CO2 incubator at 37 °C for 24 h. After arrival time, the following groupings were performed: [1] normal control group: added serum-free medium; [2] pure COM and pure COD group: 200 μg/mL pure COM or pure COD crystals synthesized according to ref. [ 20] were added; [3] DC control group: added 200 µg/mL CaOx crystals formed without polysaccharides; [4] crystal damage group: added 200 µg/mL CaOx crystals regulated by GLPs. After incubation for 6 h, 10 μL of CCK-8 reagent was added to each well and incubated for 1 h at 37 °C in the dark. The OD value was detected using the microplate reader at 450 nm wavelength. The formula is as follows:Cell viability=treatment group(OD)control group(OD)×100
## 2.5.3. Cell SOD Activity
The cell inoculation density and grouping were the same as with CCK-8 detection. After arrival time, the cells were collected, and 20 μL of the sample, 160 μL of the NBT/enzyme working solution and 20 μL of the reaction initiation working solution were taken, with the OD value recorded as Ai. The same volume of SOD detection buffer instead of the sample was taken as a blank control, and the OD value was recorded as A1. An amount of 40 μL of SOD detection buffer instead of 20 μL of the sample and 20 μL of the reaction initiation working solution was taken as a blank control, and the OD value was recorded as A2. After incubation at 37 °C for 30 min, the absorbance was measured at 560 nm, and 600 nm was set as the reference wavelength.
Calculation of inhibition percentage:inhibition percentage% = (A1 − Ai)/(A1 − A2) × 100 Calculation of SOD activity: SOD activity unit in the detection system = inhibition percentage/(1–inhibition percentage) units
## 2.5.4. The Amount of MDA in Cells
The cell inoculation density and grouping were the same as with CCK-8 detection. After arrival time, cells were collected and 0.1 mL of different concentrations of standards were added to make standard curves. An amount of 0.1 mL of homogenate, lysate or PBS was added as a blank control, and 0.1 mL of samples were added for determination. Subsequently, 0.2 mL of MDA detection working solution was added to the above groups, mixed, heated at 100 °C for 15 min, cooled to room temperature, and centrifuged at 1000× g for 10 min. An amount of 200 μL of supernatant was added to the 96-well plate, and the absorbance was measured at 532 nm using an enzymometer.
## 2.5.5. Detection of Osteopontin (OPN) Expression on Cell Surface
Cells were seeded in a confocal plate at 1.0 × 105 cells/mL and 1 mL/well. The cell grouping was the same as with CCK-8 detection. After incubation for 6 h, the cells were washed with PBS, fixed with $4\%$ paraformaldehyde for 10 min, washed with PBS three times and then blocked with goat serum for 20 min. Subsequently, the cells were incubated with OPN antibody (1:100) overnight at 4 °C, washed three times with PBS, incubated with FITC antibody (1:100) in a 37 °C incubator for 30 min in the dark, washed three times with PBS and finally incubated with a small amount of DAPI at room temperature for 10 min. The cells were washed three times with PBS, and then the expression of OPN was observed under a laser scanning confocal microscope. The fluorescence semi-quantitative analysis of OPN was performed using ImageJ software.
## 2.5.6. Detection of Reactive Oxygen Species (ROS) Level by DCFH-DA Staining
Cells were seeded in a 6-well plate at 1.0 × 105 cells/mL and 1 mL/well. Cell grouping was the same as with CCK-8 detection. After 6 h of incubation, the cells were washed with pre-cooled PBS, and 1 mL of DCFH-DA staining solution (1:1000) was added to each well. The cells were incubated at 37 °C for 30 min in the dark. The cells were washed with PBS and observed under an inverted fluorescence microscope. The fluorescence semi-quantitative analysis of ROS was performed using ImageJ software.
## 2.5.7. Detection of Apoptosis and Necrosis by Hoechst 33342-PI Double Staining
The cell inoculation density and grouping were the same as with ROS detection. After incubation for 6 h, 800 μL of cell staining buffer was added to each well, and 5 μL of Hoechst 33,342 staining solution was added. Finally, 5 μL of PI staining solution was added and mixed well. After incubating at 4 °C for 30 min, the cells were washed with PBS and observed under an inverted fluorescence microscope. The fluorescence semi-quantitative analysis of apoptosis and necrosis was performed using ImageJ software.
## 2.5.8. Statistical Analysis
All data are expressed as the mean ± standard deviation (x¯ ± SD) of three parallel groups. A one-way ANOVA was performed using IBM SPSS Statistics 26 software. $p \leq 0.05$ indicates no significant difference, 0.01 < $p \leq 0.05$ indicates a significant difference and $p \leq 0.01$ indicates a highly significant difference.
## 3.1. Degradation of GLP0
GLP0 was degraded by different concentrations of H2O2 to obtain seven degraded polysaccharide fragments (Table 1). They were named GLP1–GLP7, with molecular weights of 106, 49.6, 10.5, 6.14, 5.06, 3.71 and 2.42 kDa, respectively.
## 3.2.1. Scavenging Hydroxyl Radical (·OH)
The hydroxyl radical is a free radical that does great harm to organisms, polysaccharides can eliminate them by providing single electrons or hydrogen atoms for free radicals [21]. Figure 1A shows the hydroxyl radical scavenging ability of the seven polysaccharides (named GLP1, GLP2, GLP3, GLP4, GLP5, GLP6 and GLP7, respectively) with Mw of 2.42, 3.71, 5.06, 6.14, 10.5, 49.6 and 106 kDa, respectively. It can be seen that: [1] For the same kind of polysaccharide, with the increase in polysaccharide concentration, the ability of scavenging hydroxyl radicals increased, indicating that the antioxidant activity of polysaccharide was concentration-dependent.
[2] For different kinds of polysaccharides, when the Mw of polysaccharides increased from 2.42 kDa to 49.6 kDa, the scavenging ability of polysaccharides on hydroxyl radicals increased continuously. However, when the Mw reached 106 kDa, its scavenging ability decreased. GLP2, with Mw = 49.6 kDa, had the strongest hydroxyl radical scavenging ability.
## 3.2.2. Scavenging DPPH Free Radical
The DPPH free radical is a stable free radical that is commonly used to test antioxidant capacity. The DPPH radical has maximum absorbance at 517 nm. When antioxidants provide hydrogen atoms for the DPPH radical to obtain the non-radical compound DPPH-H, the purple disappears and the absorbance decreases [22]. The results of scavenging DPPH free radicals by seven GLPs are shown in Figure 1B. For the same kind of polysaccharide, its DPPH free radical scavenging ability was concentration-dependent.
The scavenging rule of the DPPH free radical by different kinds of polysaccharides was consistent with that of the hydroxyl free radical. That is, GLP2 with Mw = 49.6 kDa had the strongest scavenging ability.
## 3.2.3. Scavenging ABTS Free Radical
ABTS free radical scavenging is a common method for detecting the total antioxidant capacity of antioxidants [23]. As shown in Figure 1C, the ability of the same kind of polysaccharide to scavenge ABTS free radicals was concentration-dependent. The higher the polysaccharide concentration was, the stronger the scavenging capacity. For different kinds of polysaccharides, the strongest scavenging ability was still GLP2, with Mw = 49.6 kDa.
## 3.2.4. Reducing Power
The antioxidant can provide a single electron to reduce the trivalent iron of K4[Fe(CN)6]) to divalent iron, which further reacts with FeCl3·6H2O to form Prussian blue (Fe4[Fe6(CN)3]3), which has maximum absorbance at 700 nm. Therefore, the reducing power can be indirectly reflected by measuring the absorbance at 700 nm. The greater the absorbance, the stronger the reducing power of polysaccharides.
From Figure 1D, it can be seen that the absorbance of the same kind of polysaccharide at 700 nm increased with the increase in polysaccharide concentration; that is, the reducing power increased with the increase in polysaccharide concentration and was shown to be concentration-dependent.
For different kinds of polysaccharides, with the increase in Mw from 2.42 kDa to 49.6 kDa, the absorbance of polysaccharides increased, indicating that the reducing power was enhanced. However, the absorbance of GLP1 with Mw = 106 kDa was lower than that of GLP2, with Mw = 49.6 kDa, indicating that GLP had the best reducing power when Mw was about 49.6 kDa.
## 3.2.5. Fe2+ Chelating Ability
Ferrous ions (Fe2+) can activate lipid peroxidation and accelerate the oxidation rate of lipid compounds through the Fenton reaction, so their antioxidant activity can be evaluated by detecting the ability of polysaccharides to chelate Fe2+ ions [24].
Figure 1E shows that GLPs have the ability to scavenge Fe2+ in a concentration-dependent manner. At the same concentration, the chelating ability of GLPs to Fe2+ increased with the decrease in Mw. For example, at the concentration of 2.0 mg/mL, the Fe2+chelation rate of was GLP7 ($86.3\%$) > GLP6 ($85.9\%$) > GLP5 ($84.5\%$) > GLP4 ($79.8\%$) > GLP3 ($55.5\%$) > GLP2 ($46.6\%$) > GLP1 ($41.4\%$).
## 3.3. GLPs Regulate Crystal Growth of Calcium Oxalate
According to the preliminary experimental results, four representative polysaccharides, GLP1, GLP3, GLP5 and GLP7, with relatively large differences in properties and Mw of 106, 10.5, 5.06 and 2.42 kDa, were selected to study their regulatory effects on the growth of CaOx crystals.
## 3.3.1. XRD Characterization
Figure 2 shows the XRD spectra of CaOx crystals regulated by GLPs at 1.0 g/L. The diffraction peaks of crystals without polysaccharides appeared at $d = 0.591$, 0.364, 0.296 and 0.235 nm, which are the characteristic diffraction peaks of COM crystals, corresponding to their (1¯01), [020], (2¯02) and [130] crystal planes; that is only COM crystals were formed in the crystal control group without polysaccharides (Figure 2A).
In the CaOx crystals regulated by GLPs, with the decrease in Mw, the diffraction peaks at $d = 0.617$, 0.441 and 0.277 nm in the XRD spectra gradually increased. These diffraction peaks corresponded to the [200], [211] and [411] crystal planes of COD crystals (Figure 2A), indicating that the proportion of COD crystals in CaOx crystals increased with the decrease in Mw. The semi-quantitative calculation of the K value method showed that the percentages of COD regulated by GLP1, GLP3, GLP5 and GLP7 were $16.67\%$, $60.87\%$, $67.84\%$ and $79.52\%$, respectively (Figure 2B).
## 3.3.2. SEM Observation
Figure 3 shows the SEM images of CaOx crystals regulated by four GLPs at a concentration of 1.0 g/L. In the crystal group without polysaccharides, the crystal morphology was flaky and the crystal size was small (about 0.8 μm). The crystal morphology was irregular, and there were obvious aggregation phenomena (Figure 3a). XRD (Figure 2A) shows that these crystals were COM crystals. In the presence of 1.0 g/L GLPs, straw-hat-like or tetragonal bipyramidal crystals appeared. These crystals were COD crystals, which were consistent with the morphology of COD reported in the literature [25]. The crystal size regulated by polysaccharides was larger than that of the control group (about 1.6–3.7 μm). As the Mw of polysaccharides decreased (from GLP1 to GLP7), the proportion of straw-cap COD crystals increased, and the degree of crystal aggregation decreased significantly.
## 3.3.3. Zeta Potential
Compared with the crystals without polysaccharides (–1.56 mV), the Zeta potential of the crystals regulated by the four GLPs became more negative (–21.7 mV to –30.2 mV) (Figure 4). With the decrease in Mw of GLPs, the Zeta potential became more negative, indicating that the lower the Mw of GLPs, the stronger the anti-aggregation ability of CaOx crystals regulated by GLPs.
## 3.3.4. Thermal Gravimetric Analysis
The decomposition of COM, namely CaC2O4·H2O, went through three stages, and the theoretical weight loss rates were $12.33\%$ (Formula [5]), $19.18\%$ (Formula [6]) and $30.13\%$ (Formula [7]), respectively. It can be seen from Figure 5 that the crystal decomposition without polysaccharides also went through three stages (DC group in Figure 5), and the weight loss rates were $12.82\%$, $18.42\%$ and $29.65\%$, respectively (Table 2), which was basically consistent with the theoretical weight loss rate of COM, indicating that the crystal control group without polysaccharides was COM. CaC2O4·H2O → CaC2O4 + H2O[5] CaC2O4 → CO + CaCO3[6] CaCO3 → CO2 + CaO[7] In the CaOx crystals regulated by four GLPs at a concentration of 1.0 g/L, COM and COD crystals were formed simultaneously, and GLPs may also be adsorbed in the regulated crystals, which results in different TGA curves to the DC group.
With the decrease in GLPs Mw, the weight loss rate in stage I (30–220 °C) increased gradually (13.68–$17.50\%$) (Table 2). This is because more and more COD crystals were regulated by GLPs, and COD crystal had one more bound water than COM crystal. Therefore, the weight loss rate at stage I was getting higher and higher, which is consistent with the above XRD (Figure 2) and SEM (Figure 3) results.
The crystals obtained without polysaccharides did not show weight loss at stage II (220–400 °C), whereas the crystals regulated by GLPs showed weightlessness at this temperature, which was caused by the decomposition of polysaccharides [26]. In other words, the mass loss within the temperature range can be considered as the mass of polysaccharides adsorbed in the crystals. As the Mw of GLPs decreased, the proportion of polysaccharides adsorbed into the crystal increased (5.04–$9.49\%$) (Table 2). The adsorption of polysaccharides on the crystal surface is mainly attributed to the special interaction between polysaccharides and crystals [27]. Fang et al. [ 28] showed that polysaccharides rich in –OSO3– had a high interaction with CaOx crystals, and thus, more polysaccharides could be embedded into CaOx crystals, which is consistent with the results of this paper.
In stage III (440–540 °C), the weight loss rate decreased from $18.42\%$ in the DC group to 9.93–$13.59\%$ in the GLPs group (Table 2). With the decrease in GLPs Mw, the weight loss rate was smaller, which was due to the higher proportion of crystalline water and polysaccharide loss in stage I and II.
In stage IV of decomposition (540–740 °C), with the decrease in GLPs Mw, the weight loss rate also decreased to varying degrees (26.40–$29.47\%$) (Table 2), which was consistent with the above results.
## 3.4. Cytotoxicity of CaOx Crystals Regulated by GLPs with Different Molecular Weights
The cytotoxicity of CaOx crystals regulated by GLP1, GLP3, GLP5 and GLP7 was studied. Due to the different properties of polysaccharides, the properties of the regulated CaOx crystals are also different, thus showing different cytotoxicity.
## 3.4.1. Cell Viability
Figure 6 shows the cell viability changes of normal HK-2 cells after injury induced by pure COM crystal, pure COD crystal, a crystal control group without polysaccharides (DC group) and CaOx crystals regulated by GLP1, GLP3, GLP5 and GLP7. Compared with the normal control group ($100\%$ ± $2.4\%$), the cell viability of the crystal damage group was significantly decreased (56.5–$86.8\%$), indicating that CaOx crystals caused damage to cells. The cytotoxicity of the crystals regulated by GLPs (65.3–$86.8\%$) was weaker than that of the DC group ($56.5\%$), and the cytotoxicity of CaOx crystals decreased with the decrease in Mw of GLPs.
The cytotoxicity of pure COM was the highest (with cell viability of $56.5\%$), and the cytotoxicity of pure COD (with cell viability of $78.8\%$) was greater than that of the GLP7 group (with cell viability of $86.8\%$). Even the GLP7 group only regulated $79.52\%$ of COD formation due to the embedding of polysaccharides in the latter (Figure 5) reducing cytotoxicity [29].
## 3.4.2. SOD Activity and MDA
Compared with the normal control group (9.4 U/mL) with higher SOD activity, the SOD activity of HK-2 cells in the crystal group without polysaccharides (DC group) (3.48 U/mL) was significantly decreased, which was attributed to the large number of cell deaths caused by crystal damage. The CaOx regulated by different GLPs could also cause some cell death, but the death rate was lower than that of the DC group. Therefore, the SOD activity of the GLPs groups (4.11–6.68 U/mL) was higher than that of the DC group (Figure 7a), and the SOD activity increased with the decrease in Mw of GLPs.
The content of MDA in the crystal damaged group (3.50–8.03 nmol/mL) was higher than that in the normal control group (1.22 nmol/mL) (Figure 7b). MDA in the GLPs group decreased with the decrease in GLPs Mw. Figure 7 further shows that CaOx crystals have toxic effects on normal HK-2 cells.
## 3.4.3. Osteopontin (OPN) Expression
Immunofluorescence was used to detect the expression of OPN in HK-2 cells after CaOx crystal injury (Figure 8). In the normal control group, there was only a small amount of OPN expression on the cell surface, which was reflected as weak green fluorescence on the cell surface (Figure 8A). The green fluorescence on the cell surface of the crystal damage group was significantly enhanced, and the fluorescence semi-quantitative analysis showed that the average fluorescence intensity was significantly higher than that of the normal control group (371.9–$589.9\%$) (Figure 8B), indicating that the expression of OPN was significantly up-regulated. The fluorescence of the GLP7 group, with the smallest Mw, was relatively weak ($371.9\%$).
## 3.4.4. ROS Level
Figure 9 shows the effect of CaOx crystals on the ROS level in HK-2 cells detected by DCFH-DA. Compared with the low ROS level in the normal control group, the green fluorescence of the cells in the crystal damage group was enhanced to varying degrees (Figure 9A), indicating that the ROS level in the damage group was increased. The ROS level in the crystal control group without polysaccharides ($861.4\%$) was higher than that in the GLPs group (304.2–$775.4\%$) (Figure 9B), while the ROS level in the GLP1 group with the largest Mw ($775.4\%$)was significantly higher than that in the GLP7 group with the smallest Mw ($304.2\%$).
## 3.4.5. Apoptosis and Necrosis Detected by Hoechst 33342-PI Double Staining
The effects of CaOx crystals on apoptosis and necrosis of HK-2 cells were detected using Hoechst 33,342 staining-PI double staining. The number of red-stained cells in the crystal damage group was significantly higher than that in the normal control group (Figure 10A), indicating that the cells in the crystal damage group showed different degrees of late apoptosis or necrosis. The number of red-stained cells in the GLPs crystal group (325.6–$473.8\%$) was positively correlated with the polysaccharide Mw (Figure 10B), i.e., the smaller the Mw, the fewer the red-stained cells and the less the cell damage.
## 4.1. Effect of Polysaccharide Molecular Weight on Its Antioxidant Activity
The factors affecting the biological activity of polysaccharides are diverse, such as monosaccharide composition, main chain composition, branching degree and branched chain, conformation, molecular weight and acid group content in polysaccharides. From the results of 1H NMR, 13C NMR and GC-MS [14], it can be seen that the degradation of GLP0 by H2O2 does not change the main skeleton structure of polysaccharides (all the polysaccharides composed of β-D-galactose and 6-O-sulfate-3,6-α-L-galactose) and monosaccharide composition. The groups that attach to the carbohydrate part, that is, the content of acidic groups (–OSO3– and –COO–) in the GLPs (13.07–$13.56\%$) is also similar (Table 1). Therefore, the molecular weight Mw of GLPs is the main factor affecting the antioxidant activity of GLPs and regulating the growth of CaOx crystals [12,30]. For polysaccharides with different properties, the molecular weight ranges in which they exhibit optimal bioactivity may be different. Too large or too small Mw will lead to reduced antioxidant activity of polysaccharides. In this experiment, the Mw of GLPs had the best antioxidant effect when it was about 49.6 kDa (GLP2). When the Mw was less than or greater than 49.6 kDa, its antioxidant effect gradually decreased. The reasons are as follows:
## 4.1.1. The Antioxidant Activity of Polysaccharides Decreased When Mw Was Too Large
The results in Figure 1 show that GLP2 with Mw = 49.6 kDa had the strongest free radical scavenging (·OH, DPPH and ABTS) (Figure 1A–C) and reducing power (Figure 1D). The reducing power of polysaccharides had a direct positive correlation with its antioxidant capacity. The stronger the reducing power was, the stronger the antioxidant capacity was [30,31]. The antioxidant activity of GLPs with Mw greater than or less than 49.6 kDa was weakened. When the polysaccharide Mw was too large, not only was its water solubility low, the viscosity of the solution was large, which inhibited its activity. In addition, the polysaccharide with a too-large Mw had a closer structure and stronger intramolecular hydrogen bonds, resulting in lower activity of active groups and weaker antioxidant capacity [32]. Qi et al. [ 33] studied the antioxidant activity of four different Mw sulfated *Ulva pertusa* Kjellm (Chlorophyta) (U, U1, U2 and U3, Mw were 151.7, 64.5, 58.0 and 28.2 kDa, respectively). Among them, the smallest Mw U3 had the strongest scavenging superoxide anion and hydroxyl radical activity, and its IC50 were 22.1 μg/mL and 2.8 mg/mL, respectively. The reduction ability and chelating ability of U3 were also the strongest in the four samples.
## 4.1.2. The Antioxidant Activity of Polysaccharides Decreased When Molecular Weight Was Too Small
However, it is not that the smaller the Mw of polysaccharides is, the greater its activity is. When Mw < 49.6 kDa, the antioxidant activity of GLPs decreased with the decrease in Mw. Polysaccharides with repetitive structure and more electron donors (such as hydroxyl) can provide a large number of single electrons and protons for free radicals to terminate free radical chain reactions. When the Mw of polysaccharides was too small, the unique bond linkage mode of polysaccharides and the stereo structure (i.e., conformation) of polysaccharides formed based on intramolecular hydrogen bonds were destroyed, resulting in a decrease in the biological activity of polysaccharides. Lv et al. [ 34] isolated two polysaccharides (PMP-1 and PMP-2) with Mw of 480 and 610 kDa from *Polygonum multiflorum* Thunb using column chromatography. The IC50 values of PMP-1 for scavenging superoxide anion, hydroxyl and hydrogen peroxide were 1.41, 2.65 and 1.39 mg/mL, respectively, while those of PMP-2 were only 0.47, 0.93 and 0.60 mg/mL, respectively, indicating that the antioxidant activity of the PMP-1 component with small Mw decreased. Chen et al. [ 35] extracted three polysaccharide components, LA, LB and LC, from Lentinus edodes, with Mw of 150, 220 and 290 kDa, respectively. In the concentration range of 0.25–4 mg/mL, their scavenging ability for the hydroxyl radical and chelating ability to Fe2+ were LA < LB < LC, indicating that the antioxidant capacity of the LA component with the smallest Mw was the weakest. The extracellular polysaccharide of *Saccharomyces cerevisiae* (sGSCs) had lower biological activity at Mw = 5–10 kDa, but higher activity at 100–200 kDa [36].
## 4.1.3. The Antioxidant Activity of Polysaccharides with Moderate Molecular Weight Was the Highest
In this study, GLPs Mw at about 49.6 kDa (GLP2) had the best antioxidant effect; when Mw was less than or greater than 49.6 kDa, the antioxidant effect was gradually reduced. When the Mw of polysaccharides is moderate, it can not only retain enough sugar units to form the three-dimensional structure of polysaccharides to ensure that its conformation is not destroyed, but also break the highly close molecular structure of natural large Mw polysaccharides before degradation so that the active functional groups (such as sulfuric acid group, carboxyl group and hydroxyl group) are exposed, showing the maximum degree of freedom. The spatial steric hindrance is also the smallest when reacting with organisms, and the water solubility is increased, thus exerting the maximum biological activity [37]. *In* general, the ability of antioxidants to scavenge ABTS free radicals is positively correlated with the ability to scavenge DPPH free radicals. However, GLPs with different Mw had stronger ABTS radical scavenging ability than DPPH radical scavenging ability. For example, at the concentration of 3.0 mg/mL, the ABTS radical scavenging rate of GLP2 was $91.0\%$, while the DPPH radical scavenging rate was only $47.9\%$. This is because the ABTS reagent is more suitable for hydrophilic antioxidants, and the DPPH reagent is more suitable for hydrophobic antioxidants [38]. GLPs are hydrophilic polysaccharides, and GLPs have a higher ABTS radical scavenging rate. Sheng et al. [ 39] degraded CPA with hydrogen peroxide (Mw = 16.89 kDa) and obtained four kinds of degradation sugars (CPA-1, CPA-2, CPA-3 and CPA-4) with Mw of 14.53, 12.37, 11.55 and 0.64 kDa, respectively. At the concentration of 200 μg/mL, the scavenging rates of CPA, CPA-1, CPA-2, CPA-3 and CPA-4 on superoxide anion were $9.68\%$, $7.81\%$, $10.71\%$, $8.97\%$ and $8.57\%$, respectively. It indicated that only CPA-2 with moderate Mw had the strongest free radical scavenging ability. The results of this paper are consistent with the results of Sheng et al. [ 39].
## 4.1.4. The Ability of Small Mw Polysaccharide to Chelate Fe2+Ions Was Stronger
The results for Fe2+ chelating ability of GLPs with different Mw were not completely consistent with the free radical scavenging and reducing power. With the decrease in Mw of GLPs, their ability to chelate Fe2+ ions was continuously enhanced (Figure 1E). This is significantly different from the pattern of GLPs’ scavenging free radicals (·OH, DPPH and ABTS) and reducing power (Figure 1F). This is because the smaller the Mw is, the more exposed the active groups (–OSO3– and –COOH, etc.) it contains and the greater the degree of freedom it has, and thus, the smaller the steric hindrance when it chelates with Fe2+ ions. Much of the literature also shows that low Mw polysaccharide has a stronger ability with complex Fe2+ ions. For example, Zou et al. [ 40] tested the chelating ability of three polysaccharides with Mw of 13,500, 11,700 and 11,500 Da with Fe2+ ions. When the polysaccharide concentration was 500 μg/mL, the chelating rates with Fe2+ ions were $2.47\%$, $5.48\%$ and $5.96\%$, respectively. That is, the smaller the Mw, the stronger the chelating ability of polysaccharides with Fe2+ ions.
## 4.2.1. GLPs Induce the Formation of COD Crystals
XRD (Figure 2A) and SEM (Figure 3) showed that the crystal control group without polysaccharides only formed COM crystals, while each GLP could regulate the formation of COD. As GLPs Mw decreased, the percentage of COD increased (Figure 2B). This is because GLPs contain –OSO3– with negative charge, while the (1¯01) crystal plane of COM crystal has positive charge, and the adsorption sites on the surface of COM crystal are more than those on the surface of COD crystal [41]. Therefore, GLPs can be adsorbed on the surface of COM crystals by electrostatic interaction, thereby preventing the deposition of Ca2+ ions on the surface of COM crystals and inhibiting the growth of COM crystals. In contrast, the COD surface is electrically neutral. Therefore, GLPs inhibit COM growth while promoting the formation of COD [42]. Since the adhesion of COD crystals to renal epithelial cells is much weaker than that of COM, COD is more easily excreted than COM; that is, inducing the formation of COD is more likely to reduce the risk of CaOx kidney stone formation than inducing the formation of COM.
## 4.2.2. GLPs Inhibit Crystal Aggregation
SEM also showed that GLPs inhibited crystal aggregation (Figure 3). Compared with the crystal control group without polysaccharides, the dispersion of CaOx crystals regulated by GLPs was higher. Because GLPs can be adsorbed on the crystal surface in the presence of GLPs, the absolute value of Zeta potential on the crystal surface increases (Figure 4). It is already known that the size of the Zeta potential can reflect the mutual repulsion or attraction between particles. The higher the absolute value of Zeta potential, the more stable the system; the lower the absolute value of Zeta potential, the more likely the system is to condense or agglomerate. The results of this study show that as the Mw of GLPs decreased, the Zeta potential of the crystal surface became more negative; the repulsion between crystals was larger, and the crystals were more dispersed.
## 4.3. Toxicity Difference of CaOx Crystals Regulated by GLPs on HK-2 Cells
The results of this study show that the cytotoxicity of CaOx crystals regulated by GLPs with different Mw was negatively correlated with the antioxidant activity of GLPs; that is, the smaller the Mw, the stronger the antioxidant activity of GLPs and the smaller the cytotoxicity of CaOx crystals regulated by GLPs. This is mainly due to the difference in the percentage of COM and COD in CaOx crystals regulated by different GLPs. Under the same concentration condition, the GLP1 with the largest Mw only regulates $16.67\%$ of COD, while the GLP7 with the smallest Mw regulates $79.52\%$ of COD (Figure 2B). Since the positive charge density on the COM surface is higher than that on COD [43], and the specific surface area of COM crystal is also larger than that of COD (Figure 3), the adhesion of COM to damaged cells with negatively charged molecules is stronger than that of COD [44]; that is, the cytotoxicity of COM is much larger than that of COD [20,43]. The results of this paper show that the higher the COD content of the crystal, the greater the cell viability (Figure 6) and MDA level (Figure 7b), and the smaller the SOD activity (Figure 7a), OPN expression (Figure 8), ROS level (Figure 9) and necrotic cell rate (Figure 10); that is, the cytotoxicity of CaOx crystals regulated by low Mw GLPs is less than that of CaOx crystals regulated by high Mw GLPs. Figure 11 shows the different cytotoxicity of CaOx crystals regulated by two different Mw polysaccharides (GLP1 and GLP7) on HK-2 cells. This result relates to the following factors:
## 4.3.1. The Cytotoxicity of COM Is Greater Than That of COD
With the decrease in Mw of GLPs, the percentage of COD in the regulated crystals increased (Figure 2B). Since the cytotoxicity of COD crystal is lower than that of COM crystal [45], the higher the percentage of COD in CaOx crystal, the lower the cytotoxicity. In addition, COM and COD crystals have different dynamic binding modes and pathological behavior [46]. COM has a strong binding ability for binding to the cell surface of different organic molecular arrays, thus becoming an aggregation center for stone formation [47]; COD has difficulties forming stable aggregates or strong adhesion contact with renal epithelial cells [48]. Therefore, compared with COM crystals, COD crystals cause less damage to renal tubular epithelial cells [49]. In fact, there are a large number of COD microcrystals in urine, and the proportion of COM in stones is high [48].
## 4.3.2. Crystals with Blunt Morphology Have Less Cytotoxicity
It can be seen from Figure 3 that as the Mw of GLPs decreases, the regulated CaOx crystal edges become more blunt. This is because there is a complexation–dissociation equilibrium between GLPs and Ca2+ ions on the surface of CaOx crystals (especially Ca2+ ions located at the edge and tip of CaOx crystals) during the growth of CaOx crystals regulated by GLPs. On the one hand, the formed crystals dissolve continuously due to complexation with GLPs. On the other hand, the dissolved Ca2+ ions are continuously deposited on the surface of CaOx crystals. This continuous dissolution–deposition equilibrium leads to more round and blunt crystals [49]. GLP7 has the smallest Mw and the highest activity, so it has the strongest complexation–dissociation equilibrium balancewith Ca2+ ions. The regulated crystals not only have the highest COD content, but also are more round and blunt, which can reduce the mechanical damage to cells. In addition, blunt crystals are more easily excreted through urine, which helps reduce the risk of kidney stones [29].
## 4.3.3. Small-Size Crystals Have Greater Cytotoxicity
Furthermore, the toxicity of COM and COD to cells is also closely related to the size of crystals. It can be seen from Figure 3 that the size of CaOx crystals increased with the decrease in GLPs Mw, but the crystals regulated by the four GLPs groups were larger than the control group without polysaccharides. When the crystal size increased, the cytotoxicity decreased [50].
## 4.3.4. Crystal Toxicity with High Dispersion Is Smaller
In addition, the degree of crystal aggregation also has an important effect on cytotoxicity. Aggregated crystals are more likely to adhere to the surface of renal epithelial cells and damage cells, which also increases the risk of kidney stones formation [51]. In fact, compared with healthy controls, urinary microcrystals in patients with kidney stones showed sharp edges and obvious aggregation [20].
Since the formed CaOx crystals are highly aggregated without GLPs (Figure 3), their cytotoxicity is also the largest (Figure 6). With the decrease in Mw of GLPs, the aggregation degree of the regulated CaOx crystals decreased, and the cytotoxicity of crystals also decreased.
## 4.3.5. The Crystal Cytotoxicity of Doped Polysaccharides Is Smaller
Crystal surface adsorption or crystal-doped polysaccharides can reduce the toxicity of crystals to cells. Our previous studies [52,53] have shown that a certain amount of polysaccharide (6–$21\%$) is doped or adsorbed in the CaOx crystals generated by polysaccharide regulation. In this paper, a similar conclusion was obtained through thermogravimetric analysis. The polysaccharide contents in the crystals regulated by GLP1, GLP3, GLP5 and GLP7 were $5.04\%$, $6.31\%$, $8.81\%$ and $9.49\%$, respectively (Table 2). The polysaccharides doped in the crystals can play a good role in cell protection and reduce the toxicity of CaOx crystals, including preventing the destruction of the cell morphology and cytoskeleton, inhibiting the production of ROS and the decrease in lysosome integrity and reducing the expression of OPN and transmembrane proteins (CD44).
## 5. Conclusions
The seven kinds of GLPs, with Mw of 106, 49.6, 10.5, 6.14, 5.06, 3.71 and 2.42 kDa, had good reducing power and the ability to scavenge hydroxyl radical, DPPH radical and ABTS radical. GLP2, with Mw of 49.6 kDa, had the best antioxidant effect. With the decrease in Mw of GLPs, its ability to chelate Fe2+ ions increased. These GLPs can inhibit the growth of COM crystals to varying degrees, induce the formation of COD crystals, improve the absolute value of Zeta potential on the crystal surface, reduce the aggregation between crystals and make the edges and corners of the crystals more blunt. The toxicity of CaOx crystals regulated by GLPs on HK-2 cells was reduced, resulting in increased cell viability and MDA level, as well as decreased SOD activity, OPN expression, ROS level and necrotic cell number. GLPs, especially GLP7, may be potential stone preventive drugs.
## References
1. Zeng G., Mai Z., Xia S., Wang Z., Zhang K., Wang L., Long Y., Ma J., Li Y., Wan S.P.. **Prevalence of kidney stones in China: An ultrasonography based cross-sectional study**. *BJU Int.* (2017) **120** 109-116. DOI: 10.1111/bju.13828
2. Lai Y., Zheng H., Sun X., Lin J., Li Q., Huang H., Hou Y., Zhong H., Zhang D., Fucai T.. **The advances of calcium oxalate calculi associated drugs and targets**. *Eur. J. Pharmacol.* (2022) **935** 175324. DOI: 10.1016/j.ejphar.2022.175324
3. Kvsrg P., Sujatha D., Bharathi K.. **Herbal drugs in urolithiasis-a review**. *Pharmacogn. Rev.* (2007) **1** 175-178
4. Wang Z., Zhang Y., Zhang J., Deng Q., Liang H.. **Recent advances on the mechanisms of kidney stone formation**. *Int. J. Mol. Med.* (2021) **48** 149. DOI: 10.3892/ijmm.2021.4982
5. Sun X.Y., Ouyang J.M., Li Y.B., Wen X.L.. **Mechanism of cytotoxicity of micron/nano calcium oxalate monohydrate and dihydrate crystals on renal epithelial cells**. *RSC Adv.* (2015) **5** 45393-45406. DOI: 10.1039/C5RA02313K
6. De Almeida C.L.F., Falcão D.S., Lima D.M., Montenegro D.A., Lira N.S., De Athayde-Filho P.F., Rodrigues L.C., De Souza M.F.V., Barbosa-Filho J.M., Batista L.M.. **Bioactivities from marine algae of the genus Gracilaria**. *Int. J. Mol. Sci.* (2011) **12** 4550-4573. DOI: 10.3390/ijms12074550
7. Liao X., Yang L., Chen M., Yu J., Zhang S., Ju Y.. **The hypoglycemic effect of a polysaccharide from Gracilaria lemaneiformis and its degradation products in diabetic mice**. *Food Funct.* (2015) **6** 2542-2549. DOI: 10.1039/C4FO01185F
8. Liu Q., Zhou Y., Ma L., Gu F., Liao K., Liu Y., Zhang Y., Liu H., Hong Y., Cao M.. **Sulfate oligosaccharide of Gracilaria lemaneiformis modulates type 1 immunity by restraining T cell activation**. *Carbohydr. Polym.* (2022) **288** 119377. DOI: 10.1016/j.carbpol.2022.119377
9. Tang L., Luo X., Wang M., Wang Z., Guo J., Kong F., Bi Y.. **Synthesis, characterization, in vitro antioxidant and hypoglycemic activities of selenium nanoparticles decorated with polysaccharides of Gracilaria lemaneiformis**. *Int. J. Biol. Macromol.* (2021) **193** 923-932. DOI: 10.1016/j.ijbiomac.2021.10.189
10. Long X., Hu X., Xiang H., Chen S., Li L., Qi B., Li C., Liu S., Yang X.. **Structural characterization and hypolipidemic activity of Gracilaria lemaneiformis polysaccharide and its degradation products**. *Food Chem. X* (2022) **14** 100314. DOI: 10.1016/j.fochx.2022.100314
11. Liu W., Wang H., Pang X., Yao W., Gao X.. **Characterization and antioxidant activity of two low-molecular-weight polysaccharides purified from the fruiting bodies of Ganoderma lucidum**. *Int. J. Biol. Macromol.* (2010) **46** 451-457. DOI: 10.1016/j.ijbiomac.2010.02.006
12. Sun L.Q., Wang C.H., Shi Q.J., Ma C.H.. **Preparation of different molecular weight polysaccharides from Porphyridium cruentum and their antioxidant activities**. *Int. J. Biol. Macromol.* (2009) **45** 42-47. DOI: 10.1016/j.ijbiomac.2009.03.013
13. Li N., Wang C., Georgiev M.I., Bajpai V.K., Tundis R., Simal-Gandara J., Lu X., Xiao J., Tang X., Qiao X.. **Advances in dietary polysaccharides as anticancer agents: Structure-activity relationship**. *Trends Food Sci. Technol.* (2021) **111** 360-377. DOI: 10.1016/j.tifs.2021.03.008
14. Guo D., Yu K., Sun X.-Y., Ouyang J.-M.. **Structural characterization and repair mechanism of Gracilaria lemaneiformis sulfated polysaccharides of different molecular weights on damaged renal epithelial cells**. *Oxid. Med. Cell. Longev.* (2018) **2018** 7410389. DOI: 10.1155/2018/7410389
15. Jiang J., Kong F., Li N., Zhang D., Yan C., Lv H.. **Purification, structural characterization and in vitro antioxidant activity of a novel polysaccharide from Boshuzhi**. *Carbohydr. Polym.* (2016) **147** 365-371. PMID: 27178942
16. Baroš S., Karšayová M., Jomová K., Gáspár A., Valko M.. **Free radical scavenging capacity of Papaver somniferum L. and determination of pharmacologically active alkaloids using capillary electrophoresis**. *J. Microbiol. Biotechnol. Food Sci.* (2021) **2021** 725-732
17. Xu R.-B., Yang X., Wang J., Zhao H.-T., Lu W.-H., Cui J., Cheng C.-L., Zou P., Huang W.-W., Wang P.. **Chemical composition and antioxidant activities of three polysaccharide fractions from pine cones**. *Int. J. Mol. Sci.* (2012) **13** 14262-14277. DOI: 10.3390/ijms131114262
18. Berker K.I., Demirata B., Apak R.. **Determination of total antioxidant capacity of lipophilic and hydrophilic antioxidants in the same solution by using ferric-ferricyanide assay**. *Food Anal. Method* (2012) **5** 1150-1158. DOI: 10.1007/s12161-011-9358-2
19. Elessawy F.M., Vandenberg A., El-Aneed A., Purves R.W.. **An untargeted metabolomics approach for correlating pulse crop seed coat polyphenol profiles with antioxidant capacity and iron chelation ability**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26133833
20. Sun X.Y., Ouyang J.M., Liu A.J., Ding Y.M., Gan Q.Z.. **Preparation, characterization, and in vitro cytotoxicity of COM and COD crystals with various sizes**. *Mater. Sci. Eng. C* (2015) **57** 147-156. DOI: 10.1016/j.msec.2015.07.032
21. Yarley O.P.N., Kojo A.B., Zhou C., Yu X., Gideon A., Kwadwo H.H., Richard O.. **Reviews on mechanisms of in vitro antioxidant, antibacterial and anticancer activities of water-soluble plant polysaccharides**. *Int. J. Biol. Macromol.* (2021) **183** 2262-2271. DOI: 10.1016/j.ijbiomac.2021.05.181
22. Yuan Y., Macquarrie D.. **Microwave assisted extraction of sulfated polysaccharides (fucoidan) from Ascophyllum nodosum and its antioxidant activity**. *Carbohydr. Polym.* (2015) **129** 101-107. DOI: 10.1016/j.carbpol.2015.04.057
23. Wang D., Li W., Li D., Li L., Luo Z.. **Effect of high carbon dioxide treatment on reactive oxygen species accumulation and antioxidant capacity in fresh-cut pear fruit during storage**. *Sci. Hortic.* (2021) **281** 109925. DOI: 10.1016/j.scienta.2021.109925
24. Xiong Q., Xia L., Zhou R., Hao H., Li S., Jing Y., Zhu C., Zhang Q., Shi Y.. **Extraction, characterization and antioxidant activities of polysaccharides from E. corneum gigeriae galli**. *Carbohydr. Polym.* (2014) **108** 247-256. DOI: 10.1016/j.carbpol.2014.02.068
25. Zhang J., Wang L., Zhang W., Putnis C.V.. **Role of Hyperoxaluria/Hypercalciuria in Controlling the Hydrate Phase Selection of Pathological Calcium Oxalate Mineralization**. *Cryst. Growth Des.* (2020) **21** 683-691. DOI: 10.1021/acs.cgd.0c01512
26. Xu C.P., Yang C.C., Mao D.B.. **Fraction and chemical analysis of antioxidant active polysaccharide isolated from flue-cured tobacco leaves**. *Pharmacogn. Mag.* (2014) **10** 66-69. DOI: 10.4103/0973-1296.126664
27. Zhu P., Xu J., Sahar N., Morris M.D., Kohn D.H., Ramamoorthy A.. **Time-resolved dehydration-induced structural changes in an intact bovine cortical bone revealed by solid-state NMR spectroscopy**. *J. Am. Chem. Soc.* (2009) **131** 17064-17065. DOI: 10.1021/ja9081028
28. Fang W., Zhang H., Yin J., Yang B., Zhang Y., Li J., Yao F.. **Hydroxyapatite crystal formation in the presence of polysaccharide**. *Cryst. Growth Des.* (2016) **16** 1247-1255. DOI: 10.1021/acs.cgd.5b01235
29. Chen J.Y., Sun X.Y., Ouyang J.M.. **Modulation of calcium oxalate crystal growth and protection from oxidatively damaged renal epithelial cells of corn silk polysaccharides with different molecular weights**. *Oxid. Med. Cell. Longev.* (2020) **2020** 6982948. DOI: 10.1155/2020/6982948
30. Munteanu I.G., Apetrei C.. **Analytical methods used in determining antioxidant activity: A review**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22073380
31. Li C., Wang E., Elshikh M.S., Alwahibi M.S., Wang W., Wu G., Shen Y., Abbasi A.M., Shan S.. **Extraction and purification of total flavonoids from Gnaphalium affine D. Don and their evaluation for free radicals’ scavenging and oxidative damage inhabitation potential in mice liver**. *Arab. J. Chem.* (2021) **14** 103006. DOI: 10.1016/j.arabjc.2021.103006
32. Huang S., Chen F., Cheng H., Huang G.. **Modification and application of polysaccharide from traditional Chinese medicine such as Dendrobium officinale**. *Int. J. Biol. Macromol.* (2020) **157** 385-393. DOI: 10.1016/j.ijbiomac.2020.04.141
33. Qi H.M., Zhao T.T., Zhang Q.B., Li Z., Zhao Z.Q., Xing R.. **Antioxidant activity of different molecular weight sulfated polysaccharides from Ulva pertusa Kjellm (Chlorophyta)**. *J. Appl. Phycol.* (2005) **17** 527-534. DOI: 10.1007/s10811-005-9003-9
34. Lv L., Cheng Y., Zheng T., Li X., Zhai R.. **Purification, antioxidant activity and antiglycation of polysaccharides from Polygonum multiflorum Thunb**. *Carbohydr. Polym.* (2014) **99** 765-773. DOI: 10.1016/j.carbpol.2013.09.007
35. Chen H., Ju Y., Li J., Yu M.. **Antioxidant activities of polysaccharides from Lentinus edodes and their significance for disease prevention**. *Int. J. Biol. Macromol.* (2012) **50** 214-218. DOI: 10.1016/j.ijbiomac.2011.10.027
36. Lei N., Wang M., Zhang L., Xiao S., Fei C., Wang X., Zhang K., Zheng W., Wang C., Yang R.. **Effects of low molecular weight yeast β-Glucan on antioxidant and immunological activities in mice**. *Int. J. Mol. Sci.* (2015) **16** 21575-21590. DOI: 10.3390/ijms160921575
37. Huang S.-Q., Ding S., Fan L.. **Antioxidant activities of five polysaccharides from Inonotus obliquus**. *Int. J. Biol. Macromol.* (2012) **50** 1183-1187. DOI: 10.1016/j.ijbiomac.2012.03.019
38. Hsu B., Coupar I.M., Ng K.. **Antioxidant activity of hot water extract from the fruit of the Doum palm, Hyphaene thebaica**. *Food Chem.* (2006) **98** 317-328. DOI: 10.1016/j.foodchem.2005.05.077
39. Sheng J., Sun Y.. **Antioxidant properties of different molecular weight polysaccharides from Athyrium multidentatum, (Doll.) Ching**. *Carbohydr. Polym.* (2014) **108** 41-45. DOI: 10.1016/j.carbpol.2014.03.011
40. Zou C., Du Y., Li Y., Yang J., Zhang L.. **Preparation and in vitro antioxidant activity of lacquer polysaccharides with low molecular weights and their sulfated derivatives**. *Int. J. Biol. Macromol.* (2010) **46** 140-144. DOI: 10.1016/j.ijbiomac.2009.11.010
41. Furedi-Milhofer H., Sikiric M., Tunik L., Filipovic-Vincekovic N., Garti N.. **Interactions of organic additives with ionic crystal hydrates: The importance of the hydrated layer**. *Int. J. Mod. Phys. B* (2002) **16** 359-366. DOI: 10.1142/S0217979202009871
42. Yao X.Q., Ouyang J.M., Peng H., Zhu W.Y., Chen H.Q.. **Inhibition on calcium oxalate crystallization and repair on injured renal epithelial cells of degraded soybean polysaccharide**. *Carbohydr. Polym.* (2012) **90** 392-398. DOI: 10.1016/j.carbpol.2012.05.056
43. Singh A., Tandon S., Tanzeer Kaur T., Tandon C.. **In vitro studies on calcium oxalate induced apoptosis attenuated by Didymocarpus pedicellata**. *Biointerface Res. Appl. Chem* (2022) **12** 7342-7355
44. Khan A., Byer K., Khan S.R.. **Exposure of Madin-Darby canine kidney (MDCK) cells to oxalate and calcium oxalate crystals activates nicotinamide adenine dinucleotide phosphate (NADPH)-oxidase**. *Urology* (2014) **83** 510.e1-510.e7. DOI: 10.1016/j.urology.2013.10.038
45. Sun X.Y., Gan Q.Z., Ouyang J.M.. **Calcium oxalate toxicity in renal epithelial cells: The mediation of crystal size on cell death mode**. *Cell Death Discov.* (2015) **1** 15055. DOI: 10.1038/cddiscovery.2015.55
46. Wang T., Thurgood L.A., Grover P.K., Ryall R.L.. **A comparison of the binding of urinary calcium oxalate monohydrate and dihydrate crystals to human kidney cells in urine**. *BJU Int.* (2010) **106** 1768. DOI: 10.1111/j.1464-410X.2010.09258.x
47. Šter A., Šafranko S., Bilić K., Kralj D.. **The effect of hydrodynamic and thermodynamic factors and the addition of citric acid on the precipitation of calcium oxalate dihydrate**. *Urolithiasis* (2018) **46** 243-256. DOI: 10.1007/s00240-017-0991-0
48. Sheng X., Ward M.D., Wesson J.A.. **Crystal surface adhesion explains the pathological activity of calcium oxalate hydrates in kidney stone formation**. *J. Am. Soc. Nephrol.* (2005) **16** 1904-1908. DOI: 10.1681/ASN.2005040400
49. De Bellis R., Piacentini M.P., Meli M.A., Mattioli M., Menotta M., Mari M., Valentini L., Palomba L., Desideri D., Chiarantini L.. **In vitro effects on calcium oxalate crystallization kinetics and crystal morphology of an aqueous extract from Ceterach officinarum: Analysis of a potential antilithiatic mechanism**. *PLoS ONE* (2019) **14**. DOI: 10.1371/journal.pone.0218734
50. Sun X.-Y., Ouyang J.-M., Gan Q.-Z., Liu A.-J.. **Renal epithelial cell injury induced by calcium oxalate monohydrate depends on their structural features: Size, surface, and crystalline structure**. *J. Biomed. Nanotechnol.* (2016) **12** 2001-2014. DOI: 10.1166/jbn.2016.2289
51. Chaiyarit S., Thongboonkerd V.. **Defining and systematic analyses of aggregation indices to evaluate degree of calcium oxalate crystal aggregation**. *Front. Chem.* (2017) **5** 113. DOI: 10.3389/fchem.2017.00113
52. Li C.Y., Liu L., Zhao Y.W., Chen J.Y., Sun X.Y., Ouyang J.M.. **Inhibition of calcium oxalate formation and antioxidant activity of carboxymethylated Poria cocos polysaccharides**. *Oxid. Med. Cell. Longev.* (2021) **2021** 6653593. DOI: 10.1155/2021/6653593
53. Chen X.W., Huang W.B., Sun X.Y., Xiong P., Ouyang J.M.. **Antioxidant activity of sulfated Porphyra yezoensis polysaccharides and their regulating effect on calcium oxalate crystal growth**. *Mater. Sci. Eng. C* (2021) **128** 112338. DOI: 10.1016/j.msec.2021.112338
|
---
title: Effect of Pink Perch Gelatin on Physiochemical, Textural, Sensory, and Storage
Characteristics of Ready-to-Cook Low-Fat Chicken Meatballs
authors:
- Khushboo
- Nutan Kaushik
- Kristina Norne Widell
- Rasa Slizyte
- Asha Kumari
journal: Foods
year: 2023
pmcid: PMC10001017
doi: 10.3390/foods12050995
license: CC BY 4.0
---
# Effect of Pink Perch Gelatin on Physiochemical, Textural, Sensory, and Storage Characteristics of Ready-to-Cook Low-Fat Chicken Meatballs
## Abstract
In recent years consumer demand for low-fat convenience food is increasing rapidly. This study was designed to develop low-fat ready-to-cook (RTC) chicken meatballs using pink perch gelatin. Meatballs were prepared using different concentrations of fish gelatin ($3\%$, $4\%$, $5\%$, and $6\%$). The effect of fish gelatin content on the physico-chemical, textural, cooking, and sensory properties of meatballs was studied. Further, the shelf-life of meatballs was also studied at 4 °C for 15 days and −18 °C for 60 days. The addition of fish gelatin to meatballs decreased the fat content by $67.2\%$ and $79.7\%$ and increased the protein content by $20.1\%$ and $66.4\%$ in comparison to control and Branded Meatballs, respectively. As compared to the Control Meatballs, the addition of fish gelatin also reduced hardness by $26.4\%$ and increased yield and moisture retention in the RTC meatballs by $15.4\%$ and $20.9\%$, respectively. Sensory analysis suggested that a $5\%$ fish gelatin addition in meatballs has the best acceptability among all tested treatments. Storage study indicated that the addition of fish gelatin to RTC meatballs delayed lipid oxidation during both refrigerated and frozen storage. The results suggested that pink perch gelatin can be used as a fat replacer in chicken meatballs and can potentially increase their shelf-life.
## 1. Introduction
The fat component in the food matrix plays an important role in nutrition and also contributes immensely towards organoleptic properties. However, excessive consumption of fat, especially cholesterol and saturated fats, is associated with adverse health conditions and chronic diseases such as type 2 diabetes mellitus, obesity, cardiovascular diseases, and atherosclerosis [1,2]. Therefore, various health organizations are promoting reduced consumption of fat food products. In addition, due to increased consumer awareness regarding the health ramifications of fat-rich food, a shift towards the consumption of low-fat food is being observed in the global market.
Fat plays a significant role in ground meat products such as meatballs. Meatballs are a mixture of an emulsion of ground meat, fat, water, and other ingredients such as flour and spices. It has a relatively high amount of fat, which stabilizes the meat emulsion and elevates organoleptic properties such as texture and flavor [2]. The decrease in palatability associated with fat reduction is the major concern regarding the acceptability of low-fat meat products [3]. In addition, a lowering of the fat content has also been reported to cause undesirable changes in the textural attributes of meat products such as increased firmness [4].
Important properties required for the development of low-fat meatballs are emulsion stability, water binding capacity, melt-in-mouth property, and textural modification. In order to address these issues and improve the nutritional properties of low-fat meat products, fat replacers are being used. On the basis of composition, fat replacers can be classified into three categories: carbohydrates, proteins, and lipid modification-based fat replacers. Protein-based fat replacer, however, provides an added advantage of increasing the nutritional quality of meat products by augmenting the overall protein content of the product. In addition, dietary protein has been observed to be the most satiating macronutrient due to its ability to modify metabolites (e.g., amino acid) and hormones (e.g., insulin and gastrointestinal hormones) [5]. They are also considered to influence metabolic targets for both weight loss and weight management [6]. Gelatin is one of the protein-based fat replacers which can be used in the production of low-fat meatballs.
Gelatin not only provides nutritional and textural benefits, but also imparts antioxidant activity due to its DPPH radical scavenging activity [7]. It is also reported to reduce shrinkage and improve cooking yields in meat products such as meat sausage [8]. The addition of gelatin in meat products improves their functional and textural properties by the virtue of two mechanisms: the first is gelling behavior, i.e., the ability to form gel structure that improves texture and water binding capacity, and the second is surface behavior, i.e., the ability to increase the emulsification, foaming, cohesion, and adhesion which stabilize the colloidal systems [9]. Previous studies have reported the use of fish gelatin extracted from different species such as bighead carp, grass carp, and tilapia to develop low-fat mayonnaise, low-fat milk cream, and low-fat yoghurt, respectively [10,11,12]. However, no study has been reported on the use of pink perch gelatin to develop low-fat chicken meatballs. Recently, pink perch is gaining commercial attention due to its strong gelling capacity, which makes it ideal for gelatin extraction [13]. Gelatin extracted from pink perch is reported to have high gel strength, good water-holding capacity, and emulsion activity [14,15]. In addition, the pink perch gelatin has a melting temperature close to normal human body temperature, which provides the melt-in-mouth property, thus imparting fat-like properties to low-fat products. Moreover, the addition of fish gelatin to meatballs is conducive to ecological sustainability and food security, as most of the fish skin and bones used as raw material for the production of fish gelatin are conventionally being discarded by the fish processing industry which causes environmental pollution [16]. As per our knowledge, this is the first study to report the use of pink perch gelatin as a fat replacer for the development of low-fat ready-to-cook chicken meatballs.
Other ingredients such as meat binders and starch present in meatballs are also known to moderate the functional characteristics such as water-holding capacity (WHC), texture, palatability, and appearance [17,18]. Black gram flour is a common ingredient used in meat products [19,20]. It is added to increase fat emulsification, water retention, formation of meat structure, and to reduce cost [21,22]. Black gram flour forms complex gel networks with meat proteins, which can trap water and other compounds thus forming stronger bonds between them. This phenomenon helps to achieve a higher water retention in the meat matrix during processing.
The objective of this research was to develop low-fat ready-to-cook (RTC) chicken meatballs using pink perch gelatin and to determine the efficacy of pink perch gelatin in improving the physico-chemical characteristics, functional properties, and sensory acceptance of the RTC meatballs as compared to control and Branded Meatballs. Further, the storage stability of low-fat RTC meatballs under refrigerated (4 °C for 15 days) and frozen conditions (−18 °C for 60 days) was also studied.
## 2.1. Materials
Chicken mincemeat was procured from a local market. Potato starch, hydrogenated fat, and a spice mix ((garam masala: a mixture of spices containing clove, cinnamon, cardamom, black pepper, mace, and nutmeg), cumin powder, turmeric powder, red chili powder, garlic powder, ginger powder, and common salt) were procured locally. Roasted black gram flour was prepared as per the method of Modi et al. [ 23]. Branded Meatballs were procured from a local market based on preliminary screening, popularity, and availability. The composition of the Branded Meatballs as claimed on the packaging was chicken meat ($56.16\%$), soya ($10\%$), water, edible vegetable oil, batter (wheat flour, corn flour, corn starch, salt, and emulsifier sodium carboxymethyl cellulose (INS 466)), chili, onion, garlic, ginger, coriander, iodized salt, spices and condiments, and sodium nitrite (INS 250).
## 2.2. Chemicals
Glacial acetic acid and sodium hydroxide were procured from Thermo Fischer Scientific, New Delhi, India. Thiobarbituric acid was procured from LOBA (Loba Chemie Pvt. Ltd., Mumbai, India). All the chemicals used for evaluating the quality of the product were of AR grade.
## 2.3. Pink Perch Gelatin Preparation
Pink perch gelatin was prepared as per the method of K. et al. [ 14]. Pink perch skin and bones were cut into small pieces. The pieces were mixed with distilled water in a ratio of 1:3 (w/v) and the extraction was done at pH 3, 75 °C for 30 min. The pH of the aqueous solution was adjusted using glacial acetic acid. The reaction mixture was neutralized to pH (6–6.5) after the reaction using 4M NaOH. Subsequently, the non-solubilized material was filtered. The filtrate containing solubilized gelatin was freeze-dried using a lyophilizer (SNS FD-50, S N Solutions, Noida, India).
## 2.4. Preparation of Ready-to-Cook Chicken Meatballs
Chicken mince was divided into five equal parts, one each for control, $3\%$, $4\%$, $5\%$, and $6\%$ gelatin treatment. The control sample was prepared without fish gelatin and had $4.5\%$ vegetable oil while other treatment groups were prepared by incorporation of fish gelatin in varying concentrations (3–$6\%$) without the addition of any vegetable oil (Table 1). All the experimental batches had $8\%$ roasted black gram flour as per the method of Modi et al. [ 23]. All the ingredients were mixed manually to obtain a meatball dough and meatballs (20 g each) were formed. Ready-to-cook meatball samples were stored in metalized polyester pouches and stored at 4 °C for 15 days and −18 °C for 60 days for the shelf-life studies. For sensory and other physico-chemical analysis, frozen meatballs were fried at 145 ± 5 °C for 5 min in 500 mL refined sunflower oil.
## 2.5.1. Proximate Analysis
Moisture, fat, and ash content of raw and fried meatballs were estimated as per the AOAC methods 950.46, 985.15, and 920.153, respectively [24]. Protein content was estimated by Dumas’ method of protein estimation using Rapid MAX N Exceed nitrogen combustion analyzer (Elementar India Pvt. Ltd., Haryana, India) [25]. The sample (50 mg) was weighed in a steel crucible and the crucible was placed in a combustion chamber. Aspartic acid (≥$98\%$, Sigma Aldrich chemicals Pvt. Ltd., Bangalore, India) was used as reference material to calibrate the nitrogen analyzer. The nitrogen-to-protein conversion factor used for the meatballs was 6.25 [24].
## 2.5.2. Texture Profile Analysis (TPA)
The textural profile analysis of raw and fried meatballs was done for determining the parameters such as hardness (N), cohesiveness, springiness (cm), gumminess (N), and chewiness (N.cm) using a TA-XT Plus texture analyzer (Stable Micro Systems, Surrey, UK). A 36 mm cylindrical probe was attached to a 50 kg load cell to compress the sample to $75\%$ of its original height twice in two cycles at a test speed of 1 mm/s.
## 2.5.3. 2,2-Diphenylpicrylhydrazyl (DPPH) Radical Scavenging Activity
DPPH radical scavenging activity of the sample was determined as per the method of Wojtasik-Kalinowska et al. [ 26]. A sample (2.5 g) from each treatment group of meatballs was crushed manually and homogenized in 7.5 mL of ethanol for 10 min using a vortex. The homogenized samples were centrifuged for 15 min at 6000× g at room temperature (25 °C). The supernatant (0.5 mL) was added to 3.5 mL of 0.1 mM ethanolic DPPH and mixed thoroughly for 30 s. The prepared solution was stored in the dark at room temperature for 30 min. Absorbance at 517 nm was measured using UV-VIS spectrophotometer (LMSPU1000B, Labman Scientific Instruments Pvt. Ltd., Chennai, India). The ethanol solution without the sample was taken as blank. The DPPH radical scavenging activity was calculated using Equation [1]. [ 1]DPPH radical scavenging activity =1−ASampleABlank×100 where, Asample—Absorbance of the DPPH solution with the tested sample; Ablank—Absorbance of the DPPH solution with $99.5\%$ ethanol.
## 2.6.1. Cooking Yield
The cooking yield of meatballs was determined by measuring the weight of each meatball before and after cooking. The yield was calculated as per Equation [2]. [ 2]Cooking yield (%)=Weight of fried meatballWeight of raw meatball×100
## 2.6.2. Moisture Retention
Moisture retention value is the measure of the amount of moisture retained in the fried meatballs. It was calculated as per Equation [3] described by Serdaroglu et al. [ 19]. [ 3]Moisture Retention (%)=Percent yield of meatball × Percent moisture in fried meatball100
## 2.6.3. Shrinkage
Shrinkage in the meatballs was determined by measuring the diameter of the meatballs before and after frying in the oil using a vernier caliper [19]. The shrinkage was measured as per Equation [4]. [ 4]Shrinkage (%)=Diameter of raw meatball−Diameter of fried meatballDiameter of raw meatball×100
## 2.7. Sensory Evaluation
Sensory evaluation of the meatballs was done using a 9-point hedonic scale (9–extremely desirable and 1–extremely undesirable). The evaluation was done by a panel of 30 semi-trained members (15 males and 15 females; age range: 24–45 years) from Amity University Uttar Pradesh, Noida, India. All the panelists had prior experience in meat product assessment. Fried meatballs were served warm to the semi-trained panelists. Samples were presented with codes in random order. The experiment was done in a well-lit room and water was provided as a palate cleanser between samples. Meatballs were ranked for appearance, taste, smell, texture, and overall acceptability.
## 2.8. Shelf-Life Studies
The effects of gelatin addition on the shelf-life of RTC meatballs were studied during refrigerated storage conditions (4 °C for 15 days) and frozen storage conditions (−18 °C for 60 days) and compared with Control and Branded Meatballs. At refrigerated storage conditions the samples were drawn at intervals of 0, 3, 6, 9, 12, and 15 days and at frozen storage conditions the samples were drawn at intervals of 0, 15, 30, 45, and 60 days for analysis. The meatball samples were analyzed for their physico-chemical (water holding capacity, thiobarbituric acid value, free fatty acid value, pH, and color) and microbiological parameters.
## 2.8.1. Water Holding Capacity (WHC)
WHC of meatballs was measured as per the method of Bouton et al. [ 27]. The meatball (5 g) was centrifuged at 9000× g rpm for 30 min at 4 °C and weight was measured before and after centrifugation. The water holding capacity was calculated using Equation [5]. [ 5]Water Holding Capacity (WHC) % =1−TM×100 where, T = Difference between A and B (i.e., B—A)B is the weight of the sample before fryingA is the weight of the sample after frying and centrifugingM is the total water content in the sample
## 2.8.2. Thiobarbituric Acid Value (TBARS)
The thiobarbituric acid value of the meatballs was determined as per the method of Schmedes et al. [ 28]. The meatball sample (5 g) was mixed with 10 mL of $20\%$ TCA for 30 s using a vortex. The mixture was filtered. The filtrate (2 mL) was added to 2 mL of 0.02 M aqueous thiobarbituric acid solution. The contents were incubated for 100 °C for 30 min and further cooled under tap water. Absorbance was measured at 532 nm using UV-VIS spectrophotometer and TBA value was calculated using malondialdehyde as the standard (expressed as mg MDA per kg).
## 2.8.3. Free Fatty Acid Percentage (FFA)
The free fatty acid percentage of meatballs was estimated as per the method of Bienkiewicz et al. [ 29]. The meatball sample (2 g) was mixed with 30 mL of chloroform and homogenized at 6000× g rpm for 1 min. The mixture was filtered to remove solid particles. A few drops of $1\%$ ethanolic phenolphthalein indicator were added to the filtrate. The filtrate was titrated against 0.01N potassium hydroxide. Free fatty acid was expressed as % oleic acid. The free fatty acid percentage was calculated as per Equation [6]. [ 6]FFAs oleic acid (%)=(Vi− Vf)×28.2W Vi: Initial titrant volume (mL)Vf: Final titrant volume (mL)W: The amount of sample (g)Conversion factor: 28.2
## 2.8.4. pH
The Meatball sample (2 g) was homogenized with 20 mL of distilled water for 1 min at room temperature (25 °C) using a vortex. The mixture was used to measure the pH with a digital pH meter (Labman Scientific Instruments Pvt. Ltd., Chennai, India).
## 2.8.5. Color Analysis
The color of the meatballs was measured using CIEL*a*b* system-based NS810 portable spectrophotometer (Shenzhen Threenh Technology Co. Ltd., Shenzhen, China), calibrated against white and black standards provided with the instrument. The color was measured on three samples at three different locations. The results were expressed in terms of L* (lightness), a* (redness/greenness), and b* (yellowness/blueness).
## 2.8.6. Microbial Assessment
The homogenized sample (1 g) was dissolved in 10 mL peptone water. Serial dilutions were prepared from 10−1 to 10−4 dilution. After that 100 µL of the sample from dilutions was plated on different agar plates by spread plate techniques. Media used for total plate count, yeast and mold, Salmonella, and E. coli count were nutrient agar, Czapek Dox agar, XLD agar, and EMB agar, respectively. Plates were incubated for 24 h at 37 °C for total plate count, Salmonella, and E. coli and at 28 °C for 3 days for yeast and mold. The results were expressed as colony forming unit/gram (CFU/g).
## 2.9. Statistical Analysis
All experiments were done in triplicates. Data were analyzed for mean and standard deviation using Microsoft office excel 2016. Data were compared for analysis of variance using IBM SPSS (Version 26.0). The mean value was further compared using the Duncan Multiple Range test. The significance level in the study was $p \leq 0.05.$
## 3.1.1. Raw Meatballs
The proximate composition of the raw chicken meatballs varied significantly ($p \leq 0.05$) when replacing vegetable oil with fish gelatin (Table 2). Fish gelatin-incorporated meatballs had $67.2\%$ lower fat content than the control and $79.7\%$ lower fat content than the Branded Meatballs. However, no significant difference was observed between the fat content of meatballs with different concentrations of fish gelatin. Since fish gelatin is composed of more than $90\%$ protein, the protein content of fish gelatin-incorporated RTC meatballs was also significantly higher than the Control and the Branded Meatballs by $20.1\%$ and $66.4\%$, respectively ($p \leq 0.05$). The moisture content of the gelatin-incorporated RTC meatballs ($64.5\%$) was also significantly higher than the Control ($62.0\%$) and Branded Meatballs ($62.1\%$). This could be attributed to the water-holding capacity of fish gelatin [30]. A higher moisture content improves the overall acceptability of the product by increasing juiciness. The ash content of the Control Meatballs was observed to be significantly higher than the gelatin-incorporated and the Branded Meatballs ($p \leq 0.05$).
## 3.1.2. Fried Meatballs
Similar to raw meatballs, a significant variation in the proximate composition of fried meatballs was observed with the replacement of vegetable oil with fish gelatin (Table 2). The results suggested that the addition of fish gelatin significantly reduced the fat content of fried meatballs as compared to the fried control sample and branded sample, respectively ($p \leq 0.05$). The fat content of fried Branded Meatballs was observed to be two times more than the fat content of fried fish gelatin-incorporated meatballs. It was also observed that an increase in fish gelatin concentration led to a decrease in fat absorption in the fried meatballs. This observation could be explained by the fact that during the frying process, gelatin forms a gel matrix that seals the moisture content of meat and prevents further penetration of external fat inside the system. During frying, due to loss of moisture, overall protein concentration in the meatballs increased with respect to raw meatballs. Similar to raw meatballs, the protein content of gelatin-incorporated fried meatballs was also significantly higher than the fried control and the Branded Meatballs ($p \leq 0.05$). A linear relation was observed between the protein content and the fish gelatin concentration in meatballs.
A decrease in the moisture content of the fried meatballs with respect to raw meatballs was observed in all treatment groups. This was due to moisture removal during the process of frying. The result also suggested a positive correlation between the moisture content and fish gelatin concentration ($r = 0.99$) in the meatballs. No significant difference in the ash content of fried meatballs was observed with the addition of different concentrations of fish gelatin. Our results were in accordance with Jridi et al. [ 8] and Pereira et al. [ 31] who reported alteration in the chemical composition of sausage and frankfurters, respectively, with the addition of gelatin. The results from the proximate analysis suggested that the addition of pink perch gelatin to meatballs can effectively reduce the fat content along with improving the protein and moisture content.
## 3.2. Texture Profile Analysis (TPA)
The textural characteristics of fried meatballs have a significant impact on the consumer preference. Textural properties of meat products are affected by various factors such as product composition, processing technique, and cooking parameters [32]. The frying of the meat product at a high temperature significantly alters the textural properties due to moisture loss, gel matrix development, and surface hardening because of crust formation. The results obtained in this study also indicated that the process of frying has led to modification in the textural parameters of all meatballs irrespective of the treatment. As compared to the raw meatballs, there was a significant increase in hardness, springiness, cohesiveness, gumminess, and chewiness of all meatballs after frying ($p \leq 0.05$) (Table 3). The textural properties of fried meatballs were also influenced by the addition of fish gelatin (Table 3). The results indicated a decrease in the hardness of the fried meatballs with an increase in the concentration of fish gelatin. This could be due to higher moisture retention with an increase in fish gelatin concentration leading to a softer texture. The hardness of the fried fish gelatin-incorporated meatball was observed to be significantly lower than the fried control sample without fish gelatin but slightly higher than the Branded Meatballs. A similar result has been reported for the textural properties of fish balls prepared with the incorporation of tilapia gelatin [33]. Springiness is representative of the deformation in the system after the removal of external compressive force whereas cohesiveness indicates resistance due to internal bonds. In this study, no significant difference was observed between the springiness of fried meatballs of different treatments, whereas the cohesiveness of fried Branded Meatballs was observed to be slightly higher than fried control and fried fish gelatin-incorporated meatballs. The addition of fish gelatin resulted in a good quality of the meatballs which can easily reform their structure after the removal of compressive forces. Based on the findings, it was observed that the chewiness of fried fish gelatin-incorporated meatballs was significantly lower than the fried Branded and fried Control Meatballs. The presence of crust has been reported to affect the food’s mechanical properties, as well as its texture and acceptability [34]. Therefore, the development of the crust may be related to the hardness and chewiness of deep-fried meatballs. An inverse relation was also observed between the gumminess of the fried meatballs and the gelatin concentration (Table 3).
## 3.3. 2,2-Diphenylpicrylhydrazyl (DPPH) Radical Scavenging Activity
The DPPH scavenging-based antioxidant activity of the fish gelatin-based chicken meatballs ranged from 21.1–$28.1\%$ in raw samples and 10.8–$18.0\%$ in fried samples (Figure 1). The substitution of vegetable oil with fish gelatin was able to increase the antioxidant activity of the meatballs, therefore controlling oxidation reaction and retarding the development of rancidity. The antioxidant activity of meatballs is by the virtue of gelatin’s ability to quench DPPH free radicals by donating protons [7]. The results indicated a positive correlation ($r = 0.99$) between fish gelatin concentration and the DPPH scavenging effect in fried meatballs. The highest value of antioxidant activity ($28.1\%$) was observed in $6\%$ fish gelatin-incorporated meatballs. It was observed that all treatment groups of meatballs had a certain amount of antioxidant activity. This could be due to the presence of spices in the control and Fish Gelatin Meatballs whereas in the Branded Meatball the chemical preservative might have imparted a certain amount of antioxidant effect. It was also observed that antioxidant activity of all meatballs, irrespective of the treatment, reduced after frying process. This could be due to antioxidant degradation or usage during the heat treatment. The antioxidant capacity results showed that the pink perch gelatin has a potential to improve the storage and nutritional quality of meatballs.
## 3.4.1. Cooking Yield
Cooking yield is an important parameter in terms of the commercial value of a product for the industry as it helps in predicting the behavior of the products during frying [35]. The determination of cooking yield is based on the estimation of weight loss incurred by meatballs during frying. It is dependent on the ability of the protein matrix in meatballs to immobilize and retain fat and water during the processing. It was observed in our study that the addition of fish gelatin significantly increased the cooking yield of meatballs in comparison to the Control Meatballs (Table 4). The highest cooking yield of fish gelatin-incorporated meatballs was recorded at $6\%$ fish gelatin concentration ($85.6\%$), which was also observed to be higher than the Branded Meatball ($83.8\%$). An increase in fish gelatin concentration was observed to have a positive correlation ($r = 0.96$) with meatball yield. Fish gelatin causes water and oil retention which helps in reducing weight loss in meatballs during frying. Therefore, the difference in moisture retention has led to variations in meatball yield. Results were in agreement with the observation of Jirdi et al. [ 8], who reported a proportional relationship between added gelatin concentration and cooking yield in meat sausage.
## 3.4.2. Moisture Retention
The results suggested that the meatballs incorporated with fish gelatin had a higher moisture retention capacity as compared to the Control Meatballs and Branded Meatballs (Table 4). This could be explained by the ability of gelatin to covalently form matrices that swell in an aqueous solution, which results in the formation of a gelatin network and hence, increases the moisture retention capacity of the meatballs [8]. The results also depicted a linear relation ($r = 0.99$) between moisture retention and fish gelatin concentration of meatballs. In this study, the highest moisture retention capacity was observed in meatballs incorporated with $6\%$ fish gelatin ($56.5\%$), which was also observed to be higher than the Branded Meatballs ($51.7\%$).
## 3.4.3. Shrinkage
In this study, shrinkage was observed in all treatment groups of meatballs irrespective of the source and formulation (Table 4). The process of frying causes loss of moisture and denaturation of protein, resulting in shrinkage of the meatballs. The results suggested that the meatball incorporated with fish gelatin showed a significantly lower shrinkage in comparison to the Control Meatballs. This could be due to the water holding capacity and moisture retention capacity of fish gelatin, leading to a reduction in moisture loss. An inverse relationship was observed between fish gelatin concentration and shrinkage (r = −0.93). The lowest percentage of shrinkage was observed in meatballs with $6\%$ fish gelatin ($2.1\%$).
## 3.5. Sensory Evaluation
The sensory evaluation of fried meatballs suggested that the addition of fish gelatin and variation in its concentration had a significant effect on all parameters, i.e., texture, appearance, taste, smell, and overall acceptability (Figure 2). In comparison to other variants of meatballs (fish gelatin-incorporated meatballs and Branded Meatballs), Control Meatballs had the lowest acceptability scores. Meatballs incorporated with fish gelatin had a higher score for appearance in comparison to control and blank meatballs, which could be due to the higher moisture retention. An increase in moisture retention also led to higher retention of the natural juices of meat protein which facilitated the release of flavor compounds. This was also evident from the high taste scores of meatballs formulated with fish gelatin. As per the panelist, no fishy odor was perceptible in fish gelatin-incorporated meatballs. Considering all attributes, meatballs formulated with $5\%$ fish gelatin were most acceptable to panelists. With a further increase in fish gelatin concentration, a decrease in the acceptability of the meatball was observed. This could be due to the fact that as the fish gelatin concentration increased, the chewiness of the meatballs increased as well and they took on a darker color appearance.
## 3.6. Shelf-Life Study
The shelf-life of meat and meat products is majorly influenced by oxidation and microbial contamination, resulting in deterioration of nutritional value, development of unacceptable color, flavor and odor, and toxin production [36]. In this study, on the basis of the highest consumer acceptability during sensory evaluation, a $5\%$ Fish Gelatin Meatball was selected for shelf-life analysis and was compared with the Control Meatball to analyze the effect of gelatin addition on the shelf-life of the meatballs. The shelf-life of Branded Meatball was also determined to analyze the comparability of fish gelatin-incorporated meatballs with respect to commercially available meatball variants. Estimation of shelf-life of $5\%$ Fish Gelatin Meatball, Control Meatball, and Branded Meatball was done at refrigerated (4 °C for 15 days) and frozen temperatures (−18 °C for 60 days) by estimating changes in water holding capacity, thiobarbituric acid value, free fatty acid percentage, pH, color, and microbial count.
## 3.6.1. Water Holding Capacity (WHC)
WHC in meat products influence consumer acceptance by positively regulating visual desirability, yield, drip losses, and sensory properties of meatballs. The fish gelatin-incorporated meatball showed a higher WHC in comparison to the Control and the Branded Meatball samples at both storage conditions ($p \leq 0.05$) (Figure 3a,b). An overall decrease in WHC was observed in all meatballs with the increase in storage time. The rate of decrease in WHC at refrigerated conditions was higher than at frozen conditions. A sharp decrease in WHC at refrigerated conditions after 15 days may be because of the changes in protein structure. Protein denaturation can be a result of an increase in acidity of the system due to enzymatic and microbial spoilage [37]. This results in a reduced ability of meat to hold water molecules. At the frozen condition, the WHC of meatballs is reduced due to the formation of ice-crystals, resulting in myofibrillar shrinkage and partial protein denaturation [38]. Gelatin slows down the ice-crystal formation due to its ability to hold a high amount of water [39], therefore preventing WHC of meatball which was evident in this study. The result suggested gelatin incorporation was effective in improving the WHC of meatballs during both refrigerated and frozen storage conditions.
## 3.6.2. Thiobarbituric Acid Value
The oxidative rancidity of lipids is a serious problem that limits the storage stability of meat and meat products. The thiobarbituric acid (TBARS) value is one of the most commonly used measures of lipid oxidation leading to rancidity. From a health and sensory acceptance perspective, a lower value of TBARS is more preferable. During storage at refrigerated conditions, TBARS values of fish gelatin-incorporated meatballs, Control, and Branded Meatballs varied significantly on 6, 9, 12, and 15 days ($p \leq 0.05$) (Figure 4a). At both frozen and refrigerated storage temperatures, the rate of oxidation in fish gelatin-incorporated meatballs was observed to be lower than in the Control and Branded Meatball samples (Figure 4a,b). This could be due to the antioxidant activity of fish gelatin and its ability to act as a physical barrier between lipid molecules and pro-oxidants present in the system, thus lowering the lipid oxidation rate [40]. A gradual increase in the TBARS values was observed in all treatment groups of meatballs with an increased storage time at both storage conditions. However, the rate of increase in TBARS values of meatballs stored at refrigerated conditions was significantly higher in comparison to the frozen storage conditions ($p \leq 0.05$). It was also observed that at the end of the storage period the TBA values for all the meatballs were within the acceptable limits at both refrigerated and frozen storage conditions [19]. The results suggested that the fish gelatin was able to effectively retard lipid oxidation at both storage conditions and fish gelatin-incorporated meatballs can retain their oxidative stability at refrigerated conditions for 15 days and at frozen storage temperature for 60 days.
## 3.6.3. Free Fatty Acid Percentage
During storage, meat and meat products undergo enzymatic and microbial degradation due to microorganisms containing lipolytic enzymes resulting in the formation of free fatty acids (FFA) [36]. The estimation of the free fatty acid content helps in determining the storage stability of the product. In this study, an increase in the FFA value with prolonged time was observed in both refrigerated storage and frozen storage. However, the rate of increase of FFA content was significantly higher in the refrigerated storage for 15 days (4 °C) condition than in the frozen storage at 60 days (−18 °C) condition ($p \leq 0.05$) (Figure 5). A relatively lower rate of increase in FFA content at frozen conditions indicated that frozen storage must be chosen if products are intended to store for more than 2–4 days. The results also suggested that the fish gelatin-incorporated meatballs showed a lower rate of increase in FFA as compared to the Branded and the Control Meatball samples.
## 3.6.4. pH
A decreasing trend in the pH of meatballs was observed at refrigerated storage conditions (4 °C for 15 days) and at frozen (−18 °C for 60 days) storage conditions (Figure 6). The pH value of meatballs stored at 4 °C varied between 6.4 to 5.6 during the storage period of 15 days (Figure 6a). During storage, a decrease in pH may be due to the accumulation of deaminated protein, organic acid, and microbial metabolites formed as a result of enzymatic activity and the growth of microorganisms. Microorganisms metabolize carbohydrates and other compounds present in meat to produce lactic acid and acetic acid which results in a decrease in the pH of the system [41]. The results showed that the decrease in pH of fish gelatin-incorporated meatballs was lower than the Control and Branded Meatballs. This could be due to an increase in the acidity of the meatballs caused by free fatty acid produced as a result of lipid peroxidation during storage. Since the rate of increase of free fatty acid production during refrigerated storage was higher in the Branded and Control Meatballs (Figure 5a), a comparatively rapid decrease in their pH was also observed. Similar results were reported by Rubel et al. [ 42] in mutton meatballs during refrigerated storage.
pH values of the meatballs stored at −18 °C ranged from 6.5 to 6.2 during the storage period of 60 days (Figure 6b). The results showed that the pH value of the meatballs stored at −18 °C remained relatively stable over the entire 60 days in all treatment groups ($p \leq 0.05$). In addition, there was no significant difference in pH values ($p \leq 0.05$) between all combinations of meatballs during the whole storage period, indicating that less quality deterioration can be expected while storing meatballs in frozen condition for up to 60 days.
## 3.6.5. Color Analysis
The color characteristics influence customer acceptance and preference of the product. A number of factors affect color of the product such as ingredients and their interaction, packaging, processing, and exposure to light [43]. Results indicated that the fish gelatin-incorporated meatballs had a higher lightness value in comparison to the Control Meatballs (Table 5). This could be because of the addition of fish gelatin which was white in color and also due to the swelling of gelatin after getting in contact with water molecules and causing the scattering of light rays. It was also observed that the a* value (redness) of meatballs decreased with the addition of fish gelatin. Jirdi et al. [ 8] reported similar results regarding the a* value of meat sausages with the addition of cuttlefish gelatin. During storage at refrigerated conditions, a decrease in the lightness value and an increase in the a* value were observed in all treatment groups. This could be due to the browning of the meatballs because of oxidation reactions during the storage period. No significant variation was observed in the color parameters of meatballs during storage at frozen conditions (Table 6).
## 3.6.6. Microbial Assessment
Total Plate Count (TPC), Salmonella, yeast and mold (Y&M), and E. coli count of meatballs stored at 4 °C for 15 days and −18 °C for 60 days are shown in Table 7 and Table 8. Microbiological assessment of the product during storage was done to evaluate both the quality and safety of the food product. The maximum permitted microbiological limit set by the Food Safety and Standards Authority of India (FSSAI) for frozen meatballs for Total Plate *Count is* 1.0 × 104 CFU/g, for yeast and mold it is 100.0 CFU/g, and an absence of E. coli and *Salmonella is* required. During refrigerated storage, the lowest TPC and Y&M count were observed in Branded Meatballs followed by fish gelatin-incorporated meatballs. Control Meatballs had the highest TPC and Y&M count ($p \leq 0.05$) at 4 °C storage temperature compared to the branded and fish gelatin-incorporated meatballs. E. coli and *Salmonella were* not observed in any treatment groups, indicating hygienic preparation conditions, good quality ingredients, and the absence of any pathogenic microbes. From a consumer safety point of view, industries only supply meat products with a microbial load within an acceptable range [42]. On day three of storage in refrigerated conditions, the microbial load was beyond acceptable limits as prescribed by FSSAI. On day zero, during frozen conditions, no microbes were observed apart from the TPC and Y&M count. Furthermore, with an increase in storage duration, the overall growth of microorganisms decreased. This might be due to the reduced survival rate of microbes in frozen conditions [44]. Results from microbial studies suggested that meatballs stored at a refrigerated temperature were best consumed in the same day, and for long-term storage frozen temperatures are preferable. However, further processing and cooking using thermal methods such as frying can also alter the microbial load of the products. In addition, after cooking, it is advised to use good manufacturing practices (GMP) and good hygiene practices (GHP) to avoid cross-contamination.
## 4. Conclusions
In this study, the efficacy of pink perch gelatin as a fat replacer for the development of low-fat ready-to-cook chicken meatballs was evaluated on the basis of nutritional, technological, sensorial, and microbiological properties. The results suggested that the addition of fish gelatin improved the nutritional profile of the meatballs by reducing the fat content by $67.2\%$ along with an increase of $20.1\%$ in protein content in comparison to the control group. Fish gelatin addition also improved sensory properties and consumer acceptability of the product. At $5\%$ fish gelatin concentration, the overall acceptability of the fish gelatin-incorporated meatballs was higher than the Branded Meatballs. The addition of fish gelatin provided good structuring and improved the textural properties of the meatball. The cooking, yield, and moisture retention of the meatballs significantly increased with the incorporation of fish gelatin. During storage at refrigerated and frozen conditions, fish gelatin improved the shelf-life of meatballs by effectively reducing oxidation due to its radical scavenging activity. The results also suggest that the addition of gelatin preserved the quality of the meatball by stabilizing pH and water holding capacity during storage. Overall, it can be concluded that the addition of fish gelatin can be used by meat industries to produce ready-to-cook chicken meatballs with better nutritional profiles and functional properties.
## References
1. Nedeljković N., Hadnađev M., Hadnađev T.D., Šarić B., Pezo L., Sakač M., Pajin B.. **Partial replacement of fat with oat and wheat bran gels: Optimization study based on rheological and textural properties**. *LWT* (2017.0) **86** 377-384. DOI: 10.1016/j.lwt.2017.08.004
2. Zhao D., Guo C., Liu X., Xiao C.. **Effects of insoluble dietary fiber from kiwi fruit pomace on the physicochemical properties and sensory characteristics of low-fat pork meatballs**. *J. Food Sci. Technol.* (2021.0) **58** 1524-1537. DOI: 10.1007/s13197-020-04665-2
3. Niu Y., Fang H., Huo T., Sun X., Gong Q., Yu L.. **A novel fat replacer composed by gelatin and soluble dietary fibers from black bean coats with its application in meatballs**. *LWT* (2020.0) **122** 109000. DOI: 10.1016/j.lwt.2019.109000
4. Petersson K., Godard O., Eliasson A.C., Tornberg E.. **The effects of cereal additives in low-fat sausages and meatballs. Part 1: Untreated and enzyme-treated rye bran**. *Meat Sci.* (2014.0) **96** 423-428. DOI: 10.1016/j.meatsci.2013.08.020
5. Westerterp-Plantenga M.S., Nieuwenhuizen A., Tome D., Soenen S., Westerterp K.R.. **Dietary protein, weight loss, and weight maintenance**. *Annu. Rev. Nutr.* (2009.0) **29** 21-41. DOI: 10.1146/annurev-nutr-080508-141056
6. Kehlet U., Pagter M., Aaslyng M.D., Raben A.. **Meatballs with 3% and 6% dietary fibre from rye bran or pea fibre-Effects on sensory quality and subjective appetite sensations**. *Meat Sci.* (2017.0) **125** 66-75. DOI: 10.1016/j.meatsci.2016.11.007
7. Shiao W.C., Wu T.C., Kuo C.H., Tsai Y.H., Tsai M.L., Hong Y.H., Huang C.Y.. **Physicochemical and antioxidant properties of gelatin and gelatin hydrolysates obtained from extrusion-pretreated fish (**. *Mar. Drugs* (2021.0) **19**. DOI: 10.3390/md19050275
8. Jridi M., Abdelhedi O., Souissi N., Kammoun M., Nasri M., Ayadi M.A.. **Improvement of the physicochemical, textural and sensory properties of meat sausage by edible cuttlefish gelatin addition**. *Food Biosci.* (2015.0) **12** 67-72. DOI: 10.1016/j.fbio.2015.07.007
9. Gómez-Guillén M.C., Giménez B., López-Caballero M.A., Montero M.P.. **Functional and bioactive properties of collagen and gelatin from alternative sources: A review**. *Food Hydrocoll.* (2011.0) **25** 1813-1827. DOI: 10.1016/j.foodhyd.2011.02.007
10. Ataie M.J., Shekarabi S.P.H., Jalili S.H.. **Gelatin from bones of bighead carp as a fat replacer on physicochemical and sensory properties of low-fat mayonnaise**. *J. Microbiol. Biotechnol. Food Sci.* (2021.0) **8** 979-983. DOI: 10.15414/jmbfs.2019.8.4.979-983
11. Hosseini Shekarabi S.P., Rostami N., Shaviklo A.R., Mhd Sarbon N.. **Application of an optimum level of acidic extracted grass carp (**. *Iran. J. Fish. Sci.* (2021.0) **20** 1277-1290
12. Yin M., Yang D., Lai S., Yang H.. **Rheological properties of xanthan-modified fish gelatin and its potential to replace mammalian gelatin in low-fat stirred yogurt**. *LWT* (2021.0) **147** 111643. DOI: 10.1016/j.lwt.2021.111643
13. Jaziri A.A., Shapawi R., Mohd Mokhtar R.A., Noordin W.N., Huda N.. **Tropical marine fish surimi by-products: Utilization and potential as functional food application**. *Food Rev. Int.* (2021.0) 2012794. DOI: 10.1080/87559129.2021.2012794
14. Khushboo Kaushik N., Widell K.N., Slizyte R., Kumari A.. **Optimization of green single-step gelatin extraction method using RSM model for valorization of pink perch (**. *ACS Food Sci. Technol.* 2023
15. Koli J.M., Basu S., Nayak B.B., Patange S.B., Pagarkar A.U., Gudipati V.. **Functional characteristics of gelatin extracted from skin and bone of Tiger-toothed croaker (**. *Food Bioprod. Process.* (2012.0) **90** 555-562. DOI: 10.1016/j.fbp.2011.08.001
16. Kumari A., Kaushik N., Slizyte R.. **Production and Microencapsulation of Protein Hydrolysate of Pink Perch (**. *Waste Biomass Valorization* (2022.0) **14** 209-226. DOI: 10.1007/s12649-022-01853-3
17. Modi V.K., Yashoda K.P., Naveen S.K.. **Effect of carrageenan and oat flour on quality characteristics of meat kofta**. *Int. J. Food Prop.* (2009.0) **12** 228-242. DOI: 10.1080/10942910802252155
18. Petracci M., Bianchi M., Mudalal S., Cavani C.. **Functional ingredients for poultry meat products**. *Trends Food Sci. Technol.* (2013.0) **33** 27-39. DOI: 10.1016/j.tifs.2013.06.004
19. Serdaroğlu M., Yıldız-Turp G., Abrodímov K.. **Quality of low-fat meatballs containing legume flours as extenders**. *Meat Sci.* (2005.0) **70** 99-105. DOI: 10.1016/j.meatsci.2004.12.015
20. Aslinah L.N.F., Mat Yusoff M., Ismail-Fitry M.R.. **Simultaneous use of adzuki beans (**. *J. Food Sci. Tech.* (2018.0) **55** 3241-3248. DOI: 10.1007/s13197-018-3256-1
21. Sanjeewa W.T., Wanasundara J.P., Pietrasik Z., Shand P.J.. **Characterization of chickpea (**. *Food Res. Int.* (2010.0) **43** 617-626. DOI: 10.1016/j.foodres.2009.07.024
22. Dzudie T., Scher J., Hardy J.. **Common bean flour as an extender in beef sausages**. *J. Food Eng.* (2002.0) **52** 143-147. DOI: 10.1016/S0260-8774(01)00096-6
23. Modi V.K., Mahendrakar N.S., Rao D.N., Sachindra N.M.. **Quality of buffalo meat burger containing legume flours as binders**. *Meat Sci.* (2004.0) **66** 143-149. DOI: 10.1016/S0309-1740(03)00078-0
24. 24.
AOAC International
Official Methods of Analysis of AOAC International19th ed.AOAC InternationalGaithersburg, MD, USA2005. *Official Methods of Analysis of AOAC International* (2005.0)
25. Thompson M., Owen L., Wilkinson K., Wood R., Damant A.. **Testing for bias between the Kjeldahl and Dumas methods for the determination of nitrogen in meat mixtures, by using data from a designed interlaboratory experiment**. *Meat Sci.* (2004.0) **68** 631-634. DOI: 10.1016/j.meatsci.2004.05.016
26. Wojtasik-Kalinowska I., Onopiuk A., Szpicer A., Wierzbicka A., Półtorak A.. **Frozen storage quality and flavor evaluation of ready to eat steamed meat products treated with antioxidants**. *CyTA-J. Food.* (2021.0) **19** 152-162. DOI: 10.1080/19476337.2020.1869103
27. Bouton P.E., Harris P.T., Shorthose W.R.. **Effect of ultimate pH upon the water-holding capacity and tenderness of mutton**. *J. Food Sci.* (1971.0) **36** 435-439. DOI: 10.1111/j.1365-2621.1971.tb06382.x
28. Schmedes A., Hølmer G.. **A new thiobarbituric acid (TBA) method for determining free malondialdehyde (MDA) and hydroperoxides selectively as a measure of lipid peroxidation**. *J. Am. Oil Chem. Soc.* (1989.0) **66** 813-817. DOI: 10.1007/BF02653674
29. Bienkiewicz G., Tokarczyk G., Czerniejewska–Surma B., Suryn J.. **Changes in the EPA and DHA content and lipids quality parameters of rainbow trout (**. *Heliyon* (2019.0) **5** e02964. DOI: 10.1016/j.heliyon.2019.e02964
30. Hue C.T., Hang N.T.M., Razumovskaya R.G.. **Physicochemical Characterization of Gelatin Extracted from European Perch (**. *Turkish J. Fish. Aquat. Sci.* (2017.0) **17** 1117-1125. DOI: 10.4194/1303-2712-v17_6_05
31. Pereira A.G.T., Ramos E.M., Teixeira J.T., Cardoso G.P., Ramos A.D.L.S., Fontes P.R.. **Effects of the addition of mechanically deboned poultry meat and collagen fibers on quality characteristics of frankfurter-type sausages**. *Meat Sci.* (2011.0) **89** 519-525. DOI: 10.1016/j.meatsci.2011.05.022
32. Ikhlas B., Huda N., Noryati I.. **Chemical composition and physicochemical properties of meatballs prepared from mechanically deboned quail meat using various types of flour**. *Int. J. Poult. Sci.* (2011.0) **10** 30-37. DOI: 10.3923/ijps.2011.30.37
33. Feng X., Fu C., Yang H.. **Gelatin addition improves the nutrient retention, texture and mass transfer of fish balls without altering their nanostructure during boiling**. *LWT* (2017.0) **77** 142-151. DOI: 10.1016/j.lwt.2016.11.024
34. Bordin K., Tomihe Kunitake M., Kazue Aracava K., Silvia Favaro Trindade C.. **Changes in food caused by deep fat frying-A review**. *Arch. Latinoam. Nutr.* (2013.0) **63** 5-13. PMID: 24167953
35. Ulu H.. **Effects of carrageenam and guar gum on the cooking and textual properties of low fat meatballs**. *Food Chem.* (2006.0) **95** 600-605. DOI: 10.1016/j.foodchem.2005.01.039
36. Papuc C., Goran G.V., Predescu C.N., Nicorescu V., Stefan G.. **Plant polyphenols as antioxidant and antibacterial agents for shelf-life extension of meat and meat products: Classification, structures, sources, and action mechanisms**. *Compr. Rev. Food Sci. Food Saf.* (2017.0) **16** 1243-1268. DOI: 10.1111/1541-4337.12298
37. Bowker B., Zhuang H.. **Relationship between water-holding capacity and protein denaturation in broiler breast meat**. *Poult. Sci.* (2015.0) **94** 1657-1664. DOI: 10.3382/ps/pev120
38. Zhang Y., Magro A., Puolanne E., Dalle Zotte A., Ertbjerg P.. **Myofibrillar protein characteristics of fast or slow frozen pork during subsequent storage at− 3 °C**. *Meat Sci.* (2021.0) **176** 108468. DOI: 10.1016/j.meatsci.2021.108468
39. Damodaran S., Wang S.. **Ice crystal growth inhibition by peptides from fish gelatin hydrolysate**. *Food Hydrocoll.* (2017.0) **70** 46-56. DOI: 10.1016/j.foodhyd.2017.03.029
40. Sato A.C.K., Moraes K.E.F.P., Cunha R.L.. **Development of gelled emulsions with improved oxidative and pH stability**. *Food Hydrocoll.* (2014.0) **34** 184-192. DOI: 10.1016/j.foodhyd.2012.10.016
41. Torusdağ G.B., Gümüş S., Boran G.. **Effect of gelatin-based active coatings formulated with rosemary extract on quality of cold stored meatballs**. *Food Sci. Tech.* (2021.0) **42** 27421. DOI: 10.1590/fst.27421
42. Rubel S.A., Yu Z.N., Murshed H.M., Islam S.M., Sultana D., Rahman S.M.E., Wang J.. **Addition of olive (**. *J. Food Sci. Technol.* (2021.0) **58** 4002-4010. DOI: 10.1007/s13197-020-04863-y
43. Prasad B., Rashmi M.D., Yashoda K.P., Modi V.K.. **Effect of casein and oat flour on physicochemical and oxidative processes of cooked chicken kofta**. *J. Food Process. Preserv.* (2011.0) **35** 359-368. DOI: 10.1111/j.1745-4549.2009.00475.x
44. Nedwell D.B.. **Effect of low temperature on microbial growth: Lowered affinity for substrates limits growth at low temperature**. *FEMS Microbiol. Ecol.* (1999.0) **30** 101-111. DOI: 10.1111/j.1574-6941.1999.tb00639.x
|
---
title: 'Dietary Intake of Anthocyanidins and Renal Cancer Risk: A Prospective Study'
authors:
- Xin Xu
- Yi Zhu
- Shiqi Li
- Dan Xia
journal: Cancers
year: 2023
pmcid: PMC10001018
doi: 10.3390/cancers15051406
license: CC BY 4.0
---
# Dietary Intake of Anthocyanidins and Renal Cancer Risk: A Prospective Study
## Abstract
### Simple Summary
In this large prospective study based on the PLCO trial, both categorical analysis and continuous analysis indicated that higher dietary anthocyanidin consumption was associated with a lower risk of renal cancer. To the best of our knowledge, this is the first prospective study that aimed to explore a potential association between dietary anthocyanidin intake and renal cancer risk.
### Abstract
Evidence on the association between anthocyanidin intake and renal cancer risk is limited. The aim of this study was to assess the association of anthocyanidin intake with renal cancer risk in the large prospective Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The cohort for this analysis consisted of 101,156 participants. A Cox proportional hazards regression model was used to estimate the hazard ratios (HRs) and the $95\%$ confidence intervals (CIs). A restricted cubic spline model with three knots (i.e., 10th, 50th, and 90th percentiles) was used to model a smooth curve. A total of 409 renal cancer cases were identified over a median follow-up of 12.2 years. In the categorical analysis with a fully adjusted model, a higher dietary anthocyanidin consumption was associated with a lower risk of renal cancer (HRQ4vsQ1: 0.68; $95\%$ CI: 0.51–0.92; p for trend < 0.010). A similar pattern was obtained when anthocyanidin intake was analyzed as a continuous variable. The HR of one-SD increment in the anthocyanidin intake for renal cancer risk was 0.88 ($95\%$ CI: 0.77–1.00, $$p \leq 0.043$$). The restricted cubic spline model revealed a reduced risk of renal cancer with a higher intake of anthocyanidins and there was no statistical evidence for nonlinearity (p for nonlinearity = 0.207). In conclusion, in this large American population, a higher dietary anthocyanidin consumption was associated with a lower risk of renal cancer. Future cohort studies are warranted to verify our preliminary findings and to explore the underlying mechanisms in this regard.
## 1. Introduction
The incidence of and costs related to renal cancer have increased during the last two decades. As the population ages, the prevalence of established risk factors such as obesity, hypertension and chronic kidney disease increases, and the expansion of routine imaging for many disorders means that the renal cancer burden will increase significantly [1,2]. The management of renal cancer has evolved rapidly in recent years with several immunotherapy-based combinations of strategies approved as first-line therapies for the metastatic disease. However, renal cancer remains one of the most lethal urological malignancies. According to the updated data reported by the World Health Organization, there were more than 140,000 renal cancer related deaths worldwide in 2012 [3]. With rising rates of recurrence, aside from developing a personalized therapeutic treatment plan with minimal adverse events [4], it is fundamentally important to improve cancer prevention by identifying the potential factors associated with its risk.
Recent evidence has suggested that dietary flavonoid intake may be associated with decreased risk of chronic and degenerative diseases [5]. Flavonoids are classified into 12 major subclasses based on chemical structures and different subclasses may have different effects on human diseases. Anthocyanins are colored water-soluble pigments belonging to flavonoids, which provide red, blue and purple colors to fruits and vegetables. Anthocyanin pigments have been widely used as natural food colorants [6]. Recently, these colored pigments were found to have potent antioxidant properties, which give various beneficial health effects on cardiovascular [7] and neurodegenerative diseases, as reported by scientific studies from cell culture, animal models and clinical trials [8]. Similarly, dietary anthocyanidin intake has been found to be associated with a lower risk of several cancers, including lung cancer [9], head and neck cancer [10], and esophageal cancer [11]. To the best of our knowledge, evidence on the association between anthocyanidin intake and renal cancer risk is limited. An early hospital-based case-control study from Italy was undertaken on this topic and found no significant association between anthocyanidin consumption and renal cell carcinoma [12] based on 767 RCC cases and 1534 hospital controls. Therefore, the aim of this study was to assess the association of anthocyanidin intake with renal cancer risk in the large prospective Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.
## 2.1. Study Design and Population
The PLCO Cancer Screening Trial was a multicenter randomized controlled trial designed to assess whether screening exams could reduce the mortality from prostate, lung, colorectal, and ovarian cancers, and its study design and implementation were described previously [13]. There were two arms in the PLCO trial: the intervention arm and the control arm. A total of 76,682 men and 78,215 women aged between 55 to 74 years were enrolled in the PLCO study between November 1993 and September 2001 in ten screening centers across the United States of America (Washington, Pittsburgh, Honolulu, Denver, Marshfield, Minneapolis, Birmingham, Salt Lake City, Detroit, and St Louis). The principal recruitment strategy targeted individuals from the general population residing in the nearby areas of the screening centers. Participants in the intervention arm were screened during the first 3–4 years and participants in both arms of the study were subsequently followed for up to 10 more years to determine the potential benefits or harms of the screening exams. From its inception, the PLCO was designed not only as a RCT for the screening for four cancers but also more broadly as a research enterprise consisting of the trial, being a large, well-characterized cohort with all-cancer outcomes [14].
The current analysis is a secondary analysis of a primary database from the PLCO study. In total, 4918 participants were excluded because of a lack of baseline questionnaire data. Participants who did not complete a valid questionnaire or had been diagnosed with any cancer were also excluded ($$n = 48$$,237). We further excluded individuals with an implausible energy intake (i.e., lowest or highest $1\%$) ($$n = 546$$), with renal pelvis cancer ($$n = 34$$), and without follow-up time ($$n = 6$$). Overall, the cohort for this analysis consisted of 101,156 participants. Written informed consent was obtained from all the study participants, and the study protocol was approved by the Institutional Review Board of the NCI. The data used in this study were applied from the PLCO website with the permission of the NIH PLCO study group (CDAS project “PLCO-1020”).
## 2.2. Data Collection
Participants were arranged to complete a self-administered questionnaire containing personal baseline information. From the PLCO study, we collected information regarding age, gender, body mass index (BMI), race/ethnicity, education level, smoking status, and history of hypertension.
The Diet History Questionnaire (DHQ) version 1.0 (National Cancer Institute, 2007) was used to collect the dietary information, including the total daily energy intake and the daily intake of anthocyanidins. The DHQ recorded the frequency and quantity of 124 food items and supplements used over the past 12 months [15]. The daily frequency of food consumption was then multiplied by the representative sex-specific portion size of the food item using food composition data, which was based on the United States Department of Agriculture 1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII) and the University of Minnesota’s Nutrition Data Systems for Research [16]. The DHQ was found to fare as well as or better than two widely used food frequency questionnaires (FFQs) when the PLCO trial was conducted [15]. Cyanidin, delphinidin, malvidin, peonidin, petunidin, and pelargonidin are the six common types of anthocyanidins [6,8]. In this study, the daily intake of these subclasses was collected through the DHQ. The amounts for processed foods were assumed to be $50\%$ of the raw foods to account for the losses during processing [9]. The total daily intake of anthocyanidins was the sum of all six classes.
## 2.3. Renal Cancer Ascertainment
In this study, the outcome was the incidence of renal cancer. In the PLCO trial, the confirmation of the diagnosis of renal cancer was obtained from the study update forms which were mailed to participants annually asking about cancer diagnosis in the prior year. The follow-up time was based on reports from physicians and the results were confirmed by periodic linkage to the state cancer registries and the death certificates. In this study, a renal cancer case was defined as a malignant neoplasm of unspecified kidney, except for the renal pelvis (2022 ICD-10-CM Diagnosis Code C64.9). Follow-up started one year after completion of the DHQ and continued until the participants were diagnosed with cancer, withdrew from the trial, died from any cause, or completed the 10-year follow-up, whichever came first.
## 2.4. Statistical Analysis
A Cox proportional hazards regression model was used to estimate the hazard ratios (HRs) and $95\%$ confidence intervals (CIs). The models were adjusted for potential confounders including age (continuous), sex, race (white versus non-white), BMI (<25.0 kg/m2 versus ≥25.0 kg/m2), education level (≤high school versus ≥some college), smoking status (ever versus current versus never), hypertension status (yes versus no), and total energy intake (continuous). The proportional hazards (PH) assumption was examined using the Schoenfeld residual test [17]. To assess the statistical significance of the potential differences across subgroups, Wald tests were performed on the interaction terms between anthocyanidin intake and the stratifying covariates. A restricted cubic spline model [18] with three knots (i.e., 10th, 50th, and 90th percentiles) was used to evaluate the non-linearity of the associations. In a sensitivity analysis, we excluded cases diagnosed within the first two years of follow-up and then repeated the analysis. All statistical analyses were performed using the software STATA version 15 (Stata Corp, College Station, TX, USA). All tests were two-sided.
## 3.1. Study Characteristics
A total of 409 renal cancer cases were identified over a median follow-up of 12.2 years. Anthocyanidins from the diet ranged from 0 to 237.36 mg/day (median value: 12.17 mg/day). Table 1 shows the characteristics of participants by quartiles of anthocyanidin consumption. Overall, compared to participants with a lower intake of anthocyanidins, those with a higher consumption tended to be older, and were more likely to be female, non-Hispanic white, and never smokers at baseline. They also had a lower BMI but a higher rate of hypertension (all $p \leq 0.001$).
## 3.2. Dietary Anthocyanidin Intakes and Renal Cancer Risk
As shown in Table 2, in categorical analyses with a fully adjusted model, a higher dietary anthocyanidin consumption was associated with a lower risk of renal cancer (HRQ4vsQ1: 0.68; $95\%$ CI: 0.51–0.92; p for trend <0.010). A similar pattern was obtained when anthocyanidin intake was analyzed as a continuous variable. The HR of one-SD increment in the anthocyanidin intake for renal cancer risk was 0.88 ($95\%$ CI: 0.77–1.00, $$p \leq 0.043$$). The proportional hazards assumption was verified using Schoenfeld residuals.
Table 3 shows the effects of the subclasses of anthocyanidin intake on renal cancer risk. The intake of delphinidin, peonidin, and petunidin was statistically significantly associated with at least a $30\%$ reduction in the risk of renal cancer, with comparison of the highest vs. lowest quartiles (HRQ4vsQ1 for delphinidin: 0.59; $95\%$ CI: 0.43–0.79; HRQ4vsQ1 for peonidin: 0.68; $95\%$ CI: 0.50–0.93; HRQ4vsQ1 for petunidin: 0.69; $95\%$ CI: 0.50–0.94). However, there was no significant association between the consumption of cyanidin, malvidin or pelargonidin and renal cancer risk.
## 3.3. Additional Analyses
The restricted cubic spline model revealed a reduced risk of renal cancer with a higher anthocyanidin intake (Figure 1). There was no statistical evidence for nonlinearity (p for nonlinearity = 0.207). In the subgroup analyses as shown in Table 4, a significant interaction was observed between anthocyanidin consumption and hypertension status ($$p \leq 0.002$$). Specifically, the favorable association between anthocyanidin intake and renal cancer risk was more pronounced in the participants with a history of hypertension than in those without. No significant interaction was observed for other stratification factors including sex, BMI and smoking status (all $p \leq 0.05$). In a sensitivity analysis, there was little change in the findings by excluding individuals with a follow-up of less than two years (HRQ4vsQ1: 0.68; $95\%$ CI: 0.51–0.92).
## 4. Discussion
In this post hoc analysis of the PLCO trial, we found that the dietary intake of total anthocyanidins was inversely associated with renal cancer risk, and this association was also true for the subclasses including delphinidin, peonidin and petunidin. Importantly, the association was evident in the dose-response analysis and the sensitivity analysis.
In the subgroup analyses, a significant interaction was observed between anthocyanidin consumption and hypertension status. A more favorable association between anthocyanidin intake and renal cancer risk was observed in participants with hypertension. The reason for these findings was not clear. Participants with a history of hypertension may be more likely to have a healthy lifestyle and dietary pattern, which can promote the intake of anthocyanidin-rich foods. No significant interaction was observed for other important renal cancer risk factors including high BMI and smoking behavior. In the subgroup analysis according to the subclasses of anthocyanidins, a significant association was only observed for half of the types of anthocyanidins, including delphinidin, peonidin, and petunidin, which indicated that only part of the subclasses of anthocyanidins have health benefits.
According to the literature reviewed, the evidence has accumulated worldwide on the beneficial effects of anthocyanidins on chronic diseases [19,20], including cardiovascular disease [21], diabetes [22], nonalcoholic fatty liver disease [23], and neurological disease [24], as well as various types of cancer. Zhang et al. [ 9] recently reported that dietary intake of total anthocyanidins and of all six subclasses including cyanidin, delphinidin, malvidin, peonidin, petunidin, and pelargonidin were related to a reduced risk of lung cancer in the PLCO cohort. A large meta-analysis of observational studies suggested an inverse association between anthocyanidin consumption and the risk of esophageal cancer [11]. Higher intake of dietary anthocyanidins may also reduce the risk of colorectal cancer [25]. However, the relationship between anthocyanidin intake and renal cancer risk remains unclear, especially given the lack of evidence from prospective studies.
Our study, firstly, provided important data on the association between dietary anthocyanidin intake and renal cancer risk from a large-scale prospective American cohort. We also examined the association visually in a dose-response manner and according to gender and subclasses of anthocyanins, with adjustment for potential confounders. Previously, there were only two small case-control studies performed on this topic with no significant association observed [12,26]. The reasons for the inconsistency in the findings between our study and the previous two are unknown. It could be due to the unknown residual confounding, insufficient statistical power, in addition to the selection bias and recall bias imposed by case-control studies.
Several mechanisms have been proposed to explain a potential inverse association between dietary anthocyanin intake and cancer incidence. Anthocyanins may influence the composition of the gut microbiome, which may mediate the metabolic benefits of anthocyanins [27]. In a mouse model, Khandelwal et al. [ 28] found that the intake of the anthocyanidins pelargonidin and cyanidin reduced the genotoxic stress induced by environmental toxicants, such as diepoxybutane, urethane and endogenous nitrozation. Li et al. [ 29] suggested that anthocyanins exhibited anticarcinogenic properties by suppressing the proinflammatory, STAT3, and NF-kB signaling pathways and promoting the activity of essential detoxification enzymes. Farhan et al. [ 30] proposed that anthocyanidins could suppress human cancers by trigger copper mediated and ROS-dependent selective cell death of cancer cells. A recent umbrella review summarized that anthocyanin improved plasmatic lipids, glucose metabolism, and endothelial function [31], which all may help in cancer prevention.
As with any study of this type, our study had several limitations. Firstly, anthocyanidin consumption was only assessed once at baseline in the PLCO study and dietary information may have changed over time. In addition, anthocyanidin consumption was assessed by a self-administrated FFQ in our study, which was typically prone to response bias. Secondly, although we had adjusted for a wide range of potential confounders, our results could be susceptible to residual confounding as the present study was performed with an observational design. Thirdly, the potential co-linearity between anthocyanidins and other nutrients could mediate the observed associations, which was not determined in this study. Finally, physical activity has been inversely associated with the renal cancer risk [32,33]. Long-term dialysis [34] was a potential risk factor for renal cancer. However, these data were not available in the PLCO study and thus we could not adjust for these potential confounders.
## 5. Conclusions
In conclusion, in this large American population, a higher dietary consumption of total anthocyanidins, as well as three subclasses including delphinidin, peonidin and petunidin, was associated with a lower risk of renal cancer. Future cohort studies are warranted to verify our preliminary findings and to explore the underlying mechanisms in this regard.
## References
1. Wallen E.M., Pruthi R.S., Joyce G.F., Wise M.. **Kidney cancer**. *J. Urol.* (2007) **177** 2006-2019. DOI: 10.1016/j.juro.2007.01.126
2. Capitanio U., Bensalah K., Bex A., Boorjian S.A., Bray F., Coleman J., Gore J.L., Sun M., Wood C., Russo P.. **Epidemiology of Renal Cell Carcinoma**. *Eur. Urol.* (2019) **75** 74-84. DOI: 10.1016/j.eururo.2018.08.036
3. Ferlay J., Soerjomataram I., Dikshit R., Eser S., Mathers C., Rebelo M., Parkin D.M., Forman D., Bray F.. **Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012**. *Int. J. Cancer* (2015) **136** E359-E386. DOI: 10.1002/ijc.29210
4. Chowdhury N., Drake C.G.. **Kidney Cancer: An Overview of Current Therapeutic Approaches**. *Urol. Clin. N. Am.* (2020) **47** 419-431. DOI: 10.1016/j.ucl.2020.07.009
5. Grosso G., Micek A., Godos J., Pajak A., Sciacca S., Galvano F., Giovannucci E.L.. **Dietary Flavonoid and Lignan Intake and Mortality in Prospective Cohort Studies: Systematic Review and Dose-Response Meta-Analysis**. *Am. J. Epidemiol.* (2017) **185** 1304-1316. DOI: 10.1093/aje/kww207
6. Mattioli R., Francioso A., Mosca L., Silva P.. **Anthocyanins: A Comprehensive Review of Their Chemical Properties and Health Effects on Cardiovascular and Neurodegenerative Diseases**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25173809
7. Goetz M.E., Judd S.E., Safford M.M., Hartman T.J., McClellan W.M., Vaccarino V.. **Dietary flavonoid intake and incident coronary heart disease: The REasons for Geographic and Racial Differences in Stroke (REGARDS) study**. *Am. J. Clin. Nutr.* (2016) **104** 1236-1244. DOI: 10.3945/ajcn.115.129452
8. Khoo H.E., Azlan A., Tang S.T., Lim S.M.. **Anthocyanidins and anthocyanins: Colored pigments as food, pharmaceutical ingredients, and the potential health benefits**. *Food Nutr. Res.* (2017) **61** 1361779. DOI: 10.1080/16546628.2017.1361779
9. Zhang Y., Zhu M., Wan H., Chen L., Luo F.. **Association between Dietary Anthocyanidins and Risk of Lung Cancer**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14132643
10. Sun L., Subar A.F., Bosire C., Dawsey S.M., Kahle L.L., Zimmerman T.P., Abnet C.C., Heller R., Graubard B.I., Cook M.B.. **Dietary Flavonoid Intake Reduces the Risk of Head and Neck but Not Esophageal or Gastric Cancer in US Men and Women**. *J. Nutr.* (2017) **147** 1729-1738. DOI: 10.3945/jn.117.251579
11. Cui L., Liu X., Tian Y., Xie C., Li Q., Cui H., Sun C.. **Flavonoids, Flavonoid Subclasses, and Esophageal Cancer Risk: A Meta-Analysis of Epidemiologic Studies**. *Nutrients* (2016) **8**. DOI: 10.3390/nu8060350
12. Bosetti C., Rossi M., McLaughlin J.K., Negri E., Talamini R., Lagiou P., Montella M., Ramazzotti V., Franceschi S., LaVecchia C.. **Flavonoids and the risk of renal cell carcinoma**. *Cancer Epidemiol. Biomark. Prev.* (2007) **16** 98-101. DOI: 10.1158/1055-9965.EPI-06-0769
13. Prorok P.C., Andriole G.L., Bresalier R.S., Buys S.S., Chia D., Crawford E.D., Fogel R., Gelmann E.P., Gilbert F., Hasson M.A.. **Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial**. *Control Clin. Trials* (2000) **21** 273S-309S. DOI: 10.1016/S0197-2456(00)00098-2
14. Zhu C.S., Pinsky P.F., Kramer B.S., Prorok P.C., Purdue M.P., Berg C.D., Gohagan J.K.. **The prostate, lung, colorectal, and ovarian cancer screening trial and its associated research resource**. *J. Natl. Cancer Inst.* (2013) **105** 1684-1693. DOI: 10.1093/jnci/djt281
15. Subar A.F., Thompson F.E., Kipnis V., Midthune D., Hurwitz P., McNutt S., McIntosh A., Rosenfeld S.. **Comparative validation of the Block, Willett, and National Cancer Institute food frequency questionnaires: The Eating at America’s Table Study**. *Am. J. Epidemiol.* (2001) **154** 1089-1099. DOI: 10.1093/aje/154.12.1089
16. Subar A.F., Midthune D., Kulldorff M., Brown C.C., Thompson F.E., Kipnis V., Schatzkin A.. **Evaluation of alternative approaches to assign nutrient values to food groups in food frequency questionnaires**. *Am. J. Epidemiol.* (2000) **152** 279-286. DOI: 10.1093/aje/152.3.279
17. Schoenfeld D.. **Chi-squared goodness-of-fit tests for the proportional hazards regression model**. *Biometrika* (1980) **67** 145-153. DOI: 10.1093/biomet/67.1.145
18. Marrie R.A., Dawson N.V., Garland A.. **Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables**. *J. Clin. Epidemiol.* (2009) **62** 511-517.e511. DOI: 10.1016/j.jclinepi.2008.05.015
19. Panchal S.K., John O.D., Mathai M.L., Brown L.. **Anthocyanins in Chronic Diseases: The Power of Purple**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14102161
20. Gonçalves A.C., Nunes A.R., Falcão A., Alves G., Silva L.R.. **Dietary Effects of Anthocyanins in Human Health: A Comprehensive Review**. *Pharmaceuticals* (2021) **14**. DOI: 10.3390/ph14070690
21. Krga I., Milenkovic D.. **Anthocyanins: From Sources and Bioavailability to Cardiovascular-Health Benefits and Molecular Mechanisms of Action**. *J. Agric. Food Chem.* (2019) **67** 1771-1783. DOI: 10.1021/acs.jafc.8b06737
22. Blahova J., Martiniakova M., Babikova M., Kovacova V., Mondockova V., Omelka R.. **Pharmaceutical Drugs and Natural Therapeutic Products for the Treatment of Type 2 Diabetes Mellitus**. *Pharmaceuticals* (2021) **14**. DOI: 10.3390/ph14080806
23. Mehmood A., Zhao L., Wang Y., Pan F., Hao S., Zhang H., Iftikhar A., Usman M.. **Dietary anthocyanins as potential natural modulators for the prevention and treatment of non-alcoholic fatty liver disease: A comprehensive review**. *Food Res. Int.* (2021) **142** 110180. DOI: 10.1016/j.foodres.2021.110180
24. Suresh S., Begum R.F., Singh S.A., Chitra V.. **Anthocyanin as a therapeutic in Alzheimer’s disease: A systematic review of preclinical evidences**. *Ageing Res. Rev.* (2022) **76** 101595. DOI: 10.1016/j.arr.2022.101595
25. Chang H., Lei L., Zhou Y., Ye F., Zhao G.. **Dietary Flavonoids and the Risk of Colorectal Cancer: An Updated Meta-Analysis of Epidemiological Studies**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10070950
26. Rossi M., Bosetti C., Negri E., Lagiou P., La Vecchia C.. **Flavonoids, proanthocyanidins, and cancer risk: A network of case-control studies from Italy**. *Nutr. Cancer* (2010) **62** 871-877. DOI: 10.1080/01635581.2010.509534
27. Dong Y., Wu X., Han L., Bian J., He C., El-Omar E., Gong L., Wang M.. **The Potential Roles of Dietary Anthocyanins in Inhibiting Vascular Endothelial Cell Senescence and Preventing Cardiovascular Diseases**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14142836
28. Khandelwal N., Abraham S.K.. **Intake of anthocyanidins pelargonidin and cyanidin reduces genotoxic stress in mice induced by diepoxybutane, urethane and endogenous nitrosation**. *Environ. Toxicol. Pharmacol.* (2014) **37** 837-843. DOI: 10.1016/j.etap.2014.02.012
29. Li W., Peng C., Zhaojie L., Wei W.. **Chemopreventive and therapeutic properties of anthocyanins in breast cancer: A comprehensive review**. *Nutr. Res.* (2022) **107** 48-64. DOI: 10.1016/j.nutres.2022.08.005
30. Farhan M., Rizvi A., Ali F., Ahmad A., Aatif M., Malik A., Alam M.W., Muteeb G., Ahmad S., Noor A.. **Pomegranate juice anthocyanidins induce cell death in human cancer cells by mobilizing intracellular copper ions and producing reactive oxygen species**. *Front. Oncol.* (2022) **12** 998346. DOI: 10.3389/fonc.2022.998346
31. Sandoval-Ramírez B.A., Catalán Ú., Llauradó E., Valls R.M., Salamanca P., Rubió L., Yuste S., Solà R.. **The health benefits of anthocyanins: An umbrella review of systematic reviews and meta-analyses of observational studies and controlled clinical trials**. *Nutr. Rev.* (2022) **80** 1515-1530. DOI: 10.1093/nutrit/nuab086
32. Behrens G., Leitzmann M.F.. **The association between physical activity and renal cancer: Systematic review and meta-analysis**. *Br. J. Cancer* (2013) **108** 798-811. DOI: 10.1038/bjc.2013.37
33. Moore S.C., Lee I.M., Weiderpass E., Campbell P.T., Sampson J.N., Kitahara C.M., Keadle S.K., Arem H., Berrington de Gonzalez A., Hartge P.. **Association of Leisure-Time Physical Activity with Risk of 26 Types of Cancer in 1.44 Million Adults**. *JAMA Intern. Med.* (2016) **176** 816-825. DOI: 10.1001/jamainternmed.2016.1548
34. Taborelli M., Toffolutti F., Del Zotto S., Clagnan E., Furian L., Piselli P., Citterio F., Zanier L., Boscutti G., Serraino D.. **Increased cancer risk in patients undergoing dialysis: A population-based cohort study in North-Eastern Italy**. *BMC Nephrol.* (2019) **20**. DOI: 10.1186/s12882-019-1283-4
|
---
title: Chemerin and Chemokine-like Receptor 1 Expression in Ovarian Cancer Associates
with Proteins Involved in Estrogen Signaling
authors:
- Florian Weber
- Susanne Schueler-Toprak
- Christa Buechler
- Olaf Ortmann
- Oliver Treeck
journal: Diagnostics
year: 2023
pmcid: PMC10001027
doi: 10.3390/diagnostics13050944
license: CC BY 4.0
---
# Chemerin and Chemokine-like Receptor 1 Expression in Ovarian Cancer Associates with Proteins Involved in Estrogen Signaling
## Abstract
Chemerin, a pleiotropic adipokine coded by the RARRES2 gene, has been reported to affect the pathophysiology of various cancer entities. To further approach the role of this adipokine in ovarian cancer (OC), intratumoral protein levels of chemerin and its receptor chemokine-like receptor 1 (CMKLR1) were examined by immunohistochemistry analyzing tissue microarrays with tumor samples from 208 OC patients. Since chemerin has been reported to affect the female reproductive system, associations with proteins involved in steroid hormone signaling were analyzed. Additionally, correlations with ovarian cancer markers, cancer-related proteins, and survival of OC patients were examined. A positive correlation of chemerin and CMKLR1 protein levels in OC (Spearman’s rho = 0.6, $p \leq 0.0001$) was observed. Chemerin staining intensity was strongly associated with the expression of progesterone receptor (PR) (Spearman´s rho = 0.79, $p \leq 0.0001$). Both chemerin and CMKLR1 proteins positively correlated with estrogen receptor β (ERβ) and estrogen-related receptors. Neither chemerin nor the CMKLR1 protein level was associated with the survival of OC patients. At the mRNA level, in silico analysis revealed low RARRES2 and high CMKLR1 expression associated with longer overall survival. The results of our correlation analyses suggested the previously reported interaction of chemerin and estrogen signaling to be present in OC tissue. Further studies are needed to elucidate to which extent this interaction might affect OC development and progression.
## 1. Introduction
Ovarian cancer (OC) is the leading cause of death by a gynecological malignancy in the developed world [1]. Due to missing screening methods and the aggressive behavior of the disease, the majority are diagnosed in advanced stages [2]. OC has a five-year survival rate of only $10\%$ when the most common serous type spreads rapidly throughout the peritoneal cavity. Overall, this disease has a poor prognosis, with a five-year survival rate of approximately $50\%$. If diagnosed in earlier stages when the cancer is still confined to the ovary, this survival rate could rise to about $90\%$, but today this occurs in only $20\%$ of patients [2,3].
Increasing evidence suggests that ovarian cancer, like tumors of different origins, is affected by adipokine chemerin [4,5,6]. Chemerin (RARRES2) is a well-described adipokine [7]. It was initially identified as a chemoattractant protein for immune cells that binds to chemokine-like receptor 1 (CMKLR1) expressed by these cells. In the meantime, diverse functions of chemerin have been defined, and chemerin was shown to regulate angiogenesis, adipogenesis, insulin response, and blood pressure [8,9,10,11,12,13]. Although with CCRL2 and GPR1, two further chemerin receptors have been identified, CMKLR1 has been considered to be the most important receptor of this adipokine since chemerin binding to CMKLR1 particularly leads to broad G-protein activation [14]. CMKLR1, located in the cell membrane, is internalized upon chemerin binding. Ligand binding initiates activation of G-proteins and β-arrestin pathways, inducing cellular responses via second messenger pathways such as intracellular calcium mobilization, phosphorylation of mitogen-activated protein kinase (MAPK)1/MAPK2 (ERK$\frac{1}{2}$), tyrosine-protein kinase receptor (TYRO) 3, MAPK14/p38 MAPK and phosphoinositid-3-kinase (PI3K) [14,15]. Emerging studies have proven the role of chemerin in tumorigenesis, whose expression often differs between tumor and non-tumor tissues [4,16]. In most tumor entities, chemerin/RARRES2 is down-regulated compared to normal tissue, e.g., in tumors of the breast, melanoma, lung, prostate, liver, adrenal, and in melanoma, and this decrease of chemerin expression has been suggested to be part of the tumor´s immune escape [4,17].
Estrogens are known to affect the progression of ovarian cancer [18], although to a much lesser extent than breast cancer. These effects are dependent on the expression of estrogen receptors (ERs) α and β. Estrogens activate the proliferation of ovarian cancer cells via ERα, often being overexpressed in this cancer entity [18,19]. Expression of ERβ, which is the predominant ER in the ovary [20], is often down-regulated in OC. ERβ is associated with an improved overall survival (OS) [21,22] in line with in vitro data demonstrating that its activation reduces ovarian cancer cell proliferation and activates apoptosis [21,23,24,25]. There is a relationship between estrogen-related receptors (ERRs) α, β, and γ with various cancer-related genes as well as ERα in ovarian cancer [26]. ERRs interact with ERα and several other nuclear receptors [27,28]. Thereby, among others, a vast number of different genes modulating metabolic processes are regulated, and several different pathways are controlled [29]. ERRα, which has attracted the greatest attention to date, acts as a master regulator of cellular metabolism, thereby also promoting tumor growth [30]. Chemerin was shown to decrease ovarian steroidogenesis via CMKLR1 [31,32] and thus may be protective in hormone-dependent cancers. A tumor-suppressive effect of chemerin was also reported by a recent in vitro study demonstrating chemerin to reduce the growth of ovarian cancer cell spheroids via activating the release of interferon (IFN)α, leading to induction of a broad, IRF9/ISGF3-mediated anti-tumoral transcriptome response [6]. However, a recent Chinese in vitro study reported a tumor-promoting role of chemerin in ovarian cancer cell lines in terms of proliferation via upregulation of programmed death ligand 1 (PD-L1) [5].
On the mRNA level, data on the expression of RARRES2 and CMKLR1 in ovarian cancer tissue have been extensively collected, e.g., by The Cancer Genome Atlas (TCGA) project https://www.cancer.gov/tcga). However, studies based on protein data of both genes in OC are rare. Thus, to further approach the possible role of chemerin and CMKLR1 in this cancer entity, analyses of their protein levels in OC cancer tissue and identification of correlated proteins are necessary. In the current study, protein levels of chemerin and CMKLR1 were assessed by immunohistochemistry of tissue microarrays (TMA), including tissues of 208 ovarian cancer patients. Furthermore, their association with patients´ survival and with the expression of ovarian cancer markers, cancer-related proteins, and components of estrogen signaling pathways was tested.
## 2.1. Tissue Samples
In this study, ovarian cancer samples collected in the Department of Pathology of the University of Regensburg were examined. Generally, Caucasian women with sporadic ovarian cancer and available information on grading, stage, and histological subtype from 1995 to 2013 were included. Patients’ clinical data were available from tumor registry database information provided by the Tumor Center Regensburg (Bavaria, Germany). This high-quality population-based regional cancer registry was founded in 1991, and it covers a population of more than 2.2 million people in Upper Palatinate and Lower Bavaria. Information about the diagnosis, course of the disease, therapies, and long-term follow-up are documented. Patient data originate from the University Hospital Regensburg, 53 regional hospitals, and more than 1000 practicing doctors in the region. Based on medical reports, pathology, and follow up-records, these population-based data are routinely documented and fed into the cancer registry (Table 1).
## 2.2. Tissue Microarray and Immunohistochemistry
The tissue microarray (TMA) was created using standard procedures that have been previously described [33,34]. From all patients included in this study, an experienced pathologist (FW) evaluated H&E sections of tumor tissues, and representative areas were marked. From these areas, core biopsies on the corresponding paraffin blocks were removed and transferred into the grid of a recipient block according to a predesigned array of about 60 specimens in each of the five TMA paraffin blocks. For immunohistochemistry, 4 μm sections of the TMA blocks were incubated with the indicated antibodies according to the mentioned protocols in the given dilutions (Table 2), followed by incubation with a horseradish peroxidase (HRP) conjugated secondary antibody and another incubation with 3,3′-diaminobenzidine (DAB) as substrate, which resulted in a brown-colored precipitate at the antigen site. An experienced clinical pathologist (FW) evaluated immunohistochemical staining according to localization and specificity (Table 3). For the determination of the staining intensity of ERRα and ERRγ, a score from 0 (negative) to 3 (strongly positive) was used. Since staining intensities for ERRβ were generally lower, a score from 0 to 2 was used. For steroid hormone receptors ERα, nuclear ERβ, and PR, the immunoreactivity score, according to Remmele et al., was used [35]. Expression of proliferation marker Ki-67 using antibody clone MIB-1 was assessed in the percentage of tumor cells with positive nuclear staining. Her2/neu expression was scored according to the DAKO score routinely used for breast cancer cases. EGFR was scored according to Spaulding et al. on a 4-tiered scale from 0 to 3 [36]. For p53 and polyclonal CEA, the “quick score” was used, where results are scored by multiplying the percentage of positive cells (P) by the intensity (I) according to the formula: Q = P × I; maximum = 300 [37]. CA-125 and ERβ were described as positive or negative, irrespective of staining intensity. Chemerin and CMKLR1 cellular staining intensity (non-specific nuclear staining was not considered) was scored on a 3-tiered scale from 1 (weak) to 3 (strong intensity) (Figure 1).
## 2.3. In Silico Analyses
To compare the expression of RARRES2 and CMKLR1 in normal ovary, OC, and OC metastases at the mRNA level, the TNMplot webtool (https://tnmplot.com/analysis/) was used to analyze gene chip data from GEO datasets, including 744 OC patients, 46 samples from the normal ovary and 44 OC metastases [38]. The statistical significance of the comparison was determined using the nonparametric Kruskal–Wallis test. To test the association of RARRES2 and CMKLR1 mRNA levels in OC patients with overall survival by means of the webtool KMplot (https://kmplot.com/analysis/index.php?p=service&cancer=ovar (accessed on 2 February 2023)), gene chip data from TCGA and 14 GEO datasets were analyzed. Both mRNA and survival data were available from 2021 OC patients. The following parameters were used for this analysis: splitting of the patients’ collective in a high and a low expression group was performed by choosing the “auto select best cutoff” option; all patient subgroups and treatment groups were included, and biased arrays were excluded. For RARRES2, the Affymetrix ID 209496_at was indicated, and for CMKLR1, the Affy ID 210659_at [39].
## 2.4. Statistical Analysis
Apart from multivariate survival analyses, statistical analysis was performed using GraphPad Prism 5® (GraphPad Software, Inc., La Jolla, CA, USA). The non-parametric Kruskal–Wallis rank-sum test was used for testing differences in the expression among three or more groups. For pairwise comparison, the non-parametric Mann–Whitney U rank-sum test was used. Correlation analysis was performed using the Spearman correlation. Univariate survival analyses were performed using the Kaplan–Meier method. The chi-squared statistic of the log rank was used to investigate differences between survival curves. Hazard ratios were calculated using the Mantel–Haenszel method. A p-value below 0.05 was considered significant. Multivariate Cox regression survival analysis was performed using IBM® SPSS® Statistics 25 (SPSS®, IBM® Corp., Armonk, NY, USA) using the Enter method.
## 3.1. Intratumoral RARRES2 mRNA Levels in Ovarian Cancer and Metastasis Tissues Are Significantly Reduced When Compared to Normal Ovary
Given that a sufficient amount of normal ovarian tissues or metastatic tissues could not be obtained, it was decided to use the benefits of open-source gene chip expression data, and it was thereby possible to compare mRNA expression of RARRES2 (coding for chemerin) and CMKLR1 in 744 OC tissues, 46 samples from the normal ovary and 44 tissue samples of OC metastases. This analysis of open-source data using TNMplot webtool (https://tnmplot.com/analysis/) [38] accessed on 15. September 2022 revealed decreased RARRES2 mRNA levels in the OC (Dunn test $$p \leq 0.0002$$) and the metastasis group (Dunn test $$p \leq 0.0646$$) compared to normal ovarian tissue, interpreted as an attempt for evasion from the immune response. Regarding CMKLR1 mRNA levels, only the metastasis samples exhibited a reduced expression (Dunn test $p \leq 0.0001$) of this receptor (Figure 2).
## 3.2. Protein Levels of Chemerin and CMKLR1 in Ovarian Cancer Tissue
Both chemerin and CMKLR1 were shown to be widely detectable in OC tissues as assessed on the protein level by means of immunohistochemistry of tissue microarrays (TMAs). Positive staining of chemerin was found in all cases ($32.7\%$ with weak staining, $40.5\%$ moderate, and $26.8\%$ with strong staining). CMKLR1 was also detected in all tumors, among them $22.2\%$ with weak staining, $38.0\%$ with moderate, and $39.9\%$ with strong staining. There was a strong correlation between chemerin and CMKLR1 levels in all tumors (rho = 0.5959, $p \leq 0.0001$), as well as the largest subgroup of serous OC (rho = 0.6285, $p \leq 0.0001$). No significant differences in protein levels of either chemerin or CMKLR1 between G2 and G3 graded tumors, different FIGO stages, or in patients with different nodal statuses were observed. Moreover, the invasion of lymph or blood vessels did not depend on the expression of either protein.
## 3.3. Protein Levels of Chemerin and CMKLR1 in Ovarian Cancer Tissue Subject to Levels of Ovarian Cancer Markers, Cancer-Related Proteins and Components of Estrogen Signaling Pathways
Subsequently, mean protein levels of chemerin and CMKLR1 in ovarian cancer subgroups were compared with high vs. low expression of the ovarian cancer markers, cancer-related proteins, and components of estrogen signaling pathways that were analyzed in this study.
First, results showed that mean levels of chemerin and CMKLR1 were elevated in ovarian cancers with higher cytoplasmic ERβ expression when compared to the lower expressing subgroup ($$p \leq 0.0143$$ and $$p \leq 0.0133$$, respectively) (Table 3). Mean protein levels of CMKLR1 were increased in ovarian cancer specimens with higher expression of the proliferation marker Ki67 ($$p \leq 0.0304$$). Protein levels of chemerin and CMKLR1 were elevated in the ERRα-high subgroup ($p \leq 0.0001$ and $p \leq 0.0001$, respectively). In ovarian cancers with higher expression of ERRβ, increased levels of chemerin and CMKRL1 ($$p \leq 0.0091$$ and $p \leq 0.0001$, respectively) were observed. CMKLR1 levels were found to be elevated in tumors with higher expression of ERRγ ($$p \leq 0.0031$$). Finally, the mean protein expression of chemerin was elevated in ovarian cancers with higher expression of CMKRL1 ($p \leq 0.0001$), and the mean protein levels of CMKRL1 was increased in ovarian cancer with higher expression of chemerin ($p \leq 0.0001$). No differences in chemerin and CMKLR1 expression levels could be observed between tumor subgroups with different levels of ERα, nuclear ERβ, PR, CEA, CA125, CA72-4, p53, Her2, or EGFR.
## 3.4. Correlation of Chemerin and CMKLR1 Protein Levels with Intratumoral Expression of Proteins Involved in Estrogen Signaling, Ovarian Cancer Markers, and Other Cancer-Related Genes
Since chemerin is known to affect ovarian steroidogenesis and was reported to correlate with steroid hormone receptors in breast cancer, correlations of both proteins with protein expression of PR, ERα, ERβ, PR, ERRα, β, and γ were examined first. Furthermore, intratumoral chemerin and CMKLR1 levels were tested for correlation with ovarian cancer markers CA125 (MUC16), polyclonal CEA (CEACAM1,3,4,6,7 and 8), and CA72-4 and with the cancer-related genes EGFR, HER2, Ki-67 and p53. By means of Spearman’s rank correlation analysis, a strong association of chemerin with progesterone receptor (PR) levels (Spearman’s rho = 0.7952, $p \leq 0.0001$) was observed. Chemerin and CMKLR1 were found to be moderately associated with intratumoral protein expression of ERβ, particularly in the largest serous subgroup, which was true both for nuclear (chemerin: rho = 0.2127, $$p \leq 0.0213$$; CMKLR1: rho = 0.2630, $$p \leq 0.0039$$) and cytoplasmic (chemerin: rho = 0.2731, $$p \leq 0.0029$$; CMKLR1: rho = 0.27, $$p \leq 0.003$$) ERβ expression. Notably, a considerable positive correlation between both chemerin and CMKLR1 with the estrogen-related receptors (ERR)s α, β, and γ was observed. Chemerin positively correlated with ERRα (rho = 0.384, $p \leq 0.0001$), ERRβ (rho = 0.3343, $p \leq 0.0001$), and ERRγ (rho = 0.383, $p \leq 0.0001$). CMKLR1 was associated with the expression of ERRα (rho = 0.5207, $p \leq 0.0001$), ERRβ (rho = 0.4239, $p \leq 0.0001$), and ERRγ (rho = 0.4198, $p \leq 0.0001$). Additionally, a weak positive association with cancer marker CEACAM5 (rho = 0.1594, $p \leq 0.0498$) was observed. Expression of the other proteins mentioned above was not significantly associated with either chemerin or CMKLR1 (Table 4).
## 3.5. Correlation of RARRES2 and CMKLR1 mRNA Levels with Expression of Genes Involved in Sex Steroid Hormone Metabolism and Signaling Assessed by In Silico Analysis
In silico analyses on the mRNA level (using gene chip data from 744 ovarian cancer patients accessed on the platform https://tnmplot.com) [38] on 15 September 2022 corroborated the positive correlation between chemerin (RARRES2) and CMKLR1 that had been observed on the protein level (Spearman’s rho = 0.26, $p \leq 0.0001$). With regard to genes involved in estrogen signaling, this analysis also substantiated the positive correlation of CMKLR1 with ERβ (ESR2) (rho = 0.33, $p \leq 0.0001$) and of CMKLR1 with ERRα (ESRRA) (rho = 0.33, $p \leq 0.0001$), which was further corroborated using the GEPIA2 platform [40] analyzing datasets from 426 serous OC patients (CMKLR1/ESR2 rho = 0.35 and CMKLR1/ESRRA rho = 0.31, both $p \leq 0.0001$). Using the same platform and data, a positive, albeit weaker correlation of CMKLR1 with ERRβ (ESRRB) (rho = 0.2, $p \leq 0.001$) in serous OC, but not with ERRγ (ESRRG) was found. In contrast to the chemerin protein data from IHC, mRNA levels of the RARRES2 gene in ovarian cancer were not correlated with PGR, ESR2, ESRRA, ESRRB, ESRRG, nor CEACAM5 after analysis of both patient collectives on the mentioned platforms ($p \leq 0.05$ for all).
## 3.6. Survival Analyses
Association of chemerin and CMKLR1 in ovarian cancer tissue with overall and progression-free survival.
Analyzing the protein data assessed in this study by IHC of TMAs, when OC patients exhibiting different levels of intratumoral chemerin or CMKLR1 were compared with regard to OS by means of Kaplan–Meier analysis, no significant differences were found. Subsequently, the survival of patients with serous ovarian cancers was investigated. However, neither chemerin nor CMKLR1 levels did influence the OS of the patients in this cohort (Figure S1). The levels of these proteins also did not correlate with progression-free survival (PFS), neither when including all ovarian cancer cases nor when analyzing only serous ovarian cancers.
Since a weakness of this study is the relatively low number of OC samples, it was speculated that the association between chemerin and CMKLR1 expression with survival could be visible using a larger patient collective. Thus, the online tool kmplot.com providing microarray mRNA and OS data of 2021 OC patients from the Gene Expression Omnibus and The Cancer Genome Atlas [39] was used and accessed on 1 September 2022. This analysis revealed high mRNA levels of RARRES2 in OC tissue to be significantly associated with a shorter OS (HR = 1.32, $$p \leq 5.8$$ × 10−5). In contrast, high mRNA expression of CMKLR1 was associated with longer OS (HR = 0.8, $$p \leq 0.0002$$) (Figure 3).
## 4. Discussion
In this study, possible associations between the adipokine chemerin and its receptor CMKLR1 with other proteins involved in steroid hormone signaling were examined in OC tissues and in silico, as the role of these proteins in cancer is yet mostly unclear. It was found that in serous ovarian cancer, both chemerin and CMKLR1 protein positively correlated with ERβ protein expression and with levels of ERRα, β, and γ; additionally, chemerin protein expression was notably associated with that of PR. On the mRNA level, CMKLR1, not RARRES2 mRNA, correlated with ERRβ and γ. These findings thus showed an association of chemerin/CMKLR1 with a nuclear estrogen receptor (ERβ), an important estrogen target gene (PR), and with modulators of estrogen signaling, which plays essential roles in OC.
Chemerin has been shown to modulate steroidogenesis, especially secretion of progesterone, in the porcine ovary in both stimulatory and inhibitory ways [41], and it has been proposed that chemerin via CMKLR1 plays a role in the development of polycystic ovary syndrome via inhibition of progesterone secretion [42]. Since progesterone is known to be of importance in OC development, the association between chemerin/CMKLR1 and PR was investigated. In our cohort of 208 patients, a strong correlation between chemerin staining intensity and PR protein expression could be shown. PR expression in OC was found to be associated with a more favorable prognosis [43], and further studies may confirm the role of chemerin herein.
It has long been demonstrated that estrogens, their different receptors (ERs), and related receptors (ERRs) are major players in the origin and development of OC in various ways, which led to an investigation of possible associations of chemerin and CMKLR1 with different ERs and ERRs, on which there are few data published to date. One study by Hoffmann et al. indicated an anti-proliferative effect of chemerin partly via ERs [44]. In our study, both chemerin and CMKLR1 levels in tumor tissues positively correlated with estrogen receptor β (ERβ), which could be confirmed on the mRNA level for CMKLR1 and ESR2 by in silico analysis. According to past publications, this could indicate a protective role of chemerin and CMKLR1 similar to ERβ [21,22,23,24].
Concerning ERRs, both chemerin and its receptor positively correlated with estrogen-related receptor α (ERRα), particularly in serous OC tissue, an association being also validated in silico on the mRNA level for CMKLR1. This is in line with a previous study [26], where ERRα was detected abundantly in OC tissues. Also, protein levels of chemerin and its receptor were associated with ERRβ and ERRγ, with a stronger correlation present in serous OC. As these two receptors are indicative of poorer survival [26], the exact mechanisms of chemerin interaction with ERRs and other modulatory factors are to be further elucidated since these findings are contradictory in their putative pro-tumoral effects to the association found with ERβ protein expression and ESR2 gene expression.
In silico analyses comparing mRNA expression of the RARRES2 gene in normal ovary, OC, and OC metastases revealed a notable decrease of RARRES2 expression in OC and in metastatic tissue, whereas CMKLR1 RNA levels were considerably reduced in OC metastases only. Low expression of chemerin in tumor tissue is in accordance with findings from other cancer entities and was suggested to indicate a protective role of chemerin in cancer progression. Gao et al., however, described a higher expression of chemerin protein in OC compared to normal tissues. Intratumoral chemerin protein levels were not associated with the overall (OS) or progression-free survival (PFS) of OC patients. In line with our data, chemerin was found to be low-expressed in melanoma and liver cancer, but according to the Human Protein Atlas, it was not prognostic in these cancers [45]. Analysis of open-source mRNA and survival data from 2021 OC patients moreover identified a favorable effect of high CMKLR1 and low RARRES2 mRNA levels on patients’ survival. Taken together, the association of chemerin and CMKLR1 with ovarian cancer prognosis seems to be complex, and factors such as hormonal status or comorbidities such as adiposity, dyslipidemia, or hypertension must be considered.
The fact that an association of chemerin or CMKLR1 protein levels with OC survival was not observed, but instead, a significant correlation on the mRNA level of a larger patients´ collective might be explained by the different collective size. Furthermore, mRNA levels do not always correlate with the level of the coded protein. During phases such as cell proliferation or differentiation, post-transcriptional mechanisms may cause deviations from this association. The sampling of tissues for RNA and protein analysis is a further source of variations [46]. Chemerin is a secreted protein and may be taken up by cancer cells. Thus, there are different explanations for why mRNA and protein analysis of chemerin in OC did not always reveal concordant results. The first two arguments also apply to the further proteins analyzed in this study. For CMKLR1, it is important to note that only tumor cell expressed protein was quantified. At the mRNA levels, tumor cells, as well as further cells such as immune cells of the respective tissues, are included and contribute to variations of mRNA and protein data. Differences in protein level assessment of chemerin via immunohistochemistry and RARRES2 gene expression on the mRNA level can be explained by the fact that chemerin is mainly produced by extratumoral tissues, e.g., adipocytes and hepatocytes [8]. Therefore, intratumoral protein levels measured by immunohistochemical staining are expectedly higher than mRNA levels when comparing normal and cancer tissues, and associations of intratumoral chemerin levels with OS and PFS are not mirrored by mRNA gene expression data.
Tumors including OC are able to escape the intrinsic anti-tumor activity of the immune system by means of so-called immune evasion strategies [47,48] and cancer immunoediting, often attributed to the interaction of tumor cells with tumor-infiltrating lymphocytes as well as immunomodulatory factors such as PD-L1, CTLA-4, and CXCR4 [49,50]. This might be a possible explanation for the missing effect of different intratumoral chemerin levels on OS or PFS, as well as the decrease of RARRES2 on the mRNA level in the in silico analysis of OC, compared to normal ovarian tissue.
In this context, it might be of interest to investigate the composition of tumor-infiltrating lymphocytes and their interaction with chemerin via CMKLR1 in further studies.
Limitations of this study are the medium-sized cohort of OC patients and the lack of normal ovarian tissue in the immunohistochemical analysis, which has been compensated for in the additional in silico analyses on the mRNA level. As always in the case of adipokines and the like, it remains to be further determined how serum levels of chemerin must be taken into account, as serum chemerin levels were not available for our OC cohort.
## 5. Conclusions
Chemerin protein and its receptor CMKLR1 were demonstrated to be abundantly detectable by immunohistochemistry in ovarian cancer tissues and to positively correlate with intratumoral expression of PR, ERβ and ERRs, corroborating interaction with estrogen signaling pathways as previously suggested. Analysis of publicly available gene expression data demonstrated a significant downregulation of RARRES2 mRNA expression in OC and metastatic tissue, whereas CMKLR1 expression was found to be reduced in metastases only. Tumoral chemerin and CMKLR1 protein levels were not related to OS, but lower RARRES2 and higher CMKLR1 mRNA levels were associated with longer OS. Our data are able to encourage further studies examining the role of the interactions suggested in this study for the development and progression of ovarian cancer.
## References
1. Siegel R.L., Miller K.D., Jemal A.. **Cancer statistics, 2019**. *CA Cancer J. Clin.* (2019) **69** 7-34. DOI: 10.3322/caac.21551
2. Torre L.A., Trabert B., DeSantis C.E., Miller K.D., Samimi G., Runowicz C.D., Gaudet M.M., Jemal A., Siegel R.L.. **Ovarian cancer statistics, 2018**. *CA Cancer J. Clin.* (2018) **68** 284-296. DOI: 10.3322/caac.21456
3. Bäumler M., Gallant D., Druckmann R., Kuhn W.. **Ultrasound screening of ovarian cancer**. *Horm. Mol. Biol. Clin. Investig.* (2019) **41**. DOI: 10.1515/hmbci-2019-0022
4. Treeck O., Buechler C., Ortmann O.. **Chemerin and Cancer**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20153750
5. Gao C., Shi J., Zhang J., Li Y., Zhang Y.. **Chemerin promotes proliferation and migration of ovarian cancer cells by upregulating expression of PD-L1**. *J. Zhejiang Univ. Sci. B* (2022) **23** 164-170. DOI: 10.1631/jzus.B2100392
6. Schmitt M., Gallistl J., Schüler-Toprak S., Fritsch J., Buechler C., Ortmann O., Treeck O.. **Anti-Tumoral Effect of Chemerin on Ovarian Cancer Cell Lines Mediated by Activation of Interferon Alpha Response**. *Cancers* (2022) **14**. DOI: 10.3390/cancers14174108
7. Buechler C., Feder S., Haberl E.M., Aslanidis C.. **Chemerin Isoforms and Activity in Obesity**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20051128
8. Bozaoglu K., Curran J.E., Stocker C.J., Zaibi M.S., Segal D., Konstantopoulos N., Morrison S., Carless M., Dyer T.D., Cole S.A.. **Chemerin, a novel adipokine in the regulation of angiogenesis**. *J. Clin. Endocrinol. Metab.* (2010) **95** 2476-2485. DOI: 10.1210/jc.2010-0042
9. Fatima S.S., Rehman R., Baig M., Khan T.A.. **New roles of the multidimensional adipokine: Chemerin**. *Peptides* (2014) **62** 15-20. DOI: 10.1016/j.peptides.2014.09.019
10. Ferland D.J., Mullick A.E., Watts S.W.. **Chemerin as a Driver of Hypertension: A Consideration**. *Am. J. Hypertens.* (2020) **33** 975-986. DOI: 10.1093/ajh/hpaa084
11. Mariani F., Roncucci L.. **Chemerin/chemR23 axis in inflammation onset and resolution**. *Inflamm. Res.* (2015) **64** 85-95. DOI: 10.1007/s00011-014-0792-7
12. Nakamura N., Naruse K., Kobayashi Y., Miyabe M., Saiki T., Enomoto A., Takahashi M., Matsubara T.. **Chemerin promotes angiogenesis in vivo**. *Physiol. Rep.* (2018) **6** e13962. DOI: 10.14814/phy2.13962
13. Yoshimura T., Oppenheim J.J.. **Chemerin reveals its chimeric nature**. *J. Exp. Med.* (2008) **205** 2187-2190. DOI: 10.1084/jem.20081736
14. De Henau O., Degroot G.-N., Imbault V., Robert V., De Poorter C., Mcheik S., Galés C., Parmentier M., Springael J.-Y.. **Signaling Properties of Chemerin Receptors CMKLR1, GPR1 and CCRL2**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0164179
15. Serafin D.S., Allyn B., Sassano M.F., Timoshchenko R.G., Mattox D., Brozowski J.M., Siderovski D.P., Truong Y.K., Esserman D., Tarrant T.K.. **Chemerin-activated functions of CMKLR1 are regulated by G protein-coupled receptor kinase 6 (GRK6) and β-arrestin 2 in inflammatory macrophages**. *Mol. Immunol.* (2019) **106** 12-21. DOI: 10.1016/j.molimm.2018.12.016
16. Goralski K.B., Jackson A.E., McKeown B.T., Sinal C.J.. **More Than an Adipokine: The Complex Roles of Chemerin Signaling in Cancer**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20194778
17. Pachynski R.K., Wang P., Salazar N., Zheng Y., Nease L., Rosalez J., Leong W.-I., Virdi G., Rennier K., Shin W.J.. **Chemerin Suppresses Breast Cancer Growth by Recruiting Immune Effector Cells into the Tumor Microenvironment**. *Front. Immunol.* (2019) **10** 983. DOI: 10.3389/fimmu.2019.00983
18. Chan K.K.L., Siu M.K.Y., Jiang Y.-X., Wang J.-J., Wang Y., Leung T.H.Y., Liu S.S., Cheung A.N.Y., Ngan H.Y.S.. **Differential expression of estrogen receptor subtypes and variants in ovarian cancer: Effects on cell invasion, proliferation and prognosis**. *BMC Cancer* (2017) **17**. DOI: 10.1186/s12885-017-3601-1
19. O’Donnell A.J.M., Macleod K.G., Burns D.J., Smyth J.F., Langdon S.P.. **Estrogen receptor-alpha mediates gene expression changes and growth response in ovarian cancer cells exposed to estrogen**. *Endocr. Relat. Cancer* (2005) **12** 851-866. DOI: 10.1677/erc.1.01039
20. Kyriakidis I., Papaioannidou P.. **Estrogen receptor beta and ovarian cancer: A key to pathogenesis and response to therapy**. *Arch. Gynecol. Obstet.* (2016) **293** 1161-1168. DOI: 10.1007/s00404-016-4027-8
21. Halon A., Nowak-Markwitz E., Maciejczyk A., Pudelko M., Gansukh T., Györffy B., Donizy P., Murawa D., Matkowski R., Spaczynski M.. **Loss of estrogen receptor beta expression correlates with shorter overall survival and lack of clinical response to chemotherapy in ovarian cancer patients**. *Anticancer Res.* (2011) **31** 711-718. PMID: 21378361
22. Schüler-Toprak S., Weber F., Skrzypczak M., Ortmann O., Treeck O.. **Estrogen receptor β is associated with expression of cancer associated genes and survival in ovarian cancer**. *BMC Cancer* (2018) **18**. DOI: 10.1186/s12885-018-4898-0
23. Schüler-Toprak S., Moehle C., Skrzypczak M., Ortmann O., Treeck O.. **Effect of estrogen receptor β agonists on proliferation and gene expression of ovarian cancer cells**. *BMC Cancer* (2017) **17**. DOI: 10.1186/s12885-017-3246-0
24. Treeck O., Pfeiler G., Mitter D., Lattrich C., Piendl G., Ortmann O.. **Estrogen receptor {beta}1 exerts antitumoral effects on SK-OV-3 ovarian cancer cells**. *J. Endocrinol.* (2007) **193** 421-433. DOI: 10.1677/JOE-07-0087
25. Liu J., Viswanadhapalli S., Garcia L., Zhou M., Nair B.C., Kost E., Rao Tekmal R., Li R., Rao M.K., Curiel T.. **Therapeutic utility of natural estrogen receptor beta agonists on ovarian cancer**. *Oncotarget* (2017) **8** 50002-50014. DOI: 10.18632/oncotarget.18442
26. Schüler-Toprak S., Weber F., Skrzypczak M., Ortmann O., Treeck O.. **Expression of estrogen-related receptors in ovarian cancer and impact on survival**. *J. Cancer Res. Clin. Oncol.* (2021) **147** 2555-2567. DOI: 10.1007/s00432-021-03673-9
27. Yamamoto T., Mori T., Sawada M., Kuroboshi H., Tatsumi H., Yoshioka T., Matsushima H., Iwasaku K., Kitawaki J.. **Estrogen-related receptor-γ regulates estrogen receptor-α responsiveness in uterine endometrial cancer**. *Int. J. Gynecol. Cancer* (2012) **22** 1509-1516. DOI: 10.1097/IGC.0b013e31826fd623
28. Tanida T., Matsuda K.I., Yamada S., Hashimoto T., Kawata M.. **Estrogen-related Receptor β Reduces the Subnuclear Mobility of Estrogen Receptor α and Suppresses Estrogen-dependent Cellular Function**. *J. Biol. Chem.* (2015) **290** 12332-12345. DOI: 10.1074/jbc.M114.619098
29. Ranhotra H.S.. **The estrogen-related receptors: Orphans orchestrating myriad functions**. *J. Recept. Signal Transduct. Res.* (2012) **32** 47-56. DOI: 10.3109/10799893.2011.647350
30. Liu G., Sun P., Dong B., Sehouli J.. **Key regulator of cellular metabolism, estrogen-related receptor α, a new therapeutic target in endocrine-related gynecological tumor**. *Cancer Manag. Res.* (2018) **10** 6887-6895. DOI: 10.2147/CMAR.S182466
31. Reverchon M., Cornuau M., Ramé C., Guerif F., Royère D., Dupont J.. **Chemerin inhibits IGF-1-induced progesterone and estradiol secretion in human granulosa cells**. *Hum. Reprod.* (2012) **27** 1790-1800. DOI: 10.1093/humrep/des089
32. Tang M., Huang C., Wang Y.-F., Ren P.-G., Chen L., Xiao T.-X., Wang B.-B., Pan Y.-F., Tsang B.K., Zabel B.A.. **CMKLR1 deficiency maintains ovarian steroid production in mice treated chronically with dihydrotestosterone**. *Sci. Rep.* (2016) **6** 21328. DOI: 10.1038/srep21328
33. Mirlacher M., Simon R.. **Recipient block TMA technique**. *Methods Mol. Biol.* (2010) **664** 37-44. DOI: 10.1007/978-1-60761-806-5_4
34. Simon R., Mirlacher M., Sauter G.. **Immunohistochemical analysis of tissue microarrays**. *Methods Mol. Biol.* (2010) **664** 113-126. DOI: 10.1007/978-1-60761-806-5_12
35. Remmele W., Stegner H.E.. **Vorschlag zur einheitlichen Definition eines Immunreaktiven Score (IRS) für den immunhistochemischen Ostrogenrezeptor-Nachweis (ER-ICA) im Mammakarzinomgewebe**. *Pathologe* (1987) **8** 138-140. PMID: 3303008
36. Spaulding D.C., Spaulding B.O.. **Epidermal growth factor receptor expression and measurement in solid tumors**. *Semin. Oncol.* (2002) **29** 45-54. DOI: 10.1053/sonc.2002.35647
37. Charafe-Jauffret E., Tarpin C., Bardou V.-J., Bertucci F., Ginestier C., Braud A.-C., Puig B., Geneix J., Hassoun J., Birnbaum D.. **Immunophenotypic analysis of inflammatory breast cancers: Identification of an ‘inflammatory signature’**. *J. Pathol.* (2004) **202** 265-273. DOI: 10.1002/path.1515
38. Bartha Á., Győrffy B.. **TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22052622
39. Lánczky A., Győrffy B.. **Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation**. *J. Med. Internet Res.* (2021) **23** e27633. DOI: 10.2196/27633
40. Tang Z., Kang B., Li C., Chen T., Zhang Z.. **GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis**. *Nucleic Acids Res.* (2019) **47** W556-W560. DOI: 10.1093/nar/gkz430
41. Rytelewska E., Kiezun M., Kisielewska K., Gudelska M., Dobrzyn K., Kaminska B., Kaminski T., Smolinska N.. **Chemerin as a modulator of ovarian steroidogenesis in pigs: An in vitro study**. *Theriogenology* (2021) **160** 95-101. DOI: 10.1016/j.theriogenology.2020.10.040
42. Estienne A., Mellouk N., Bongrani A., Plotton I., Langer I., Ramé C., Petit C., Guérif F., Froment P., Dupont J.. **Involvement of chemerin and CMKLR1 in the progesterone decrease by PCOS granulosa cells**. *Reproduction* (2021) **162** 427-436. DOI: 10.1530/REP-21-0265
43. Luo H., Li S., Zhao M., Sheng B., Zhu H., Zhu X.. **Prognostic value of progesterone receptor expression in ovarian cancer: A meta-analysis**. *Oncotarget* (2017) **8** 36845-36856. DOI: 10.18632/oncotarget.15982
44. Hoffmann M., Rak A., Ptak A.. **Bisphenol A and its derivatives decrease expression of chemerin, which reverses its stimulatory action in ovarian cancer cells**. *Toxicol. Lett.* (2018) **291** 61-69. DOI: 10.1016/j.toxlet.2018.04.004
45. Pontén F., Jirström K., Uhlen M.. **The Human Protein Atlas—A tool for pathology**. *J. Pathol.* (2008) **216** 387-393. DOI: 10.1002/path.2440
46. Liu Y., Beyer A., Aebersold R.. **On the Dependency of Cellular Protein Levels on mRNA Abundance**. *Cell* (2016) **165** 535-550. DOI: 10.1016/j.cell.2016.03.014
47. Vinay D.S., Ryan E.P., Pawelec G., Talib W.H., Stagg J., Elkord E., Lichtor T., Decker W.K., Whelan R.L., Kumara H.M.C.S.. **Immune evasion in cancer: Mechanistic basis and therapeutic strategies**. *Semin. Cancer Biol.* (2015) **35** S185-S198. DOI: 10.1016/j.semcancer.2015.03.004
48. Sun J., Yan C., Xu D., Zhang Z., Li K., Li X., Zhou M., Hao D.. **Immuno-genomic characterisation of high-grade serous ovarian cancer reveals immune evasion mechanisms and identifies an immunological subtype with a favourable prognosis and improved therapeutic efficacy**. *Br. J. Cancer* (2022) **126** 1570-1580. DOI: 10.1038/s41416-021-01692-4
49. Schreiber R.D., Old L.J., Smyth M.J.. **Cancer immunoediting: Integrating immunity’s roles in cancer suppression and promotion**. *Science* (2011) **331** 1565-1570. DOI: 10.1126/science.1203486
50. Rennier K., Shin W.J., Krug E., Virdi G., Pachynski R.K.. **Chemerin Reactivates PTEN and Suppresses PD-L1 in Tumor Cells via Modulation of a Novel CMKLR1-mediated Signaling Cascade**. *Clin. Cancer Res.* (2020) **26** 5019-5035. DOI: 10.1158/1078-0432.CCR-19-4245
|
---
title: Prognostic Value of Sarcopenia and Metabolic Parameters of 18F-FDG-PET/CT in
Patients with Advanced Gastroesophageal Cancer
authors:
- Ricarda Hinzpeter
- Seyed Ali Mirshahvalad
- Roshini Kulanthaivelu
- Vanessa Murad
- Claudia Ortega
- Ur Metser
- Zhihui Amy Liu
- Elena Elimova
- Rebecca K. S. Wong
- Jonathan Yeung
- Raymond W. Jang
- Patrick Veit-Haibach
journal: Diagnostics
year: 2023
pmcid: PMC10001050
doi: 10.3390/diagnostics13050838
license: CC BY 4.0
---
# Prognostic Value of Sarcopenia and Metabolic Parameters of 18F-FDG-PET/CT in Patients with Advanced Gastroesophageal Cancer
## Abstract
We investigated the prognostic value of sarcopenia measurements and metabolic parameters of primary tumors derived from 18F-FDG-PET/CT among patients with primary, metastatic esophageal and gastroesophageal cancer. A total of 128 patients (26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) with advanced metastatic gastroesophageal cancer who underwent 18F-FDG-PET/CT as part of their initial staging between November 2008 and December 2019 were included. Mean and maximum standardized uptake value (SUV) and SUV normalized by lean body mass (SUL) were measured. Skeletal muscle index (SMI) was measured at the level of L3 on the CT component of the 18F-FDG-PET/CT. Sarcopenia was defined as SMI < 34.4 cm2/m2 in women and <45.4 cm2/m2 in men. A total of $\frac{60}{128}$ patients ($47\%$) had sarcopenia on baseline 18F-FDG-PET/CT. Mean SMI in patients with sarcopenia was 29.7 cm2/m2 in females and 37.5 cm2/m2 in males. In a univariable analysis, ECOG (<0.001), bone metastases ($$p \leq 0.028$$), SMI ($$p \leq 0.0075$$) and dichotomized sarcopenia score ($$p \leq 0.033$$) were significant prognostic factors for overall survival (OS) and progression-free survival (PFS). Age was a poor prognostic factor for OS ($$p \leq 0.017$$). Standard metabolic parameters were not statistically significant in the univariable analysis and thus were not evaluated further. In a multivariable analysis, ECOG ($p \leq 0.001$) and bone metastases ($$p \leq 0.019$$) remained significant poor prognostic factors for OS and PFS. The final model demonstrated improved OS and PFS prognostication when combining clinical parameters with imaging-derived sarcopenia measurements but not metabolic tumor parameters. In summary, the combination of clinical parameters and sarcopenia status, but not standard metabolic values from 18F-FDG-PET/CT, may improve survival prognostication in patients with advanced, metastatic gastroesophageal cancer.
## 1. Introduction
Esophageal, gastroesophageal and gastric cancers are major causes of cancer-associated morbidity and death worldwide [1]. Despite the ongoing development of novel therapeutic strategies, the prognosis of these entities remains poor, with a 5-year survival rate between 5–$46\%$ [2]. In addition, up to $50\%$ of all patients are diagnosed with advanced stage of disease at the time of initial presentation, precluding curative treatment [3].
Fluorine-18-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) is an established and important tool in the workup of esophageal, gastroesophageal and gastric cancers, providing significant diagnostic and prognostic value amongst these patients [4,5]. Additionally, the CT component allows for the assessment of skeletal muscle and sarcopenia.
Skeletal muscle depletion, also known as sarcopenia, is the involuntary loss of muscle mass. This is one of the main components of cancer cachexia syndrome, which is associated with mobility disorder, loss of independence and even increased risk of death [6]. Prior studies have emphasized the influence of nutritional state and body composition on overall survival in various tumor entities [7,8]. Significant weight loss due to dysphagia and altered eating habits is a well-documented clinical problem in patients with gastroesophageal cancers, with a prevalence of up to $79\%$ prior to surgery [8,9,10,11]. Although evidence has shown a significant correlation between sarcopenia and major postoperative complications, the prognostic value of sarcopenia has not been definitively established in patients with advanced disease [12,13].
As a result, it is highly desirable to further investigate potential prognostic factors which could support therapeutic decision making in patients with esophageal and gastroesophageal cancers. Therefore, the aim of our study was to determine the prognostic value of sarcopenia measurements and metabolic activity parameters of primary gastroesophageal cancer in patients with advanced metastatic disease.
## 2. Materials and Methods
Between November 2008 and December 2019, 128 patients with primary metastatic esophageal or gastroesophageal cancer who underwent 18F-FDG-PET/CT as part of their initial staging were included from an institutional registry. Patients with primary metastatic disease who were missing staging 18F-FDG-PET/CT ($$n = 35$$) were excluded from the study. Demographic data of the study cohort are provided in Table 1. Different aspects about sarcopenia and PET/CT radiomics in patients with gastroesophageal cancer from the same patient population were evaluated in a different manuscript [14].
This retrospective study was approved by the institutional review board and the need to obtain informed consent from patients was waived (REB# 19-5575).
## 2.1. Imaging Acquisition
Whole body 18F-FDG PET/CT was acquired prior to treatment on a Siemens mCT40 (Siemens Healthineers, Erlangen, Germany). Images were obtained from the skull base to the upper thighs. Iodinated oral contrast media was administered for bowel opacification; no intravenous contrast media were used. Patients received 300–400 Mbq (4–5 MBq/kg) of 18F-Fluorodeoxyglucose (FDG) after having fasted for 6 hours, and PET/CT image acquisition was performed after approximately 60 min. Overall, 5–9 bed positions were obtained, depending on patient height, with an acquisition time of 2–3 min per bed position. CT parameters were 120 kVp tube voltage, 3.0 mm slice width, 2 mm collimation, 0.8 s rotation time and 8.4 mm feed/rotation.
## 2.2. Image Analysis and Sarcopenia Measurements
The mean, max, and peak standardized uptake values (SUV) and SUV normalized by lean body mass (SUL), were collected from the primary tumor in each patient, using a common commercially available imaging software (Mirada XD Workstation, Mirada Medical, Ltd., Oxford, UK). SUV were obtained manually with a volume-of-interest (VOI) covering the entire tumor volume as defined by PET. Sarcopenia measurements were taken from the CT component of the 18F-FDG PET/CT. Assessment of skeletal muscle mass was performed at the level of the third lumbar vertebra using Slice-O-Matic (TomoVision, version 5.0, Magog, QC, Canada) Hounsfield units (HU) were used to identify skeletal muscle (threshold −29 to 150 HU) (Figure 1). Skeletal muscle index (SMI) was calculated by normalizing the muscle area (cm2) for the subject’s height in squared meters (m2). SMI cutoff values for sarcopenia were used as follows [15]: SMI of 34.4 cm2/m2 in females and SMI of 45.4 cm2/m2 in males. Image analysis was performed by one radiologist with 5 years of experience in oncologic imaging.
## 2.3. Statistical Analysis
Summary statistics were used to describe demographics and disease characteristics. Kaplan–Meier (KM) method was used to estimate overall survival (OS) and progression-free survival (PFS) and there $95\%$ confidence intervals (CI). Univariable analysis (UVA) was used to identify potential prognostic factors for OS and PFS, including clinical variables, SUV parameters and anthropometric indices. Parameters with a p-value of <0.05 were included in a subsequent analysis to build a multivariable analysis (MVA). Model performance was quantified and visualized using area under the time-dependent receiver operating characteristic (ROC) curve (AUC), and calculated using leave-one-out cross-validation which served as an internal validation method. All statistical analyses were carried out in R version 4.0.2 [16] and a p-value of <0.05 was considered statistically significant.
## 3.1. Baseline Characteristics of the Study Cohort
Overall, 128 patients (26 females, 102 males; mean age 64 ± 11 years, range: 29–91 years) with advanced metastatic gastroesophageal squamous cell carcinoma ($$n = 44$$) and adenocarcinoma ($$n = 84$$) were included in this study. The majority of patients had an ECOG score of 0 or 1 ($22\%$ and $57\%$, respectively) and $21\%$ had an ECOG score of 2 or above.
All patients were deemed palliative and underwent either chemotherapy or radiotherapy or a combination of both. A total of $\frac{2}{128}$ patients underwent additional salvage esophagectomy and esophago-gastrectomy.
At the time of diagnosis, $\frac{117}{128}$ ($91\%$) patients presented with regional lymph node metastases and concurrent distant metastatic disease to extra-regional lymph nodes, liver, bone, brain or peritoneum. In addition, $\frac{6}{128}$ cases had distant metastases only and in $\frac{5}{128}$ cases the N-stage was undetermined (Table 1).
## 3.2. Image Analysis and Sarcopenia Measurements
All primary tumors were associated with increased metabolic activity on staging 18F-FDG-PET/CT with a mean SUVmax of 15.4, ranging from 4.1 to 54.4. Further SUV parameters are summarized in Table 2.
Overall, $\frac{60}{128}$ ($47\%$) patients had an SMI score below the cutoff value for sarcopenia, indicating low skeletal muscle mass and poor nutritional status. The mean SMI score in patients with sarcopenia was 29.7 cm2/m2 in females and 37.5 cm2/m2 in males.
## 3.3. Analysis on Survival Prognostication
The median ($95\%$ confidence interval) OS and PFS in our cohort was 9.0 (6.9, 10.7) months and 6.0 (4.7, 7.0) months, respectively. OS and PFS showed statistically significant differences with regard to sarcopenia status. Median OS was 9.9 (7.8, 12.4) months in non-sarcopenic patients and 6.8 (4.9, 10.1) months in patients with sarcopenia ($$p \leq 0.032$$). Median PFS was 7.1 (4.6, 9.2) months in non-sarcopenic patients and 5.1 (4.5, 6.8) months in patients with sarcopenia ($$p \leq 0.02$$). Statistical analysis did not show significant differences when comparing patients with squamous cell carcinoma and adenocarcinoma regarding OS and PFS ($$p \leq 0.67$$ and 0.68, respectively). Consequently, further statistical analysis was performed on the entire cohort.
UVA using Cox proportional hazards revealed the following parameters as poor prognostic factors for OS and PFS: ECOG performance status ($p \leq 0.001$), bone metastases ($$p \leq 0.028$$) and sarcopenic status (dichotomized sarcopenia score ($$p \leq 0.033$$) and SMI (0.0075)). Additionally, age was associated with decreased OS in the overall cohort ($$p \leq 0.017$$). Metabolic parameters derived from baseline 18F-FDG-PET/CT, however, were not significantly associated with decreases in OS and PFS (Table 3).
On MVA, ECOG performance status ($p \leq 0.001$) and bone metastases ($$p \leq 0.01$$ for OS and 0.019 for PFS) remained significant poor prognostic factors for OS and PFS in the overall cohort (Table 4). To this clinical model, we added the sarcopenia status of the patient determined by the SMI score ($$p \leq 0.065$$ for OS and 0.03 for PFS). The combined model (clinical parameters + sarcopenia status) outperformed the model with solely clinical parameters over a clinical course of 33 months, indicating improved OS and PFS prognostication when taking into account the patients’ nutritional status. The results were an OS AUC of 0.76, 0.71 and 0.84 for the combined model compared to 0.7, 0.67 and 0.82 for the clinical model at 6, 12 and 33 months of follow-ups, respectively (Figure 2), and PFS AUC of 0.67, 0.69 and 0.83 for the combined model compared to 0.63; 0.65 and 0.7 for the clinical model at 6, 12 and 33 months of follow-ups, respectively (Figure 3).
## 4. Discussion
In our study, we assessed the prognostic value of sarcopenia—an indication for poor nutritional state—in combination with clinical variables and metabolic parameters, derived from 18F-FDG-PET/CTs among patients with advanced metastatic esophageal and gastroesophageal cancers. The main finding of our study was that sarcopenia (low SMI value) is a prognostic marker for poor OS and PFS. Furthermore, improved prognostication of OS and PFS was observed when sarcopenia status was combined with clinical variables as opposed to clinical variables only. However, standard metabolic parameters from 18F-FDG-PET/CTs obtained from the primary tumor were not associated with an overall improvement in outcome prediction.
Sarcopenia describes a progressive and generalized loss of skeletal muscle mass and function, which is associated with an increase in adverse outcomes, including a high risk of falls, frailty and mortality [17]. The impact of sarcopenia in cancer patients has been studied across a broad range of malignancies and has been shown to be an independent poor prognostic factor among both patients deemed curative and those undergoing palliative treatment [18,19,20]. A recent study by Gu et al. [ 21] indicates prognostic significance of combined pretreatment body mass index (BMI) and BMI loss in patients with esophageal cancer. However, patients’ BMIs were not found to be significantly different in sarcopenic versus non-sarcopenic patients; neither was it associated with overall survival. This emphasizes the need for advanced screening measurements, besides height and weight, especially since sarcopenia in obese patients is a known phenomenon [18,22].
The impact of sarcopenia in gastroesophageal cancer has been the subject of several previous studies [13,23,24,25,26,27]. A recent study by Sato et al. [ 25] showed significantly worse overall survival rates in a cohort of 48 patients with locally advanced esophageal squamous cell carcinoma who underwent definite chemoradiotherapy—with a 3-year survival rate of 36.95 % vs. $63.9\%$. Similarly, Koch et al. [ 24] investigated the impact of sarcopenia as a prognostic factor for survival in a cohort of 83 patients with locally advanced non-metastatic gastric or gastroesophageal junction (GEJ) cancer, who underwent curative treatment with perioperative chemotherapy and surgery. The authors reported a significantly shorter median survival in patients with sarcopenia compared to non-sarcopenic patients (35 vs. 52 months). Further, perioperative complications occurred more frequently in sarcopenic patients. This is in line with the results of our study, showing a significant decrease in OS (6.8 vs. 9.9, $$p \leq 0.032$$) and PFS (5.1 vs. 7.1 months, $$p \leq 0.02$$) in gastroesophageal cancer patients with primary palliative treatment intent when sarcopenia is present. Notably, sarcopenic patients in the present study showed lower median OS and PFS compared to the prior studies [24,25], which is likely related to the presence of distant metastasis in our cohort. Additionally, our study proposes the combination of standard clinical parameters with imaging-derived sarcopenia measurements to enhance outcome predictions in these patients over a clinical course of 33 months for OS (AUC 0.7 vs. 076 for 6 months; 0.67 vs. 0.71 for 12 months and 0.82 vs. 0.84 for 33 months) and PFS (AUC 0.63 vs. 0.67 for 6 months; 0.65 vs. 0.69 for 6 months and 0.7 vs. 0.83 for 33 months).
Only a few studies so far have reported contrasting study results [28,29,30], including a study by Grotenhuis et al. [ 31], who investigated 120 patients undergoing esophagectomies following neoadjuvant chemoradiotherapy for primary esophageal cancer. The results of their study indicate that the presence of sarcopenia is not associated with negative short- and long-term outcomes. Although these studies applied similar measurement techniques for the assessment of sarcopenia (using CT images at the level of the third lumbar vertebra), cutoff SMI values varied between publications. Applying different threshold values for the assessment of sarcopenia is part of an ongoing debate. Whereas some authors used self-developed software tools, recent studies have performed their measurements with frequently used commercially available software, at least partly minimizing the effect of different evaluation approaches. The cutoff values used in the present study are one of the most frequently used within the literature. Further, none of these studies reported the presence of distant metastatic disease in their patient cohort, which may indicate that sarcopenia plays an even more prominent role in outcome prognostication, particularly in advanced metastatic disease.
Although dual X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) have previously been investigated for imaging assessment of sarcopenia, MRI and CT are considered the most suitable methods for analyzing quantitative and qualitative changes in body composition [30]. One reason for that might be also that CT and MRI are the most frequently used cross-sectional imaging methods in cancer patients, and thus availability of sarcopenia measurements from this standard-of-care imaging is certainly higher than compared with the other methods.
18F-FDG-PET/CT is an established and routinely used imaging technique for the staging of several different malignancies, including gastroesophageal cancer. 18F-FDG-PET/CT has resulted in a significant improvement in imaging assessment and management of gastroesophageal cancer patients at initial staging, treatment planning, restaging as well as response assessment [32]. In the present study, 18F-FDG-PET/CT was routinely performed to stage patients with esophageal and gastroesophageal cancer. Assessment of sarcopenia was performed on the CT component of this study. Therefore, in the future, this could potentially provide patients with the one-stop shop imaging-derived means to predict OS and PFS as part of routine clinical management. A study by Mallet et al. [ 33] had a similar approach, using staging 18F-FDG-PET/CT for the assessment of sarcopenia in patients with locally advanced esophageal cancer treated with chemoradiation. This is in line with the results of our study, indicating poor prognosis in sarcopenic patients. However, the analysis in our study was performed on a larger sample size than compared to the aforementioned study. Further, we investigated a more homogenous set of patients by only including those with metastatic esophageal and gastroesophageal cancer—adding potential value to the literature, particularly in patients with advanced disease.
Notably, it would be highly desirable to obtain clinical, nutritional as well as functional imaging data simultaneously; however, as the results of our study demonstrated, adding standard metabolic parameters to the model with clinical information and sarcopenia measurements does not improve the prediction of OS and PFS. In contradiction, several prior studies demonstrated that metabolic parameters of 18F-FDG-PET/CT do improve overall survival prediction in patients with gastroesophageal cancer [34,35,36]. A systematic review by Pan et al. [ 36] analyzed 39 studies to assess the prognostic value of SUV for survival in patients with esophageal cancer. It has been found that pretreatment SUV measurements can serve as prognostic survival markers in this patient population. However, the SUV threshold was chosen arbitrarily between patients with high and low survival based on the median SUV values in the majority of the studies. Additionally, some studies also used maximum SUV values, reflecting a possible bias. Further, several studies obtained SUV measurements at metastasis sites, rather than the primary tumor, reflecting another difference to our study. Li et al. [ 34] showed that metabolic parameters of sequential 18F-FDG-PET/CTs predict overall survival in esophageal cancer patients treated with chemo-radiation. MVA (which was performed in the aforementioned study), however, revealed that metabolic tumor volume was the only independent prognostic parameter from the initial staging 18F-FDG-PET/CT, whereas SUVmax was not found to be significant, which is in line with the results of our study. This may lead to the notion that besides SUV values, additional more-advanced metabolic markers, such as metabolic tumor volume or total lesion glycolysis, should be included as surrogate parameters for outcome prediction.
The following study limitations must be acknowledged. Firstly, there are inherent drawbacks due to the retrospective nature of the study. Secondly, we included patients with both squamous cell carcinoma and adenocarcinoma, leading to relative inhomogeneity of the study cohort. Thirdly, sarcopenia measurements were not performed on post-treatment imaging, since 18F-FDG-PET/CT is not funded for restaging purposes in the healthcare system where our study was conducted.
## 5. Conclusions
In conclusion, our study indicates that sarcopenia derived from standard-of-care clinical 18F-FDG-PET/CTs is a prognostic marker of poor outcomes in patients with advanced metastatic esophageal and gastroesophageal cancer. Combining the patients’ nutritional states with clinical variables—but not with metabolic activity parameters from 18F-FDG-PET/CT—resulted in overall improved prognostic ability regarding OS and PFS.
## References
1. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F.. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J. Clin.* (2021) **71** 209-249. DOI: 10.3322/caac.21660
2. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A.. **Cancer statistics, 2022**. *CA Cancer J. Clin.* (2022) **72** 7-33. DOI: 10.3322/caac.21708
3. Lund O., Hasenkam J.M., Aagaard M.T., Kimose H.H.. **Time-related changes in characteristics of prognostic significance in carcinomas of the oesophagus and cardia**. *Br. J. Surg.* (1989) **76** 1301-1307. DOI: 10.1002/bjs.1800761227
4. Kwon H.R., Pahk K., Park S., Kwon H.W., Kim S.. **Prognostic value of metabolic information in advanced gastric cancer using preoperative 18F-FDG PET/CT**. *Nucl. Med. Mol. Imaging* (2019) **53** 386-395. DOI: 10.1007/s13139-019-00622-w
5. Han S., Kim Y.J., Woo S., Suh C.H., Lee J.J.. **Prognostic value of volumetric parameters of pretreatment 18F-FDG PET/CT in esophageal cancer: A systematic review and meta-analysis**. *Clin. Nucl. Med.* (2018) **43** 887-894. DOI: 10.1097/RLU.0000000000002291
6. Cruz-Jentoft A.J., Baeyens J.P., Bauer J.M., Boirie Y., Cederholm T., Landi F., Martin F.C., Michel J.P., Rolland Y., Schneider S.M.. **Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on sarcopenia in older people**. *Age Ageing* (2010) **39** 412-423. DOI: 10.1093/ageing/afq034
7. Fearon K., Strasser F., Anker S.D., Bosaeus I., Bruera E., Fainsinger R.L., Jatoi A., Loprinzi C., MacDonald N., Mantovani G.. **Definition and classification of cancer cachexia: An international consensus**. *Lancet Oncol.* (2011) **12** 489-495. DOI: 10.1016/S1470-2045(10)70218-7
8. Yip C., Goh V., Davies A., Gossage J., Mitchell-Hay R., Hynes O., Maisey N., Ross P., Gaya A., Landau D.. **Assessment of sarcopenia and changes in body composition after neoadjuvant chemotherapy and associations with clinical outcomes in oesophageal cancer**. *Eur. Radiol.* (2014) **24** 998-1005. DOI: 10.1007/s00330-014-3110-4
9. Sheetz K.H., Zhao L., Holcombe S.A., Wang S.C., Reddy R.M., Lin J., Orringer M.B., Chang A.C.. **Decreased core muscle size is associated with worse patient survival following esophagectomy for cancer**. *Dis. Esophagus* (2013) **26** 716-722. DOI: 10.1111/dote.12020
10. Elliott J.A., Doyle S.L., Murphy C.F., King S., Guinan E.M., Beddy P., Ravi N., Reynolds J.V.. **Sarcopenia: Prevalence, and impact on operative and oncologic outcomes in the multimodal management of locally advanced esophageal cancer**. *Ann. Surg.* (2017) **266** 822-830. DOI: 10.1097/SLA.0000000000002398
11. Levolger S., van Vugt J.L.A., de Bruin R.W.F., Ijzermans J.N.M.. **Systematic review of sarcopenia in patients operated on for gastrointestinal and hepatopancreatobiliary malignancies**. *Br. J. Surg.* (2015) **102** 1448-1458. DOI: 10.1002/bjs.9893
12. Pamoukdjian F., Bouillet T., Lévy V., Soussan M., Zelek L., Paillaud E.. **Prevalence and predictive value of pre-therapeutic sarcopenia in cancer patients: A systematic review**. *Clin. Nutr.* (2017) **37** 1101-1113. DOI: 10.1016/j.clnu.2017.07.010
13. Makiura D., Ono R., Inoue J., Kashiwa M., Oshikiri T., Nakamura T., Kakeji Y., Sakai Y., Miura Y.. **Preoperative sarcopenia is a predictor of postoperative pulmonary complications in esophageal cancer following esophagectomy: A retrospective cohort study**. *J. Geriatr. Oncol.* (2016) **7** 430-436. DOI: 10.1016/j.jgo.2016.07.003
14. Hinzpeter R., Mirshahvalad S.A., Kulanthaivelu R., Ortega C., Metser U., Liu Z.A., Elimova E., Wong R.K.S., Yeung J., Jang R.W.-J.. **Prognostic Value of [18F]-FDG PET/CT Radiomics Combined with Sarcopenia Status among Patients with Advanced Gastroesophageal Cancer**. *Cancers* (2022) **14**. DOI: 10.3390/cancers14215314
15. Derstine B.A., Holcombe S.A., Ross B.E., Wang N.C., Su G.L., Wang S.C.. **Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population**. *Sci. Rep.* (2018) **8** 11369. DOI: 10.1038/s41598-018-29825-5
16. 16.
R Core Team
R: A Language and Environment for Statistical ComputingFoundation for Statistical ComputingVienna, Austria2020. *R: A Language and Environment for Statistical Computing* (2020)
17. Cruz-Jentoft A.J., Sayer A.A.. **Sarcopenia**. *Lancet* (2019) **393** 2636-2646. DOI: 10.1016/S0140-6736(19)31138-9
18. Martin L., Birdsell L., MacDonald N., Reiman T., Clandinin M.T., McCargar L.J., Murphy R., Ghosh S., Sawyer M.B., Baracos V.E.. **Cancer cachexia in the age of obesity: Skeletal muscle depletion is a powerful prognostic factor, independent of body mass index**. *J. Clin. Oncol.* (2013) **31** 1539-1547. DOI: 10.1200/JCO.2012.45.2722
19. Joglekar S., Nau P.N., Mezhir J.J.. **The impact of sarcopenia on survival and complications in surgical oncology: A review of the current literature**. *J. Surg. Oncol.* (2015) **112** 503-509. DOI: 10.1002/jso.24025
20. Tan B.H., Birdsell L.A., Martin L., Baracos V.E., Fearon K.C.. **Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer**. *Clin. Cancer Res.* (2009) **15** 6973-6979. DOI: 10.1158/1078-0432.CCR-09-1525
21. Gu W.-S., Fang W.-Z., Liu C.-Y., Pan K.-Y., Ding R., Li X.-H., Duan C.-H.. **Prognostic significance of combined pretreatment body mass index (BMI) and BMI loss in patients with esophageal cancer**. *Cancer Manag. Res.* (2019) **11** 3029-3041. DOI: 10.2147/CMAR.S197820
22. Baracos V., Arribas L.. **Sarcopenic obesity: Hidden muscle wasting and its impact for survival and complications of cancer therapy**. *Ann. Oncol.* (2018) **29** ii1-ii9. DOI: 10.1093/annonc/mdx810
23. Kudou K., Saeki H., Nakashima Y., Edahiro K., Korehisa S., Taniguchi D., Tsutsumi R., Nishimura S., Nakaji Y., Akiyama S.. **Prognostic significance of sarcopenia in patients with esophagogastric junction cancer or upper gastric cancer**. *Ann. Surg. Oncol.* (2017) **24** 1804-1810. DOI: 10.1245/s10434-017-5811-9
24. Koch C., Reitz C., Schreckenbach T., Eichler K., Filmann N., Al-Batran S.E., Götze T., Zeuzem S., Bechstein W.O., Kraus T.. **Sarcopenia as a prognostic factor for survival in patients with locally advanced gastroesophageal adenocarcinoma**. *PLoS ONE* (2019) **14**. DOI: 10.1371/journal.pone.0223613
25. Sato S., Kunisaki C., Suematsu H., Tanaka Y., Miyamoto H., Kosaka T., Yukawa N., Tanaka K., Sato K., Akiyama H.. **Impact of sarcopenia in patients with unresectable locally advanced esophageal cancer receiving chemoradiotherapy**. *In Vivo* (2018) **32** 603-610. PMID: 29695567
26. Deng H.-Y., Zha P., Peng L., Hou L., Huang K.-L., Li X.-Y.. **Preoperative sarcopenia is a predictor of poor prognosis of esophageal cancer after esophagectomy: A comprehensive systematic review and meta-analysis**. *Dis. Esophagus* (2018) **32** 1-10. DOI: 10.1093/dote/doy115
27. Paireder M., Asari R., Kristo I., Rieder E., Tamandl D., Ba-Ssalamah A., Schoppmann S.F.. **Impact of sarcopenia on outcome in patients with esophageal resection following neoadjuvant chemotherapy for esophageal cancer**. *Eur. J. Surg. Oncol.* (2017) **43** 478-484. DOI: 10.1016/j.ejso.2016.11.015
28. Siegal S.R., Dolan J.P., Dewey E.N., Guimaraes A.R., Tieu B.H., Schipper P.H., Hunter J.G.. **Sarcopenia is not associated with morbidity, mortality, or recurrence after esophagectomy for cancer**. *Am. J. Surg.* (2018) **215** 813-817. DOI: 10.1016/j.amjsurg.2017.12.017
29. Saeki H., Nakashima Y., Kudou K., Sasaki S., Jogo T., Hirose K., Edahiro K., Korehisa S., Taniguchi D., Nakanishi R.. **Neoadjuvant chemoradiotherapy for patients with cT3/nearly T4 esophageal cancer: Is sarcopenia correlated with postoperative complications and prognosis?**. *World J. Surg.* (2018) **42** 2894-2901. DOI: 10.1007/s00268-018-4554-5
30. Grotenhuis B.A., Shapiro J., van Adrichem S., de Vries M., Koek M., Wijnhoven B.P., van Lanschot J.J.. **Sarcopenia/muscle mass is not a prognostic factor for short-and long-term outcome after esophagectomy for cancer**. *World J. Surg.* (2016) **40** 2698-2704. DOI: 10.1007/s00268-016-3603-1
31. Sergi G., Trevisan C., Veronese N., Lucato P., Manzato E.. **Imaging of sarcopenia**. *Eur. J. Radiol.* (2016) **85** 1519-1524. DOI: 10.1016/j.ejrad.2016.04.009
32. Kitajima K., Nakajo M., Kaida H., Minamimoto R., Hirata K., Tsurusaki M., Doi H., Ueno Y., Sofue K., Tamaki Y.. **Present and future roles of FDG-PET/CT imaging in the management of gastrointestinal cancer: An update**. *Nagoya J. Med. Sci.* (2017) **79** 527-543. DOI: 10.18999/nagjms.79.4.527
33. Mallet R., Modzelewski R., LeQuesne J., Mihailescu S., Decazes P., Auvray H., Benyoucef A., Di Fiore F., Vera P., DuBray B.. **Prognostic value of sarcopenia in patients treated by Radiochemotherapy for locally advanced oesophageal cancer**. *Radiat. Oncol.* (2020) **15** 116. DOI: 10.1186/s13014-020-01545-z
34. Li Y., Zschaeck S., Lin Q., Chen S., Chen L., Wu H.. **Metabolic parameters of sequential 18F-FDG PET/CT predict overall survival of esophageal cancer patients treated with (chemo-) radiation**. *Radiat. Oncol.* (2019) **14** 35. DOI: 10.1186/s13014-019-1236-x
35. Lee S., Choi Y., Park G., Jo S., Lee S.S., Park J., Shim H.K.. **18F-FDG PET/CT Parameters for predicting prognosis in esophageal cancer patients treated with concurrent chemoradiotherapy**. *Technol. Cancer Res. Treat.* (2021) **20** 15330338211024655. DOI: 10.1177/15330338211024655
36. Pan L., Gu P., Huang G., Xue H., Wu S.. **Prognostic significance of SUV on PET/CT in patients with esophageal cancer: A systematic review and meta-analysis**. *Eur. J. Gastroenterol. Hepatol.* (2009) **21** 1008-1015. DOI: 10.1097/MEG.0b013e328323d6fa
|
---
title: 'Xanthine Oxidase Inhibitory Peptides from Larimichthys polyactis: Characterization
and In Vitro/In Silico Evidence'
authors:
- Xiaoling Chen
- Weiliang Guan
- Yujin Li
- Jinjie Zhang
- Luyun Cai
journal: Foods
year: 2023
pmcid: PMC10001067
doi: 10.3390/foods12050982
license: CC BY 4.0
---
# Xanthine Oxidase Inhibitory Peptides from Larimichthys polyactis: Characterization and In Vitro/In Silico Evidence
## Abstract
Hyperuricemia is linked to a variety of disorders that can have serious consequences for human health. Peptides that inhibit xanthine oxidase (XO) are expected to be a safe and effective functional ingredient for the treatment or relief of hyperuricemia. The goal of this study was to discover whether papain small yellow croaker hydrolysates (SYCHs) have potent xanthine oxidase inhibitory (XOI) activity. The results showed that compared to the XOI activity of SYCHs (IC50 = 33.40 ± 0.26 mg/mL), peptides with a molecular weight (MW) of less than 3 kDa (UF-3) after ultrafiltration (UF) had stronger XOI activity, which was reduced to IC50 = 25.87 ± 0.16 mg/mL ($p \leq 0.05$). Two peptides were identified from UF-3 using nano-high-performance liquid chromatography–tandem mass spectrometry. These two peptides were chemically synthesized and tested for XOI activity in vitro. Trp-Asp-Asp-Met-Glu-Lys-Ile-Trp (WDDMEKIW) ($p \leq 0.05$) had the stronger XOI activity (IC50 = 3.16 ± 0.03 mM). The XOI activity IC50 of the other peptide, Ala-Pro-Pro-Glu-Arg-Lys-Tyr-Ser-Val-Trp (APPERKYSVW), was 5.86 ± 0.02 mM. According to amino acid sequence results, the peptides contained at least $50\%$ hydrophobic amino acids, which might be responsible for reducing xanthine oxidase (XO) catalytic activity. Furthermore, the inhibition of the peptides (WDDMEKIW and APPERKYSVW) against XO may depend on their binding to the XO active site. According to molecular docking, certain peptides made from small yellow croaker proteins were able to bind to the XO active site through hydrogen bonds and hydrophobic interactions. The results of this work illuminate SYCHs as a promising functional candidate for the prevention of hyperuricemia.
## 1. Introduction
Uric acid has been identified as a recognized or prospective biomarker for various pathological conditions. Lifestyle factors such as high fructose intake, alcohol addiction, and a high-purine diet can all contribute to high levels of uric acid [1]. Hyperuricemia develops when serum uric acid concentrations surpass solubility limits (6.8 mg/dL at physiological pH). Chronic hyperuricemia may raise the risk of gout, which can lead to gout stones, acute arthritis, and other complications [2,3]. The major pathway of uric acid regulation involves the modulation of purine metabolism via xanthine oxidase (XO, EC 1.17.3.2), which is a molybdenum-containing homodimeric cytoplasmic enzyme with a molecular weight (MW) of approximately 300 kDa [4,5]. It predominantly catalyzes the conversion of xanthine and hypoxanthine to uric acid in the human body. Therefore, substances effectively inhibiting XO can be used to prevent hyperuricemia, as exemplified by drugs such as allopurinol, which can provide short-term relief from the pain caused by gout [6]. However, these synthetic drugs often cause a variety of negative effects; for example, allopurinol is highly susceptible to drug cross-reactivity and may cause rashes [7,8].
As a consequence, researchers are trying to create new inhibitors from natural sources that are safe, effective, and less expensive, such as food-derived bioactive peptides with a high XO inhibitory (XOI) effect and minimal side effects. XOI peptides are generally derived from protein hydrolysates by separation, purification, and identification, and include dairy products [9], nuts [10], and aquatic products [11,12]. For example, the peptides YF, WPDARG, ACECD, and FPSV were discovered in the hydrolysates of aquatic products and have been shown to alleviate hyperuricemia [11,13,14]. These bioactive peptides generated from dietary protein hydrolysates are typically easily absorbed and are safer than pharmaceuticals [15,16]. Furthermore, quantitative structure–activity relationships and molecular docking approaches, which are widely used in the screening and discovery of natural small molecule active compounds, have contributed to the illumination of new peptides. Thus, it is important to explore physiologically active peptides from aquatic materials.
The small yellow croaker (Larimichthys polyactis, SYC), a Sciaenidae fish, is widely distributed as a benthic warm temperate fish in the coastal waters of China [17], favored by consumers because of its high nutritional value, umami taste, and tender texture [18]. However, certain characteristics of SYC limit its current utilization, such as its small size, susceptibility to perishability, and potent fishy smell [19]. Typically, it is processed into items such as fish cake, fish balls, and canned food [20,21,22]. Therefore, to improve the utilization and economic value of SYC, it is critical to develop higher-value-added products.
In this work, we combined traditional testing methods with computer simulation techniques to acquire XOI peptides from SYC muscle. First, papain hydrolysates from SYC were graded by ultrafiltration (UF) technology. Next, the group with the greatest XOI effect was identified and the amino acid sequences of two peptides were obtained. The contribution of the synthesized peptides to the XOI activities of SYC was calculated. Furthermore, molecular docking analysis was used to model the interactions between these peptides and the XO active site and, thus, shed light on the XO inhibition mechanisms of SYC peptides. The XOI effects of the XOI peptides from SYC in vitro were elucidated.
## 2.1. Materials and Chemicals
Frozen SYC 9 ± 1 cm in length was obtained from the Zhejiang Xianghai Food Co., Ltd. in Wenzhou, China. Papain (100,000 U/g), Alcalase (100,000 U/g), Neutrase (50,000 U/g), and xanthine oxidase (X1875-5UN, derived from bovine milk) were purchased from Solarbio Co., Ltd. (Beijing, China). We purchased 0.2 M potassium phosphate buffer (pH = 7.4) from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Xanthine (≥$98\%$) and allopurinol (chromatographically pure) were purchased from Sigma Aldrich Co., Ltd. (St. Louis, MO, USA). Sodium hydroxide, anhydrous ethanol, and boric acid were of analytical grade and purchased from Sinopharm chemical reagent Co., Ltd. (Shanghai, China).
## 2.2. Pretreatment of Raw Materials
The SYCs were thawed overnight at 4 °C and then manually filleted. Next, the filets were boiled to kill enzymes, freeze-dried, and ground into a fine powder (sieved through an 80-mesh sieve). The prepared powder was vacuum sealed and stored at −80 °C before subsequent experiments.
## 2.3. Determination of Raw Materials Protein
The determination of protein was carried out using Kjeldahl nitrogen (Kjeltec 8400 Analyzer Unit, Foss Analytical AB, Hoganas, Sweden) according to the Chinese Standard for Food Safety Determination of Protein in Food (GB 5009.5-2016). Briefly, approximately 500 mg of lyophilized sample was digested by the addition of the digestion mixture and 12 mL of concentrated hydrochloric acid at 420 °C for 80 min and then cooled and subjected to distillation with 50 mL of $40\%$ NaOH and auto-titration experiments using 0.1005 M HCl.
## 2.4. Preparation of Papain Hydrolysates from SYC
The SYC peptides were prepared in accordance with the research of Hu et al. [ 14] with appropriate modifications. The critical hydrolysis parameters for the preparation of papain SYCH were optimized according to our previous unpublished study. The substrate concentration (1:20 w/v, protein weight basis) was hydrated at 50 °C for 15 min with gentle stirring, adjusted to pH 6.8 with papain at 3000 U/g on a protein basis for 6 h at 50 °C. The mixture was then heated at 95 °C for 10 min to inactivate enzymes and centrifuged at 3950× g for 20 min at 4 °C. The supernatant was collected, concentrated, and freeze-dried to obtain SYCHs. SYCHs were stored at −80 °C before subsequent experiments.
## 2.5. Preparation of Peptide Fractions of SYCHs
The enzymatic solution was fractionated through an ultrafiltration centrifuge tube with MW cut-offs of 10 kDa, 3 kDa, and 1 kDa (Pall, New York, NY, USA). The fractions corresponding to three MW distributions, i.e., >10 kDa (UF-1), 3–10 kDa (UF-2), and <3 kDa (UF-3), were concentrated and freeze-dried to obtain peptide fractions, which were stored at −80 °C before subsequent experiments.
## 2.6. Determination of Amino Acids Composition of SYC and SYCHs
Amino acids composition was determined using the method reported by Hou et al. [ 23] with some modifications, using an Agilent 1100 high-performance liquid chromatography (HPLC) instrument (Wilmington, DE, USA) coupled with a VWD detector (Agilent Technologies, Inc., Wilmington, DE, USA) and a column of Agilent Zoubax Elicpse AAA (4.6 × 150 mm, 3.5 μm). The determination of 17 hydrolysis AAs of 100 mg SYC and SYCHs was performed with 6 M HCl for 22 h, while *Trp analysis* of 100 mg SYC was performed by alkaline hydrolysis using 5 M NaOH for 20 h. After passing through a 0.22 μm filter, 10 μL of the sample was loaded into the column and eluted at a flow rate of 1.0 mL/min. The temperature was 40 °C, ultraviolet, 338 nm (0–19 min), 266 nm (19.01–25 min); mobile phase A (40 mM sodium dihydrogen phosphate (pH 7.8)); mobile phase B (acetonitrile: methanol: water = 45:45:10). All of the AAs were detected at 338 nm, except Pro, which was detected at 266 nm. The AAs were identified and quantified by authentic AA standards comparing the retention time and peak.
## 2.7. Determination of XOI Activity IC50 In Vitro
The XOI activity levels of SYCHs were determined and calculated with the methods reported by Liu and Wei [24,25] with slight modifications. Xanthine was dissolved in 0.2 M potassium phosphate buffer (pH = 7.4) to a concentration of 0.48 mM. In addition, samples (SYCH, UF-1, UF-2, and UF-3) were also dissolved in 0.2 M potassium phosphate buffer (pH = 7.4). Next, 50 μL of sample solution and 50 μL of XO solution (0.07 U/mL) were mixed and incubated at 37 °C for 5 min, then 150 μL of xanthine solution was added to the mixture to continue the reaction. The absorbance of formed uric acid in the samples was monitored at 290 nm with a multifunctional microplate reader (Tecan Co., Ltd., Männedorf, Switzerland). The results were recorded for 10 min. The assay was performed in triplicate. The formula for the calculation of XOI activity is as follows:XO $50\%$ inhibition=(dA/dt)blank−(dA/dt)sample(dA/dt)blank × $100\%$ where (dA/dt)blank and (dA/dt)sample are the reaction rate without and with the test sample inhibitor, respectively. IC50 values were calculated from the mean values of data. XOI activity IC50 (the concentration of active compound required to observe $50\%$ XO inhibition) was determined by plotting the percentage inhibition as a function of concentration of the test compound.
## 2.8. Determination of MW Distributions
The MW distributions of SYCH and UF-3, which showed the lowest XOI activity IC50 (detailed in Section 3.3), were determined as described by Bao et al. [ 26] with slight modifications. Gel permeation chromatography (Waters 1515, Waters Co., Milford, MA, USA) with a 2414 differential refractive index detector and an Ultrahydrogel gel permeation chromatography column (7.8 × 300 mm, Waters Co., Milford, MA, USA) was used. The measurement conditions were as follows: 5 mg/mL of the sample (SYCH and UF-3) concentration; mobile phase, 0.1 M sodium nitrate solution; flow rate, 1 mL/min; oven temperature, 40 °C; detector temperature, 40 °C; and the standard, polyethylene glycol.
## 2.9. Identification of the AA Sequence and Molecular Mass of SYCH
UF-3 showed the strongest XOI activity (detailed in Section 3.3). Thus, the AA sequence and molecular mass of UF-3 were identified by a nano-HPLC-MS/MS equipped with a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). The samples were injected into a chromatographic analytical column (C18, 75 µm × 25 cm, 2 μm, 100 Å, Thermo Fisher Scientific) at a flow rate of 300 nL/min. The elution conditions were as follows: mobile phase A ($0.1\%$ formic acid in water); mobile phase B ($0.1\%$ formic acid in acetonitrile); and a column temperature of 40 °C. The liquid phase separation gradient was as follows: start from $6\%$ to $25\%$ mobile phase B over 42 min, followed by an increase to $45\%$ mobile phase B over 11 min and an increase to $80\%$ mobile phase B over 0.5 min, then hold at $80\%$ mobile phase B for 6.5 min at a sustained flow rate of 300 nL/min.
Peptides were acquired in data d acquisition (DDA) mode with each scan cycle containing one full MS scan ($R = 60$ K, AGC (automatic gain control) = 3 × 106, max IT = 20 ms, scan range = 350–1800 mass/charge) and 25 subsequent MS/MS scans ($R = 15$ K, AGC = 2 × 105, max IT = 50 ms). The mass spectral data were searched by Max Quant (V1.6.6) software.
## 2.10. Physicochemical Properties Prediction of Active Peptides
Bioinformatics methodologies depend on data maintained in a variety of databases. We conducted computational investigations using database-based search tools; all programs were executed on 30 August 2022. We predicted the hemolytic properties of peptides using an online prediction website (http://codes.bio/hemopred/, accessed on 30 August 2022). We employed the toxicity prediction tool ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/index.html, accessed on 30 August 2022) to predict the potential toxicity of XOI peptides [27]. Meanwhile, we predicted the isoelectric point (pI) of the peptides using Prot Param (http://web.expasy.org/protparam/, accessed on 30 August 2022) [12]. We used Innovagen (http://www.innovagen.com/proteomics-tools, accessed on 30 August 2022) to predict the water solubility of the screened potential bioactive peptides [28]. Additionally, we predicted the potential biological activity of all peptides using the Peptide Ranker (http://distilldeep.ucd.ie/PeptideRanker/, accessed on 30 August 2022), with scores between 0 and 1 [12]. The closer the calculated value was to 1, the higher the activity exhibited by the fragments.
## 2.11. Peptides Synthesis
The two peptides (purity > $95\%$) Trp-Asp-Asp-Met-Glu-Lys-Ile-Trp (WDDMEKIW, WW8) and Ala-Pro-Pro-Glu-Arg-Lys-Tyr-Ser-Val-Trp (APPERKYSVW, AW10) from the enzymatic hydrolysates of papain SYCHs identified by nano-HPLC-MS/MS (detailed in Section 2.9 and Section 3.4) were chemically synthesized at Sangon Biotech Co., Ltd. (Shanghai, China).
## 2.12. Molecular Docking and Interaction Visual Analysis
We used the docking program Auto Dock Vina to simulate molecular modeling studies in order to further understand the probable binding mechanism of peptides with XO [10]. The X-ray crystal structure of XO from bovine milk with quercetin (PDB: 3 NVY) was downloaded from the RCSB Protein Data Bank (http://www.rcsb.org/pdb) (accessed on 10 September 2022) [28]. The water molecules and all small molecules in XO were removed via Auto Dock tools (v1.5.6). The 3D structures of the inhibitor molecules were built and optimized by minimizing energy in ChemBio3D Ultra 14.0 [11]. Then, the ligands were docked with the XO crystal structure. Peptides and ligand inhibitors were then docked with the PDB structures, giving a Vina score, which is the predicted affinity of the molecule to bind to the PDB structure, calculated in kcal/mol. A more negative score indicates that a ligand is more likely to dock with the enzyme and achieve more favorable interactions [10]. The highest scoring docked model of a ligand was chosen herein to represent its most favorable binding mode predicted by Auto Dock Vina [10]. We carried out functional visualization of the peptides and 3NVY docking results using Pymol2.3.0 [29] to analyze their interaction patterns with binding site residues.
## 2.13. Statistical Analysis
All experimental data were analyzed using SPSS 25.0 (SPSS, Inc., Chicago, IL, USA) and Origin 2021 (Origin Lab, Northampton, MA, USA) software. Data are presented as the mean ± standard deviation (SD). One-way analysis of variance (ANOVA) with least significant difference (LSD) procedures was used to determine the significance of the main effects, and $p \leq 0.05$ was considered statistically significant.
## 3.1. The Potential of SYC to Decrease Uric Acid Levels
The protein content of SYC was determined to be $88.96\%$ ± $1.40\%$. Table 1 summarizes the 18 AA composition of SYC. SYC is rich in a variety of AAs, with a total amino acid (TAA) content of 824.45 ± 10.50 mg/g, including 327.39 ± 5.41 mg/g of essential amino acids (EAA) for humans, which accounted for $41.09\%$ of the total. The major AAs of SYC protein were Glu ($21.08\%$), Asp ($9.41\%$), Lys ($9.07\%$), and Leu ($8.11\%$). SYC was rich in HAAs ($34.22\%$), AAAs ($9.39\%$), and BAAs ($17.38\%$), indicating that SYC might be a source of uric-acid-lowering peptides. Hydrophobic amino acids (HAAs) (Met, Leu, and Ala), aromatic amino acids (AAAs) (Trp, Phe, and Tyr), and basic amino acids (BAAs) (Lys, His, and Arg) play essential roles in the uric-acid-lowering process of peptides [23,30,31]. A hydrophobic pocket formed by AA residues near the XO active core acts as a critical structural domain that is accessible to peptides with more HAAs [23]. These AAs can bind to XO via hydrophobic interactions, for example, altering its spatial structure and thereby limiting its activity. Furthermore, it has previously been claimed that proteins with charged AAs and AAAs, particularly Glu, constitute a valuable source of active XOI hydrolysis products [30]. Given that XO generates reactive oxygen species (ROS) by utilizing molecular oxygen as an electron acceptor, the hyperuricemia-treating medicine allopurinol also has antioxidant activity [32]. Because of the significance of phenolic and indole groups as hydrogen donors, AAAs exhibit significant antioxidant action. Furthermore, AAs with charged residues interact with metal ions and restrict oxidative activity [33]. The physiological action of the peptides benefits from a decrease in ROS, which may be related to the effect that decreases uric acid [30]. These results support the evidence indicating that SYC is a potential source of uric-acid-lowering peptides.
## 3.2. The Optimal Conditions for the Preparation of SYCH
Targeted hydrolysis of endogenous proteins employing proteases is a typical approach for generating peptides with specified active bioactivities [34]. As indicated in Figure 1A, papain was more efficient than Neutrase and Alcalase in hydrolyzing muscle proteins, and papain hydrolysates had the lowest IC50 values (IC50 = 33.40 ± 0.26 mg/mL) for XOI activity, due to variations in the protein sequence in the substrate and the accessibility of the enzyme to the active site. Subsequently, a one-way experiment was conducted on the enzymatic hydrolysis time of papain based on the XOI activity and achieved the best XOI active hydrolysates at 6 h ($p \leq 0.05$), as shown in Figure 1B, which may be because the SYC muscle was more sufficiently hydrolyzed, and more short peptides with high XOI activity were formed as the hydrolysis time increased. *In* general, papain degrades proteins more thoroughly than other endoproteases. Additionally, papain has been identified as one of the most appropriate enzymes, with a low cost per unit activity [35]. Moreover, some research has used papain to create XOI peptides. For example, it was demonstrated that protein hydrolysates derived from bonito hydrolysates prepared with papain exhibited XOI activity [36].
The biological activity of peptides in fish hydrolysates primarily depends on their structural properties such as AA content, sequence, and hydrophobicity [37]. Table 2 shows the AA composition of SYCH, which has a total acid hydrolyzable AA content of 782.73 ± 16.20 mg/g, an EAA content of 288.50 ± 8.57 mg/g ($36.86\%$), and an HAA content of 248.92 ± 6.66 ($31.81\%$). Studies confirmed that HAA facilitates the interaction with hydrophobic targets (e.g., cell membranes), thereby enhancing their bioavailability [13]. Additionally, SYCH is high in AAAs and BAAs, which may play a key role in the peptide’s functional capabilities [30,36]. AAAs have benzene rings in their molecules. An AAA at one end of the peptide segment may be more beneficial for binding of the peptide to the enzyme’s active domain since the presence of a benzene ring structure in these peptide segments is thought to have a substantial XO inhibition rate [11]. These findings imply that SYCHs may have bioavailability and anti-hyperuricemia activity, conducive to the next step of obtaining active peptides.
## 3.3. MW Distribution of XOI Peptides
Low-molecular-weight peptides have been shown to have improved bioactivity and a better capacity to penetrate the gastrointestinal membrane [38]. Bioactive peptides have been refined using UF. Figure 2A shows that the SYCHs mostly consist of peptides of different MWs, with the MW distribution being centered below 3 kDa. MW distributions of SYCHs and UF-3 are depicted in Figure S1, and relative peak table of SYCHs and UF-3 MW distributions in Tables S1 and S2, respectively. He et al. [ 4] suggested that low-molecular-weight peptides may be important contributors to the significant XOI activity of XOI peptides. The peptides in SYCHs were fractionated and the obtained fractions were individually tested in terms of their XOI activities. Figure 2B displays the outcomes of the XOI activity of the SYCH and the three fractions after UF. Comparing these four fractions revealed that the UF-3 (IC50 = 25.87 ± 0.16 mg/mL) had the strongest ability to inhibit XO. UF-3 also showed low molecular weight (Figure 2A), which is consistent with the trend of inhibitory activity of XOI. He et al. [ 4] collected and examined eight fractions with different molecular weight distributions from the lyophilized ethanol-soluble fraction powder of tuna protein hydrolysates and found that the fractions of small peptides (<1 kDa) had remarkable XOI activity compared to the original hydrolysates and other fractions. Other studies found that peptides from skipjack tuna below 3 kDa had a greater inhibitory effect on XO than other fractions [34]. Similar to previous findings, our investigation found that the fraction with 3 kDa inhibited XO more than the SYCH and other fractions, indicating that the fraction with 3 kDa has structural properties recognized by XO and thus functions as a substrate for XO. Therefore, we selected UF-3 for the next identification stage.
The AA compositions of SYCH and UF-3 (showing the strongest XOI activity) were evaluated by the acid hydrolysis method to confirm whether there is a potential link between AA composition and uric acid decrease. The makeup of the AA group shifted with the change in MW, Glu, Asp, and Lys were the most prevalent AAs in both SYCH and UF-3, as indicated in Table 2. Following UF, the proportions of BAA, AAA, and HAA increased by $2.64\%$, $5.72\%$, and $6.48\%$, respectively, in UF-3 (Table 2). A molecular docking approach was used to mimic the structure–activity relationships of 20 amino acids, 400 dipeptides, and 8000 tripeptides with XO [9,10]. AAA and HAA were shown to be more likely to connect with the critical amino acid residues around the active core of XO, resulting in a substantial inhibitory action against XO. This could be the reason for the higher XOI activity of UF-3 than others.
## 3.4. Identification of XOI Peptides Sequences and Validation of XOI Activity
Seven peptides (WDDMEKIW, APPERKYSVW, IADRMQKELT, LNSADLIK, LSNLGIVI, IGALRAVA, and HHTFYNELR) derived from SYC were obtained and their activity values, hemolysis, and toxicity were predicted. Their basic data (AA sequence, MW, PI, predicted activity values, hemolysis, toxicity, and IC50 for XOI activity) are summarized in Table 3. According to Table 3, all of the peptides had a molecular weight between 760 and 1250 and were predicted to be non-toxic. Only WW8 and AW10 were predicted to have higher potential physiological activity, and all were non-hemolytic except LSNLGIVI and LNSADLIK. The results indicated that WW8 and AW10 (the identification results in Figure 3) are non-toxic and non-hemolytic and have potential physiological activity. WW8 and AW10 presented adequate water solubility. Water solubility has been found to have an effect on bioactive peptide absorption, and dissolution is a limiting factor for physiological function performance [39]. Peptides with considerable water solubility may have high biological availability [15,40]. The XOI activities of the peptides were highest in WW8 (IC50 = 3.16 ± 0.03 mM) and AW10 (IC50 = 5.86 ± 0.02 mM), as shown in Figure 4. The results showed a higher XOI effect than peptide ACECD from *Skipjack tuna* hydrolysates, which had XOI activity of IC50 of 13.40 mM [13]. IADRMQKELT and LNSADLIK had no XOI activity at 15 mg/mL, and LSNLGIVI, IGALRAVA, and HHTFYNELR did not even dissolve in water at 3 mg/mL, so these five peptides were neglected for the subsequent experiments.
The XOI activity of WW8 and AW10 was significantly higher than that of the other peptides lacking Trp residue. These findings suggest that a crucial component of effective XOI activity is the presence of Trp residues in the peptides [9,10]. Nongonierma [9] reported that Trp inhibited XO by 70.3 ± $1.1\%$ at a concentration of 0.25 mg/mL. Li [10] claimed that relatively lower IC50 values were mainly located in the peptides containing Trp residue, reporting that the IC50 values of peptides WDD, WDQW, PPKNW, WPPKN, and WSREEQE were lower than those of peptides HCPF and ADIYTE, and that these Trp-containing peptides had relatively higher XOI activity. One possible explanation for this is that Trp with an indole group has a similar C6 and C5 ring structure to the drug allopurinol. Hou et al. [ 23] came to the same conclusion and demonstrated that peptides with Trp residue at the C-terminus inhibited XO. Similarly, the WW8 and AW10 had Trp residue at the C-terminus, which was the most critical factor contributing to the inhibitory effect. Furthermore, prior research suggested that peptides with HAA function well as XO inhibitors, because peptides with higher amounts of HAA may be able to access the hydrophobic domain of the XO active center more easily [36,41]. Interestingly, the peptides with high XOI activity all contain these specific residues. WW8 and AW10 were abundant in HAA ($50\%$ HAA residues), including Try, Tyr, Pro, and Ile, which is consistent with prior results on XOI peptides.
The higher suppression of XO peptides compared to SYCH implies that peptides generated from SYC, notably WW8 and AW10, could be potential XO inhibitors. Thus, the peptides WW8 and AW10, which are non-toxic and non-hemolytic and have adequate good water solubility and XOI activity, were subjected to molecular docking to explain the relationship between peptides and XO.
## 3.5. Molecular Docking and Visual Analysis
Molecular docking simulates and visualizes the binding sites and binding profiles of small molecule ligands to biological macromolecule receptors. Figure 5A,B depicts the interaction between the two peptides (WW8 and AW10) with XO, with binding energies of −7.3 and −7.9 kcal/mol (detailed in Table 4), respectively, indicating a strong binding relationship. Generally, the lower the binding energy for the same docking model, the more stable the complex [23].
The forces created between the peptides and XO (PDB: 3NVY), as well as the interactions and matching bonds, are depicted in Figure 5C and Table 4. The peptides WW8 and AW10 interacted with the protein, and WW8 established hydrogen connections with Ile1190, Ala1189, Leu744, and Gln1201, with hydrogen bond lengths of 3.5 Å, 2.7 Å, 2.0 Å, and 2.4 Å, respectively. WW8 also established electrostatic interaction with His579 and hydrophobic interactions with 24 AAs, including Val1200, Gly1197, Glu1196, Phe1219, Ile1235, Ile1229, Phe1232, Pro1230, His741, Ala1231, Tyr743, Phe238, Phe742, Tyr592, Met1038, Gly1039, Gly796, Gly1039, Met794, Gly795, Ala582, Gln585, Gln1194, and Gly1193. These AA residues appeared around the binding of AW10 to XO, including Arg912 and Met1038 with which they formed hydrogen bonds with lengths of 1.9 Å, 2.1 Å, and 3.4 Å, respectively. Moreover, hydrophobic interactions with 22 AAs were distributed around the binding sites of AW10 in XOD, including Ala582, His579, Gln585, Met794, Gly796, Gly795, Leu744, Tyr743, Tyr592, Gly039, Gln194, Gly193, Gln021, Phe798, Ala1198, Glu1196, Ile1235, Phe1239, Gly1197, Val1200, Phe1232, and Ala1231, indicating that the hydrophobic force is another important factor driving the binding of AW10 to XO.
It was hypothesized that although all peptides interact with different AA residues of XO, they all bind to XO mainly through hydrogen bonding and hydrophobic forces, thus inhibiting the catalytic activity of XO. The lower XOI activity of IC50 for WW8 compared to AW10 could be attributed to a greater number of hydrogen bonds and hydrophobic forces between the WW8 and the XO interaction than AW10.
## 4. Conclusions
Peptides (WDDMEKIW and APPERKYSVW) with XOI activity were identified after enzymatic hydrolysis and UF separation of SYC proteins, and the IC50 values (IC50 = 3.16 ± 0.03 mM and 5.86 ± 0.02 mM, respectively) of XOI activity were calculated in vitro. These findings were validated by molecular docking of the two peptides chosen for the strongest XOI activity, which highlighted the importance of hydrophobic bonds and hydrogen bonds in the establishment of a stable complex conformation and the resulting inhibitory effect of the peptides. We anticipate that these peptides can be employed to manage hyperuricemia as natural XO inhibitors.
Bioactive peptides will continue to constitute an important area of study in the future, with an expanding array of uses in food, medicine, and cosmetics. Although peptides derived from SYC proteins exhibit XOI activity, in vivo experiments and clinical trial data are required to confirm these findings and explain unknown mechanisms. Clinical trial data are also necessary to demonstrate the efficacy of active peptides and ensure their bioavailability and safety profile. These are all objectives that must be fulfilled in the future. In terms of preserving peptide activity, micro- and nano-encapsulation of bioactive peptides may be an effective way to manage their release and avoid degradation in order to optimize their bioavailability and effectiveness. The advancement of oral administration and bioactive peptide delivery introduces both possibilities and limitations to be addressed in future research.
## References
1. Kaneko K., Takayanagi F., Fukuuchi T., Yamaoka N., Yasuda M., Mawatari K.I., Fujimori S.. **Determination of total purine and purine base content of 80 food products to aid nutritional therapy for gout and hyperuricemia**. *Nucleosides Nucleotides Nucleic Acids* (2020) **39** 1449-1457. DOI: 10.1080/15257770.2020.1748197
2. Zhang Y., Chen S., Yuan M., Xu Y., Xu H.. **Gout and diet: A comprehensive review of mechanisms and management**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14173525
3. Lee Y., Hwang J., Desai S.H., Li X., Jenkins C., Kopp J.B., Winkler C.A., Cho S.K.. **Efficacy of xanthine oxidase inhibitors in lowering serum uric acid in chronic kidney disease: A systematic review and meta-analysis**. *J. Clin. Med.* (2022) **11**. DOI: 10.3390/jcm11092468
4. He W., Su G., Sun-Waterhouse D., Waterhouse G.I.N., Zhao M., Liu Y.. **In vivo anti-hyperuricemic and xanthine oxidase inhibitory properties of tuna protein hydrolysates and its isolated fractions**. *Food Chem.* (2019) **272** 453-461. DOI: 10.1016/j.foodchem.2018.08.057
5. Ichida K., Matsuo H., Takada T., Nakayama A., Murakami K., Shimizu T., Yamanashi Y., Kasuga H., Nakashima H., Nakamura T.. **Decreased extra-renal urate excretion is a common cause of hyperuricemia**. *Nat. Commun.* (2012) **3** 764. DOI: 10.1038/ncomms1756
6. Zhao M., Zhu D., Sun-Waterhouse D., Su G., Lin L., Wang X., Dong Y.. **In vitro and in vivo studies on adlay-derived seed extracts: Phenolic profiles, antioxidant activities, serum uric acid suppression, and xanthine oxidase inhibitory effects**. *J. Agric. Food Chem.* (2014) **62** 7771-7778. DOI: 10.1021/jf501952e
7. Wang J., Chen Y., Zhong H., Chen F., Regenstein J., Hu X., Cai L., Feng F.. **The gut microbiota as a target to control hyperuricemia pathogenesis: Potential mechanisms and therapeutic strategies**. *Crit. Rev. Food Sci. Nutr.* (2022) **62** 3979-3989. DOI: 10.1080/10408398.2021.1874287
8. Zhang Y., Li Q., Wang F., Xing C.. **A zebrafish (**. *Biochem. Biophys. Res. Commun.* (2019) **508** 494-498. DOI: 10.1016/j.bbrc.2018.11.050
9. Nongonierma A.B., Fitzgerald R.J.. **Tryptophan-containing milk protein-derived dipeptides inhibit xanthine oxidase**. *Peptides* (2012) **37** 263-272. DOI: 10.1016/j.peptides.2012.07.030
10. Li Q., Shi C., Wang M., Zhou M., Liang M., Zhang T., Yuan E., Wang Z., Yao M., Ren J.. **Tryptophan residue enhances in vitro walnut protein-derived peptides exerting xanthine oxidase inhibition and antioxidant activities**. *J. Funct. Foods* (2019) **53** 276-285. DOI: 10.1016/j.jff.2018.11.024
11. Zhao Q., Meng Y., Liu J., Hu Z., Du Y., Sun J., Mao X.. **Separation, identification and docking analysis of xanthine oxidase inhibitory peptides from pacific cod bone-flesh mixture**. *LWT* (2022) **167** 113862. DOI: 10.1016/j.lwt.2022.113862
12. Zhao Q., Jiang X., Mao Z., Zhang J., Sun J., Mao X.. **Exploration, sequence optimization and mechanism analysis of novel xanthine oxidase inhibitory peptide from**. *Food Chem.* (2022) **404** 134537. DOI: 10.1016/j.foodchem.2022.134537
13. Zhong H., Abdullah Y., Zhang L., Deng M., Zhao J., Tang H., Zhang F., Feng J.. **Exploring the potential of novel xanthine oxidase inhibitory peptide (ACECD) derived from**. *Food Chem.* (2021) **347** 129068. DOI: 10.1016/j.foodchem.2021.129068
14. Hu X., Zhou Y., Zhou S., Chen S., Wu Y., Li L., Yang X.. **Purification and identification of novel xanthine oxidase inhibitory peptides derived from Round Scad (**. *Mar. Drugs* (2021) **19**. DOI: 10.3390/md19100538
15. Jia L., Wang L., Liu C., Liang Y., Lin Q.. **Bioactive peptides from foods: Production, function, and application**. *Food Funct.* (2021) **12** 7108-7125. DOI: 10.1039/D1FO01265G
16. Cunha S.A., Pintado M.E.. **Bioactive peptides derived from marine sources: Biological and functional properties**. *Trends Food Sci. Technol.* (2022) **119** 348-370. DOI: 10.1016/j.tifs.2021.08.017
17. Wang Y.Y., Tayyab Rashid M., Yan J.K., Ma H.. **Effect of multi-frequency ultrasound thawing on the structure and rheological properties of myofibrillar proteins from small yellow croaker**. *Ultrason. Sonochem.* (2021) **70** 105352. DOI: 10.1016/j.ultsonch.2020.105352
18. Chen L., Zeng W., Rong Y., Lou B.. **Compositions, nutritional and texture quality of wild-caught andcage-cultured small yellow croaker**. *J. Food Compos. Anal.* (2022) **107** 104370. DOI: 10.1016/j.jfca.2021.104370
19. Allegrini S., Garcia-Gil M., Pesi R., Camici M., Tozzi M.G.. **The good, the bad and the new about uric acid in cancer**. *Cancers* (2022) **14**. DOI: 10.3390/cancers14194959
20. Kim B.S., Oh B.J., Lee J.H., Yoon Y.S., Lee H.I.. **Effects of various drying methods on physicochemical characteristics and textural features of yellow croaker (**. *Foods* (2020) **9**. DOI: 10.3390/foods9020196
21. Wang S.-M., Li J., Zhao Q., Lv D.-D., Rakariyatham K.. **The effect of frying process on lipids in small yellow croaker (**. *J. Aquat. Food Prod. Technol.* (2021) **31** 83-95. DOI: 10.1080/10498850.2021.2011519
22. Gao Y., Zhang M., Chen G., Wang Y.. **Effect of micronization on physicochemical properties of small yellow croaker (**. *Adv. Powder. Technol.* (2013) **24** 932-938. DOI: 10.1016/j.apt.2013.01.009
23. Hou M., Xiang H., Hu X., Chen S., Wu Y., Xu J., Yang X.. **Novel potential XOD inhibitory peptides derived from**. *Food Biosci.* (2022) **47** 101639. DOI: 10.1016/j.fbio.2022.101639
24. Liu N., Wang Y., Yang M., Bian W., Zeng L., Yin S., Xiong Z., Hu Y., Wang S., Meng B.. **New rice-derived short peptide potently alleviated hyperuricemia induced by potassium oxonate in rats**. *J. Agric. Food Chem.* (2019) **67** 220-228. DOI: 10.1021/acs.jafc.8b05879
25. Wei L., Ji H., Song W., Peng S., Zhan S., Qu Y., Chen M., Zhang D., Liu S.. **Hypouricemic, hepatoprotective and nephroprotective roles of oligopeptides derived from**. *Food Funct.* (2021) **12** 11838-11848. DOI: 10.1039/D1FO02539B
26. Bao X., Si X., Ding X., Duan L., Xiao C.. **pH-responsive hydrogels based on the self-assembly of short polypeptides for controlled release of peptide and protein drugs**. *J. Polym. Res.* (2019) **26** 278. DOI: 10.1007/s10965-019-1953-8
27. Çağlar A.F., Çakır B., Gülseren İ.. **LC-Q-TOF/MS based identification and in silico verification of ACE-inhibitory peptides in Giresun (**. *Eur. Food Res. Technol.* (2021) **247** 1189-1198. DOI: 10.1007/s00217-021-03700-6
28. Yu Z., Kan R., Wu S., Guo H., Zhao W., Ding L., Zheng F., Liu J.. **Xanthine oxidase inhibitory peptides derived from tuna protein: Virtual screening, inhibitory activity, and molecular mechanisms**. *J. Sci. Food Agr.* (2021) **101** 1349-1354. DOI: 10.1002/jsfa.10745
29. Gui M., Gao L., Rao L., Li P., Zhang Y., Han J.W., Li J.. **Bioactive peptides identified from enzymatic hydrolysates of sturgeon skin**. *J. Sci. Food Agr.* (2022) **102** 1948-1957. DOI: 10.1002/jsfa.11532
30. Huang Y., Fan S., Lu G., Sun N., Wang R., Lu C., Han J., Zhou J., Li Y., Ming T.. **Systematic investigation of the amino acid profiles that are correlated with xanthine oxidase inhibitory activity: Effects, mechanism and applications in protein source screening**. *Free Radic. Biol. Med.* (2021) **177** 326-336. DOI: 10.1016/j.freeradbiomed.2021.11.004
31. Li Q., Kang X., Shi C., Li Y., Majumder K., Ning Z., Ren J.. **Moderation of hyperuricemia in rats via consuming walnut protein hydrolysate diet and identification of new antihyperuricemic peptides**. *Food Funct.* (2018) **9** 107-116. DOI: 10.1039/C7FO01174A
32. Allahyari M., Samadi-Noshahr Z., Hosseinian S., Salmani H., Noras M., Khajavi-Rad A.. **Camel milk and allopurinol attenuated adenine-induced acute renal failure in rats**. *Iran. J. Sci. Technol. A* (2021) **45** 1539-1548. DOI: 10.1007/s40995-021-01155-8
33. Li Q., Li X., Wang J., Liu H., Kwong J.S., Chen H., Li L., Chung S.C., Shah A., Chen Y.. **Diagnosis and treatment for hyperuricemia and gout: A systematic review of clinical practice guidelines and consensus statements**. *BMJ Open* (2019) **9** e026677. DOI: 10.1136/bmjopen-2018-026677
34. Chalamaiah M., Dinesh Kumar B., Hemalatha R., Jyothirmayi T.. **Fish protein hydrolysates: Proximate composition, amino acid composition, antioxidant activities and applications: A review**. *Food Chem.* (2012) **135** 3020-3038. DOI: 10.1016/j.foodchem.2012.06.100
35. Elavarasan K., Shamasundar B.A.. **Antioxidant properties of papain mediated protein hydrolysates from fresh water carps (**. *J. Food Sci. Technol.* (2022) **59** 636-645. DOI: 10.1007/s13197-021-05053-0
36. Li Y., Kang X., Li Q., Shi C., Lian Y., Yuan E., Zhou M., Ren J.. **Anti-hyperuricemic peptides derived from bonito hydrolysates based on in vivo hyperuricemic model and in vitro xanthine oxidase inhibitory activity**. *Peptides* (2018) **107** 45-53. DOI: 10.1016/j.peptides.2018.08.001
37. Wei L., Ji H., Song W., Peng S., Zhan S., Qu Y., Chen M., Zhang D., Liu S.. **Identification and molecular docking of two novel peptides with xanthine oxidase inhibitory activity from Auxis thazard**. *Food Sci. Technol.* (2022) **42** e106921. DOI: 10.1590/fst.106921
38. Vásquez P., Zapata J.E., Chamorro V.C., García Fillería S.F., Tironi V.A.. **Antioxidant and angiotensin I-converting enzyme (ACE) inhibitory peptides of rainbow trout (**. *LWT* (2022) **154** 112834. DOI: 10.1016/j.lwt.2021.112834
39. Etemadian Y., Ghaemi V., Shaviklo A.R., Pourashouri P., Sadeghi Mahoonak A.R., Rafipour F.. **Development of animal/ plant-based protein hydrolysate and its application in food, feed and nutraceutical industries: State of the art**. *J. Clean. Prod.* (2021) **278** 123219. DOI: 10.1016/j.jclepro.2020.123219
40. Islam M.S., Wang H., Admassu H., Sulieman A.A., Wei F.A.. **Health benefits of bioactive peptides produced from muscle proteins: Antioxidant, anti-cancer, and anti-diabetic activities**. *Process Biochem.* (2022) **116** 116-125. DOI: 10.1016/j.procbio.2022.03.007
41. Wu Y., He H., Hou T.. **Purification, identification, and computational analysis of xanthine oxidase inhibitory peptides from kidney bean**. *J. Food Sci.* (2021) **86** 1081-1088. DOI: 10.1111/1750-3841.15603
|
---
title: 'The Effect of Ginger (Zingiber officinale Roscoe) Aqueous Extract on Postprandial
Glycemia in Nondiabetic Adults: A Randomized Controlled Trial'
authors:
- Alda Diakos
- Maria Leonor Silva
- José Brito
- Margarida Moncada
- Maria Fernanda de Mesquita
- Maria Alexandra Bernardo
journal: Foods
year: 2023
pmcid: PMC10001081
doi: 10.3390/foods12051037
license: CC BY 4.0
---
# The Effect of Ginger (Zingiber officinale Roscoe) Aqueous Extract on Postprandial Glycemia in Nondiabetic Adults: A Randomized Controlled Trial
## Abstract
Ginger has shown beneficial effects on blood glucose control due to its antioxidant and anti-inflammatory properties. The present study investigated the effect of ginger aqueous extract on postprandial glucose levels in nondiabetic adults and characterized its antioxidant activity. Twenty-four nondiabetic participants were randomly assigned into two groups (NCT05152745), the intervention group ($$n = 12$$) and the control group ($$n = 12$$). Both groups were administered 200 mL of an oral glucose tolerance test (OGTT), after which participants in the intervention group ingested 100 mL of ginger extract (0.2 g/100 mL). Postprandial blood glucose was measured while fasting and after 30, 60, 90, and 120 min. The total phenolic content, flavonoid content, and antioxidant activity of ginger extract were quantified. In the intervention group, the incremental area under the curve for glucose levels decreased significantly ($p \leq 0.001$) and the maximum glucose concentration significantly reduced ($p \leq 0.001$). The extract possessed a polyphenolic content of 13.85 mg gallic acid equivalent/L, a flavonoid content of 3.35 mg quercetin equivalent/L, and a high superoxide radical inhibitory capacity ($45.73\%$). This study showed that ginger has a beneficial effect on glucose homeostasis under acute conditions and encourages the use of ginger extract as a promising source of natural antioxidants.
## 1. Introduction
The postprandial blood glucose concentration has been reported as a key factor in glucose homeostasis control, which seems to be effective in preventing the development and progression of long-term diabetes complications [1]. According to epidemiological data, there is an association between cardiovascular and all-cause death and postprandial hyperglycemia status in nondiabetic patients [2]. In addition, the hyperglycemic status combined with clinical parameters can also predict an increased risk of developing diabetes [3].
It has been reported that postprandial glycemia profiles can be influenced by several factors, such as carbohydrate absorption, insulin and glucagon secretion and/or action, and glucose metabolism in different tissues [1]. Although the peak glucose concentration of nondiabetic individuals occurs about 60 min after the meal, the meal composition influences the magnitude and timing of the peak [1].
There is also evidence that, during hyperglycemic conditions, the oxygen free radicals are overproduced, leading to oxidative stress and cellular damage. This oxidative stress has been correlated with the development of diabetes complications [4].
Ginger (Zingiber officinale Roscoe) is a traditional herb belonging to Zingiberaceae family that has revealed beneficial effects on human health [5]. This herb has been used to treat nausea and vomiting, pain, metabolic syndrome, osteoarthritis, and obesity conditions [6,7,8,9,10]. In addition, it has been proposed that ginger possesses antioxidant and anti-inflammatory properties [11,12]. The main classes of the components responsible for ginger’s bioactivities include shogaols, gingerols, zingerone, and zingiberene [13,14]. It has been shown that these bioactive ginger compounds possess antidiabetic properties that are thought to enhance insulin secretion through the modulation of KATP channels [15]. In addition, 6-Gingerol potentiates the glucagon-like peptide 1 (GLP-1)-mediated glucose-stimulated insulin-secretion pathway in the pancreatic beta cell [16]. Another proposed mechanism of action postulates that the possible stimulation of Rab27a GTPase, in isolated islets, may contribute to the exocytosis of insulin-containing dense core granules. Increased Rab27a GTPase may also increase the translocation of the glucose transporter 4 (GLUT4) vesicle to the membrane of skeletal myocytes [16].
Currently, there is promising evidence of the beneficial properties of ginger extract, which seems to be effective in lowering blood glucose levels [17]. According to the Zhu et al. study, ethanolic ginger extract (200 mg/Kg body weight) demonstrated a significant antihyperglycemic effect in streptozotocin (STZ)—diabetic rats—for 20 days [17]. Ginger aqueous extract (500 mg/Kg body weight) significantly reduced blood glucose level after ginger treatment on the 8th day compared with the baseline in alloxan-induced diabetic rats [18]. However, recently published data on human studies have shown conflicting results regarding blood glucose control [19]. In Karimi et al. ’s study, the ingestion of a ginger supplement (four capsules) (3 g/day) for 7 weeks did not significantly change blood glucose in the ginger group (6.5 ± 0.4 mmol/L) compared to the placebo group (6.5 ± 1 mmol/L) [20]. Additionally, in another study, the ingestion of a ginger capsule (1000 mg per day) for 10 weeks significantly reduced the fasting blood glucose by up to $20\%$ in the nondiabetic adult ginger group at the end of the experimental protocol [21]. Conversely, in the Bordia et al. study, the ingestion of 5 g ginger powder (4 g per day) in nondiabetic patients for 3 months did not affect fasting and postprandial blood glucose levels [22].
Among the studies found in the literature focusing on the effect of ginger on blood sugar, few works have been developed on the effect of this herb on postprandial glycemia. Hung and co-workers [2022] demonstrated that a spice mix meal containing ginger significantly reduced postprandial glucose levels in obese and overweight adults [23]. In accordance with the lack of literature concerning ginger’s effect on the glucose response, the main aim of the present study was to investigate the effect of ginger (Zingibre officinalle Roscoe) aqueous extract (0.2 g/100 mL) on postprandial glucose levels in nondiabetic adults. The second aim was to characterize the antioxidant activity of the ingested ginger extract.
## 2.1. Ethical Consideration
This clinical trial was approved by the Egas Moniz School of Health and Science Ethics Committee (Project Code 519, approval on 23 November 2016). The participation was voluntary and informed consent was obtained from all participants after receiving oral and written information about the study. Data confidentiality and anonymity were guaranteed through a codification attributed to each participant. The experimental procedure involving humans was carried out according to the Declaration of Helsinki and CONSORT guidelines. This clinical trial is registered on Clinicaltrials.gov (NCT05152745).
## 2.2. Participants and Study Design
This randomized controlled clinical trial, blind to the researcher who performed the statistical analysis, was conducted at Campus Universitário Egas Moniz, Monte de Caparica, Portugal. Twenty-four nondiabetic male and female participants between ages 18 and 40 years were selected. After eligibility criteria were confirmed, participants were sequentially numbered and randomly placed in an intervention group ($$n = 12$$) or a control group ($$n = 12$$).
The eligibility and inclusion criteria included subjects of both genders, without glucose metabolism alteration (fasting blood glucose < 126 mg/dL or 6.99 mmol/L). Exclusion criteria included subjects who fasted less than 8 or more than 10 h, were under medication for glycemia control, had gastrointestinal symptoms or disease, pregnant or lactating women, and subjects with an allergy to ginger. Participants were asked not to ingest ginger on the day before the intervention.
After 8 h fasting, the intervention group performed an oral glucose tolerance test (OGTT), immediately followed by ginger extract administration; the control group performed an OGTT administration alone.
## 2.3. Ginger Extract Preparation
The ginger powder (Zingibre officinalle Roscoe) was obtained from a Portuguese company of Indian origin (batch number LI1GIGRNT150012) and stored under standard environmental conditions (21–23 °C, 50–$60\%$ humidity) until needed. Ginger powder was individually weighed (0.2 g each dose) and added to 100 mL water, thus producing the ginger aqueous extract, which was boiled for 10 min. After cooling at room temperature, the ginger extract solution was distributed to each participant. This method was adapted from Wilkinson, J. M. [2000] [24]. The ginger extract obtained was subject to total phenolic and flavonoid content determination, as well as radical inhibition assay.
## 2.4. Intervention
Blood samples were collected from each participant after overnight fasting (8 h), using capillary drop blood, before the intervention (t0). The control group ingested an oral glucose solution (75 g of dextrose in 200 mL water) [25] and the intervention group ingested a ginger aqueous extract solution immediately after the oral glucose solution (75 g of dextrose in 200 mL water). Blood samples were collected at 30, 60, 90, and 120 min after glucose solution and/or ginger extract ingestion in both groups. The blood glucose level analysis was performed using a strip for a glucose meter (Onetouch Select Plus Flex), a sterilized lancet, and glucose meter equipment.
## 2.5. Data Collection
General characteristics of the participants were collected through a questionnaire, including age and anthropometric parameters (weight, height, and body mass index). A 24 h dietary recall questionnaire was administered to participants the day before the intervention. The 24 h recall was instructed by an investigator to complete the food record. The ingested food quantity was estimated using a picture book. The Food Processor SQL (version 10.5.0) was used in order to obtain total energy (Kcal), total carbohydrates (g), total protein (g), and total lipid (g) mean intake.
## 2.6. Chemical Analysis
Folin–Ciocalteu and gallic acid-1-hydrate (C6H2(OH)3COOH·H2O) were from PanReac (Cascais, Portugal). Quercetin dihydrate (C15H10O7·2H2O) was from Extrasynthese (Lyon, France). Anhydrous aluminum chloride, potassium acetate, sodium carbonate, and Tris(hydroxymethyl)amino methane were from Merck (Alges, Portugal). Phenazine methosulfate (PMS), nicotinamide-adenine dinucleotide hydride (NADH), and nitro-blue tetrazolium chloride (NBT) were from Sigma Aldrich (Lisbon, Portugal). All reagents were pro-analysis grade. All absorbance measurements were performed in a Perkin–Elmer (Lisbon, Portugal) Lambda 25. The reagents were weighed on an analytical balance (Sartorius, ±0.00001 g) (Lisbon, Portugal).
## 2.7. Total Phenolic Content Determination
The total phenolic content quantification of 7 ginger extract samples was determined according to the Folin–Ciocalteu method [26]. The total phenolic content was expressed as mg gallic acid equivalent (GAE)/L of ginger extract.
## 2.8. Flavonoid Content Determination
The total flavonoid content quantification of 7 ginger extract samples was determined according to the Prabha method [26]. The total flavonoid content was expressed as mg quercetin equivalent (QCE)/L of ginger extract.
## 2.9. Radical Inhibition Assay
The superoxide anion (O2∙−) scavenging activity of the ginger extract was determined based on the Morais and Alam methods [27,28]. The superoxide anion was generated by reacting phenazine methosulfate (PMS), nicotinamide adenine dinucleotide hydride (NADH), and oxygen, causing a reduction of NBT in Formazan. A volume of 0.5 mL of ginger extract was added to 2 mL of a solution containing NADH (189 μM) and nitroblue tetrazolium (NBT) (120 μM) with Tris-HCl (40 mM, pH = 8). The reaction started after the addition of 0.5 mL of PMS (60 μM). After 5 min of incubation, control absorbance was measured at 560 nm at room temperature. The percentage of superoxide anion inhibition capacity was calculated using the following equation:Inhibition capacity (%)=Absorvance (control)−Absorvance(sample)Absorvance (control)×100
## 2.10. Statistical Analysis
Statistical analysis of the data was performed using SPSS® (Statistical Package for Social Sciences), version 25.0 software. Descriptive statistics data are reported as the mean ± SD (standard deviation) or SEM (standard error of the mean). Repeated measures of ANOVA of mixed type were used to assess the difference between the 2 groups for postprandial blood glucose at different times. After assumption verification, differences between the 2 groups for total energy, total carbohydrates, total protein and total lipid intake, maximum concentration (Cmax), variation of maximum concentration (ΔCmax), and incremental area under the curve (AUCi) of glucose were assessed using the independent samples t-test. The AUCi was calculated using GraphPad Prim (version 7.03) software. All statistical tests were performed at the $5\%$ level of significance.
The sample size required for the study was calculated by simulation using G-Power Software version 3.1.9.4 with a statistical significance of $5\%$ for an expected medium to a large effect size of $20\%$. Additionally, a low correlation (0.40) was assumed among repeated measures and a sphericity correction epsilon of 0.65.
## 3.1. Participant Enrollment
In accordance with the CONSORT participant sample description, a total of twenty-four participants were enrolled in and completed the study, twelve for each group, as shown in Figure 1.
## 3.2. Participant Characteristics
*The* general characteristics of nondiabetic male and female participants are shown in Table 1. A total of 24 participants, 12 subjects in the intervention group (four male, eight female) and 12 subjects in the control group (five male, seven female), completed the study. Participants from both groups did not significantly differ in age ($$p \leq 0.173$$), body mass index ($$p \leq 0.116$$), weight ($$p \leq 0.725$$), or height ($$p \leq 0.386$$).
The total nutritional composition of meals at the day before the intervention was analyzed in each participant of both groups. Non-significant differences ($p \leq 0.05$) were observed in carbohydrates and lipids between groups, as shown in Table 2. The total protein mean and total energy intake was significantly higher in the intervention group compared to the control group ($p \leq 0.05$).
## 3.3. Glycemic Response
Blood glucose levels were measured during an oral glucose tolerance test (OGTT) in the control and intervention groups, as shown in Table 3. The repeated measures ANOVA of mixed type showed that there was a significant interaction between the independent and the repeated measures factors ($p \leq 0.001$), which means that there are differences in postprandial blood glucose levels between groups, depending on the moment of measurement. Additionally, the differences in blood glucose levels between different measurement times change depending on the group.
The intervention group showed a significantly decreased blood glucose incremental area under the curve ($p \leq 0.001$) and variation of blood glucose maximum concentration ($p \leq 0.001$) compared to the control group (Table 4).
## 3.4. Total Phenols, Flavonoid, and Antioxidant Activity
The total phenol and flavonoid contents of the ginger extract used in this study are shown in Table 5. The results revealed a high total phenol (13.85 ± 0.1 mg GAE/L extract) and flavonoid (3.35 ± 0.2 mg QCE/L extract) content.
Additionally, the ginger extract showed a high inhibitory capacity for superoxide radical scavenging ($45.73\%$) and an IC50 of 15.66 mgGAE/L.
## 4. Discussion
The main aim of our study was to investigate if ginger extract improved the postprandial glucose concentration in nondiabetic adults. The findings of our study revealed that the ingestion of ginger aqueous extract (0.2 g/100 mL) improved the glycemic response in nondiabetic subjects compared to the control group. Data analysis showed a significant interaction between the independent and repeated measures factors ($p \leq 0.001$), which means that there are differences in postprandial blood glucose mean values between groups, depending on the moment of measurement. In addition, the results showed that the postprandial glycemia between different moments changed depending on the group.
The ginger extract reduced the blood glucose incremental area under the curve (AUCi) in the intervention group (169.75 ± 17.3) compared to the control group (334.43 ± 32.4) ($p \leq 0.001$), and the glucose maximum concentration in the intervention group (7.72 ± 0.28 mmol/L) compared to the control group (9.57 ± 0.43) ($p \leq 0.001$). These results may be associated with the potential properties of ginger’s bioactive compounds, namely the insulin-mimetic action, leading to increased glucose uptake through the upregulation of GLUT4 expression [16].
Furthermore, the results obtained from the postprandial glycemic response during the oral glucose tolerance test allow us to conclude that they are different between groups and suggest a beneficial effect on the postprandial glycemic response after ingestion of ginger extract. According to the literature, the glycemic response depends on the nutritional macronutrient composition of the meals [29]. In fact, in the present study, the average total protein intake on the day before the intervention (89.86 ± 10.63). in the intervention group was significantly ($$p \leq 0.011$$) higher than the control group (56.33 ± 4.85). The effect of protein intake on blood glucose has been studied in the literature using different methodological approaches. Khan et al. [ 1992] showed that the ingestion of 50 g of protein in the form of cottage cheese did not significantly reduce plasma glucose concentration compared with the control group (water alone) for 8 h [27]. In addition, Khoury et al. [ 2010] demonstrated that postprandial glucose peaks were significantly lower following a high-protein meal, compared with a high-carbohydrate meal [29]. Different studies have also evaluated the effect of protein ingestion in glycemic response through blood glucose concentration analysis for 180 min post-meal. The whey protein and milk protein co-ingestion with mixed meals improves postprandial glycemia [28,30]. On the other hand, in Paterson M. et al. ’s study, dietary protein does not seem to influence glycemic control in nondiabetic individuals [31]. In this context, due to the diversity of methods and results in the literature, the influence of protein intake on postprandial glucose is not fully understood. For this reason, although the results showed a beneficial effect of ginger extract ingestion on glycemia, further studies with homogeneous and comparable sample sizes, methodologies, and dietary patterns should be employed.
According to the literature, not many clinical trials have investigated the effect of ginger extract on postprandial glycemia. Most studies evaluate the effect of ginger on fasting glycaemia in diabetic patients. Additionally, the findings regarding ginger’s effect on glucose homeostasis seem to be contradictory. A recent meta-analysis that included eight randomized trials, with a total of 454 type 2 diabetic participants, revealed that ginger ingestion did not significantly improve glycaemia response in patients with type 2 diabetes mellitus ($$p \leq 0.16$$). Additionally, this study also showed that HbA1c significantly improved in the participants with ginger ingestion ($$p \leq 0.02$$) from the baseline to the follow-up, suggesting that ginger may have a beneficial impact on glucose control over a longer period of time [20].
Other studies have reported that ginger powder significantly reduces fasting glucose concentration. In a double-blind placebo-controlled randomized clinical trial, type 2 diabetic patients revealed significant differences in serum glucose ($p \leq 0.001$) in the intervention group compared with the control group after 3 months of the intervention (3 g per day of powdered ginger) [32]. Additionally, in Arablou et al. ’s study, the ingestion of 1.6 mg powdered ginger (capsule) per day for 12 weeks significantly lowered ($$p \leq 0.02$$) fasting plasma glucose, compared with the placebo group [33]. The ingestion of 2 g of ginger supplement for 12 weeks in type 2 diabetic patients also reduced the concentration of serum blood glucose ($$p \leq 0.000$$) [34]. In addition to this beneficial effect on glycemia, ginger powder has been shown to decrease serum insulin resistance [35] and significantly improve insulin levels and hemoglobin A1c [33]. In a randomized double-blind placebo-controlled trial with 64 type 2 diabetic patients (28 patients in the ginger group; 30 patients in the placebo group), the ginger supplementation in lower doses (2 g/day) for 2 months had a beneficial effect on insulin levels, but no significant change on fasting blood glucose. The dietary intakes of the participants revealed no significant difference in macronutrient intake between groups, both at the baseline and at the end of the study [36].
The discrepancy in the literature results could be attributed to heterogenicity of the study designs, ginger chemical composition, doses, formulations, extraction processes, and population samples [37]. Nevertheless, according to recent data, the consumption of ginger seems safe and acts beneficially on human health and well-being, highlighting the potential effect the glycemic control [38].
The mechanism of action of ginger extract responsible for glucose homeostasis control effects can be supported by animal and in vitro studies. The administration of 200 mg/kg of gingerol for 4 weeks significantly potentiates GLP-1-mediated glucose-stimulated insulin-secretion pathway in pancreatic beta cells of treated type 2 diabetic mice, compared to untreated type 2 diabetic mice [16]. The increase in insulin secretion through endocrine hormones can be related to a beneficial effect on plasma glucose concentration regulation. In C2C12 cells, the polyphenol-rich Indian ginger extract increased insulin-stimulated glucose uptake [39]. Moreover, different studies explored several underlying mechanisms promoted by different ginger bioactive compounds, which can play a role in glucose control in peripheral tissues. The [6]-Gingerol increased the glucose-stimulated insulin secretion [16]. This compound upregulated and activated cAMP, PKA, and CREB in the pancreatic islets, which can contribute to the insulin-secretion pathway [16]. In addition, [6]-Gingerol regulated the Rab27a GTPase in pancreatic islets, leading to the exocytosis of insulin-containing dense-core granules [16]. Additionally, S-[8]-gingerol seems to increase the protein level of GLUT 4 in a dose-dependent manner in L6 myotubes [40].
Moreover, our study confirms that ginger aqueous extract possesses a high-antioxidant activity through the free radical scavenging capacity. This finding could be correlated with a high-polyphenolic content observed in ginger extract since, according to the literature, there is a significant correlation between free radical scavenging capacity and total phenolic content [41].
According to Manjunathan et al., the antioxidant properties and phenolic content of ginger aqueous extract could also be attributed to gingerol bioactive compound activity [42]. These findings are in accordance with the Fathi study, in which hydroethanolic extract of ginger demonstrated a good level of DPPH scavenging activity and total phenolic content per gram of dry extract [43]. The bioactive compounds identified in ginger, namely [6]-gingerol, [8]-gingerol, [10]-gingerol, and [6]-shogaol showed important scavenging activities with IC50 of 26.3, 19.47, 10.47, and 8.05 µM against DPPH radical and with IC50 of 4.05, 2.5, 1.68, and 0.85 µM against superoxide radical, respectively [44]. Since hyperglycemia induces free radical formation, including the superoxide anion [4], the administration of ginger extract may also contribute beneficially to oxidative damage prevention through its high inhibitory capacity for superoxide radical scavenging ($45.73\%$).
Limitations of this study include the unblinded design regarding investigators and the study participants, which was not possible given the nature of the study. The authors did not evaluate the plasma insulin concentration and plasma glucagon-like peptide (GLP-1), which are important in analyzing the effect of ginger extract on GLP-1 and insulin secretion, allowing us to understand its mechanism of action. Additionally, it would be interesting to test other ginger aqueous extract doses in order to explore the eventual postprandial glycemia ginger extract dose-dependence. Further research should be undertaken with a larger sample size and performed over a longer period as part of a mixed-meal daily intake, in order to verify the effect of ginger extract in the long term.
## 5. Conclusions
The current study indicates that the ingestion of ginger (Zingiber officinale Roscoe) aqueous extract (0.2 g/100 mL) reduces blood glucose incremental area under the curve and postprandial maximum glucose level variation in nondiabetic subjects. In addition, ginger extract possesses substantial antioxidant activity through free radical scavenging activity. The present study contributes to the support of the beneficial properties of ginger (Zingiber officinale Roscoe), suggesting that this herb extract may be effective against hyperglycemic status in nondiabetic subjects.
## References
1. **Postprandial Blood Glucose**. *Diabetes Care* (2001) **24** 775-778. DOI: 10.2337/diacare.24.4.775
2. Chien K.L., Lee B.C., Lin H.J., Hsu H.C., Chen M.F.. **Association of Fasting and Post-Prandial Hyperglycemia on the Risk of Cardiovascular and All-Cause Death among Non-Diabetic Chinese**. *Diabetes Res. Clin. Pract.* (2009) **83** e47-e50. DOI: 10.1016/j.diabres.2008.11.023
3. Schmidt M.I., Bracco P.A., Yudkin J.S., Bensenor I.M., Griep R.H., Barreto S.M., Castilhos C.D., Duncan B.B.. **Intermediate Hyperglycaemia to Predict Progression to Type 2 Diabetes (ELSA-Brasil): An Occupational Cohort Study in Brazil**. *Lancet Diabetes Endocrinol.* (2019) **7** 267-277. DOI: 10.1016/S2213-8587(19)30058-0
4. Papachristoforou E., Lambadiari V., Maratou E., Makrilakis K.. **Association of Glycemic Indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with Oxidative Stress and Diabetic Complications**. *J. Diabetes Res.* (2020) **2020**. DOI: 10.1155/2020/7489795
5. Anh N.H., Kim S.J., Long N.P., Min J.E., Yoon Y.C., Lee E.G., Kim M., Kim T.J., Yang Y.Y., Son E.Y.. **Ginger on Human Health: A Comprehensive Systematic Review of 109 Randomized Controlled Trials**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12010157
6. Chang W.P., Peng Y.X.. **Does the Oral Administration of Ginger Reduce Chemotherapy-Induced Nausea and Vomiting?: A Meta-Analysis of 10 Randomized Controlled Trials**. *Cancer Nurs.* (2019) **42** E14-E23. DOI: 10.1097/NCC.0000000000000648
7. Terry R., Posadzki P., Watson L.K., Ernst E.. **The Use of Ginger (Zingiber Officinale) for the Treatment of Pain: A Systematic Review of Clinical Trials**. *Pain Med.* (2011) **12** 1808-1818. DOI: 10.1111/j.1526-4637.2011.01261.x
8. Ebrahimzadeh Attari V., Malek Mahdavi A., Javadivala Z., Mahluji S., Zununi Vahed S., Ostadrahimi A.. **A Systematic Review of the Anti-Obesity and Weight Lowering Effect of Ginger (Zingiber Officinale Roscoe) and Its Mechanisms of Action**. *Phytother. Res.* (2018) **32** 577-585. DOI: 10.1002/ptr.5986
9. Leach M.J., Kumar S.. **The Clinical Effectiveness of Ginger (Zingiber Officinale) in Adults with Osteoarthritis**. *Int. J. Evid. Based Healthc.* (2008) **6** 311-320. DOI: 10.1111/j.1479-6988.2008.00106.x
10. Wang J., Ke W., Bao R., Hu X., Chen F.. **Beneficial Effects of Ginger Zingiber Officinale Roscoe on Obesity and Metabolic Syndrome: A Review**. *Ann. N. Y. Acad. Sci.* (2017) **1398** 83-98. DOI: 10.1111/nyas.13375
11. Abdi T., Mahmoudabady M., Marzouni H.Z., Niazmand S., Khazaei M.. **Ginger (Zingiber Officinale Roscoe) Extract Protects the Heart Against Inflammation and Fibrosis in Diabetic Rats**. *Can. J. Diabetes* (2021) **45** 220-227. DOI: 10.1016/j.jcjd.2020.08.102
12. Mahluji S., Ostadrahimi A., Mobasseri M., Attari V.E., Payahoo L.. **Anti-Inflammatory Effects of Zingiber Officinale in Type 2 Diabetic Patients**. *Adv. Pharm. Bull.* (2013) **3** 273-276. DOI: 10.5681/apb.2013.044
13. Liu Y., Liu J., Zhang Y.. **Research Progress on Chemical Constituents of Zingiber Officinale Roscoe**. *Biomed. Res. Int.* (2019) **2019** 5370823. DOI: 10.1155/2019/5370823
14. Butt M.S., Sultan M.T.. **Ginger and Its Health Claims: Molecular Aspects**. *Crit. Rev. Food Sci. Nutr.* (2011) **51** 383-393. DOI: 10.1080/10408391003624848
15. Nam Y.H., Hong B.N., Rodriguez I., Park M.S., Jeong S.Y., Lee Y.G., Shim J.H., Yasmin T., Kim N.W., Koo Y.T.. **Steamed Ginger May Enhance Insulin Secretion through Katp Channel Closure in Pancreatic β-Cells Potentially by Increasing 1-Dehydro-6-Gingerdione Content**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12020324
16. Samad M., Mohsin M.N.A., Razu B.A., Hossain M.T., Mahzabeen S., Unnoor N., Muna I.A., Akhter F., Kabir A.U., Hannan J.M.A.. **[6]-Gingerol, from Zingiber Officinale, Potentiates GLP-1 Mediated Glucose-Stimulated Insulin Secretion Pathway in Pancreatic β-Cells and Increases RAB8/RAB10-Regulated Membrane Presentation of GLUT4 Transporters in Skeletal Muscle to Improve Hyperglycemia in Leprdb/Db Type 2 Diabetic Mice**. *BMC Complement Altern Med.* (2017) **17** 395. DOI: 10.1186/s12906-017-1903-0
17. Bhandari U., Kanojia R., Pillai K.K.. **Effect of Ethanolic Extract of Zingiber Officinale on Dyslipidaemia in Diabetic Rats**. *J. Ethnopharmacol.* (2005) **97** 227-230. DOI: 10.1016/j.jep.2004.11.011
18. Jafri S.A., Abass S., Qasim M.. **Pakistan Veterinary Journal Hypoglycemic Effect of Ginger (Zingiber Officinale) in Alloxan Induced Diabetic Rats (Rattus Norvagicus)**. *Pak. Vet. J.* (2010) **31** 160-162
19. Huang F.Y., Deng T., Meng L.X., Ma X.L.. **Dietary Ginger as a Traditional Therapy for Blood Sugar Control in Patients with Type 2 Diabetes Mellitus**. *Medicine* (2019) **98** e15054. DOI: 10.1097/MD.0000000000015054
20. Karimi N., Roshan V.D., Bayatiyani Z.F.. **Individually and Combined Water-Based Exercise with Ginger Supplement, on Systemic Inflammation and Metabolic Syndrome Indices, among the Obese Women with Breast Neoplasms**. *Int. J. Cancer Manag.* (2015) **8** e3856. DOI: 10.17795/ijcp-3856
21. Imani H., Tabibi H., Najafi I., Atabak S., Hedayati M., Rahmani L.. **Effects of Ginger on Serum Glucose, Advanced Glycation End Products, and Inflammation in Peritoneal Dialysis Patients**. *Nutrition* (2015) **31** 703-707. DOI: 10.1016/j.nut.2014.11.020
22. Bordia A., Verma S.K., Srivastava K.C.. **Effect of Ginger (Zingiber Officinale Rosc.) and Fenugreek (**. *Prostaglandins Leukot Essent Fat. Acids* (1997) **56** 379-384. DOI: 10.1016/S0952-3278(97)90587-1
23. Huang Y., Tsai M.F., Thorat R.S., Xiao D., Zhang X., Sandhu A.K., Edirisinghe I., Burton-Freeman B.M.. **Endothelial Function and Postprandial Glucose Control in Response to Test-Meals Containing Herbs and Spices in Adults with Overweight/Obesity**. *Front. Nutr.* (2022) **9** 811433. DOI: 10.3389/fnut.2022.811433
24. Wilkinson J.M.. **Effect of Ginger Tea on the Fetal Development of Sprague-Dawley Rats**. *Reprod. Toxicol.* (2000) **14** 507-512. DOI: 10.1016/S0890-6238(00)00106-4
25. **American Diabetes Association Diagnosis and Classification of Diabetes Mellitus**. *Diabetes Care* (2010) **33** S62-S69. DOI: 10.2337/dc10-S062
26. Prabha M.R., Vasantha K.. **Antioxidant, Cytotoxicity and Polyphenolic Content of Calotropis Procera (Ait.) R. Br. Flowers**. *J. Appl. Pharm. Sci.* (2011) **1** 136-140
27. Khan M.A., Gannon M.C., Nuttall F.Q.. **Glucose Appearance Rate Following Protein Ingestion in Normal Subjects**. *J. Am. Coll. Nutr.* (1992) **11** 701-706. DOI: 10.1080/07315724.1992.10718270
28. King D.G., Walker M., Campbell M.D., Breen L., Stevenson E.J., West D.J.. **A Small Dose of Whey Protein Co-Ingested with Mixed-Macronutrient Breakfast and Lunch Meals Improves Postprandial Glycemia and Suppresses Appetite in Men with Type 2 Diabetes: A Randomized Controlled Trial**. *Am. J. Clin. Nutr.* (2018) **107** 550-557. DOI: 10.1093/ajcn/nqy019
29. El Khoury D., Hwalla N.. **Metabolic and Appetite Hormone Responses of Hyperinsulinemic Normoglycemic Males to Meals with Varied Macronutrient Compositions**. *Ann. Nutr. Metab.* (2010) **57** 59-67. DOI: 10.1159/000317343
30. Kung B., Anderson G.H., Paré S., Tucker A.J., Vien S., Wright A.J., Goff H.D.. **Effect of Milk Protein Intake and Casein-to-Whey Ratio in Breakfast Meals on Postprandial Glucose, Satiety Ratings, and Subsequent Meal Intake**. *J. Dairy Sci.* (2018) **101** 8688-8701. DOI: 10.3168/jds.2018-14419
31. Paterson M., Bell K.J., O’Connell S.M., Smart C.E., Shafat A., King B.. **The Role of Dietary Protein and Fat in Glycaemic Control in Type 1 Diabetes: Implications for Intensive Diabetes Management**. *Curr. Diab. Rep.* (2015) **15** 61. DOI: 10.1007/s11892-015-0630-5
32. Shidfar F., Rajab A., Rahideh T., Khandouzi N., Hosseini S., Shidfar S.. **The Effect of Ginger (Zingiber Officinale) on Glycemic Markers in Patients with Type 2 Diabetes**. *J. Complement. Integr. Med.* (2015) **12** 165-170. DOI: 10.1515/jcim-2014-0021
33. Arablou T., Aryaeian N., Valizadeh M., Sharifi F., Hosseini A., Djalali M.. **The Effect of Ginger Consumption on Glycemic Status, Lipid Profile and Some Inflammatory Markers in Patients with Type 2 Diabetes Mellitus**. *Int. J. Food Sci. Nutr.* (2014) **65** 515-520. DOI: 10.3109/09637486.2014.880671
34. Khandouzi N., Shidfar F., Rajab A., Rahideh T., Hosseini P., Taheri M.M.. **The Effects of Ginger on Fasting Blood Sugar, Hemoglobin A1c, Apolipoprotein B, Apolipoprotein A-I and Malondialdehyde in Type 2 Diabetic Patients**. *Iran. J. Pharm. Res.* (2015) **14** 131-140. PMID: 25561919
35. Mozaffari-Khosravi H., Talaei B., Jalali B.A., Najarzadeh A., Mozayan M.R.. **The Effect of Ginger Powder Supplementation on Insulin Resistance and Glycemic Indices in Patients with Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial**. *Complement Ther. Med.* (2014) **22** 9-16. DOI: 10.1016/j.ctim.2013.12.017
36. Mahluji S., Attari V.E., Mobasseri M., Payahoo L., Ostadrahimi A., Golzari S.E.. **Effects of Ginger (Zingiber Officinale) on Plasma Glucose Level, HbA1c and Insulin Sensitivity in Type 2 Diabetic Patients**. *Int. J. Food Sci. Nutr.* (2013) **64** 682-686. DOI: 10.3109/09637486.2013.775223
37. Li H., Liu Y., Luo D., Ma Y., Zhang J., Li M., Yao L., Shi X., Liu X., Yang K.. **Ginger for Health Care: An Overview of Systematic Reviews**. *Complement Ther. Med.* (2019) **45** 114-123. DOI: 10.1016/j.ctim.2019.06.002
38. Crichton M., Davidson A.R., Innerarity C., Marx W., Lohning A., Isenring E., Marshall S.. **Orally Consumed Ginger and Human Health: An Umbrella Review**. *Am. J. Clin. Nutr.* (2022) **115** 1511-1527. DOI: 10.1093/ajcn/nqac035
39. Venkateswaran M., Jayabal S., Hemaiswarya S., Murugesan S., Enkateswara S., Doble M., Periyasamy S.. **Polyphenol-Rich Indian Ginger Cultivars Ameliorate GLUT4 Activity in C2C12 Cells, Inhibit Diabetes-Related Enzymes and LPS-Induced Inflammation: An in Vitro Study**. *J. Food Biochem.* (2021) **45** e13600. DOI: 10.1111/jfbc.13600
40. Li Y., Tran V.H., Duke C.C., Roufogalis B.D.. **Gingerols of Zingiber Officinale Enhance Glucose Uptake by Increasing Cell Surface GLUT4 in Cultured L6 Myotubes**. *Planta Med.* (2012) **78** 1549-1555. DOI: 10.1055/s-0032-1315041
41. Dudonné S., Vitrac X., Coutiére P., Woillez M., Mérillon J.M.. **Comparative Study of Antioxidant Properties and Total Phenolic Content of 30 Plant Extracts of Industrial Interest Using DPPH, ABTS, FRAP, SOD, and ORAC Assays**. *J. Agric. Food Chem.* (2009) **57** 1768-1774. DOI: 10.1021/jf803011r
42. Manjunathan T., Guru A., Arokiaraj J., Gopinath P.. **6-Gingerol and Semisynthetic 6-Gingerdione Counteract Oxidative Stress Induced by ROS in Zebrafish-PubMed**. *Chem. Biodivers* (2021) **18** e2100650. DOI: 10.1002/cbdv.202100650
43. Fathi R., Akbari A., Nasiri K., Chardahcherik M.. **Ginger (Zingiber Officinale Roscoe) Extract Could Upregulate the Renal Expression of NRF2 and TNFα and Prevents Ethanol-Induced Toxicity in Rat Kidney**. *Orig. Res. Artic.* (2020) **11** 134-145
44. Dugasani S., Pichika M.R., Nadarajah V.D., Balijepalli M.K., Tandra S., Korlakunta J.N.. **Comparative Antioxidant and Anti-Inflammatory Effects of [6]-Gingerol, [8]-Gingerol, [10]-Gingerol and [6]-Shogaol**. *J. Ethnopharmacol.* (2010) **127** 515-520. DOI: 10.1016/j.jep.2009.10.004
|
---
title: The Impact of Physical Exercises with Elements of Dance Movement Therapy on
Anthropometric Parameters and Physical Fitness among Functionally Limited Older
Nursing Home Residents
authors:
- Natalia Wołoszyn
- Justyna Brożonowicz
- Joanna Grzegorczyk
- Justyna Leszczak
- Andrzej Kwolek
- Agnieszka Wiśniowska-Szurlej
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001087
doi: 10.3390/ijerph20053827
license: CC BY 4.0
---
# The Impact of Physical Exercises with Elements of Dance Movement Therapy on Anthropometric Parameters and Physical Fitness among Functionally Limited Older Nursing Home Residents
## Abstract
Changes in the composition of the body mass of functionally limited older patients may contribute to a decrease in functional fitness and the development of chronic diseases. This research aimed to assess the differences in anthropometric parameters and physical fitness of older patients, over the age of 65, in a 12-week clinical intervention study. Method: The study participants were nursing home inhabitants aged 65–85 who were functionally limited. Persons meeting the inclusion criteria were assigned to one of the three groups: Group 1–basic exercises/BE group ($$n = 56$$); Group 2—physical exercises with elements of dancing/PED group ($$n = 57$$); Group 3—control group/CO group ($$n = 56$$) routine care. The data were collected at the beginning of the study and at the 12-week mark. The outcome was observed for hand grip strength (HGS), arm curl test (ACT), Barthel Index (BI), Berg Balance Scale (BBS), triceps skin fold (TSF), waist-to-hip-ratio (WHR), and arm muscle area (AMA). Results: The study included 98 women and 71 men. The average age of the participants was 74.40 years. The analysis of the effects of the 12-week exercise program showed the greatest changes in HGS, ACT, and BI in the exercise groups, especially in the PED group compared to the BE group. Statistically significant differences in the examined parameters of the PED vs. BE vs. CO groups were demonstrated in favour of the exercising groups. In conclusion, a 12-week program of group physical exercises, both PED and BE, improves physical fitness indicators and anthropometric indicators.
## 1. Introduction
Changes in the composition of the body mass of older patients may contribute to a decrease in functional fitness and the development of chronic diseases. Scientific research confirms a strong relationship between a decrease in muscle strength, physical fitness, and the loss of lean body mass [1]. A decrease in skeletal muscle mass and function is seen in older patients and is associated with sarcopenia, which is a health concern [2]. One of the main risk factors for sarcopenia is a low level of physical activity along with a decline in muscle fibers that begins in middle age. The decrease in muscle mass and strength increases the risk of fractures, the quality of life decreases, and independent living becomes more difficult [2,3] With age, the distribution and function of adipose tissue reorganize, reaching a peak by the age of 70 and then beginning to decline. In the following decades of life, excess adipose tissue is deposited in the epicardium, bone marrow, liver, and muscles, which leads to the loss of lean body mass and organ dysfunction, and consequently to obesity and a decrease in the fitness of older patients [4,5].
During the aging process, there are changes in the composition of body mass, which cause a decrease in bone mineral density and muscle mass and an increase in fat mass [6,7]. In an aging society, one of the most significant metabolic diseases is osteoporosis, which leads to the weakening of bone microstructures, reducing bone mineral density (BMD), and increasing the risk of fractures [8,9]. As indicated by Tsutsumi et al., it is important to systematically assess anthropometric parameters in older patients, as their results are easy to obtain and correlate with the risk of mortality and the duration of hospitalization [10]. In addition, Colleluori and Villareal emphasized that it is important to conduct research that takes the impact of physical exercise on anthropometric indicators among functionally limited older patients into account [11]. Combining various interventions/exercises in old age gives the opportunity to improve functional fitness. The use of aerobic exercises can achieve improvements in peak oxygen consumption as well as have beneficial effects on blood pressure, lipids, glucose tolerance, bone density, depression, and quality of life [12]. A properly designed resistance training program for older patients should include an individualized periodic approach involving the performance of 2–3 sets of 1–2 multi-joint exercises per major muscle group, reaching an intensity of 70–$85\%$ of 1-repetition maximum (1RM) 2–3 times per week, and include strength training performed at higher speeds in moderate-intensity concentric movements (i.e., 40–$60\%$ 1RM). Resistance training programs for older patients should follow the principles of individualization, periodization, and progression. A properly designed resistance training program can counteract age-related changes such as contractile function, atrophy, and morphology of aging human skeletal muscles. In addition, it can increase strength, muscle power, and neuromuscular functioning in older patients [13]. Exercise of the hypertrophy type is the main modality that aims to increase skeletal muscle size [14]. However, in some cases, even 3-month resistance training was not effective in achieving a practically significant nor statistically significant increase in lean mass compared to sham-intervention in healthy older participants [15]. According to the World Health Organization (WHO), older adults should engage in at least 150–300 min of moderate-intensity aerobic physical activity (PA) per week, or at least 75–150 min of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate-intensity and vigorous-intensity aerobic physical activity per week. Two days a week, they should do moderate or higher-intensity muscle-strengthening exercises involving all of the major muscle groups, as they provide additional health benefits.
To increase functional capacity and prevent falls, as part of their weekly physical activity, older adults should engage in multi-component physical activity 3 or more days a week that emphasizes functional balance and moderate-to-high-intensity strength training [16]. Low functional fitness is a common and complex geriatric condition characterized by the failure of multiple systems in the body along with reduced capacity for recovery. The functionally limited older patients are particularly exposed to the risk of adverse health effects, deterioration of cognitive functioning, physical disability, and, as a consequence, hospitalization and institutionalization [17]. Despite the fact that the decline in psychophysical health is inextricably linked to the aging process, regular physical activity contributes to slowing down this process and improving functional fitness in everyday life [18]. In the literature on the subject, there is a great deal of scientific evidence confirming the beneficial effects of physical activity (PA) on the body in old age. Pepera et al. studied forty older patients, long-term care residents, who were assigned to two groups: intervention (IG) and control (CO). The IG group performed a twice-weekly, two-month, multi-component exercise program consisting of exercises for functional mobility, balance, muscle strength, and flexibility; the CO did not perform any movement intervention. During the two-month exercise program, systolic blood pressure and function improved in the older patients and residents of long-term care facilities in the group IG [19]. Similarly, Thomas et al., reviewing randomized studies among people over 65 years of age, note a similar observation. They report that balance can be improved by various means of exercise training and that promoting physical activity in older people is essential [20].
Despite the widely publicized benefits of PA, a significant proportion of older patients do not meet the minimum and recommended PA levels. Therefore, effective interventions are needed to increase physical activity in older patients. One such form is the introduction of dance elements into standard exercise programs. According to scientific reports, this action combines the benefits of physical activity with improving mood, thereby affecting the quality of life [21]. Elements of choreotherapy, such as music and movement exercises or movement improvisations, effectively improve balance and functional fitness and are conducive to social integration. Group exercises with elements of dance alleviate the feeling of social isolation and give a sense of belonging to a group [22]. In addition, dance, regardless of style, can significantly improve muscle strength, endurance, balance, and other aspects of functional fitness in older patients [23]. Any style of dance can induce positive functional adaptations in older people, especially in terms of balance. A result of dancing can also be the improvement of metabolism. Dance could be a potential movement intervention to promote health benefits for aging individuals [24].
Existing research does not provide sufficient exercise guidelines for nursing home residents to improve existing health resources and prevent or delay the loss of physical function in functionally limited older patients [25].
In their systematic review of the scientific literature, Fernández-Argüelles et al. noted the positive impact of factors such as balance, gait and dynamic mobility, strength, and physical capacity on the risk of falls; however, based on the evidence, the authors were unable to confirm that dance has significant benefits on these factors due to some unsatisfactory aspects of the research, such as methodological quality, low sample size, lack of homogeneity with respect to variables and measurement tools, and the existence of variability in the study design and type of dance [26]. Similarly, Rodrigues-Krause et al. claim that there are no randomized clinical trials (RCTs) evaluating metabolic and anthropometric outcomes related to dance interventions [24].
Despite the knowledge about the impact of physical activity on aging, there are few reports in the available literature on the influence of physical exercises with elements of dancing on physical fitness and anthropometric indicators. To our knowledge, this is the first study to focus on older adults and tailor interventions for people with functional limitations. In addition, in the assessment, we combine physical indicators and anthropometric parameters. Therefore, this research project aimed to assess the differences in anthropometric measurements and physical fitness of a sample of Polish older patients over the age of 65 in a 12-week clinical intervention study.
## 2.1. Study Design and Sample Description
The study was designed in accordance with consort guidelines for reporting randomized controlled trials. The study was conducted in 5 randomly selected nursing homes in Podkarpacie (south-eastern Poland). The sample size was estimated from a priori power analysis to detect statistically significant effects of exercise [27]. The sample size was chosen according to the Cohen method, using standard assumptions: 0.05 for significance level, 0.8 for the power of the test, and 0.5 for effect size which accounts, according to Cohen, for medium effect size. Sample size calculation for the main outcome measure was based on changes in HGS scores.
The inclusion criteria for participation in the study are as follows: age between 65 and 85 years, cognitive functioning status enabling participants to execute instructions and answer questions, moderate limitations in functional fitness (Barthel score in the range of 21–75 points), no serious diseases that would make it impossible to participate in the study, giving informed consent to participate in the study, and living in a nursing home. The exclusion criteria included the inability to perform active movements with the upper and lower limbs and the presence of unstable internal diseases (recent myocardial infarction, uncontrolled arrhythmias, acute congestive heart failure, unstable angina, acute pulmonary embolism or pulmonary infarction, uncontrolled hypertension, uncontrolled diabetes mellitus, acute systemic infections accompanied by fever, body aches, or swollen lymph nodes) [28,29,30].
People who met the criteria for participation in the study were randomly assigned to one of three groups: basic exercises (BE)—56 people, physical exercises with elements of dancing (PED)—57 people, and control group (CO)—56 people. Randomization was carried out by implementing the stratified method with the use of the statistical package R 3.2.2. Three-in-one blocks were randomized, which made it possible to obtain an even distribution of older patients in the studied groups. The order of randomization was determined using a computerized schedule of random numbers. An independent biostatistician implemented randomization, hid the block size from the executive module, and used randomly mixed block sizes. The person conducting the randomization was not involved in the recruitment process or assessment and did not carry out interventions in any of the groups. Of the 169 randomized participants, 156 ($92.3\%$) completed the study after 12 weeks. The completion rate of the study was as follows: BE group, 52 people; PED group, 54 people; CO group, 50 people. The most common reasons for withdrawal were related to loss of interest in the study and change of place of residence. No adverse events related to participation in the exercise programs were reported. The stages of including participants in the study and assigning them to particular groups are presented in the CONSORT flow diagram (Figure 1).
The study was approved by the Bioethics Committee of the University of Rzeszów (No. $\frac{9}{11}$/2017). In accordance with the Declaration of Helsinki, all participants were informed about the purpose and procedure of the study and gave their informed consent to participate in the study. In the absence of informed consent, the participant was excluded from the further research procedure.
The study protocol has been registered with the Sri Lanka Clinical Trials Registry (SLCTR/$\frac{2018}{014}$).
## 2.2. Intervention
In each of the groups, a different program of action was implemented.
Group 1 Basic exercises (BE)—exercises were performed in a sitting position, in groups of 6–8 people. The exercises were carried out without background music and without the use of accessories. In accordance with the recommendations of the World Health Organization (WHO) regarding physical activity in older patients, the program included exercises based on aerobic exercise, strengthening exercises for the upper and lower limbs, trunk exercises, as well as balance exercises [16]. Each session consisted of a warm-up (10 min), a main part (20 min), and a final part (5 min). Each exercise was preceded by instructions. As part of the warm-up, simple upper and lower limb exercises were performed, leading to faster breathing and heart rate and preparing muscles and joints for the main exercises (e.g., marching in place, circulation in the shoulder joints, turning the arm, flexion and extension in the elbow joints, and opening and clenching the fist). Aerobic warm-up exercises were supplemented with breathing exercises. In the main part, exercises were performed to strengthen the upper and lower limbs (e.g., lifting the torso with the hands on the seat, slowly moving to a standing position, or tilting the torso forward and backward in a sitting position) and for balance and coordination (e.g., alternating weight on the right and left buttock and alternate lifting of the opposite arm and leg). A total of 6–12 repetitions of each exercise were performed in two series (the first week started with 6 repetitions, and in each subsequent week of implementation, the program was performed with one more repetition of each exercise). The pace of exercise was adjusted to the abilities and well-being of those exercising and did not exceed 11–13 points on the Borg scale of exercise intensity. Between the series of each exercise, there was a 30 s break in which breathing exercises were performed. In the final part of each session, calming and relaxing exercises were performed (e.g., calm breaths combined with exercises maintaining the range of motion in the joints—slow bends and twists of the trunk) the purpose of which was to normalize the heart rhythm [31,32].
Group 2: Physical exercises with elements of dancing (PED)—in this group, exercises were carried out in a sitting position. In the initial part of the classes, a warm-up (5 min) was carried out based on simple movements of the upper and lower limbs and simple movement improvisations to the rhythm of music. The exercises began with rhythmic hand clapping and walking in place to the rhythm of the music. Then, those exercising imitated the physiotherapist conducting the classes and performed movements that simulated, for example, driving a car, flying an airplane, rustling trees, and picking fruit. The main part (20 min) was based on simple movements and dances performed in a sitting position. The movements to the music were mainly based on hand clapping, waving the hands, and stamping the feet. During the sessions, dance movements to the rhythm of music were implemented, e.g., cha-cha, Zumba, jive, macarena, and Hawaiian dance. Each musical arrangement consisted of 6–8 movement sequences appropriate to the type of music. Before starting the exercises, the physiotherapist taught the participants the appropriate sequence of movements without the use of music. During the exercises, equipment was also used: canes, TheraBands, and balls. Each program was structured in such a way as to include elements of strengthening (e.g., stretching a TheraBand or squeezing a ball) and balancing exercises (e.g., tilting the trunk forward and diagonally with raised arms). In order to keep the exercisers interested, the music and exercise routines were changed weekly for the first 6 weeks, with the sequence repeated in succession for the next 6 weeks. The nature of the music and its tempo were selected depending on the current perceptual and motor abilities of older patients exercising. After each session, the subjective intensity of exercise in the study group was assessed using the Borg scale. The intensity of the exercises was planned so as not to exceed 11–13 points on the Borg scale. At the end of each session, there was a short calming part (5 min) in which breathing exercises and simple stretching exercises were performed to calm relaxing music [33].
In both groups 1 and 2, the program was carried out twice a week for 30 min in the morning over a 12-week period. Before the start of each exercise session, the participants were asked about their current well-being, and each participant had their blood pressure and pulse measured in order to qualify for exercise on a given day. The classes were conducted by a physiotherapist trained in the research procedure and experienced in working with older patients.
Group 3: Control group (CO)—in this group, no therapeutic program was implemented. People assigned to this group followed their standard daily schedule.
## 2.3. Outcome Measures
All data were collected before the intervention and after the 12-week exercise program.
## 2.3.1. Socio-Demographic Data, Data on Health, and Nutrition
In order to characterize the study group, socio-demographic data such as age, sex, marital status, education level, and time of stay in a nursing home were collected. Data on coexisting diseases in the examined participants were obtained on the basis of the analysis of the medical records of the nursing home residents. The height and weight of the participants were also measured, and then the BMI (kg/m2) was determined. The BMI value was classified in accordance with the standards proposed by the Committee of Diet and Health, according to which the normal body weight for people over 65 corresponds to a BMI in the range of 24–29 kg/m2, underweight—below 24 kg/m2, and overweight—above 29 kg/m2 [34]. On the basis of interviews with the participants, data on the subjective and objective assessment of their nutritional status (the number of meals, the amount of fluids, and the intake of particular types of products) were also collected. The survey form contained questions about chronic (long-term) diseases, e.g., cardiovascular diseases, including chronic stroke, myocardial infarction, controlled hypertension, and atherosclerosis under control.
## 2.3.2. Muscle Strength (Hand Grip Strength HGS)
The measurement was carried out using a hand dynamometer (JAMAR PLUS + Digital Hand Dynamometer, Patterson Medical). The measurement was performed in a sitting position on a chair without armrests, with the elbow joint of the dominant arm bent at 90 degrees, the forearm in a neutral position, and the wrist extended between 0 and 30 degrees. The participant was asked to clench their hand as much as possible and hold it for 6 s. The procedure was repeated three times with a one-minute rest between trials. The result was given in kilograms and as the average of the three measurements [35].
## 2.3.3. Manual Endurance of the Upper Limb (Arm Curl Test ACT)
The measurement was carried out in a sitting position on a chair without armrests, with feet flat on the floor. The measurement was performed with a weight of 2 kg for women and 3.5 kg for men. The participant’s task was to flex the forearm with the dominant hand in supination and return to the starting position (extension of the forearm in pronation). The result of the test was the number of repetitions in 30 s [36].
## 2.3.4. Functional Assessment (Index Barthel IB)
The scale assesses the participant’s degree of independence in basic daily activities. It includes 10 activities such as dressing, bathing, mobility, eating, using the toilet, moving around, and controlling urine and stool excretion. Depending on the degree of independence in each of the assessed activities, the participant was assessed at the level of 0–15 points, with the higher score indicating greater independence. A total of 100 points can be obtained on the scale. On the basis of the total score obtained on the scale, the participant can be classified into one of three categories of need for care [37].
## 2.3.5. Body Balance Assessment (Berg Balance Scale BBS)
The scale assesses the participant’s static and dynamic balance in 14 trials. In each of the trials, the participant’s balance is assessed on a scale of 0–4, with a higher assigned point value indicating a better level of balance during the performance of a given trial. The results obtained in each trial are added together, and in total the participant may obtain 56 points [38].
## 2.3.6. Triceps Skinfold (TSF)
The measurement was carried out using calipers. In order to carry out the measurement, a point halfway between the posterior edge of the acromion process and the ulnar process of the left hand was determined. Then, at this point, the skin-fat fold was pulled vertically with the thumb and forefinger and its measurement was made by applying the arms of the calipers and tightening them until they were fully stabilized. The result was read to the nearest 1 mm [39].
## 2.3.7. The Waist-to-Hip Ratio (WHR)
The value was determined on the basis of the ratio of the waist circumference measured in centimeters, measured at the height of the last rib, to the maximum circumference of the hips. The normal values were 0.71–0.85 for women and 0.78–0.94 for men. Higher values were interpreted as abdominal obesity and lower values as gluteofemoral obesity [40].
## 2.3.8. Arm Muscle Area (AMA)
In order to determine the AMA value, the mid-arm circumference was measured. The distance between the ulnar process and the shoulder projection was determined. The circumference of the arm was measured at a point halfway along that distance. Next, the arm muscle circumference was determined according to the formula: arm muscle circumference = arm circumference-ח (triceps skin fold thickness). The AMA value was determined according to the formula: arm muscle area (AMA) = arm muscle circumference2 /ח [41,42].
## 2.4. Statistical Analysis
Statistical analysis of the collected material was performed using the Statistica 13.3 package. The database and the graphical elaboration of the results were prepared in Microsoft Excel and Microsoft Word. Descriptive characteristics were presented as means and standard deviations, or numbers and percentages.
The model of elaboration of results for dependent trials (to examine the assessment of effects before and after therapy) and for independent trials (between groups) was adopted. To examine the effects before and after therapy (measuring scale of the dependent variable of a quantitative nature), the t-Student test for dependent samples was used, and in case of lack of normal distribution of differences in dependent variables, the Wilcoxon non-parametric test was used. Differences between the groups for differences in the studied parameters were assessed using t-Student tests for independent samples (independent variable on two levels) or one-way ANOVA (independent variable on at least three levels). When the assumption of normality of distribution (verified by the Shapiro–Wilk test) of the dependent variables was not met, the following non-parametric tests were used: Mann–Whitney U or Kruskal–Wallis. Statistically significant results for differences in at least three groups were supplemented with post-hoc Tukey tests or post-hoc multiple comparisons of mean ranks for all samples. Pearson’s chi-square tests of independence were used in the analysis if the data represented a non-ranked category (measuring scale of nominal variables).
Table 1 description: Sociodemographic and clinical data of the participants were taken into account and differences between the three groups (BE; PED; CO) were examined. Quantitative data meeting the criteria of normality of distribution within groups (tested by Shapiro–Wilk test) and homogeneity of variance (tested by Levene’s test) were qualified to use the one-way ANOVA variance analysis test. If one of the above assumptions was violated, the Kruskal–Wallis test was used. Significantly statistical results for the one-way analysis of variance were completed using the Tukey post-hoc test, and the Kruskal–Wallis post-hoc test was used for the Kruskal–Wallis test. The significance of differences between the two nominal variables was calculated using Pearson’s Chi square test.
Table 2 description: A statistical model was adopted in which the effect of intervention between Measure I and Measure II was tested. For the normality of the distributions of differences (tested by the Shapiro–Wilk test), the Student’s t-test for dependent samples was used. If the assumption of the normality of the distribution of differences was violated, the non-parametric Wilcoxon test was used.
Table 3 and Table 4 descriptions: Firstly, the difference between the 1st and 2nd survey was calculated (2nd measurement minus 1st measurement). It was then examined whether these differences from the two measurements differed significantly between the groups. It was decided that the study groups be treated individually by pairing them (PED vs. BE; PED vs. CO; BE vs. CO); therefore, the Student’s t-test for independent samples was used if the assumption of normality of distribution (checked with the Shapiro–Wilk test) and homogeneity of variance (checked with the Levene’s test) was met. If the normality of the distribution was not observed in the study groups, the decision was made to use the non-parametric Mann–Whitney test.
## 3. Results
The study included 98 women ($57.99\%$) and 71 ($42.01\%$) men. The average age of the participants was 74.40 years (SD = 7.45). For the entire study group the average number of daily consumed meals was 3.11 per person (SD = 0.79). According to the subjective assessment of nutritional status, 77 people ($45.56\%$) believed that they were malnourished, and 54 participants ($31.95\%$) did not report any disorders in their eating. In the study group, 141 people ($83.40\%$) were right-handed, and 28 participants ($16.60\%$) indicated the left hand as dominant. The mean grip strength for the right hand was 12.81 kg (SD = 5.27) and 11.20 kg for the left hand (SD = 4.41). The average BBS score for all groups was 12.41 points (SD = 5.61), and for the BI scale 54.03 points (SD = 11.69).
Before starting the exercise programs, all study groups were comparable with each other in terms of socio-demographic characteristics (except education level), functional fitness, including upper limb strength, endurance and balance, muscle mass index, and skin fold index. The participants from the CO group showed a lower level of education than the BE and PED groups (CO < BE, PED). Before the intervention, the BMI and body mass scores in the CO group were higher than in the BE group (CO < BE; CO < BE, PED, respectively). In terms of arm muscle area (AMA), the results were statistically significantly higher in the PED group than in the BE and CO groups (PED < BE, CO). However, before the start of the study, WHR results were significantly higher in the PED group compared to the BE group. The demographic data of the participants and the basic parameters are summarized in Table 1.
After 12 weeks of intervention, the measured parameters were compared with the baseline data. The analysis of the results showed that in the BE and PED group there was a moderately significant increase in BBS, ACT, HGS, AMA parameters and a decrease in TSF and BMI values. However, in the CO group there was a decrease in BBS, ACT, HGS and an increase in TSF and BMI. Detailed data are presented in Table 2.
## Mean Difference Scores for Each Group across Time
The analysis of the effects of the 12-week exercise program showed the greatest changes in HGS, ACT, BI and BMI in the exercise groups, especially in the PED group compared to the BE group. However, in the CO group, a decrease in the value of most of the parameters studied was observed. In the BE group, after 12 weeks of physical exercise, an improvement was demonstrated: in HGS R by 5.54 kg and in HGS L by 6.60 kg; in the ACT test by 2.56 repetitions; and in the BBS scale by an increase of 1.90 points. After the intervention, a decrease in body weight by an average of 2.50 kg was shown, which in BMI showed a decrease of 0.96 points, and an average decrease in WHR of −0.03. The greatest positive changes after 12 weeks of intervention were shown in the PED group: HGS R by an average of 6.54 kg and HGS L by 7.74. For the PED group, the number of repetitions in the ACT test increased by 3.63, and in the BBS scale the number of points increased by an average of 2.67. There was a decrease in body weight by an average of 3.50 kg and BMI by 1.51 points. However, an increase in the WHR index by 0.02 points and AMA area by 885.32 was found. In the CO group, after 12 weeks of observation, there was a decrease in HGS R and L, ACT, BI, BBS, and AMA, while an increase in anthropometric indicators such as body mass, BMI, triceps skin fold, and WHR was noted. The mean difference scores for each group after 12 weeks are shown in Table 3.
After 12 weeks of exercise and observation, statistically significant differences were found between the PED and BE groups in all parameters examined. After the intervention period, statistically significant differences were found between functional status and anthropometric indicators between the PED and CO groups. No statistically significant difference was found between the PED group and the CO group in WHR results. Statistically significant differences in the examined parameters evaluating functionally limited older patients in the BE vs. CO groups were demonstrated. The differences between the groups after 12 weeks from the start of the study are presented in Table 4.
## 4. Discussion
Lack of physical stimulation causes health disorders and functional impairment, which negatively affects functioning in everyday life and dependence on third parties. Declining health and physical limitations may make it difficult for some older patients to participate in exercise programs designed for people without mobility limitations [43]. In this study, changes in anthropometric parameters and physical fitness in a group of functionally limited older patients were assessed after 12 weeks of participation in a PA program or with no intervention. The obtained results indicate that the program, both in the form of general fitness exercises (basic exercises) and in the form of physical exercises with elements of dancing, improves the functional fitness and anthropometric parameters of older patients.
Our research used group exercises designed for functionally limited older patients living in nursing homes and demonstrated improvements in physical fitness. The results of a systematic review by Shakeel et al. support the effectiveness of group exercise. The authors propose the implementation of this type of program as an effective and economical way to provide nursing home residents with physical and social health benefits [44]. The results of a meta-analysis by Jansen et al. confirm the potential of group intervention programs to increase physical activity among residents of nursing homes. [ 45] In our study, we have shown that the addition of elements of dancing has a positive effect on the physical and functional fitness of functionally limited older patients. In addition, music during physical exercise can reduce the perceived effort and improve mood, as well as reduce anxiety and the symptoms of depression. Rucello et al. point out that music is an important tool to support the involvement of older people in physical exercise [46].
The results of our studies showed an improvement in muscle strength and manual endurance of the upper limb in the exercise groups and a decrease in HGS R and L and ACT in the CO group. Similar results were obtained by Chiu et al. In a randomized control study, the authors used their own program of exercises in a sitting position among 64 residents of long-term care homes. The program lasted 12 weeks and the exercises were carried out twice a week. Music was used to increase the motivation of the participants to exercise. The researchers also showed a statistically significant improvement in hand grip strength in the intervention group compared to the control group [47]. Chen et al. investigated the effectiveness of a group exercise program based on the use of elastic bands in resistance training. Elderly people in wheelchairs performed exercises three times a week for 40 min. The results showed high effectiveness of the intervention in terms of improving functional fitness, including improvement of hand grip strength, upper and lower body flexibility, and lung function [48]. Falconer et al. and Williams et al. showed that manual dexterity is strongly correlated with the degree of independence in older people. Limitation of manual dexterity also causes a limitation in the scope of instrumental activities of daily living and an increase in dependence on other people [49,50]. Similar results were obtained by Scherder et al., who showed that a reduction of manual dexterity is associated with increases in dependency in everyday activities, institutionalization, and mortality [51].
The analysis of the impact of a 3-month exercise program on everyday activities assessed using the Barthel scale showed improvement in the exercising groups (BE and PED group). There were no significant changes in the CO group after 12 weeks of observation. Similar results were obtained by Venturelli et al., whose study aimed to assess the impact of upper body training on the fitness of older women with mobility limitations. The results of the authors’ research showed a significant improvement in activities of daily living and no changes in the control group [52]. In a meta-analysis, Crocker et al. showed that physical rehabilitation has a positive effect on improving the performance of everyday activities of older patients living in nursing homes. The authors believe that exercise is an important factor in reducing disability among older people in long-term care [53]. The study by Machacova et al. assessed the impact of a 3-month intervention with elements of dance among older patients in a nursing home. The authors claim that relatively simple dance-based exercises can reduce physical decline [54]. Murrock et al. based their research on the theory of music, mood, and movement. The intervention was aimed at evaluating the impact of dance exercises on the functional fitness of older patients. The authors demonstrated improved functional fitness following a 12-week exercise program [55].
On the basis of our own research, there was an improvement in balance in the BE and PED groups. In the CO group, deterioration was observed in the area under study. On the basis of a meta-analysis, Chou et al. showed the positive effect of various types of physical interventions on improving balance among frail older patients. Despite the existence of multi-directional exercise programs, the most effective one for the elderly population has not been clearly identified [56]. Telenius et al. and Grönstedt et al. evaluated the impact of exercise programs on static and dynamic balance in elderly people living in nursing homes. They showed a statistically significant improvement in this parameter between the exercise group and the control group [57,58]. Eyigor et al. assessed the effect of folk dance on balance in a randomized study. After an 8-week dance program, the examined group of older women had improved their physical fitness and balance [59].
We showed that after 12 weeks of exercise, a decrease in body weight was observed in the study group, and, consequently, a decrease in BMI; greater changes were noted in the PED group (by −1.51 on average) than in the BE group (by −0.96 on average). Similar results were obtained by Valdés-Badilla et al. after 16 weeks of physical exercise among the entire study group [60]. Research published by Merchant et al. on BMI and waist circumference showed that a high BMI was associated with better functional and cognitive status [61]. On the other hand, LaCroix et al. showed that loss of physical fitness strongly correlates with very high BMI [62]. An earlier study by Colleluori et al. confirms that physical exercise is beneficial in reducing body weight while maintaining muscle mass. In addition, the authors suggest training protocols and recommend combining resistance and aerobic training with a proper diet [11]. In our research, we have shown that the lack of an adequate level of physical activity increases body weight and increases BMI by an average of 0.55 points.
Our study showed that in the BE group, the WHR index decreased, while it increased in the PED group, but this change was not statistically significant. As for the thickness of the triceps skin fold, the results were lower for the exercise groups and slightly higher for the CO group. Ruiz-Montero et al. showed that after 24 weeks of Pilates exercises and aerobic exercise, there was a statistically significant improvement in anthropometric parameters. However, the authors showed that in the case of skin fold, some participants had higher scores, while others showed a decrease in their scores. In addition, the authors showed that the waist circumference of the participants did not decrease and the WHR index was higher after the examination [1]. In their study, Kirton et al. did not observe significant differences after 12 weeks of physical exercise intervention in the WHR index between people in a group with individualized exercises and people exercising in a group. The authors suggest that individual exercise in older people with a low activity index is more beneficial in improving cardio-respiratory fitness than in reducing body weight [63]. Rugbeer et al. assessed the effect of group exercise among older patients on the anthropometric profile, demonstrating a decrease in the thickness of the skin fold in the group that exercised three times a week. The authors suggest that exercising three times a week could therefore protect against excessive body fat, which reduces the risk of cardiovascular disease in older patients in long-term care facilities [64].
Our research showed a significant increase in the AMA index in the PED group compared to the BE group and a decrease in the CO group. The decrease in the value of the variables evaluating muscle mass in older age groups is alarming because the loss of muscle mass affects the limitation of functional fitness in everyday life. Muscle strength and muscle mass are prognostic measurements of independence and mobility in older patients [65]. Additionally, Sigh et al. showed a relationship between anthropometric parameters, including the AMA index, and depressive symptoms among older patients. The authors also showed that AMA is a better indicator of health monitoring compared to the traditionally used BMI and WHR indicators [66].
Despite the extensive literature on the impact of physical activity on older patients’ physical fitness and anthropometric measurements, there are few reports focusing on functionally limited older patients. Older people, especially those with physical limitations living in nursing homes, need more support and motivation to take up physical exercise. Physical activity of older patients should be comprehensive, including moderate-intensity aerobic exercise, strengthening exercises, and exercises that improve balance. Both physical exercises with elements of dancing and basic exercise are in line with the above recommendations and are a valuable form of exercise for functionally limited older patients. In addition, exercising with music increases motivation and improves well-being [67].
One of the biggest problems related to the aging of society and low awareness of the need for physical activity in older patients is the progressive limitation of fitness in everyday activities, which in turn increases the degree of dependence on third parties. Our own research shows that older patients taking up physical exercise contributes to the extension of the period of independence in everyday activities, which is reflected in an improvement of the quality and satisfaction of life in older patients. There is a need to look for effective and inexpensive solutions to improve the functional fitness and quality of life of older patients. The results of these studies confirm the need for systematic rehabilitation in order to avoid functional and psychosocial disability. The practical aspect of the above research is the identification of simple and inexpensive exercise programs for older patients with reduced mobility.
This study had some limitations. Firstly, the groups were not initially homogeneous in terms of all parameters tested. Secondly, the authors did not carry out an assessment after 36 weeks. When designing further studies, sampling points will be scheduled at 6- and 12-month marks in order to assess long-term effects.
## 5. Conclusions
In conclusion, a 12-week program of group physical exercises, both physical exercises with elements of dancing and basic exercise, improve physical fitness indicators and anthropometric indicators.
Functionally limited older patients living in nursing homes could participate in group exercise programs tailored to their individual needs and abilities. In addition, building on this multi-component strength, balance, and endurance program, further research will help formulate important recommendations for action and guidance for promoting the health of nursing home residents.
In addition, the results of our study contributed to the introduction of systematic dance therapy using the described intervention in nursing homes in the Podkarpacie region (south-eastern Poland). This is the first step in developing proven intervention procedures.
## References
1. Ruiz-Montero P.J., Castillo-Rodriguez A., Mikalački M., Nebojsa C., Korovljev D.. **24–weeks Pilates–aerobic and educative training to improve body fat mass in elderly Serbian women**. *Clin. Interv. Aging* (2014) **9** 243-248. DOI: 10.2147/CIA.S52077
2. Papadopoulou S.K.. **Sarcopenia. A Contemporary Health Problem among Older Adult Populations**. *Nutrients* (2020) **5**. DOI: 10.3390/nu12051293
3. Faulkner J.A., Larkin L.M., Claflin D.R., Brooks S.V.. **Age-related changes in the structure and function of skeletal muscles**. *Clin. Exp. Pharmacol. Physiol.* (2007) **34** 1091-1096. DOI: 10.1111/j.1440-1681.2007.04752.x
4. Cartwright M.J., Tchkonia T., Kirkland J.L.. **Aging in adipocytes: Potential impact of inherent, depot–specific mechanisms**. *Exp. Gerontol.* (2007) **42** 463-471. DOI: 10.1016/j.exger.2007.03.003
5. Merchant R.A., Seetharaman S., Au L., Wong M.W.K., Wong B.L.L., Tan L.F., Chen M.Z., Ng S.E., Soong J.T.Y., Hui R.J.Y.. **Relationship of Fat Mass Index and Fat Free Mass Index With Body Mass Index and Association With Function, Cognition and Sarcopenia in Pre–Frail Older Adults**. *Front. Endocrinol.* (2021) **12** 765415. DOI: 10.3389/fendo.2021.765415
6. Villa-Forte A.. **Effects of Aging on the Musculoskeletal System**. (2022)
7. Basu R., Basu A., Nair K.S.. **Muscle changes in aging**. *J. Nutr. Health Aging* (2002) **6** 336-341. PMID: 12474025
8. Kanis J.. *Assessment of Osteoporosis at the Primary Health Care Level* (2007)
9. Kim S.W., Park H.Y., Jung W.S., Lim K.. **Effects of Twenty-Four Weeks of Resistance Exercise Training on Body Composition, Bone Mineral Density, Functional Fitness and Isokinetic Muscle Strength in Obese Older Women: A Randomized Con-trolled Trial**. *Int. J. Environ. Res. Public Health* (2022) **21**. DOI: 10.3390/ijerph192114554
10. Tsutsumi R., Tsutsumi Y.M., Horikawa Y.T., Takehisa Y., Hosaka T., Harada N., Sakai T., Nakaya Y.. **Decline in anthropometric evaluation predicts a poor prognosis in geriatric patients**. *Asia Pac. J. Clin. Nutr.* (2012) **21** 44-51. PMID: 22374559
11. Colleluori G., Villareal D.T.. **Aging, obesity, sarcopenia and the effect of diet and exercise intervention**. *Exp. Gerontol.* (2021) **155** 111561. DOI: 10.1016/j.exger.2021.111561
12. Fleg J.L.. **Aerobic exercise in the elderly: A key to successful aging**. *Discov. Med.* (2012) **70** 223-228
13. Fragala M.S., Cadore E.L., Dorgo S., Izquierdo M., Kraemer W.J., Peterson M.D., Ryan E.D.. **Resistance Training for Older Adults: Position Statement From the National Strength and Conditioning Association**. *J. Strength Cond. Res.* (2019) **8** 2019-2052. DOI: 10.1519/JSC.0000000000003230
14. Krzysztofik M., Wilk M., Wojdała G., Gołaś A.. **Maximizing Muscle Hypertrophy: A Systematic Review of Advanced Resistance Training Techniques and Methods**. *Int. J. Environ. Res. Public Health* (2019) **24**. DOI: 10.3390/ijerph16244897
15. Kujawski S., Kujawska A., Kozakiewicz M., Jakovljevic D.G., Stankiewicz B., Newton J.L., Kędziora-Kornatowska K., Zalewski P.. **Effects of Sitting Callisthenic Balance and Resistance Exercise Programs on Cognitive Function in Older Participants**. *Int. J. Environ. Res. Public Health* (2022) **22**. DOI: 10.3390/ijerph192214925
16. 16.WHO Guidelines on Physical Activity and Sedentary Behaviour: At a GlanceWorld Health OrganizationGeneva, Switzerland2020Available online: https://apps.who.int/iris/bitstream/handle/10665/337001/9789240014886-eng.pdf(accessed on 6 September 2022). *WHO Guidelines on Physical Activity and Sedentary Behaviour: At a Glance* (2020)
17. Salminen M., Laine J., Vahlberg T., Viikari P., Wuorela M., Viitanen M., Viikari L.. **Factors associated with institutionalization among home–dwelling patients of Urgent Geriatric Outpatient Clinic: A 3–year follow–up study**. *Eur. Geriatr. Med.* (2020) **11** 745-751. DOI: 10.1007/s41999-020-00338-7
18. 18.
World Health Assembly
The Global Strategy and Action Plan on Ageing and Health 2016–2020: Towards a World in Which Everyone Can Live a Long and Healthy LifeWorld Health OrganizationGeneva, Switzerland2016. *The Global Strategy and Action Plan on Ageing and Health 2016–2020: Towards a World in Which Everyone Can Live a Long and Healthy Life* (2016)
19. Pepera G., Christina M., Katerina K., Argirios P., Varsamo A.. **Effects of multicomponent exercise training intervention on hemodynamic and physical function in older residents of long–term care facilities: A multicenter randomized clinical controlled trial**. *J. Bodyw. Mov. Ther.* (2021) **28** 231-237. DOI: 10.1016/j.jbmt.2021.07.009
20. Thomas E., Battaglia G., Patti A., Brusa J., Leonardi V., Palma A., Bellafiore M.. **Physical activity programs for balance and fall prevention in elderly: A systematic review**. *Medicine* (2019) **98** e16218. DOI: 10.1097/MD.0000000000016218
21. Karmarkar A.M., Dicianno B.E., Cooper R.. **Demographic profile of older adults using wheeled mobility devices**. *J. Aging Res.* (2011) **2011** 560358. DOI: 10.4061/2011/560358
22. Britten L., Addington C., Astill S.. **Dancing in time: Feasibility and acceptability of a contemporary dance programme to modify risk factors for falling in community dwelling older adults**. *BMC Geriatr.* (2017) **11**. DOI: 10.1186/s12877-017-0476-6
23. Hwang P.W., Braun K.L.. **The Effectiveness of Dance Interventions to Improve Older Adults’ Health: A Systematic Lit-erature Review**. *Altern Ther. Health Med.* (2015) **5** 64-70
24. Rodrigues-Krause J., Krause M., Reischak-Oliveira A.. **Dancing for Healthy Aging: Functional and Metabolic Perspec-tives**. *Altern. Ther. Health Med.* (2019) **1** 44-63
25. Cordes T., Bischoff L.L., Schoene D., Schott N., Voelcker–Rehage C., Meixner C., Appelles L.M., Bebenek M., Berwinkel A., Hildebrand C.. **A multicomponent exercise intervention to improve physical functioning, cognition and psychosocial well–being in elderly nursing home residents: A study protocol of a randomized controlled trial in the PROCARE**. *BMC Geriatr.* (2019) **19**. DOI: 10.1186/s12877-019-1386-6
26. Fernández-Argüelles E.L., Rodríguez-Mansilla J., Antunez L.E., Garrido-Ardila E.M., Muñoz R.P.. **Effects of dancing on the risk of falling related factors of healthy older adults: A systematic review**. *Arch. Gerontol Geriatr.* (2015) **1** 1-8. DOI: 10.1016/j.archger.2014.10.003
27. Faul F., Erdfelder E., Lang A.G., Buchner A.. **G*Power 3: A flexible sta-tistical power analysis program for the social, behavioral, and biomedical sciences**. *Behav. Res. Methods.* (2007) **39** 175-191. DOI: 10.3758/BF03193146
28. **American College of Sports Medicine position stand. Exercise and physical activity for older adults**. *Med. Sci. Sports Exerc.* (1998) **30** 992-1008. PMID: 9624662
29. Pollock M.L., Franklin B.A., Balady G.J., Chaitman B.L., Fleg J.L., Fletcher B., Limacher M., Piña I.L., Stein R.A., Williams M.. **AHA science advisory, Resistance exercise in individuals with and without cardiovascular disease: Benefits, rationale, safety, and prescription: An advisory from the Committee on exercise, Rehabilitation, and Prevention, Council on Clinical Cardiology**. *Circulation* (2000) **101** 828-833. DOI: 10.1161/01.CIR.101.7.828
30. Elsawy B., Higgins K.E.. **Physical activity guidelines for older adults**. *Am. Fam. Physician* (2010) **81** 55-59. PMID: 20052963
31. Anthony K., Robinson K., Logan P., Gordon A.L., Harwood R.H., Masud T.. **Chair–based exercises for frail older people: A systematic review**. *BioMed Res. Int.* (2013) **2013** 309506. DOI: 10.1155/2013/309506
32. Mulasso A., Roppolo M., Rainoldi A., Rabaglietti E.. **Effects of a Multicomponent Exercise Program on Prevalence and Severity of the Frailty Syndrome in a Sample of Italian Community–Dwelling Older Adults**. *Healthcare* (2022) **10**. DOI: 10.3390/healthcare10050911
33. Prebola A.. *Dance Therapy Action Plan: Improving Body Posture and Quality of Life in Older Patients* (2014)
34. 34.
National Research Council
Diet and Health: Implications for Reducing Chronic Disease RiskThe National Academies PressWashington, DC, USA1989. *Diet and Health: Implications for Reducing Chronic Disease Risk* (1989)
35. Leong D.P., Teo K.K., Rangarajan S., Kutty V.R., Lanas F., Hui C., Quanyong X., Zhenzhen Q., Jinhua T., Noorhassim I.. **Reference ranges of handgrip strength from 125,462 healthy adults in 21 countries: A prospective urban rural epidemiologic (PURE) study**. *J. Cachexia Sarcopenia Muscle* (2016) **7** 535-546. DOI: 10.1002/jcsm.12112
36. Różańska–Kirschke A., Kocur P., Wilk M., Dylewicz P.. **The Fullerton Fitness Test as an index of fitness in the elderly**. *Med. Rehab.* (2006) **10** 9-16
37. Stone S.P., Ali B., Auberleek I., Thompsell A., Young A.. **The Barthel index in clinical practice: Use on a rehabilitation ward for elderly people**. *J. R. Coll. Physicians Lond.* (1994) **28** 419-423. PMID: 7807430
38. Berg K., Wood–Dauphinee S.L., Williams J.I.. **The Balance Scale: Reliability assessment with elderly residents and patients with an acute stroke**. *Scand. J. Rehabil. Med. Suppl.* (1995) **27** 27-36
39. Ruiz L., Colley J.R., Hamilton P.J.. **Measurement of triceps skinfold thickness. An investigation of sources of variation**. *Br. J. Prev. Soc. Med.* (1971) **25** 165-167. DOI: 10.1136/jech.25.3.165
40. Lean M.E., Han T.S., Morrison C.E.. **Waist circumference as a measure for indicating need for weight managment**. *BMJ* (1995) **311** 158-161. DOI: 10.1136/bmj.311.6998.158
41. Burr M.L., Phillips K.M.. **Anthropometric norms in the elderly**. *Br. J. Nutr.* (1984) **51** 165-169. DOI: 10.1079/BJN19840020
42. Volpini M.M., Frangella V.S.. **Nutritional assessment of institutionalized elderly**. *Einstein* (2013) **11** 32-40. DOI: 10.1590/S1679-45082013000100007
43. Grandes G., García–Alvarez A., Ansorena M., Ortega Sánchez–Pinilla R., Torcal J., Soledad Arietaleanizbeaskoa M., Sánchez A.. **Any increment in physical activity reduces mortality risk of physically inactive patients: Prospective cohort study in primary care**. *Br. J. Gen. Pract.* (2022) **1** 0118
44. Shakeel S., Newhouse I., Malik A., Heckman G.. **Identifying Feasible Physical Activity Programs for Long–Term Care Homes in the Ontario Context**. *Can. Geriatr. J.* (2015) **18** 73-104. DOI: 10.5770/cgj.18.158
45. Jansen C.P., Claßen K., Wahl H.W., Hauer K.. **Effects of interventions on physical activity in nursing home residents**. *Eur. J. Ageing* (2015) **12** 261-271. DOI: 10.1007/s10433-015-0344-1
46. Ruscello B., D’Ottavio S., Padua E., Tonelli C., Pantanella L.. **The influence of music on exercise in a group of sedentary elderly women: An important tool to help the elderly to stay active**. *J. Sports Med. Phys. Fitness* (2014) **54** 536-544. PMID: 25034556
47. Chiu S.C., Yang R.S., Yang R.J., Chang S.F.. **Effects of resistance training on body composition and functional capacity among sarcopenic obese residents in long–term care facilities: A preliminary study**. *BMC Geriatr.* (2018) **18**. DOI: 10.1186/s12877-018-0714-6
48. Chen K.M., Li C.H., Chang Y.H., Huang H.T., Cheng Y.Y.. **An elastic band exercise program for older adults using wheelchairs in Taiwan nursing homes: A cluster randomized trial**. *Int. J. Nurs. Stud.* (2015) **52** 30-38. DOI: 10.1016/j.ijnurstu.2014.06.005
49. Falconer J., Hughes S.L., Naughton B.J., Singer R., Chang R.W., Sinacore J.M.. **Self report and performance–based hand function tests as correlates of dependency in the elderly**. *J. Am. Geriatr. Soc.* (1991) **39** 695-699. DOI: 10.1111/j.1532-5415.1991.tb03624.x
50. Williams M.E., Hadler N.M., Earp J.L.. **Manual abillt! as ri marker of dependency in geriatric women**. *J. Chronic Semin. Arthrltlh. Rhrum.* (1981) **I** 1-7
51. Scherder E., Dekker W., Eggermont L.. **Higher–level hand motor function in aging and (preclinical) dementia: Its relationship with (instrumental) activities of daily life––A mini–review**. *Gerontology* (2008) **54** 333-341. DOI: 10.1159/000168203
52. Venturelli M., Lanza M., Muti E., Schena F.. **Positive Effects of Physical Training in Activity of Daily Living–Dependent Older Adults**. *Exp. Aging Res.* (2010) **36** 190-205. DOI: 10.1080/03610731003613771
53. Crocker T., Forster A., Young J., Brown L., Ozer S., Smith J., Green J., Hardy J., Burns E., Glidewell E.. **Physical rehabilitation for older people in long–term care**. *Cochrane Database Syst. Rev.* (2013) **28** CD004294. DOI: 10.1002/14651858.CD004294.pub3
54. Machacova K., Vankova H., Volicer L., Veleta P., Holmerova I.. **Dance as Prevention of Late Life Functional Decline Among Nursing Home Residents**. *J. Appl. Gerontol.* (2017) **36** 1453-1470. DOI: 10.1177/0733464815602111
55. Murrock C.J., Graor C.H., Sues-Mitzel A.. **Effects of dance on upper extremity activities in underserved adults**. *J. Am. Assoc. Nurse Pract.* (2015) **27** 10. DOI: 10.1002/2327-6924.12232
56. Chou C.H., Hwang C.L., Wu Y.T.. **Effect of exercise on physical function, daily living activities, and quality of life in the frail older adults: A meta–analysis**. *Arch. Phys. Med. Rehabil.* (2012) **93** 237-244. DOI: 10.1016/j.apmr.2011.08.042
57. Telenius E.W., Engedal K., Bergland A.. **Long–term effects of a 12 weeks high–intensity functional exercise program on physical function and mental health in nursing home residents with dementia: A single blinded randomized controlled trial**. *BMC Geriatr.* (2015) **15**. DOI: 10.1186/s12877-015-0151-8
58. Grönstedt H., Frändin K., Bergland A., Helbostad J.L., Granbo R., Puggaard L., Andresen M., Hellström K.. **Effects of individually tailored physical and daily activities in nursing home residents on activities of daily living, physical performance and physical activity level: A randomized controlled trial**. *Gerontology* (2013) **59** 220-229. DOI: 10.1159/000345416
59. Eyigor S., Karapolat H., Durmaz B., Ibisoglu U., Cakir S.. **A randomized controlled trial of Turkish folklore dance on the physical performance, balance, depression and quality of life in older women**. *Arch. Gerontol. Geriatr.* (2009) **48** 84-88. DOI: 10.1016/j.archger.2007.10.008
60. Valdés-Badilla P., Guzmán-Muñoz E., Ramírez-Campillo R., Godoy-Cumillaf A., Concha-Cisternas Y., Ortega-Spuler J., Herrera-Valenzuela T., Duran-Agüero S., Vargas-Vitoria R., Henrique Magnani-Branco B.. **Changes in anthropometric parameters and physical fitness in older adults after participating in a 16–week physical activity program**. *Rev. Fac. Med.* (2020) **68** 375-382. DOI: 10.15446/revfacmed.v68n3.75817
61. Merchant R.A., Wong M.W.K., Lim J.Y., Morley J.E.. **Association of Central Obesity and High Body Mass Index With Function and Cognition in Older Adults**. *Endocr Connect.* (2021) **10** 909-917. DOI: 10.1530/EC-21-0223
62. LaCroix A.Z., Guralnik J.M., Berkman L.F., Wallace R.B., Satterfield S.. **Maintaining mobility in late life. II. Smoking, alcohol consumption, physical activity, and body mass index**. *Am. J. Epidemiol.* (1993) **137** 858-869. DOI: 10.1093/oxfordjournals.aje.a116747
63. Kirton M.J., Burnley M.T., Ramos J.S., Weatherwax R., Dalleck L.C.. **The Effects of Standardised versus Individualised Aerobic Exercise Prescription on Fitness–Fatness Index in Sedentary Adults: A Randomised Controlled Trial**. *J. Sports Sci. Med.* (2022) **21** 347-355. DOI: 10.52082/jssm.2022.347
64. Rugbeer N., Ramklass S.S., Van Heerden H.J., Mckune A.J.. **The effect of group exercise frequency on anthropometric profile of apparently healthy elderly permanently residing in institutionalised nursing homes**. *Afr. J. Phys. Health Educ. Recreat. Danc.* (2016) **22** 32
65. Menezes T.N., Marucci M.. **Trends in body fat and muscle mass among elderly individuals in Fortaleza, Ceará State, Brazil**. *Cad. Saude Publica* (2007) **23** 2887-2895. DOI: 10.1590/S0102-311X2007001200010
66. Singh K., Singh S.P., Kaur G., Bose K.. **Association of body mass index and upper arm body composition with depressive symptoms in old age home and family based elderly**. *Homo* (2019) **70** 155-162. DOI: 10.1127/homo/2019/1054
67. Holmerová I., Machácová K., Vanková H., Veleta P., Jurasková B., Hrnciariková D., Volicer L., Andel R.. **Effect of the Exercise Dance for Seniors (EXDASE) program on lower–body functioning among institutionalized older adults**. *J. Aging Health* (2010) **22** 106-119. DOI: 10.1177/0898264309351738
|
---
title: 'Cost-Effectiveness of Prolonged Physical Activity on Prescription in Previously
Non-Complying Patients: Impact of Physical Activity Mediators'
authors:
- Linda Ryen
- Stefan Lundqvist
- Åsa Cider
- Mats Börjesson
- Maria E. H. Larsson
- Lars Hagberg
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001088
doi: 10.3390/ijerph20053801
license: CC BY 4.0
---
# Cost-Effectiveness of Prolonged Physical Activity on Prescription in Previously Non-Complying Patients: Impact of Physical Activity Mediators
## Abstract
In Sweden, physical activity on prescription (PAP) is used to support patients in increasing their levels of physical activity (PA). The role of healthcare professionals in supporting PA behavior change requires optimization in terms of knowledge, quality and organization. This study aims to evaluate the cost-effectiveness of support from a physiotherapist (PT) compared to continued PAP at a healthcare center (HCC) for patients who remained insufficiently active after 6-month PAP treatment at the HCC. The PT strategy was constituted by a higher follow-up frequency as well as by aerobic physical fitness tests. The analysis was based on an RCT with a three-year time horizon, including 190 patients aged 27–77 with metabolic risk factors. The cost per QALY for the PT strategy compared to the HCC strategy was USD 16,771 with a societal perspective (including individual PA expenses, production loss and time cost for exercise, as well as healthcare resource use) and USD 33,450 with a healthcare perspective (including only costs related to healthcare resource use). Assuming a willingness-to-pay of USD 57,000 for a QALY, the probability of cost-effectiveness for the PT strategy was 0.5 for the societal perspective and 0.6 for the healthcare perspective. Subgroup analyses on cost-effectiveness based on individual characteristics regarding enjoyment, expectations and confidence indicated potential in identifying cost-effective strategies based on mediating factors. However, this needs to be further explored. In conclusion, both PT and HCC interventions are similar from a cost-effectiveness perspective, indicating that both strategies are equally valuable in healthcare’s range of treatments.
## 1. Introduction
Globally, non-communicable diseases contribute to more than $70\%$ of total deaths [1], with cardiovascular diseases as the most common cause of death and metabolic risk factors considered the most prominent for the global burden of disease [1,2]. Regular physical activity (PA) provides a basis for positive health effects, including the prevention and treatment of a plurality of non-communicable diseases [3,4,5]. However, only a minority of all adults reaches the internationally recommended PA level, including 150 min of moderate-intensity PA or 75 min of vigorous-intensity PA per week [6,7]. The economic burden of physical inactivity to societies around the world is substantial [8]. Although several PA interventions are considered cost-effective, there are factors complicating the interpretation of results in published research, such as short time perspectives, the measurement of single treatment effects only, the variability of interventions in different population groups and a lack of cost estimates and savings in the cost-effectiveness analyses [9,10,11].
The physical activity on prescription (PAP) method used in Swedish healthcare by licensed healthcare professionals includes patient-centered counselling, individualized PA recommendations with a written prescription and individualized structured follow-ups. From the patient’s perspective, it seems crucial to individualize all parts of the PAP treatment in order to reinforce behavior changes towards increased PA [12,13,14]. A systematic review of Swedish PAP found a high level of evidence that physically inactive patients in the healthcare setting increased their PA levels [15,16].
Previous studies of PAP have evaluated the effects of shorter interventions, but do not provide guidance on how healthcare providers should act when patients do not reach sufficient levels of PA within this time frame. Hence, there is a need for further studies on long-term PAP interventions with longer follow-up periods [17,18]. Lifestyle change is usually an ongoing process that takes several years [19] and is affected by mediating factors associated with increased PA, such as enjoyment, outcome expectations and confidence in succeeding in changing the PA level [20,21,22]. These factors, defined as intervening causal variables, are important in creating a cause–effect pathway between an intervention and PA [23,24] and could optimally be part of the patient-centered work of tailoring interventions with different levels of support. Immediate rewards of PA (e.g., enjoyment) predict long-term adherence to the PA, whereas delayed rewards (e.g., health benefits) do not [25]. Therefore, it is likely that those who experience high enjoyment do not need any support at all to adhere to PA, and the lower the experienced enjoyment is, the greater is the need of support for sustainable PA. Outcome expectations represent the belief that a behavior change, e.g., increased PA, will lead to a certain outcome [26]. Although not consistent across studies, outcome expectations are considered important in predicting PA behavior [27,28]. Confidence, or self-efficacy expectations, is described as the confidence in one’s capability to change one’s behavior (e.g., PA) [29]. Having confidence in the readiness to change the PA level has been shown to be strongly associated with PA [30]. In this study, the mediating factors of enjoyment, outcome expectations and confidence were measured and have been described in detail previously [31,32]. As behavior changes take time [19], the question is how healthcare providers should act when the desired effect on PA levels is not achieved after a certain period of time, even though the patient is motivated to continue with PAP. As far as we know, there are no previous studies showing what healthcare should do when a lifestyle intervention has failed, which, according to the literature, is a common situation [17,33,34].
Economic evaluations of health interventions compare the costs and consequences of different strategies in order to provide decision-makers with information regarding choices affecting health and the use of resources. Traditionally, these analyses provide answers as to which method is most cost-effective for the average patient. However, recently updated international guidelines on the reporting of health economic evaluation results, known as the Consolidating Health Economic Evaluation Standards or CHEERS statement, include new recommendations on subgroup analyses, acknowledging that heterogeneity among patients means that strategies might be cost-effective for specific groups while not for others [35].
The main aim of this study is to evaluate the cost-effectiveness of a three-year prolonged program of enhanced PAP support delivered by a physiotherapist (PT) compared to continued (standard) PAP treatment at the healthcare center (HCC) for patients who remained insufficiently physically active after a prior six-month period of PAP treatment in a primary healthcare setting. A secondary aim was to explore whether enjoyment, expectations and confidence have potential in identifying cost-effective strategies on a subgroup level.
## 2.1. Study Design and Study Population
This cost–utility analysis was based on a randomized controlled trial (RCT) [31] of PAP treatment conducted with two intervention arms: one PT group and one HCC group. The time horizon was three years, and the analysis was performed from both a healthcare and a societal perspective. The study was approved by the Regional Ethical Review Board in Gothenburg, Sweden (ref: 529-09).
The present analysis forms part of a long-term follow-up study including 444 patients, which has been described previously [32,36]. Out of these patients, 190 patients did not achieve the internationally recommended minimum PA level after six months of PAP treatment, and were thus included in this study. These 190 patients were living in an urban area of Gothenburg, Sweden. The patients were 27–77 years of age, and had at least one metabolic risk factor (Table 1). Before inclusion in the study, they received standard PAP treatment for six months during 2010–2014 at one of 15 designated healthcare centers in Gothenburg. At the six-month follow-up, $56\%$ of the 190 patients had increased their PA level to some extent, but none of the included patients reached a sufficiently high PA level according to the internationally recommended minimum of 150 min/week. PA level was assessed via two questions regarding moderate- and vigorous-intensity PA during the past week. The patients agreed, orally and in writing, to participate in the RCT at the six-month follow-up, and were then randomized to either enhanced PAP treatment provided by a physiotherapist (PT group, $$n = 98$$) or continued ordinary PAP treatment delivered by nurses at the healthcare center (HCC group, $$n = 92$$). Randomization was based on block randomization, with an automated computer-based stratification of age, sex and BMI. Each patient was then contacted by the PT or HCC for further intervention. A more detailed description of the study population has been published previously [31].
## 2.2. Intervention
The PA and PAP interventions were offered to the patients according to the Physical Activity in the Prevention and Treatment of Disease (FYSS) handbook and the concept of the Swedish PAP model [37,38]. The intervention is described in detail elsewhere [31].
In the HCC group, PAP treatment was provided by nurses, whose area of expertise was nursing, who were trained on the health effects of PA and on treatment with PAP. The treatment included an individualized dialogue concerning PA, an individually dosed PA recommendation including a written prescription and an individually adjusted follow-up. The majority of the patients received continued PAP treatment at follow-ups 2–3 times a year during the intervention period.
The physiotherapists, whose area of expertise was work physiology, who provided treatment in the PT group, were also educated in PAP treatment. The PT intervention included the first two individually adapted parts described for the HCC group—that is, the individualized dialogue and the individual PA recommendation. The third part (the follow-up) differed between the two interventions, and in the PT group, this was arranged via a fixed follow-up schedule. This schedule contained a total of ten follow-up sessions during the three-year intervention: six during the first year of intervention, three during the second year and the final one at the three-year follow-up. The PT group also received five additional aerobic physical fitness tests during the intervention period, using an ergometer bicycle. The results from the physical fitness test formed the basis for a continuing dialogue with the patient concerning PA and for an individual dosage of PA regarding frequency, duration and intensity, recorded in a written prescription.
## 2.3. Measurements
The patients’ own costs, health-related quality of life (HRQOL), healthcare resource use and absence from work were measured at baseline and at the one-, two- and three-year follow-ups. Costs were estimated based on data from the follow-up questionnaires and administrative sources, as described below for each type of cost included. Unit prices used for estimation are summarized in Table 2. Costs were expressed as 2018 prices and a yearly discount rate of $3\%$ was applied. HRQOL was measured by the Swedish version of the Short Form 36 (SF-36 Standard Swedish Version 1.0) [39], transformed to quality-adjusted life years (QALYs) with SF-6D [40] and a UK tariff [41]. The UK tariff is the one commonly applied also to a Swedish population as there are no Swedish tariffs available. Details on the HRQOL in both groups at each time point are available in Supplemental Table S1.
Estimations of the cost-effectiveness are presented both from a healthcare perspective and from a societal perspective. The healthcare perspective includes the healthcare resource use in terms of intervention costs as well as costs for visits to primary care or hospital. For the societal perspective, of which healthcare resource use forms a part, individual expenses for PA, production loss due to sick leave and the time cost of exercise are added.
The amount of healthcare resource use in outpatient care was based on the self-reported number of visits to primary healthcare centers and hospitals stated in the yearly follow-up questionnaires. The number of visits to the physiotherapist in the PT group was reported from the administrative source in the study. The costs for all healthcare resource use were estimated based on unit costs differentiated by professions according to standard production prices negotiated for the trade of healthcare between county councils [42] and stated in 2018 prices.
Individual expenses related to PA, such as the costs of equipment or transportation, were reported by the patients in the yearly follow-ups. Patients stated their expenses for the last month, which were then multiplied by 12 to estimate yearly expenses. Since different individuals had entered the interventions in different years, all expenses were converted to 2018 prices using the Swedish consumer price index (CPI). Conversion to USD was based on the mean exchange rate on 1 January 2018 (1 USD = 8.78 SEK).
The cost of increased exercise time was estimated based on the experience of exercise time in comparison to the experience of leisure activity forgone and of household work [43]. The mean net salary in Sweden was used in the estimation [44]. Time spent on exercise was measured with the International Physical Activity Questionnaires [45]. When experience of PA time was rated higher than leisure activity forgone, there was no time cost. When experience of PA was rated lower than household work (cleaning), the time cost was set to the same as for half net salary. When experience of PA was rated in between the experience of household work and that of leisure activity forgone, the cost was set to the part of the half net work salary that corresponded to the relative position between experience of household work and leisure activity forgone.
Individuals were asked about the amount of sick leave from paid work in the yearly follow-ups, and their answers were then converted to full days of absence from work. Each full day of sick leave was then valued in accordance to the human capital approach, based on average wages including payroll taxes [42,46]. Production loss was only estimated for those who stated that they were absent from paid work.
## 2.4. Mediating Factors for Increased PA
Based on the positive relationship between PA and health, mediators for increased PA can also be seen as mediators for improved health. Enjoyment was measured using the Physical Activity Enjoyment Scale (PACES) [47], modified by Motl et al. [ 48], including 16 positively or negatively worded items rated on a 5-point Likert scale (1: Does not apply at all, 5: Truly applies). The negatively worded items were reverse-scored, and the responses were added to a score that ranged from 16 to 80.
Outcome expectations were assessed with the Outcome Expectations for Exercise-2 Scale (OEE-2) [49,50], including 13 positively or negatively worded items also rated on a 5-point Likert scale (1: Strongly agree, 2: Strongly disagree). The negative OEE items were reverse-scored, and the numerical ratings for each response were summarized and divided by the number of items where a highly valued outcome expectation from the patient gave a low total score.
Confidence (the readiness to change PA level) was measured via a 100-mm visual analogue scale (VAS) with the question “How confident are you about succeeding with changing PA level?” [ 51,52]. The VAS line was anchored at each end with words that described the minimum (not at all) and maximum (very) extremes. The mediating factors have been described in detail previously [32].
## 2.5. Health Economic Analysis Methods
In a cost–utility analysis, i.e., a cost-effectiveness analysis with QALY as the outcome measure, costs and effects for at least two alternative treatments are compared in terms of their costs and effects, resulting in an incremental cost-effectiveness ratio (ICER). Here, the costs included for each treatment were actual healthcare resource use, intervention costs, individual expenses related to PA, estimations of production loss due to work absence and individualized time cost for PA. The effect was measured in terms of changes in HRQOL expressed as quality-adjusted life years (QALYs).
The cost-effectiveness of the PT group compared to the HCC group is presented in terms of the incremental cost-effectiveness ratio (ICER), which represents the cost of achieving one additional QALY when applying PAP supported by PT compared with continued PAP by HCC. This is expressed by ICER=CostPT group−CostHCC groupQALYPT group−QALYHCC group To include the mediating factors in the analysis, patients were divided into two subgroups for each factor: the half who, at the start of the study, experienced the lowest versus the highest enjoyment, outcome expectations and confidence, respectively, according to the median value in each of the measurements. ICERs were then estimated comparing the costs and effects of the PT intervention compared to HCC treatment for all subgroups, respectively, following the below example for the patients reporting high enjoyment (≥58). The corresponding ICERs were then estimated for low enjoyment (<58), high confidence (≥55), low confidence (<55), high expectations (<2.08) and low expectations (≥2.08). ICER=CostPT high enjoyment−CostHCC high enjoymentQALYPT high enjoyment−QALYsHCC high enjoyment Bootstrapping was performed to acknowledge uncertainty in both costs and effects. This procedure takes the variance in the trial data into account by repeatedly drawing random samples (of the same size as the original) with replacements of costs and effects from the two groups. In this case, 1000 new samples were drawn. Using the net monetary benefit method, QALYs are then replaced by varying willingness-to-pay (WTP) levels for gaining a QALY, in this case ranging from USD 0 to USD 1,000,000. The results of this analysis are presented as cost-effectiveness acceptability curves (CEACs) (Figure 1) showing the probabilities for the PT treatment to be the most cost-effective choice at different WTP thresholds [53]. When the curve is above the 0.5 line (on the vertical axis), this means that PT is more likely than HCC to be the most cost-effective choice for the WTP on the horizontal axis.
All randomized participants were kept in their original study groups. For missing data needed to estimate costs and effects, stochastic imputation (by using a single dataset from multiple imputation) was performed based on the assumption that data were missing at random. All analysis was performed on the imputed dataset.
For the subgroup analyses on mediating factors, only complete cases on each mediator, respectively, were used, and patients with costs more than three standard deviations from the mean were excluded.
## 3. Results
At the three-year follow-up, $70\%$ of the patients in the PT group ($$n = 69$$) and $66\%$ of the patients in the HCC group ($$n = 61$$) attended. Of the patients attending the follow-up, $77\%$ ($p \leq 0.001$) of the PT group ($$n = 61$$) and $66.1\%$ ($p \leq 0.001$) of the HCC group ($$n = 59$$) had increased their PA level and $44.3\%$ vs. $35.6\%$ had achieved the public health recommendation of ≥150 min of moderate-intensity PA per week. There were no significant differences in PA level between the groups at the three-year follow-up ($$p \leq 0.55$$).
In the PT group, the incremental QALY gain per participant compared to the HCC group over three years was 0.016, see Table 3 below. From the societal perspective, the average cost per participant amounted to USD 13,488 in the PT group and USD 13,219 in the HCC group. From the healthcare perspective, the corresponding costs were USD 2685 in the PT group and USD 2150 in the HCC group. According to these costs and effects, the resulting ICER was USD 16,771 per additional QALY gained from the societal perspective and USD 33,450 per additional QALY gained from the healthcare perspective for the PT group compared to the HCC group.
Based on bootstrapping, taking the variability in the sample into consideration, cost-effectiveness acceptability curves (CEAC) were produced (Figure 1). In order for PT to be more likely than HCC to be cost-effective for the whole sample, the willingness to pay for a QALY needed to be higher than USD 57,000 when considering the societal perspective and higher than USD 22,000 when considering the healthcare perspective (Figure 1). This can be related to a willingness to pay of USD 57,000 for a QALY (corresponding to SEK 500,000, a threshold value commonly referred to in Sweden). Cost effectiveness scatterplots for the CEACs are available in Supplemental Figure S1.
In a second step, after splitting the sample into high/low on the mediating factors enjoyment, outcome expectations and confidence, CEACs were produced for these subgroups as well.
## 4.1. Main Outcomes
The main aim of this study was to evaluate the cost-effectiveness of a three-year prolonged program of enhanced PAP support delivered by a physiotherapist compared to continued (standard) PAP treatment at the healthcare center for patients who remained insufficiently physically active after a prior six-month period of PAP treatment in a primary healthcare setting. We have tried to shed light on what healthcare should do when a short-term lifestyle intervention is not enough for patients to achieve a desirable PA level. This study does not allow for the analysis of whether the patients “got their chance” and nothing more should be done, but we highlight whether it is most cost-effective to continue the intervention started (HCC group) or to enhance it (PT group). The cost per QALY for the PT strategy compared to the HCC strategy was USD 16,771 with a societal perspective and USD 33,450 with a healthcare perspective. Given a willingness to pay of USD 57,000 for a QALY, the probability of cost-effectiveness for the PT strategy compared to the HCC strategy was 0.5 with a societal perspective and 0.6 with a healthcare perspective.
There are no formally established thresholds, but cost-effectiveness ratios of 50,000–100,000 USD in the USA and 32,000–50,000 USD in the UK have often been accepted [54]. The World Health Organization argues that a threshold should simply be seen as an indication of poor, good or very good value for money [55]. There are no general recommendations for the threshold for the probability of cost-effectiveness for a change in routine care, but there are arguments that it should be close to 0.50 [56]. Consequently, it can be concluded that base-case results indicate that PT is cost-effective compared to HCC, but the uncertainty is large. Therefore, it was not possible to draw a definite conclusion about the most cost-effective PAP strategy in this study, as neither of the strategies was clearly superior to the other. The subgroup analyses showed that when enjoyment was high, the HCC intervention was most cost-effective, and when enjoyment was low, the PT intervention was the preferred choice. For confidence and expectations, the result was ambiguous, with small differences or different results depending on perspective. The number of participants in each subgroup was small and the result should be seen as an indicator of the possible impact of mediators. Nevertheless, the analysis showed that it may be worth considering the individual patient’s mediators for increased PA before agreeing with the patient on choosing the optimal intervention. It is probably advantageous to be able to offer either of the methods for increased individualization of the support, where the patient’s preferences are integrated as a vital part of evidence-based medicine [57].
In this study, the two interventions were quite similar in terms of cost-effectiveness. At the same time, the subgroup analyses indicated that they were not equal in effect and cost-effectiveness for everyone. In particular, the subgroup analysis based on enjoyment showed different cost-effectiveness for the respective interventions. Enjoyment has been shown to be the most important mediator for increased physical activity [58,59] and consequently the degree of enjoyment could affect whether extensive support is needed for the individual. In this study, individuals were randomized to either the PT or HCC group. This study suggests that as some individuals seem to benefit more from increased support, cost-effectiveness might be enhanced by screening for enjoyment together with other individual preferences. In clinical use, before the decision about which type of intervention to choose, screening of enjoyment could easily be performed with the PACES short version [60], with four questions instead of 16, as in this study.
As the subgroup analysis indicated, the HCC intervention was more cost-effective for patients with higher enjoyment. For patients with lower enjoyment, the PT intervention seemed to be more cost-effective. However, the PT intervention was problematic to implement, with relatively low compliance (with an attendance rate of 5.8 out of 11 follow-ups). Possible explanations could be a lack of time, transport problems or a lack of motivation. It is therefore important that only patients who have the need and motivation for this type of support are offered it. More knowledge is needed on whether the areas of expertise of different professional groups (nurses—nursing, physiotherapists—work physiology), in addition to training in PA and PAP, have significance for the patient’s opportunity to increase their PA.
## 4.2. Strengths and Weaknesses
It is not obvious how healthcare professionals should treat patients who have begun the process of changing their PA level but not succeeded in achieving the desired result in the short term. The patients in this study were motivated to continue the process of behavior change, and so we did not consider it ethically acceptable to randomize some of the patients to discontinued treatment—that is, to a “do nothing” group. This means that the analysis was limited to the way in which continued support should be provided, and not to the question of whether it is cost-effective to continue to provide support or not.
The study was carried out in a real-world setting, which makes the results generalizable. It also means that the study could not be carried out completely according to protocol. The inclusion of the patients in the study took much longer than planned, and the interventions were implemented on the basis of each patient’s condition and motivation. This resulted in an average of 5.8 PT counselling sessions instead of the planned 11 sessions, indicating that the patient group as a whole was not motivated to participate in such an extensive intervention.
As in all long-lasting interventions, there was an increase in missing data over time. This was handled by multiple imputation (but using one dataset instead of a large number of datasets, which is otherwise usual, since the calculation of the probability of cost-effectiveness requires a single outcome at the individual level) based on the assumption that data were missing at random. This could have led to biased results if those who were least physically active were those who had the most dropouts. However, there is no reason to believe that this would have differed between the two groups.
The groups differed in terms of the initial QALY level, which might be a concern since a lower level means greater potential for improvement. However, the difference was due to randomization and not to systematic factors.
As there are no specific Swedish tariffs available, preference weights for estimating QALYs are based on UK tariffs. This is standard procedure when using the SF6D in Sweden.
Many cost-effectiveness analyses of PA interventions have considered the time cost of exercise, but all of them were based on assumptions. As far as we know, this is the first attempt to base exercise costs on empirical data.
The study concerns two questions that are rarely answered in the research. Should healthcare continue to support a lifestyle intervention when it is not enough for patients to achieve a desirable PA level in the short term? The question cannot be answered based on our design, but we can see, after the first six-month period, a continued increase in PA, health and HRQOL among the patients at a relatively low cost [32]. This suggests that it can be cost-effective to prolong the intervention. The second question is whether healthcare should start with a small intervention and then increase in magnitude or invest in the most effective (and probably most expensive) intervention directly? This question also cannot be answered with certainty. However, $60\%$ of the original patient group needed, at least temporarily, only a small intervention [36]. The remaining $40\%$, who were not helped during the first 6-month period, had improved PA, health and HRQOL with prolonged intervention, suggesting that an individualized step-by-step increase may be the best use of resources in this case.
## 4.3. Strengths and Weaknesses in Relation to Other Research
As far as we know, this is the first study of the cost-effectiveness of prolonged PAP or other prolonged PA interventions. However, earlier studies indicate that it is effective and thus probably also cost-effective to support change over a long period of time [19]. It has been shown that behavior change processes and the establishment of new PA habits are individual and, in many cases, take a long time, often several years. To increase the understanding of the behavior change process and promote behavior change maintenance in PA, more frequent measurements of mediators and outcomes are needed at longer time points [61]. Marcus et al. [ 19] recommend that follow-up should take place for at least 2 years, preferably 5–10 years.
## 4.4. Future Research
Lifestyle interventions rarely succeed for all patients, and there is very little research into how healthcare providers should act in cases where they fail to support the patient´s behavior change. This study and health economic analysis is one of very few attempts to shed light on the matter. There is a great need for more research in this important area about prolonged physical activity support in previously non-complying patients. Our study represents only an initial contribution. We believe that the following questions are important to highlight in the continued research in effectiveness as well as cost-effectiveness analysis:[1]How long and to what extent should prolonged support be provided to non-complying patients?[2]How should the prolonged support be organized?[3]Are there individual factors in addition to enjoyment that can be the basis for individualizing the prolonged support?
## 5. Conclusions
Both PT and HCC interventions are quite similar from a cost-effectiveness perspective, indicating that both PAP strategies seem to be equally valuable to have in healthcare’s range of treatments. Individual preconditions for being physically active vary and so does the need, concerning time and magnitude, for professional support.
## References
1. Roth G.A., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A.. **Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018.0) **392** 1736-1788. DOI: 10.1016/S0140-6736(18)32203-7
2. Stanaway J.D., Afshin A., Gakidou E., Lim S.S., Abate D., Abate K.H., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F.. **Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018.0) **392** 1923-1994. PMID: 30496105
3. 3.
Yrkesföreningar för Fysisk Aktivitet (YFA)
Fysisk Aktivitet i Sjukdomsprevention och Sjukdomsbehandling, FYSS 2021Läkartidningen Förlag ABStockholm, Sweden2021978-91-985098-2-3. *Fysisk Aktivitet i Sjukdomsprevention och Sjukdomsbehandling, FYSS 2021* (2021.0)
4. 4.
Physical Activity Guidelines Advisory Committee
2018 Physical Activity Guidelines Advisory Committee Scientific ReportPhysical Activity Guidelines Advisory CommitteeWashington, DC, USA2018. *2018 Physical Activity Guidelines Advisory Committee Scientific Report* (2018.0)
5. 5.
WHO
Global Action Plan on Physical Activity 2018–2030: More Active People for a Healthier WorldWorld Health OrganizationGeneva, Switzerland2019. *Global Action Plan on Physical Activity 2018–2030: More Active People for a Healthier World* (2019.0)
6. Lee I.-M., Shiroma E.J., Lobelo F., Puska P., Blair S.N., Katzmarzyk P.T.. **Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy**. *Lancet* (2012.0) **380** 219-229. DOI: 10.1016/S0140-6736(12)61031-9
7. 7.
WHO
Global Recommendations on Physical Activity for HealthWorld Health OrganizationGeneva, Switzerland2010. *Global Recommendations on Physical Activity for Health* (2010.0)
8. Ding D., Lawson K.D., Kolbe-Alexander T.L., Finkelstein E.A., Katzmarzyk P.T., Van Mechelen W., Pratt M.. **The economic burden of physical inactivity: A global analysis of major non-communicable diseases**. *Lancet* (2016.0) **388** 1311-1324. DOI: 10.1016/S0140-6736(16)30383-X
9. Hagberg L.A., Lindholm L.. **Review Article: Cost-effectiveness of healthcare-based interventions aimed at improving physical activity**. *Scand. J. Public Health* (2006.0) **34** 641-653. DOI: 10.1080/14034940600627853
10. Garrett S., Elley C.R., Rose S.B., O’Dea D., Lawton B.A., Dowell A.C.. **Are physical activity interventions in primary care and the community cost-effective? A systematic review of the evidence**. *Br. J. Gen. Pract. J. R. Coll. Gen. Pract.* (2011.0) **61** e125-e133. DOI: 10.3399/bjgp11X561249
11. Vijay G., Wilson E.C., Suhrcke M., Hardeman W., Sutton S.. **Are brief interventions to increase physical activity cost-effective? A systematic review**. *Br. J. Sports Med.* (2016.0) **50** 408-417. PMID: 26438429
12. Andersen P., Lendahls L., Holmberg S., Nilsen P.. **Patients’ experiences of physical activity on prescription with access to counsellors in routine care: A qualitative study in Sweden**. *BMC Public Health* (2019.0) **19**. DOI: 10.1186/s12889-019-6535-5
13. Bohman D.M., Mattsson L., Borglin G.. **Primary healthcare nurses’ experiences of physical activity referrals: An interview study**. *Prim. Health Care Res. Dev.* (2015.0) **16** 270-280. DOI: 10.1017/S1463423614000267
14. Joelsson M., Bernhardsson S., Larsson M.E.. **Patients with chronic pain may need extra support when prescribed physical activity in primary care: A qualitative study**. *Scand. J. Prim. Health Care* (2017.0) **35** 64-74. DOI: 10.1080/02813432.2017.1288815
15. 15.
OECD
Healthy Eating and Active LifestylesOECDParis, France202210.1787/40f65568-en. *Healthy Eating and Active Lifestyles* (2022.0). DOI: 10.1787/40f65568-en
16. Onerup A., Arvidsson D., Blomqvist Å., Daxberg E.-L., Jivegård L., Jonsdottir I.H., Lundqvist S., Mellén A., Persson J., Sjögren P.. **Physical activity on prescription in accordance with the Swedish model increases physical activity: A systematic review**. *Br. J. Sports Med.* (2019.0) **53** 383-388. PMID: 30413421
17. Arsenijevic J., Groot W.. **Physical activity on prescription schemes (PARS): Do programme characteristics influence effectiveness? Results of a systematic review and meta-analyses**. *BMJ Open* (2017.0) **7** e012156. DOI: 10.1136/bmjopen-2016-012156
18. Sallis R., Franklin B., Joy L., Ross R., Sabgir D., Stone J.. **Strategies for promoting physical activity in clinical practice**. *Prog. Cardiovasc. Dis.* (2015.0) **57** 375-386. DOI: 10.1016/j.pcad.2014.10.003
19. Marcus B.H., Forsyth L.H., Stone E.J., Dubbert P.M., McKenzie T.L., Dunn A.L., Blair S.N.. **Physical activity behavior change: Issues in adoption and maintenance**. *Health Psychol.* (2000.0) **19** 32. DOI: 10.1037/0278-6133.19.Suppl1.32
20. Bauman A.E., Sallis J.F., Dzewaltowski D.A., Owen N.. **Toward a better understanding of the influences on physical activity: The role of determinants, correlates, causal variables, mediators, moderators, and confounders**. *Am. J. Prev. Med.* (2002.0) **23** 5-14. DOI: 10.1016/S0749-3797(02)00469-5
21. Biddle S., Mutrie N.. *Psychology of Physical Activity: Determinants, Well-Being and Interventions* (2007.0)
22. Sherwood N.E., Jeffery R.W.. **The behavioral determinants of exercise: Implications for physical activity interventions**. *Annu. Rev. Nutr.* (2000.0) **20** 21-44. PMID: 10940325
23. Baranowski T., Anderson C., Carmack C.. **Mediating variable framework in physical activity interventions: How are we doing? How might we do better?**. *Am. J. Prev. Med.* (1998.0) **15** 266-297. PMID: 9838973
24. Breitborde N.J., Srihari V.H., Pollard J.M., Addington D.N., Woods S.W.. **Mediators and moderators in early intervention research**. *Early Interv. Psychiatry* (2010.0) **4** 143-152. PMID: 20536970
25. Woolley K., Fishbach A.. **Immediate rewards predict adherence to long-term goals**. *Personal. Soc. Psychol. Bull.* (2017.0) **43** 151-162
26. Bandura A.. **Health promotion by social cognitive means**. *Health Educ. Behav.* (2004.0) **31** 143-164. DOI: 10.1177/1090198104263660
27. Bogataj Š., Pajek M., Buturović Ponikvar J., Pajek J.. **Outcome expectations for exercise and decisional balance questionnaires predict adherence and efficacy of exercise programs in dialysis patients**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17093175
28. Young M.D., Plotnikoff R., Collins C., Callister R., Morgan P.. **Social cognitive theory and physical activity: A systematic review and meta-analysis**. *Obes. Rev.* (2014.0) **15** 983-995. DOI: 10.1111/obr.12225
29. Bandura A.. **Self-efficacy: Toward a unifying theory of behavioral change**. *Psychol. Rev.* (1977.0) **84** 191. DOI: 10.1037/0033-295X.84.2.191
30. McAuley E., Blissmer B.. **Self-efficacy determinants and consequences of physical activity**. *Exerc. Sport Sci. Rev.* (2000.0) **28** 85-88. PMID: 10902091
31. Lundqvist S., Börjesson M., Cider Å., Hagberg L., Ottehall C.B., Sjöström J., Larsson M.E.. **Long-term physical activity on prescription intervention for patients with insufficient physical activity level—A randomized controlled trial**. *Trials* (2020.0) **21** 793. DOI: 10.1186/s13063-020-04727-y
32. Lundqvist S., Börjesson M., Larsson M.E., Cider Å., Hagberg L.. **Which patients benefit from physical activity on prescription (PAP)? A prospective observational analysis of factors that predict increased physical activity**. *BMC Public Health* (2019.0) **19**. DOI: 10.1186/s12889-019-6830-1
33. Pavey T., Taylor A., Hillsdon M., Fox K., Campbell J., Foster C., Moxham T., Mutrie N., Searle J., Taylor R.. **Levels and predictors of exercise referral scheme uptake and adherence: A systematic review**. *J. Epidemiol. Community Health* (2012.0) **66** 737-744. DOI: 10.1136/jech-2011-200354
34. Shore C.B., Hubbard G., Gorely T., Polson R., Hunter A., Galloway S.D.. **Insufficient reporting of factors associated with exercise referral scheme uptake, attendance, and adherence: A systematic review of reviews**. *J. Phys. Act. Health* (2019.0) **16** 667-676. DOI: 10.1123/jpah.2018-0341
35. Husereau D., Drummond M., Augustovski F., de Bekker-Grob E., Briggs A.H., Carswell C., Caulley L., Chaiyakunapruk N., Greenberg D., Loder E.. **Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: Updated reporting guidance for health economic evaluations**. *Int. J. Technol. Assess. Health Care* (2022.0) **38** e13. DOI: 10.1017/S0266462321001732
36. Lundqvist S., Börjesson M., Larsson M.E., Hagberg L., Cider Å.. **Physical Activity on Prescription (PAP), in patients with metabolic risk factors. A 6-month follow-up study in primary health care**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0175190
37. 37.
Yrkesföreningar för Fysisk Aktivitet (YFA)
FYSS 2017: Fysisk Aktivitet i Sjukdomsprevention och SjukdomsbehandlingLäkartidningen förlag ABStockholm, Sweden2017. *FYSS 2017: Fysisk Aktivitet i Sjukdomsprevention och Sjukdomsbehandling* (2017.0)
38. Kallings L.V.. **The Swedish approach on physical activity on prescription. (“Implementation of physical activity in health care—Facilitators and barriers” Supplement by the HPH Task Force on Health Enhancing Physical Activity in Hospitals and Health Services)**. *Clin. Health Promot.* (2016.0) **6** 31-33
39. Sullivan M., Karlsson J., Ware J.E.. **The Swedish SF-36 Health Survey—I. Evaluation of data quality, scaling assumptions, reliability and construct validity across general populations in Sweden**. *Soc. Sci. Med.* (1995.0) **41** 1349-1358. DOI: 10.1016/0277-9536(95)00125-Q
40. Brazier J., Roberts J., Deverill M.. **The estimation of a preference-based measure of health from the SF-36**. *J. Health Econ.* (2002.0) **21** 271-292. DOI: 10.1016/S0167-6296(01)00130-8
41. Kharroubi S., Brazier J.E., O’Hagan A.. **Modelling covariates for the SF-6D standard gamble health state preference data using a nonparametric Bayesian method**. *Soc. Sci. Med.* (2007.0) **64** 1242-1252. DOI: 10.1016/j.socscimed.2006.10.040
42. **Utomlänsprislista. Samverkansnämnden Västra Sjukvårdsregionen. Västra Götalandsregionen**. (2018.0)
43. Hagberg L., Lundqvist S., Lindholm L.. **What is the time cost of exercise? Cost of time spent on exercise in a primary health care intervention to increase physical activity**. *Cost Eff. Resour. Alloc.* (2020.0) **18** 14. DOI: 10.1186/s12962-020-00209-9
44. **Medellöner i Sverige (Mean Salaries in Sweden) [Internet]. Statistics Sweden**
45. Ekelund U., Sepp H., Brage S., Becker W., Jakes R., Hennings M., Wareham N.J.. **Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults**. *Public Health Nutr.* (2006.0) **9** 258-265. DOI: 10.1079/PHN2005840
46. 46.
Folkhälsomyndigheten
Future Costs of Anitbiotics RsistanceFolkhälsomyndighetenSolna, Sweden2018. *Future Costs of Anitbiotics Rsistance* (2018.0)
47. Kendzierski D., DeCarlo K.J.. **Physical activity enjoyment scale: Two validation studies**. *J. Sport Exerc. Psychol.* (1991.0) **13** 50-64. DOI: 10.1123/jsep.13.1.50
48. Motl R.W., Dishman R.K., Saunders R., Dowda M., Felton G., Pate R.R.. **Measuring enjoyment of physical activity in adolescent girls**. *Am. J. Prev. Med.* (2001.0) **21** 110-117. DOI: 10.1016/S0749-3797(01)00326-9
49. Resnick B.. **Reliability and validity of the Outcome Expectations for Exercise Scale-2**. *J. Aging Phys. Act.* (2005.0) **13** 382-394. DOI: 10.1123/japa.13.4.382
50. Resnick B., Magaziner J., Orwig D., Zimmerman S.. **Evaluating the components of the Exercise Plus Program: Rationale, theory and implementation**. *Health Educ. Res.* (2002.0) **17** 648-658. DOI: 10.1093/her/17.5.648
51. Rollnick S., Mason P., Butler C.. *Health Behavior Change: A Guide for Practitioners* (2001.0)
52. Stott N.C., Rollnick S., Rees M., Pill R.. **Innovation in clinical method: Diabetes care and negotiating skills**. *Fam. Pract.* (1995.0) **12** 413-418. DOI: 10.1093/fampra/12.4.413
53. Gray A.M., Clarke P.M., Wolstenholme J.L., Wordsworth S.. *Applied Methods of Cost-Effectiveness Analysis in Healthcare* (2010.0) **Volume 3**
54. Shiroiwa T., Sung Y.K., Fukuda T., Lang H.C., Bae S.C., Tsutani K.. **International survey on willingness-to-pay (WTP) for one additional QALY gained: What is the threshold of cost effectiveness?**. *Health Econ.* (2010.0) **19** 422-437. DOI: 10.1002/hec.1481
55. Bertram M.Y., Lauer J.A., De Joncheere K., Edejer T., Hutubessy R., Kieny M.-P., Hill S.R.. **Cost–effectiveness thresholds: Pros and cons**. *Bull. World Health Organ.* (2016.0) **94** 925. DOI: 10.2471/BLT.15.164418
56. Claxton K.. **The irrelevance of inference: A decision-making approach to the stochastic evaluation of health care technologies**. *J. Health Econ.* (1999.0) **18** 341-364. DOI: 10.1016/S0167-6296(98)00039-3
57. Sackett D.L., Rosenberg W.M., Gray J.M., Haynes R.B., Richardson W.S.. *Evidence Based Medicine: What It Is and What It Isn’t* (1996.0) **Volume 312** 71-72
58. Klompstra L., Deka P., Almenar L., Pathak D., Muñoz-Gómez E., López-Vilella R., Marques-Sule E.. **Physical activity enjoyment, exercise motivation, and physical activity in patients with heart failure: A mediation analysis**. *Clin. Rehabil.* (2022.0) **36** 1324-1331. DOI: 10.1177/02692155221103696
59. Lewis B.A., Williams D.M., Frayeh A., Marcus B.H.. **Self-efficacy versus perceived enjoyment as predictors of physical activity behaviour**. *Psychol. Health* (2016.0) **31** 456-469. DOI: 10.1080/08870446.2015.1111372
60. Chen C., Weyland S., Fritsch J., Woll A., Niessner C., Burchartz A., Schmidt S.C., Jekauc D.. **A Short Version of the Physical Activity Enjoyment Scale: Development and Psychometric Properties**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph182111035
61. Murray J.M., Brennan S.F., French D.P., Patterson C.C., Kee F., Hunter R.F.. **Mediators of behavior change maintenance in physical activity interventions for young and middle-aged adults: A systematic review**. *Ann. Behav. Med.* (2018.0) **52** 513-529. DOI: 10.1093/abm/kay012
|
---
title: 'A New Bloody Pulp Selection of Myrobalan (Prunus cerasifera L.): Pomological
Traits, Chemical Composition, and Nutraceutical Properties'
authors:
- Francesco Sottile
- Assunta Napolitano
- Natale Badalamenti
- Maurizio Bruno
- Rosa Tundis
- Monica Rosa Loizzo
- Sonia Piacente
journal: Foods
year: 2023
pmcid: PMC10001106
doi: 10.3390/foods12051107
license: CC BY 4.0
---
# A New Bloody Pulp Selection of Myrobalan (Prunus cerasifera L.): Pomological Traits, Chemical Composition, and Nutraceutical Properties
## Abstract
A new accession of myrobalan (*Prunus cerasifera* L.) from Sicily (Italy) was studied for the first time for its chemical and nutraceutical properties. A description of the main morphological and pomological traits was created as a tool for characterization for consumers. For this purpose, three different extracts of fresh myrobalan fruits were subjected to different analyses, including the evaluation of total phenol (TPC), flavonoid (TFC), and anthocyanin (TAC) contents. The extracts exhibited a TPC in the range 34.52–97.63 mg gallic acid equivalent (GAE)/100 g fresh weight (FW), a TFC of 0.23–0.96 mg quercetin equivalent (QE)/100 g FW, and a TAC of 20.24–55.33 cyanidine-3-O-glucoside/100 g FW. LC-HRMS analysis evidenced that the compounds mainly belong to the flavonols, flavan-3-ols, proanthocyanidins, anthocyanins, hydroxycinnamic acid derivatives, and organic acids classes. A multitarget approach was used to assess the antioxidant properties by using FRAP, ABTS, DPPH, and β-carotene bleaching tests. Moreover, the myrobalan fruit extracts were tested as inhibitors of the key enzymes related to obesity and metabolic syndrome (α-glucosidase, α-amylase, and lipase). All extracts exhibited an ABTS radical scavenging activity that was higher than the positive control BHT (IC50 value in the range 1.19–2.97 μg/mL). Moreover, all extracts showed iron-reducing activity, with a potency similar to that of BHT (53.01–64.90 vs 3.26 μM Fe(II)/g). The PF extract exhibited a promising lipase inhibitory effect (IC50 value of 29.61 μg/mL).
## 1. Introduction
Among fresh fruit species, plums (genus Prunus) play a non-determinant role in terms of production and cultivated surfaces, yet it is traditionally found in many areas characterized by temperate climates. It is usually considered a minor stone fruit together with apricot mostly because it is compared to peach and nectarines, which accounts for wider diffusion and growing areas [1]. The species has a complex botanical classification because several species are traced back to the name plum, and its hybridizations, natural and/or induced, are widespread [2]. Domestic or European plums (*Prunus domestica* L.) are generally used for processing into dried plums, while Japanese plums (*Prunus salicina* Lindl.) are almost exclusively used for fresh consumption. The names of the two species highlight the links between the origin and geographical distribution, with the former being more closely associated with the old continent and the latter with the Asian continent. However, these are genetically distant species characterized by different ploidy levels.
Myrobalan (*Prunus cerasifera* L.) is a diploid, widespread species in the Mediterranean. This species is credited with the origin of European plums via hybridization with the tetraploid P. Spinosa, and for this reason, myrobalan is considered genetically close to P. domestica, although with different ploidy [1]. It is widely used as rootstock for both plum and apricot trees, more rarely for peach [3,4] thanks mainly to its ability to produce adventitious roots that facilitate its propagation [5,6].
In many traditional fruit-growing areas, where intensive agriculture has not taken over, there are several edible fruiting myrobalan accessions, often small (hence referred to as cherry-plum), selected by farmers and passed down because of the consumption related to the gastronomic traditions of indigenous peoples [7]. For this reason, many studies have focused on characterizing the accessions that are locally grown and highly valued by consumers, often for the taste qualities of the fruits as well as their early ripening time [8]. This approach is very often based on the analysis of morphological data through the adoption of specific descriptor lists studied and approved on a global scale [9]. The morphological traits of flowers, leaves, fruits, and seeds are studied, as well as wood, crown, bud characteristics, tree habit, and phenotypic behavior by the age of maturity and flowering. More recently, it has been proposed to combine these observations with a molecular-scale evaluation model, which, however, becomes effective only when morphological analysis fails to separate different accessions and, in any case, only in the presence of an adequate bank of genetic information [10]. Over the past decade, on an international scale, many research centers have initiated several programs for biodiversity conservation and characterization in accordance with the provisions of the International Treaty on Plant Genetic Resources for Agriculture and Food [11]. This important agreement has given all participating countries a responsibility in the conservation of indigenous genetic resources through the development of national plans in which the main objective is to enable a description of biodiversity with comparable and recognized patterns. Many innovative approaches have been developed for preserving plum biodiversity from the risk of erosion and/or extinction [12]. It is well known that the myrobalan fruits are usually rich in fibers and antioxidants, evidencing an important role in terms of nutritional source [13]; however, cherry plums are not so widely diffused due to the low resistance of the fruit in the postharvest management. Given that germplasm conservation is a substantial contribution to preserving knowledge about all crops, the identification of new resources and their characterization is an indispensable tool for achieving sustainable development targets by reducing the risk of genetic erosion. *Indigenous* genetic diversity is now recognized as a tool for resilience, mitigating the climate crisis due to increased adaptation with less consumption of natural resources, especially soil and water. Genetic diversity can conserve those genes that are potentially useful in strengthening resistance to pathogens or adaptability to stresses [14]. On a global scale, this type of work is also of strategic importance with a view to achieving the Agenda 2030 goals [15]. The conservation of biodiversity of agricultural interest and its morphological and functional characterization is central to SDGs 1, 2, 3, 12, and 14, with the general convergence of all targets toward SDG 13 being related to urgent action on climate change mitigation and adaptation, which, in some ways, underlies all the goals [16].
The attention of consumers toward an increasingly healthy diet has led to an increase in the consumption of fruits, with reference to red fruits not only for their nutritional value but also for their characteristic taste and their well-known health properties [17]. In fact, these fruits, in addition to vitamins and minerals, are rich in compounds with several health properties, mainly phenolic acids such as p-coumaric acid, vanillic acid β-glucoside, protocatechuic acid, and caffeic acid, and flavonoids such as catechin, epicatechin, quercetin and cyanidin-3-O-glucoside [18,19,20].
Metabolic syndrome (MetS) is a complex disorder that is often associated with insulin resistance, high cholesterol and triglycerides levels, and abdominal obesity [21]. The role of oxidative stress in its pathogenesis was proved [22,23]. Găman et al. [ 24] evidenced that in the pancreatic β cells of subjects affected by type 2 diabetes, oxidative stress can reduce insulin secretion and, consequently, glucose uptake.
Although research has elucidated many of the mechanisms underlying MetS, its treatment remains a challenge, given the complexity of this disease. For this reason, many research groups are looking for bioactive compounds from food products that can play a preventive role in the onset of this syndrome. Many of these compounds belong to the class of phenols and possess antihypertensive, antihyperglycemic, antihypercholesterolemic, antioxidant, and anti-inflammatory activity, and furthermore, they can produce body weight loss or prevent against body weight gain [25,26].
Among the potentially used preventing approaches to counteract MetS and obesity, the inhibition of α-glucosidase, α-amylase, and lipase was one of the most applied. In fact, the inhibition of carbohydrate hydrolyzing enzymes delays carbohydrate digestion with a consequent hypoglycaemic effect, whereas the inhibition of pancreatic lipase reduces the absorption of ingested fats with a consequent hypolipidemic effect [27,28].
Therefore, herein, we report, for the first time, the pomological characteristics, chemical profile, and nutraceutical properties of different extracts obtained from *Prunus cerasifera* cv ‘Alimena’, a new bloody pulp cultivar from Sicily. The anthocyanin (TAC), flavonoid (TFC), and total phenol (TPC) contents were spectrophotometrically measured. The complete phytochemical profile was assessed using LC-ESI/LTQOrbitrap/MS analysis. A multitarget approach was applied to assess antioxidant activity (ABTS, DPPH, β-carotene bleaching, and FRAP assays). The inhibitory activity against key enzymes involved in MetS was also assessed.
## 2.1. Chemicals and Reagents
All chemicals utilized in this study were purchased from VWR International (Milan, Italy) and Sigma-Aldrich Chemical Co., Ltd. (Milan, Italy).
## 2.2. Plant Material
The research was carried out on a new accession of *Prunus cerasifera* L. named ‘Alimena’ and identified in an agricultural area in the territory of Alimena, Sicily (Italy) at an altitude of 675 m s/l, (37°41′25″ N; 14°05′53″ E). From the selected natural tree, budsticks have been collected for developing a small experimental orchard with 50 trees grafted onto P. cerasifera myrobalan 29C.
The orchard was established in 2015, and standard growing techniques have been applied since then. The planting density was 5 m × 5 m, fully irrigated during summer and managed by adopting spontaneous cover crops during the winter-spring season. Winter and summer pruning was performed yearly; fruit thinning was not necessarily due to a regular crop density recorded at fruit set.
## 2.3. Morphological Description
Morphological description was carried out by applying the Guidelines for the Characterization of Plant, Livestock and Microbial Genetic Resources approved based on the National Agricultural Biodiversity Plan of the Italian Ministry of Agriculture [29]. The Guidelines were drafted based on the international descriptors approved by UPOV [30] and contain references to plant traits considered essential for the characterization of plum tree accessions with the aim of distinguishing and defining their uniqueness.
Application of the guidelines involves the observation of multiple characters related to the tree and morphological characteristics of vegetative and reproductive organs. Leaves and wood samples were taken from mature trees during the vegetative-reproductive season, and at the same time, all observations of tree habits were recorded. At maturity, a sample of 100 fruits was subjected to morphological analysis (height, width, and thickness of the drupe and stone), as well as to qualitative analysis (skin and pulp color, titratable acidity, and soluble sugar content). For this purpose, fruits were randomly taken from the mass harvested at maturity from 5 plants of the same age. The samples were transported to the laboratory with refrigerated facilities so as not to suffer any damage or spoilage.
## 2.4. Extraction Procedure
A total of 500 g of ripe fruits of P. cerasifera were cleaned, stone removed, and homogenized into puree (PA, 380 g) using a food processor. To determine the water quantity, 50 g of puree, once freeze-dried, to give 7.90 g of extract (content of water: $84.2\%$). Three hundred grams of PA were extracted for seven days at r.t. with 600 mL of acetone. The solvent was evaporated to give, after freeze-drying, 35.5 g of dry extract (PB). Then, 2.5 g of PB, after dilution with deionized water (≈15 mL) and then extracted with butanol (8 × 10 mL). The butanolic extracts were evaporated to give, after freeze-drying, 0.63 g of dry material (PC). The aqueous layer was freeze-dried to give 1.8 g of extract (PD). As an alternative extraction method, 20 g of PA was mixed with organic solution [≈120 mL, acetone/methanol/water/formic acid (40:40:20:0.1, v/v/v/v)] [31,32] and the mixture was allowed to stand at 4 °C for 24 h. The crude was filtered and washed with 120 mL of the same solution. The supernatants were combined, evaporated under vacuum at 37 °C, and freeze-dried to obtain 2.51 g of new extract (PF).
## 2.5. Total Phenol (TPC), Flavonoid (TFC), and Anthocyanin Contents (TAC)
Total phenol content (TPC) and total flavonoid content (TFC) were evaluated as previously described by Leporini et al. [ 33,34]. The differential pH method was used for total anthocyanin content (TAC) quantification [35].
## 2.6. LC-HRMS Analysis
The LC-ESI/HRMS analysis was carried out on a system of liquid chromatography consisting of a Thermo Ultimate RS 3000 UHPLC coupled online to a Q-Exactive hybrid quadrupole Orbitrap high-resolution mass spectrometer (UHPLC-Q-Orbitrap) (Thermo Fisher Scientific, Bremen, Germany), fitted with a HESI II (heated electrospray ionization) probe, working in both negative and positive ionization mode.
In order to allow the chromatographic separation, a Luna C-18 column (RP-18, 2.0 × 150 mm, 5 nm; Waters; Milford, MA, USA), set at a temperature of 30 °C, and a linear gradient, obtained by using mobile phase $0.1\%$ formic acid in water, v/v (A), and $0.1\%$ formic acid in acetonitrile, v/v (B), from 5 to $55\%$ of B, in 20 min, at a flow rate of 0.2 mL/min, were used. 5 μL of each extract, dissolved in water/acetonitrile 1:1 v/v (0.25 mg/mL), were injected by the autosampler.
To allow negative and positive ions analysis, the HESI source parameters were set as follows: spray voltage at −2.50 kV and 3.30 kV, respectively; sheath gas at 50 arbitrary units (a.u.); auxiliary gas at 10 and 15 a.u., respectively; auxiliary gas heater temperature at 300 °C; capillary temperature at 300 °C; S-lens RF value at 50 a.u.
HRMS and HRMS/MS analyses were carried out by experiments of full (mass range: m/z 150–1400) and data-dependent scan (dd-MS2 topN = 5) at resolutions of 70,000 and 17,500, respectively. A normalized collision energy (NCE) of 30 was used. For each sample, three replicates were performed. Data collection and analysis were carried out by using the manufacturer’s software (Xcalibur 2.2).
## 2.7. In Vitro Evaluation of Antioxidant Potential and Relative Antioxidant Capacity Index
The antioxidant activity was assessed by using two radical scavenging test: 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity, β-carotene bleaching test and Ferric Reducing Ability Power (FRAP). All the procedures were previously detailed [36]. The Relative Antioxidant Capacity Index (RACI) was applied to evaluate the sample characterized by the highest activity [33].
## 2.8. Pancreatic Lipase Inhibitory Activity
The evaluation of pancreatic lipase inhibitory activity was assessed as previously described by Loizzo et al. [ 37].
## 2.9. Evaluation of α-Amylase and α-Glucosidase Inhibitory Activity
For α-amylase and α-glucosidase inhibitory activity tests, the procedure previously reported was adopted [36].
## 2.10. Statistical Analysis
Linear regression, assessment of repeatability, calculation of average, relative standard deviation (SD), and Pearson’s correlation coefficient (r) were calculated by using Microsoft Excel 2010 software (Redmond, WA, USA). The results were expressed as the means of three different experiments ± SD. The inhibitory concentration $50\%$ (IC50) was calculated by using Prism GraphPad Prism version 4.0 for Windows (GraphPad Software, San Diego, CA, USA).
Parametric data were statistically analyzed by using one-way analysis of variance (ANOVA) followed by Tukey’s posthoc test by using Prism GraphPad Prism version 4.0 for Windows. Differences at * $p \leq 0.05$ were statistically significant, while at ** $p \leq 0.01$, they were highly significant.
## 3.1. Morphological Data
All data related to the description of morphological traits provided by the applied descriptors are given in Table 1. All data related to the measurements of the fruits, seeds, and leaves, as well as of the fruit quality characteristics, are given in Table 2. The data reported revealed that the ‘Alimena’ accession had morphological characteristics of uniqueness, mainly related to the average fruit size (41.3 g), which is generally larger than that of the other P. cerasifera cultivars known in the Mediterranean basin. The color of the skin and pulp, therefore, give these fruits distinguishable traits that are unmatched in the varietal panorama of myrobalans, confirming, moreover, the character of low resistance to handling and transport.
## 3.2. Total Phytochemical Content (TPC, TFC, and TAC)
The pulp obtained from the P. cerasifera ‘Alimena’ fruits was subjected to different extractions with several solvents (acetone, butanol, water, methanol, etc.) to evaluate the impact of the solvent on the phytochemical content. The presence of several compounds with different structures and polarities can drastically affect their solubility [38]. Polar solvents (MeOH, EtOH, and water) were used for the isolation of polyphenols and glycosides from plants. The most common ones are mixtures of water with ethanol and methanol. Ethanol is a good solvent for polyphenol extraction and is safe for human consumption, whereas acetone and ethyl acetate have been used for the extraction of medium molecular weight metabolites, such as terpenoids and flavonoid aglycones [39].
The PF sample resulted in the richest TPC and TFC, with values of 97.63 mg gallic acid equivalent (GAE)/100 g FW and 0.96 mg quercetin equivalent (QE)/100 g FW, respectively, followed by the PC samples, whereas PD was richest in TAC (55.33 mg cyanidine-3-O-glucoside/100 g FW) (Table 3).
Previously, the TPC content of P. cerasifera ‘Mirabolano’, P. domestica cv ‘President’, and P. salicina cv ‘Shiro’ at different stages of development was analyzed [40]. All plumes exhibited the highest TPC at the date of commercial harvesting—at about 100 days for ‘Mirabolano’, 130 days for ‘President’, and more than 110 days for “Shiro”. TPC values in a range from 1.34 to 6.11 g/kg FW were found for the red and purple myrobalan plum fruits, respectively [31]. Values from 1.74 to 3.75 g/Kg FW were recorded for the Stanley and French Damson fresh plums, respectively [41].
A lower TPC content was found by Gündüz et al. [ 8], who investigated P. cerasifera selections from Turkey and found values between 136.8 to 583.1 mg GAE/kg FW for ‘Ozark Premier’, and ‘Selection No. 3’, respectively. On the contrary, our data are lower than those found for P. divaricata “Demal” and P. domestica ‘Sugar plum’, with TPC values of 169.6 and 172.4 mg GAE/100 g, respectively [40]. TFC values from 12.1 to 29.1 mg rutin equivalent/100 g were found for ‘Demal’ and P. domestica ‘Red plum’, respectively [42]. TPC values ranging from 177–365 mg GAE/100 g were found for P. divaricate yellow and black, respectively [43].
Gil et al. [ 44] analyzed several Californian flesh plums and found that ‘Black Beaut’ was richer in TPC and TCC in comparison to ‘Angeleno’, ‘Red Beaut’, ‘Wickson’, and ‘Santa Rosa’ cultivars. Moreover, several research papers demonstrated that qualitative and quantitative variability in TPC, is often related to different genetic factors and developmental stages [45].
Our data on TAC are in line with those reported for P. cerasus varieties ‘Kántorjánosi’, ‘Újfehértói fürtös’, and ‘Debreceni bötermö’ (TAC values of 21, 56, and 63 mg cyanidine-3-O-glucoside/100 g FW, respectively) but lower than those found for the varieties ‘Csengödi csokros’ and ‘Cigánymeggy (TAC values of 295 and 206 mg cyanidine-3-O-glucoside/100 g FW, respectively) [46]. A higher TAC was found for P. domestica ‘Santa Rosa’ and ‘African Rose’, with values of 164.13 and 326.83 mg cyanidine-3-O-glucoside/100 g FW, respectively, and for P. cerasifera from Georgia (109.77 mg cyanidine-3-O-glucoside/100 g FW) [47,48].
## 3.3. LC-HRMS Analysis
The LC-HRMS profiles of the PC, PD, and PF extracts highlighted the occurrence of several metabolites in P. cerasifera, the structures of which could be assigned by a comparison between the molecular formulae, fragmentation patterns, and retention times and the literature data and metabolite databases, allowing us to putatively identify hydroxycinnamic acid derivatives, flavonols, flavan-3-ols and proanthocyanidins, anthocyanins, organic acids, sugar alcohols (and their derivatives), glycosylated hydroxybenzaldehyde and benzylic alcohol derivatives, glycosyl terpenates, and glycosylated aliphatic alcohol derivatives (Table 4) [37,49,50,51,52,53,54]. Except for compounds 28 and 67, which were previously described in the Rosaceae family [55,56] but not in the genus Prunus, and for compounds 35, 36, 43, and 54 described in families other than Rosaceae [57,58,59], to the best of our knowledge, most of these compounds have already been detected in plants belonging to the genus Prunus [60,61,62,63,64,65,66,67,68,69,70,71,72,73] but not in the species cerasifera. Only compounds 1, 2, 5, 7, 10, 12, 15, 18, 20, 29, 34, 42, 58, and 75 have already been described in this species [31,45,74,75,76,77,78].
Among the most represented group of metabolites, the derivatives of hydroxycinnamic acid, some differences could be appreciated among the three P. cerasifera extracts (Table 4). PF and PC showed, in fact, a higher number of metabolites belonging to this class with respect to the PD extract. It is noteworthy that compounds 21, 25, 31, 35, and 36, containing a feruloyl unit in their structure, and compounds 29, 42, and 64, corresponding to methyl ester derivatives of quinic or coumaroyl acid, were not detectable in PD. Moreover, the caffeoyl- and coumaroyl-quinic acid isomers 6–7, and 17–19, along with the caffeoylhexose 8 and the coumaroyl dihexoside isomers 9 and 14 were detectable in PD at a minor intensity level with respect to the other two extracts, as well as the mono-acylated forms of coumaroyldihexoside isomers (22, 27, 32, and 37). Furthermore, in PD, the coumaroyldihexoside isomers with two to four acetyl groups were even less intense (41, 44, 47, 59, 61, 65, 68–69, and 72–74), while the penta-acetylated forms (76–77) were not detectable at all (Table 4). It is noteworthy that compounds 41, 44, and 47 were more evident in PC than in PF.
Regarding the second most representative group of metabolites identified in P. cerasifera—flavonols—once again, the PF extract was the most complete of the three, highlighting, besides the quercetin derivatives, the occurrence of isorhamnetin derivatives (50, 52, and 63) that were not evident in the other two extracts. Furthermore, PC showed the occurrence of flavonols at higher intensity levels than PD (Table 4). Flavan-3-ols (20 and 34) and proanthocyanidins A-type [49, 56] occurred in all tested extracts, even if at lower intensity in PD, which lacked the dimers of proanthocyanidins B-type (11, 16, and 23), which were, in turn, more evident in PF than in PC (Table 4). Glycosylated derivatives of cyanidin and delphinidin (10, 12, 15, 45, and 48) could be observed in PF, PC, and PD, with PC showing the occurrence of the rutinoside derivatives of both anthocyanins (15 and 45) at a minor intensity level.
Simple organic acids like 2 and 5 or the nonanedioic acid 70, as well as derivatives of malic acid with sugar alcohol like sorbitol or galactitol [3], or with a ketohexose like fructose [4], were detectable in all tested extracts, unlike the glycosylated forms of the dicrotalic and abscisic acids (54 and 53), with the first being more evident in PF and PC and the second no detectable in PD (Table 4).
Analogously, the glycosylated derivatives of benzyl alcohol or hydroxybenzaldehyde (13, 24, 26, and 28) (differing in sugars) could be highlighted in all the extracts, contrary to glycosylated terpenates (39 and 46), which were mainly present in PF and PC, as well as the glycosylated form of hexanol [43], likely due to their higher lipophilicity (Table 4).
## 3.4. ‘Alimena’ Myrobalan Antioxidant Potential
The antioxidant activities of myrobalan bloody pulp fruit extracts were assessed using different in vitro methods, namely FRAP, β-carotene bleaching test, ABTS, and DPPH assays (Table 5).
The PF sample showed the highest radical scavenging activity, with IC50 values of 19.61 and 1.19 μg/mL for the DPPH and ABTS assays, respectively.
*In* general, by comparing the data obtained from the two different radical scavenging tests, it was possible to note that the DPPH· radical is less sensible than the ABTS+ in our samples. In fact, IC50 values in the range 19.61–39.02 and 1.19–2.97 μg/mL for the DPPH and ABTS tests, respectively, were obtained. Data from the ABTS test are in the same order of potency as ascorbic acid. The ‘Alimena’ pulp extracts ferric-reducing potential was evaluated by FRAP testing, revealing reduced iron in the samples, with a potency comparable to the positive control BHT.
Pearson’s correlation coefficient evidenced r values of 0.79, 0.80, and 0.91 for TPC, FRAP, ABTS, and DPPH, respectively. A similar trend was also observed for TFC, wherein r values of 0.73, 0.85, and 0.95 for FRAP, ABTS, and DPPH were recorded, respectively. No positive correlation was found for TAC and the antioxidant data.
Our data on radical scavenging potential are better than those reported for Indian P. cerasifera with IC50 values of 10.09 and 45.40 μg/mL for ABTS and DPPH assays, respectively [79]. On the contrary, our data obtained by FRAP testing resulted lower than those found in the “Sugar plum” cultivar (563.8 μM Fe (II)/g) [44].
The variability of antioxidant activity during the harvesting of P. domestica cv ‘President’, P. salicina cv ‘Shiro’, and P. cerasifera myrobalan was investigated. By comparing the data on the ‘Alimena’ myrobalan with those obtained by Moscatello et al. [ 40], it emerges that at the commercial maturity stage, the radical scavenging DPPH activity of our samples is significantly higher, with IC50 values in the range 56.22–76.46 µg/mL vs. 19.61–39.02 µg/mL, while similar results can be observed with the plum ‘President’ (IC50 in the range 28.63–30.35 µg/mL). Similar results against DPPH were also found for the P. domestica ‘African Rose’ and ‘Santa Rosa’ extracts (IC50 values of 13.923 and 18.416 μg/mL, respectively) [47]. On the contrary, when comparing this with domesticated P. domestica, a higher DPPH activity was observed (IC50 in the range 5.2–6.6 µg/mL for black and red fruit, respectively) [43].
Recently, Popović et al. [ 75] explored different Prunus varieties from Serbia and found high variability in the ability of hydroalcoholic extract to counteract the DPPH· radical (IC50 in the range 0.83–29.12 mg/mL for steppe cherry and red cherry plum, respectively). However, all data are lower than those found in our extracts.
FRAP values ranging from 11.20 to 44.83 mmol TE/g fresh weight (FW) were found for yellow and purple Chinese myrobalan plum extract, respectively [28], whereas the FRAP values ranged between 0.123 and 0.835 mmol TE/kg FW for ‘Selection 33C 02’ and ‘Selection 31C 18’, respectively [8].
The integration of antioxidant data into the PF sample saw the most active result in terms of antioxidant activity, with the lowest RACI value (0.71) (Figure S1).
## 3.5. Inhibition of Target Enzymes Useful for the Prevention and Treatment of Hyperglycaemia and Obesity
In our continuous search for foods that are able to prevent MeTs, we have investigated the ability of the bloody pulp of myrobalan, a new variety of Sicilian Prunus, to counteract the enzymes linked with hyperglycaemia and hyperlipidaemia. All investigated extracts exerted inhibitory enzyme activity in a concentration-dependent manner (Table 6). According to Nowicka et al. [ 80], the extract richest in flavonols (PF) exerted the highest α-amylase inhibitory activity (IC50 value of 34.48 μg/mL). Moreover, the PF sample was found to be more proficient in inhibiting pancreatic lipase, followed by the PD sample, with IC50 values of 29.61 and 38.16 μg/mL, respectively. Values from 49.76 to 78.87 μg/mL were found for the PC and PD samples, respectively, against α-glucosidase. The results of correlation analysis evidenced that a strong positive correlation was found between TPC and α-glucosidase inhibitory activity ($r = 0.96$) and TAC and lipase inhibitory property ($r = 0.80$). A weak correlation was found between TFC and α-amylase ($r = 0.56$).
Our data on carbohydrate-hydrolyzing enzymes are in line with those reported by Kołodziejczyk-Czepas [81] for P. spinosa; they found IC50 values in a range from 15.43 to 90.95 μg/mL for hydroalcoholic extract and butanol fraction, respectively, against α-glucosidase, and from 33.47 to 110.12 μg/mL for ethyl acetate and butanol fraction, respectively, against α-amylase. A lower α-amylase inhibitory effect was found by testing the P. ceraus extracts, with IC50 values in a range from 330 to 892 μg/mL for the ‘Cigánymeggy’ and ‘Kántorjánosi’ varieties, respectively [46], and by Popović et al. [ 75], who investigated different Prunus species (IC50 values in the range 4.61–136.23 mg/mL for P. fruticose and P. pissardi ‘Carriére’, respectively). In the same work authors tested Prunus extracts against α-glucosidase and recognized a low inhibitory activity (IC50 values in the range 0.41–136.23 mg/mL).
Podsędek et al. [ 82] investigated the effect of water extract obtained from P. persica fruits from Poland and found lower α-glucosidase inhibitory activity in comparison to our data (IC50 value of 264.44 mg/mL) despite a higher TPC content.
Recently, Ullah et al. [ 83] demonstrated that P. domestica subsp. syriaca (‘Mirabelle’) was able to inhibit the key enzymes involved in MetS. In particular, the hydroethanolic extract inhibited α-amylase, α-glucosidase, and pancreatic lipase, with IC50 values of 7.01, 6.4, 6.0 mg/mL, respectively. All these values are higher than those found in this study.
A perusal analysis of the literature revealed that the inhibition of α-glucosidase should be related to the content of the hydroxycinnamic acid derivatives that are particularly abundant in PF and PC and that exert a more potent inhibitory activity against this enzyme [84].
## 4. Conclusions
The preservation of agro-biodiversity for the purpose of consumption is at the heart of the targets of SDG No. 12 (Responsible Production and Consumption), demonstrating that the spread of a model of sustainability goes through the choices of products that have very high environmental adaptation. For this reason, the identification of new accessions, their description, and in-depth knowledge of their quality characteristics represents a virtuous model of knowledge development for consumption. The growing awareness of consumers toward the possibility of preventing and/or treating pathologies through certain defined foods represents the driving force of the global market for these types of products. In this context, we have analyzed the chemical profile and bioactivity of three extracts of a new bloody pulp selection of myrobalan (P. cerasifera). The PF extract showed the most promising bioactivity, which is in agreement with its highest phytochemical content. Further in vivo studies are necessary to better understand the biological properties and potential applications for the development of functional foods or nutraceutical products that are useful to consumers with these types of health problems.
## References
1. Sottile F., Caltagirone C., Giacalone G., Peano C., Barone E.. **Unlocking plum genetic potential: Where are we at?**. *Horticulturae* (2022.0) **8**. DOI: 10.3390/horticulturae8020128
2. Szymajda M., Studnicki M., Kuras A., Żurawicz E.. **Cross-compatibility in interspecific hybridization between three**. *S. Afr. J. Bot.* (2022.0) **146** 624-633. DOI: 10.1016/j.sajb.2021.11.036
3. Iacona C., Cirilli M., Zega A., Frioni E., Silvestri C., Muleo R.. **A somaclonal myrobalan rootstock increases waterlogging tolerance to peach cultivar in controlled conditions**. *Sci. Hortic.* (2013.0) **156** 1-8. DOI: 10.1016/j.scienta.2013.03.014
4. Motisi A., Pernice F., Sottile F., Caruso T.. **Rootstock effect on stem water potential gradients in cv. “Armking” nectarine trees**. *Acta Hortic.* (2004.0) **658** 75-79. DOI: 10.17660/ActaHortic.2004.658.7
5. Nasri A., Baklouti E., Ben Romdhane A., Maalej M., Schumach H.M., Drira N., Fki L.. **Large-scale propagation of Myrobolan**. *Sci. Hortic.* (2019.0) **245** 144-153. DOI: 10.1016/j.scienta.2018.10.016
6. Carmona-Martin E., Petri C.. **Adventitious regeneration from mature seed-derived tissues of**. *Sci. Hortic.* (2020.0) **259** 108746. DOI: 10.1016/j.scienta.2019.108746
7. Impallari F.M., Monte M., Girgenti V., Del Signore M.B., Sottile F.. **Biodiversity of Sicilian fruit trees: Studies on plums**. *Acta Hortic.* (2010.0) **874** 37-44. DOI: 10.17660/ActaHortic.2010.874.3
8. Gündüz K., Saraçoğlu O.. **Variation in total phenolic content and antioxidant activity of**. *Sci. Hortic.* (2012.0) **134** 88-92. DOI: 10.1016/j.scienta.2011.11.003
9. Faust M., Suranyi D.. **Origin and dissemination of plums**. *Hortic. Rev.* (1999.0) **23** 179-231
10. Horvath A., Balsemin E., Barbot J.C., Christmann H., Manzano G., Reynet P., Laigret F., Mariette S.. **Phenotypic variability and genetic structure in plum (**. *Sci. Hortic.* (2011.0) **129** 283-293. DOI: 10.1016/j.scienta.2011.03.049
11. Moore G., Tymowski W.. *Explanatory Guide to the International Treaty on Plant Genetic Resources for Food and Agriculture* (2005.0) **Volume xii** 212
12. Gianní S., Sottile F.. **In vitro storage of plum germplasm by slow growth**. *Hort. Sci.* (2016.0) **42** 61-69. DOI: 10.17221/186/2014-HORTSCI
13. Bandeira Reidel R.V., Cioni P.L., Pistelli L.. **Volatile emission of different plant parts and fruit development from Italian cherry plums (**. *Biochem. System. Ecol.* (2017.0) **75** 10-17. DOI: 10.1016/j.bse.2017.10.001
14. Priyanka V., Kumar R., Dhaliwal I., Kaushik P.. **Germplasm Conservation: Instrumental in Agricultural Biodiversity: A Review**. *Sustainability* (2021.0) **13**. DOI: 10.3390/su13126743
15. McCouch S., Navabi Z.K., Abberton M., Anglin N.L., Barbieri R.L., Baum M., Bett K., Booker H., Brown G.L., Bryan G.J.. **Mobilizing Crop Biodiversity**. *Mol. Plant.* (2020.0) **13** 1341-1344. DOI: 10.1016/j.molp.2020.08.011
16. Priyadarshan P.M., Jain S.M.. **Cash Crops: Genetic Diversity, Erosion, Conservation and Utilization**. *An Introduction* (2022.0) 1-636. DOI: 10.1007/978-3-030-74926-2
17. Cosme F., Pinto T., Aires A., Morais M.C., Bacelar E., Anjos R., Ferreira-Cardoso J., Oliveira I., Vilela A., Gonçalves B.. **Red fruits composition and their health benefits—A review**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11050644
18. Fu H., Mu X., Wang P., Zhang J., Fu B., Du J.. **Fruit quality and antioxidant potential of**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0244445
19. Kayano S., Kikuzaki H., Fukutsuka N., Mitani T., Nakatani N.. **Antioxidant activity of prune (**. *J. Agric. Food Chem.* (2002.0) **50** 3708-3712. DOI: 10.1021/jf0200164
20. Varga E., Domokos E., Fogarasi E., Steanesu R., Fülöp I., Croitoru M.D., Laczkó-Zöld E.. **Polyphenolic compounds analysis and antioxidant activity in fruits of**. *Acta Pharm. Hung.* (2017.0) **87** 19-25. PMID: 29489094
21. Jiang X., Yang Z., Wang S., Deng S.. **“Big Data” approaches for prevention of the metabolic syndrome**. *Front. Genet.* (2022.0) **13** 810152. DOI: 10.3389/fgene.2022.810152
22. Scuteri A., Laurent S., Cucca F., Cockcroft J., Cunha P.G., Mañas L.R., Mattace Raso F.U., Muiesan M.L., Ryliškytė L., Rietzschel E.. **Metabolic Syndrome and arteries research (MARE) consortium. Metabolic syndrome across Europe: Different clusters of risk factors**. *Eur. J. Prev. Cardiol.* (2015.0) **22** 486-491. DOI: 10.1177/2047487314525529
23. Carrier A.. **Metabolic syndrome and oxidative stress: A complex relationship**. *Antioxid. Redox Signal.* (2017.0) **26** 429-431. DOI: 10.1089/ars.2016.6929
24. Găman M., Epingeac M., Diaconu C., Găman A.. **Oxidative stress levels are increased in type 2 diabetes mellitus and obesity**. *J. Hypertens* (2019.0) **37** e265. DOI: 10.1097/01.hjh.0000573380.76445.7a
25. Del Rio D., Rodriguez-Mateos A., Spencer J.P., Tognolini M., Borges G., Crozier A.. **Dietary (poly)phenolics in human health: Structures, bioavailability, and evidence of protective effects against chronic diseases**. *Antioxid. Redox Signal.* (2013.0) **18** 1818-1892. DOI: 10.1089/ars.2012.4581
26. Rahman M.M., Rahaman M.S., Islam M.R., Rahman F., Mithi F.M., Alqahtani T., Almikhlafi M.A., Alghamdi S.Q., Alruwaili A.S., Hossain M.S.. **Role of phenolic compounds in human disease: Current knowledge and future prospects**. *Molecules* (2021.0) **27**. DOI: 10.3390/molecules27010233
27. Tundis R., Loizzo M.R., Menichini F.. **Natural products as alpha-amylase and alpha- glucosidase inhibitors and their hypoglycaemic potential in the treatment of diabetes: An update**. *Mini-Rev. Med. Chem.* (2010.0) **10** 315-331. DOI: 10.2174/138955710791331007
28. de la Garza A.L., Milagro F.I., Boque N., Campión J., Martínez J.A.. **Natural inhibitors of pancreatic lipase as new players in obesity treatment**. *Planta Med.* (2011.0) **77** 773-785. DOI: 10.1055/s-0030-1270924
29. 29.RRNAvailable online: https://www.reterurale.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/9580(accessed on 3 January 2023)
30. 30.UPOVAvailable online: https://www.upov.int/edocs/tgdocs/en/tg041.pdf(accessed on 3 January 2023)
31. Wang Y., Chen X., Zhang Y., Chen X.. **Antioxidant activities and major anthocyanins of myrobalan plum (**. *J. Food Sci.* (2012.0) **77** C388-C393. DOI: 10.1111/j.1750-3841.2012.02624.x
32. Koca I., Karadeniz B.. **Antioxidant properties of blackberry and blueberry fruits grown in the Black Sea Region of Turkey**. *Sci. Hortic.* (2009.0) **121** 447-450. DOI: 10.1016/j.scienta.2009.03.015
33. Leporini M., Loizzo M.R., Sicari V., Pellicanò T.M., Reitano A., Dugay A., Deguin B., Tundis R.. *Antioxidants* (2020.0) **9**. DOI: 10.3390/antiox9040298
34. Leporini M., Tundis R., Sicari V., Pellicanò T.M., Dugay A., Deguin B., Loizzo M.R.. **Impact of extraction processes on phytochemicals content and biological activity of**. *Food Res. Int.* (2020.0) **127** 108742. DOI: 10.1016/j.foodres.2019.108742
35. Loizzo M.R., Sicari V., Pellicanò T., Xiao J., Poiana M., Tundis R.. **Comparative analysis of chemical composition, antioxidant and anti-proliferative activities of Italian**. *Food Chem. Toxicol.* (2019.0) **127** 127-134. DOI: 10.1016/j.fct.2019.03.007
36. Formoso P., Tundis R., Pellegrino M., Leporini M., Sicari V., Romeo R., Gervasi L., Corrente G., Beneduci A., Loizzo M.R.. **Preparation, characterization, and bioactivity of**. *J. Sci. Food Agr.* (2022.0) **102** 6566-6577. DOI: 10.1002/jsfa.12022
37. Loizzo M.R., Tundis R., Leporini M., D’Urso G., Gagliano Candela R., Falco T., Piacente S., Bruno M., Sottile F.. **Almond (**. *Antioxidants* (2021.0) **10**. DOI: 10.3390/antiox10081218
38. Turkmen N., Sari F., Velioglu Y.S.. **Effects of extraction solvents on concentration and antioxidant activity of black and black mate tea polyphenols determined by ferrous tartrate and FolineCiocalteu methods**. *Food Chem.* (2006.0) **99** 835-841. DOI: 10.1016/j.foodchem.2005.08.034
39. Dai J., Mumper R.J.. **Plant phenolics: Extraction, analysis and their antioxidant and anticancer properties**. *Molecules* (2010.0) **15** 7313-7352. DOI: 10.3390/molecules15107313
40. Moscatello S., Frioni T., Blasi F., Proietti S., Pollini L., Verducci G., Rosati A., Walker R.P., Battistelli A., Cossignani L.. **Changes in absolute contents of compounds affecting the taste and nutritional properties of the flesh of three plum species throughout development**. *Foods* (2019.0) **8**. DOI: 10.3390/foods8100486
41. Kim D.O., Jeong S.W., Lee C.Y.. **Antioxidant capacity of phenolic phytochemicals from various cultivars of plums**. *Food Chem.* (2003.0) **81** 321-326. DOI: 10.1016/S0308-8146(02)00423-5
42. Murathan Z.A., Mehmet E.N.. **Analyzing biological properties of some plum genotypes grown in turkey**. *Int. J. Fruit Sci.* (2020.0) **20** 3-15. DOI: 10.1080/15538362.2020.1830917
43. Smanalieva J., Iskakova J., Oskonbaeva Z., Wichern F., Darr D.. **Determination of physicochemical parameters, phenolic content, and antioxidant capacity of wild cherry plum (**. *Eur. Food Res. Technol.* (2019.0) **245** 2293-2301. DOI: 10.1007/s00217-019-03335-8
44. Gil M.I., Toma Barberan F.A., Hess-Pierce B., Kaderm A.A.. **Antioxidant capacities, phenolic compounds, carotenoids, and vitamin C contents of nectarine, peach, and plum cultivars from California**. *J. Agric. Food Chem.* (2012.0) **50** 4976-4982. DOI: 10.1021/jf020136b
45. Celik F., Gundogdu M., Alp Ş., Muradoğlu F., Geçer M., Canan I.. **Determination of phenolic compounds, antioxidant capacity and organic acids contents of**. *Acta Chromatogr.* (2017.0) **29** 1-4. DOI: 10.1556/1326.2017.00327
46. Homoki J.R., Nemes A., Fazekas E., Gyémánt G., Balogh P., Gál F., Al-Asri J., Mortier J., Wolber G., Babinszky L.. **Anthocyanin composition, antioxidant efficiency, and α-amylase inhibitor activity of different Hungarian sour cherry varieties (**. *Food Chem.* (2016.0) **194** 222-229. DOI: 10.1016/j.foodchem.2015.07.130
47. El-Beltagi H., El- Ansary A., Mostafa M., Kamel T., Safwat G.. **Evaluation of the Phytochemical, Antioxidant, Antibacterial and Anticancer Activity of Prunus domestica Fruit**. *Not. Bot. Horti. Agrobot. Cluj Napoca* (2018.0) **47** 395-404. DOI: 10.15835/nbha47111402
48. Putkaradze J., Diasamidze M., Vanidze M., Kalandia A.. **Antioxidant Activity of**. *Int. J. Life Sci.* (2021.0) **10** 52-54
49. Napolitano A., Di Napoli M., Castagliuolo G., Badalamenti N., Cicio A., Bruno M., Piacente S., Maresca V., Cianciullo P., Capasso L.. **The chemical composition of the aerial parts of**. *Phytochemistry* (2022.0) **203** 113373. DOI: 10.1016/j.phytochem.2022.113373
50. Navarro-Hoyos M., Arnáez-Serrano E., Quesada-Mora S., Azofeifa-Cordero G., Wilhelm-Romero K., Quirós-Fallas M.I., Alvarado-Corella D., Vargas-Huertas F., Sánchez-Kopper A.. **Polyphenolic QTOF-ESI MS characterization and the antioxidant and cytotoxic activities of**. *Molecules* (2021.0) **26**. DOI: 10.3390/molecules26216493
51. D’Urso G., Napolitano A., Cannavacciuolo C., Masullo M., Piacente S.. **Okra fruit: LC-ESI/LTQOrbitrap/MS/MSn based deep insight on polar lipids and specialized metabolites with evaluation of anti-oxidant and antihyperglycemic activity**. *Food Funct.* (2020.0) **11** 7856-7865. DOI: 10.1039/D0FO00867B
52. Cerulli A., Napolitano A., Hosek J., Masullo M., Pizza C., Piacente S.. **Antioxidant and in vitro preliminary anti-inflammatory activity of**. *Antioxidants* (2021.0) **10**. DOI: 10.3390/antiox10020278
53. Sun J., Lin L., Chen P.. **Study of the mass spectrometric behaviors of anthocyanins in negative ionization mode and its applications for characterization of anthocyanins and non-anthocyanin polyphenols**. *Rapid Commun. Mass Spectrom.* (2012.0) **26** 1123-1133. DOI: 10.1002/rcm.6209
54. Gómez-Caravaca A.M., Verardo V., Segura-Carretero A., Caboni M.F., Fernández-Gutiérrez A.. **Development of a rapid method to determine phenolic and other polar compounds in walnut by capillary electrophoresis–electrospray ionization time-of-flight mass spectrometry**. *J. Chromatogr. A* (2008.0) **1209** 238-245. DOI: 10.1016/j.chroma.2008.08.117
55. Bijttebier S., Van der Auwera A., Voorspoels S., Noten B., Hermans N., Pieters L., Apers S.. **A first step in the quest for the active constituents in**. *Planta Med.* (2016.0) **82** 559-572. DOI: 10.1055/s-0042-101943
56. Shi Q.Q., Lu S.Y., Peng X.R., Zhou L., Qiu M.H.. **Hydroxynitrile glucosides: Bioactive constituent recovery from the oil residue of**. *J. Agric. Food Chem.* (2021.0) **69** 2438-2443. DOI: 10.1021/acs.jafc.0c07514
57. Cho J.G., Cha B.J., Seo W.D., Jeong R.H., Shrestha S., Kim J.Y., Kang H.C., Baek N.I.. **Feruloyl sucrose esters from**. *Chem. Nat. Compd.* (2015.0) **51** 1094-1098. DOI: 10.1007/s10600-015-1500-8
58. Tao S., Huang Y., Chen Z., Chen Y., Wang Y., Wang Y.. **Rapid identification of anti-inflammatory compounds from Tongmai Yangxin Pills by liquid chromatography with high-resolution mass spectrometry and chemometric analysis**. *J. Sep. Sci.* (2015.0) **38** 1881-1893. DOI: 10.1002/jssc.201401481
59. Wu X., Wang Y., Huang X.J., Fan C.L., Wang G.C., Zhang X.Q., Zhang Q.W., Ye W.C.. **Three new glycosides from**. *J. Asian Nat. Prod. Res.* (2011.0) **13** 728-733. DOI: 10.1080/10286020.2011.586944
60. Zhang X., Su M., Du J., Zhou H., Li X., Zhang M., Hu Y., Ye Z.. **Profiling of naturally occurring proanthocyanidins and other phenolic compounds in a diverse peach germplasm by LC-MS/MS**. *Food Chem.* (2023.0) **403** 134471. DOI: 10.1016/j.foodchem.2022.134471
61. Ortega-Vidal J., Cobo A., Ortega-Morente E., Gálvez A., Martínez-Bailén M., Salido S., Altarejos J.. **Antimicrobial activity of phenolics isolated from the pruning wood residue of European plum (**. *Ind. Crops Prod.* (2022.0) **176** 114296. DOI: 10.1016/j.indcrop.2021.114296
62. De Leo M., Iannuzzi A.M., Germano M.P., D’Angelo V., Camangi F., Sevi F., Diretto G., De Tommasi N., Braca A.. **Comparative chemical analysis of six ancient italian sweet cherry (**. *Food Chem.* (2021.0) **360** 129999. DOI: 10.1016/j.foodchem.2021.129999
63. Wojdyło A., Nowicka P., Turkiewicz I.P., Tkacz K.. **Profiling of polyphenols by LC-QTOF/ESI-MS, characteristics of nutritional compounds and in vitro effect on pancreatic lipase, α-glucosidase, α-amylase, cholinesterase and cyclooxygenase activities of sweet (**. *Ind. Crops Prod.* (2021.0) **174** 114214. DOI: 10.1016/j.indcrop.2021.114214
64. Tiboni M., Coppari S., Casettari L., Guescini M., Colomba M., Fraternale D.E., Gorassini A., Verardo G., Ramakrishna S., Guidi L.. *Nanomaterials* (2021.0) **11**. DOI: 10.3390/nano11010036
65. Bottone A., Montoro P., Masullo M., Pizza C., Piacente S.. **Metabolite profiling and antioxidant activity of the polar fraction of Italian almonds (Toritto and Avola): Analysis of seeds, skins, and blanching water**. *J. Pharm. Biomed. Anal.* (2020.0) **190** 113518. DOI: 10.1016/j.jpba.2020.113518
66. Blackhall M.L., Berry R., Davies N.W., Walls J.T.. **Optimized extraction of anthocyanins from reid fruits’**. *Food Chem.* (2018.0) **256** 280-285. DOI: 10.1016/j.foodchem.2018.02.137
67. Crupi P., Bleve G., Tufariello M., Corbo F., Clodoveo M.L., Tarricone L.. **Comprehensive identification and quantification of chlorogenic acids in sweet cherry by tandem mass spectrometry techniques**. *J. Food Comp. Anal.* (2018.0) **73** 103-111. DOI: 10.1016/j.jfca.2018.06.013
68. Zhang X., Lin Z., Fang J., Liu M., Niu Y., Chen S., Wang H.. **An on-line high-performance liquid chromatography–diode-array detector–electrospray ionization–ion-trap–time-of-flight–mass spectrometry–total antioxidant capacity detection system applying two antioxidant methods for activity evaluation of the edible flowers from**. *J. Chromatogr. A* (2015.0) **1414** 88-102. DOI: 10.1016/j.chroma.2015.08.033
69. Kayano S., Kikuzaki H., Hashimoto S., Kasamatsu K., Ikami T., Nakatani N.. **Glucosyl terpenates from the dried fruits of**. *Phytochem. Lett.* (2014.0) **8** 132-136. DOI: 10.1016/j.phytol.2014.03.006
70. Treutter D., Wang D., Farag M.A., Argueta Baires G.D., Rühmann S., Neumüller M.. **Diversity of phenolic profiles in the fruit skin of**. *J. Agric. Food Chem.* (2012.0) **60** 12011-12019. DOI: 10.1021/jf303644f
71. Olszewska M., Kwapisz A.. **Metabolite profiling and antioxidant activity of**. *Nat. Prod. Res.* (2011.0) **25** 1115-1131. DOI: 10.1080/14786410903230359
72. Kikuzaki H., Kayano S., Fukutsuka N., Aoki A., Kasamatsu K., Yamasaki Y., Mitani T., Nakatani N.. **Abscisic acid related compounds and lignans in prunes (**. *J. Agric. Food Chem.* (2004.0) **52** 344-349. DOI: 10.1021/jf034954v
73. Purohit M.C., Rawat M.S.M., Pant G., Nautiyal A.K., Sakakibaraa J., Kaiya T.. **A methyl ester of melilotoside from the sapwood of**. *Phytochemistry* (1993.0) **32** 431-432. DOI: 10.1016/S0031-9422(00)95009-X
74. Lo Piccolo E., Araniti F., Landi M., Massai R., Guidi L., Abenavoli M.R., Remorini D.. **Girdling stimulates anthocyanin accumulation and promotes sugar, organic acid, amino acid level and antioxidant activity in red plum: An overview of skin and pulp metabolomics**. *Sci. Hortic.* (2021.0) **280** 109907. DOI: 10.1016/j.scienta.2021.109907
75. Popović B.M., Blagojević B., Kucharska A.Z., Agić D., Magazin N., Milović M., Serra A.T.. **Exploring fruits from genus**. *Food Chem.* (2021.0) **358** 129812. DOI: 10.1016/j.foodchem.2021.129812
76. Shen J., Zhang P., Zhang X., Yao J., Chang J.-M.. **Extraction process and composition identification of anthocyanins from Xinjiang wild**. *Pak. J. Pharm. Sci.* (2021.0) **34** 2409-2415. DOI: 10.36721/PJPS.2021.34.6.SP.2409-2415.1
77. Liu W., Nisar M.F., Wan C.. **Characterization of phenolic constituents from**. *J. Chem.* (2020.0) **2020** 5976090. DOI: 10.1155/2020/5976090
78. Chen F.F., Sang J., Zhang Y., Sang J.. **Development of a green two-dimensional HPLC-DAD/ESI-MS method for the determination of anthocyanins from**. *Int. J. Food Sci. Technol.* (2018.0) **53** 1494-1502. DOI: 10.1111/ijfs.13730
79. Saraswathi K., Sivaraj C., Arumugam P.. **Antioxidant and antibacterial activities of ethanol fruit extract of cherry Plum—**. *J. Drug Deliv. Ther.* (2020.0) **10** 45-50. DOI: 10.22270/jddt.v10i1-s.3851
80. Nowicka P., Wojdyło A., Samoticha J.. **Evaluation of phytochemicals, antioxidant capacity, and antidiabetic activity of novel smoothies from selected**. *J. Funct. Foods* (2016.0) **25** 397-407. DOI: 10.1016/j.jff.2016.06.024
81. Kołodziejczyk-Czepas J., Skrobacz K., Czerwińska M.E., Rutkowska M., Prokop A., Michel P., Olszewska M.A.. **Valorisation of the Inhibitory Potential of Fresh and Dried Fruit Extracts of**. *Pharmaceuticals* (2022.0) **15**. DOI: 10.3390/ph15101300
82. Podsędek A., Majewska I., Redzynia M., Sosnowska D., Koziołkiewicz M.. **In Vitro Inhibitory Effect on Digestive Enzymes and Antioxidant Potential of Commonly Consumed Fruits**. *J. Agric. Food Chem.* (2014.0) **62** 4610-4617. DOI: 10.1021/jf5008264
83. Ullah H., Sommella E., Santarcangelo C., D’Avino D., Rossi A., Dacrema M., Minno A.D., Di Matteo G., Mannina L., Campiglia P.. **Hydroethanolic extract of**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14020340
84. Wang T., Li X., Zhou B., Li H., Zeng J., Gao W.. **Anti-diabetic activity in type 2 diabetic mice and α-glucosidase inhibitory, antioxidant and anti-inflammatory potential of chemically profiled pear peel and pulp extracts (**. *J. Funct. Foods* (2015.0) **13** 276-288. DOI: 10.1016/j.jff.2014.12.049
|
---
title: 'Evaluating the Effectiveness of Letter and Telephone Reminders in Promoting
the Use of Specific Health Guidance in an At-Risk Population for Metabolic Syndrome
in Japan: A Randomized Controlled Trial'
authors:
- Hiroshi Murayama
- Setaro Shimada
- Kosuke Morito
- Haruna Maeda
- Yuta Takahashi
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001113
doi: 10.3390/ijerph20053784
license: CC BY 4.0
---
# Evaluating the Effectiveness of Letter and Telephone Reminders in Promoting the Use of Specific Health Guidance in an At-Risk Population for Metabolic Syndrome in Japan: A Randomized Controlled Trial
## Abstract
Japan has introduced a nationwide lifestyle intervention program (specific health guidance) for people aged 40–74 years. Medical insurers apply a reminder system to improve their utilization rates. This study examined the effectiveness of two methods of reminders (mailed letters and telephone calls) in a randomized controlled trial. Subscribers to National Health Insurance in Yokohama City, Kanagawa Prefecture, who were eligible for specific health guidance in 2021, were recruited. A total of 1377 people who met the criteria of having or being at risk of developing metabolic syndrome (male: $77.9\%$, mean age: 63.1 ± 10.0 years) were randomly assigned to one of three groups: a “no reminder” group, a “letter reminder” group, or a “telephone reminder” group. The utilization rates of specific health guidance were not significantly different between the three groups ($10.5\%$, $15.3\%$, and $13.7\%$, respectively). However, in the case of the telephone reminder group, a subgroup analysis showed that the utilization rate was significantly higher among participants who received the reminder than those who did not answer the calls. Although the effectiveness of a telephone reminder might be underestimated, this study suggests that neither method impacted the utilization rates of specific health guidance among the population at risk of metabolic syndrome.
## 1. Introduction
Cardiometabolic diseases (CMDs), such as cardiovascular disease, stroke, diabetes, and chronic kidney disease, are the leading causes of mortality worldwide [1]. The main cause of CMDs is a cluster of metabolic derangements known as metabolic syndrome. The underlying factors for the incidence of metabolic syndrome include obesity, physical inactivity, and older age [2]. Therefore, with the increasing rates of obesity [3] and adoption of sedentary lifestyles [4], in combination with the aging of the global population, there is an urgent need to screen for CMD risks and to derive novel prevention strategies. In this context, several countries, including Japan, are striving to establish screening programs and lifestyle intervention strategies in order to promote the primary prevention of CMDs.
In 2008, Japan introduced a nationwide screening program (i.e., health checkups) to identify individuals with high obesity and cardiovascular risks (known as metabolic syndrome). In addition, the country established specific health guidance (i.e., a lifestyle intervention program) to reduce cardiovascular risk factors [5]. These services are provided to all adults aged 40–74 years every year and are delegated to medical insurers by the Act on Securing Medical Care for the Elderly. In fact, medical insurers have taken various measures to improve the utilization rates of the programs by the targeted population. According to recent statistics collected in Japan, approximately 30 million people underwent screening and 1.2 million people utilized specific health guidance in 2019 [6].
Meta-analyses revealed that lifestyle intervention can reduce cardiometabolic risks [7,8,9,10]. In contrast, a large-scale community-based study concluded that individually tailored lifestyle interventions had no effect on ischemic heart disease, stroke, or mortality on the population level after 10 years [11]. In addition, Japanese studies using a quasi-experimental design reported limited effects of lifestyle interventions on cardiometabolic risk factors [12]. Thus, evidence for the effects of health guidance remains controversial. Nevertheless, since specific health guidance is already a national measure, considering better methods to improve the utilization rate is important for both current and future systems.
Many medical insurers in Japan use reminders to improve the utilization rates of specific health guidance. Systematic reviews have reported the effectiveness of such reminder systems for cancer screening [13]. In the case of general health checkups, a study in the United Kingdom showed that the utilization rate was higher when short message services were used as reminders compared to when the usual letter reminders were used [14]. It has also been reported that telephone reminders are more effective in increasing participation in health checkups than letter reminders [15]. However, the effectiveness of reminders seems to differ depending on the population’s demographic characteristics, such as race/ethnicity [16]. Previous findings mainly originate from studies conducted in Western countries. Therefore, it is necessary to confirm whether these findings are applicable to other populations, such as those in Japan.
The purpose of this study was to examine the effectiveness of reminders in promoting the utilization of specific health guidance using a randomized controlled trial design. In this study, we used two reminder methods (letters and telephone calls). In addition, this study focused on people who are considered at high risk of metabolic syndrome, given the requirement for reminders among this population.
## 2.1. Sample and Procedures
The target population was National Health Insurance subscribers in Yokohama City, Kanagawa Prefecture, Japan (approximately 510 thousand subscribers as of April 2021). Yokohama is the capital of Kanagawa Prefecture and is located 30 km southwest of central Tokyo. As of April 2021, Yokohama City had a population of approximately 3.78 million. At the time of this study, National Health Insurance covered approximately $20\%$ of the total city population.
Among the National Health Insurance subscribers in Yokohama, 460,928 were eligible for health checkups in the fiscal year (FY) of 2021 (i.e., between April 2021 and March 2022), and 113,945 received health checkups. Of these, 13,638 were eligible for specific health guidance based on national criteria. Among them, 10,763 people who were deemed to require immediate medical attention (based on the national criteria using the results of health checkups, renal function tests, blood pressure, complete blood counts, lipid panels, blood sugar levels, and liver function tests) were excluded. Consequently, we included 1377 people (355 met the criteria for metabolic syndrome and 1022 were considered to be at risk of metabolic syndrome). In FY2021, among those who underwent health checkups, $14.8\%$ and $56.9\%$ were judged as “applicable” and “at risk” of metabolic syndrome, respectively. The implementation rate of specific health guidance in Yokohama City was $9.3\%$ in FY2020.
This study adopted a random sampling method to enhance the internal validity of the findings. The participants were randomly assigned to three groups: the “no reminder” group ($$n = 458$$), the “letter reminder” group ($$n = 459$$), or the “telephone reminder” group ($$n = 460$$). Random assignment was conducted by the staff of Yokohama City. The staff provided a unique number to every participant and assigned them randomly to one of the three groups using a random number generator. This process was performed each month. The data analysts, but not the participants, were blinded to the information on the group assignment. A flow diagram of the sampling and allocation processes is shown in Figure 1.
## 2.2. Intervention
We adopted letter and telephone reminder interventions in this study. The interventions were administered by the staff of Yokohama City. The information provided to the participants via either letter or telephone call was not personalized.
## 2.2.1. Letter Reminder
A reminder was mailed to the participants’ home addresses. The main components of the letter were an “explanation of the specific health guidance (including information that the specific health guidance was free of charge)”, “the expiration date of the specific health guidance”, “information on the medical centers/hospitals/clinics where the specific health guidance is provided”, and “telephone number for inquiries”. The expiration date was determined according to the month in which the participants underwent a health checkup. The coupon for specific health guidance was valid for two months from the time of dispatch.
## 2.2.2. Telephone Reminder
The public health nurse called the participants on weekdays using the phone numbers that the participants had provided as their contact information when they were enrolled in the National Health Insurance program. The information provided to the participants was compiled into a manual. The main contents were “a brief explanation of the results of the health checkups”, “the explanation of the specific health guidance (including information that the specific health guidance was free of charge)”, “the expiration date of the specific health guidance”, and “information on the medical centers/hospitals/clinics where the specific health guidance is provided and the way to make an appointment”. In cases of disconnection, the public health nurse re-called the participant on different weekdays (up to three times). If family members answered the phone, the public health nurse told them to re-call on different days and asked them to encourage the participant to receive specific health guidance.
Of 460 individuals assigned to the telephone reminder group, the public health nurse was able to directly reach 274 participants ($59.6\%$) and to leave a message with the family members of 34 participants ($7.4\%$). The public health nurse could not reach the remaining 152 participants, and they did not receive the telephone reminder.
## 2.3.1. Outcomes
The outcome variable was whether or not the participants utilized specific health guidance in FY2021. Information on the participants’ use of specific health guidance was obtained from the Data Management System of Yokohama City.
## 2.3.2. Participants’ Characteristics
We used the participants’ demographics (sex and age) and the results of the health checkups obtained via the Data Management System. The results of the health checkups included the abdominal circumference, body mass index, diastolic blood pressure, systolic blood pressure, HbA1c, fasting blood glucose, triglyceride, high-density lipoprotein cholesterol, history of diseases (cerebrovascular diseases, cardiovascular diseases, chronic kidney failure, and dialysis therapy), smoking habits (“Do you currently smoke habitually?”; yes or no), exercise habits (“Do you exercise lightly for at least 30 min two days a week for at least one year?”; yes or no), and frequency of drinking (“How often do you drink alcohol?”; every day, sometimes, rarely, or never).
## 2.4. Statistical Analysis
First, the participants’ characteristics were compared between the three groups using the chi-square test, Fisher’s exact test, and Kruskal–Wallis test. For continuous variables, confirmed to be not normally distributed by the Shapiro–Wilk test, the non-parametric test was conducted (i.e., the Kruskal–Wallis test). Second, the outcome variable (i.e., the utilization of specific health guidance) was compared between the three groups using the chi-square test. For multiple comparisons, the Bonferroni correction was adopted with a significance level (α) of $1.7\%$ (i.e., $p \leq 0.017$ (=$\frac{0.05}{3}$)). The analysis was performed using IBM SPSS Statistics 29 (IBM Corp., Armonk, NY, USA).
Previous studies have suggested that reminders of health checkups increase the uptake rate by a factor of approximately 1.5–1.7 times [14]. The utilization rate of specific health guidance in Yokohama City in FY2020 was $9.3\%$ in total. However, it was $13.3\%$ for the subpopulation of people who met the criteria for metabolic syndrome or were at risk of metabolic syndrome. Assuming a significance level (α) of 0.05 (actually calculated as 0.017, considering multiple comparisons) and a power (1 − β) of 0.80, the total number of necessary samples was projected to be 1380 cases (460 in each group).
## 3. Results
Table 1 shows the characteristics of the participants. Overall, $77.9\%$ were male, and the average age was 63.1 ± 10.0 years. No differences were observed between the three groups in any of the variables.
Table 2 presents the utilization rates of specific health guidance among the three groups. The utilization rates were $10.5\%$ in the no-reminder group, $13.7\%$ in the letter reminder group, and $15.3\%$ in the telephone reminder group, with no significant differences between the three groups (χ2 = 4.753, $$p \leq 0.093$$). Moreover, no differences were found between any two groups in multiple comparisons.
Although they are not shown in the table, we compared the utilization rates of the participants whose calls were answered either by them directly or by their family members ($$n = 308$$, $67.0\%$) and those who were not reachable by telephone ($$n = 152$$, $33.0\%$) in the telephone reminder group. The utilization rates were $16.9\%$ (52 of 308) and $7.2\%$ (11 of 152), respectively (χ2 = 8.012, $$p \leq 0.004$$). This difference remained significant even after adjusting for sex, age, body mass index, and history of disease.
## 4. Discussion
In this study, we examined the effectiveness of two reminder methods in regard to the rate of utilization of specific health guidance (i.e., letters and telephone calls) using a randomized controlled trial design. Most medical insurers in Japan use a call–recall methodology to improve the implementation rate of specific health guidance. However, its effectiveness has not been yet sufficiently verified. This study focused on widely used reminder methods that can contribute to the establishment of evidence-based health activities.
The analysis did not demonstrate an improvement in the utilization rate after either letter or telephone reminders compared to no reminder. This result differs from those of previous studies regarding general health checkups and cancer screening [13,14,15,17], which confirmed the effectiveness of reminders. One possible reason for this inconsistency may be that individuals refrained from following specific health guidance due to the recent coronavirus disease 2019 (COVID-19) pandemic. The COVID-19 pandemic has affected many aspects of people’s behaviors in daily life. In fact, the nationwide implementation rate of specific health guidance had been increasing every year until FY2019 (i.e., before the COVID-19 outbreak); however, in FY 2020, during the outbreak, it decreased (overall: from $29.3\%$ to $27.9\%$, male: from $27.5\%$ to $26.4\%$, and female: from $32.9\%$ to $30.9\%$) [6]. The spread of COVID-19 has augmented people’s fear of going out and visiting places where people gather, and thus the utilization of specific health guidance might be impacted. Another possibility is the limited population targeted in this study, i.e., people who have metabolic syndrome or are at risk of developing metabolic syndrome. Although we focused on this population because of their higher need for lifestyle interventions, they might have special circumstances that hinder their use of specific health guidance, which could lead to an underestimation of the effectiveness of reminders. We considered these two possibilities in our analysis.
Earlier studies regarding the use of a letter invitation/reminder to attend health checkups reported a lack of impact of letters [16,18,19]; however, the letter is the most common method used to invite/remind individuals about health checkup participation. This study also revealed that the effect of the letter reminder might be weak. The letter reminder used in this study was generic. Since some studies demonstrated that tailoring messages in the letter to the individual’s level of risk could increase the participation rate in cancer screening programs [20,21], personalization of the letter may lead to a higher level of utilization of health guidance.
Previous studies revealed that telephone reminders are more effective than letter reminders [15,17]. A qualitative study conducted in the United Kingdom reported that participants could directly make an appointment for consultation or to obtain health guidance via telephone reminder, which could contribute to increased utilization [22]. However, in Yokohama City, owing to the system of specific health guidance, it was not possible to make an appointment for specific health guidance over the phone directly; the participants had to make an appointment later by themselves. The inefficiency of this process may have reduced the effectiveness of telephone reminders. In addition, the telephone reminder group included both participants who could be reached by a public health nurse and those who could not; thus, the effectiveness of the telephone reminder could have been underestimated. However, as specific health guidance is provided for those aged 40–74 years, including the working-age population (e.g., those in their 40s and 50s), from a practical perspective, it is difficult to access all of the target population when the telephone reminder is performed on weekdays. To increase the effectiveness of telephone reminders, a more flexible system, such as calling in the evening/nighttime or on weekends, should be implemented.
Although this study showed no difference in the utilization rate of specific health guidance between the three groups, this does not necessarily mean that reminders are ineffective. This study focused on individuals who met the criteria for metabolic syndrome or were considered at risk of developing metabolic syndrome. Therefore, the findings suggest that reminders directed towards this population may be given lower priority. Based on this study, future investigations could be conducted to verify which populations will benefit most from the reminders system.
This study has several limitations. First, as previously mentioned, the current study targeted only those with metabolic syndrome or subjects at risk of developing this disease. The effects on other populations need to be investigated in future studies to enhance the external validity. Second, this study was conducted in Yokohama. The possibility that the results may differ between regions with different medical resources and resident characteristics cannot be denied, and the generalizability of the findings must be carefully considered. Third, this study was performed in FY2021, the second year of the COVID-19 pandemic. People’s attitudes toward the utilization of specific health guidance could have been influenced by the outbreak. Therefore, the present findings might not necessarily be applicable to the “post-COVID-19 era”. Fourth, many other factors prevent people from using specific health guidance (e.g., the inconvenience of making an appointment for specific health guidance and inaccessibility of the implementation site). Therefore, it might not be sufficient to improve the utilization rate by implementing reminders alone. Fifth, we were not able to investigate how many participants in the letter reminder group actually read the letter. The effects of the letter reminder might have differed between those who read the letter and those who did not. This means that we might have underestimated the effectiveness of the letter. Finally, this study investigated the effectiveness of letter and telephone reminders. However, there are some other reminder options (e.g., short message service (SMS) and e-mail), and these effects should be examined in the future.
## 5. Conclusions
We examined the effectiveness of two types of reminder methods (i.e., letters and telephone calls) in regard to the utilization of specific health guidance using a randomized controlled trial design for individuals with metabolic syndrome or those who were at risk of developing it. The results suggest that low priority is assigned to the task of reminding people in the population at risk of metabolic syndrome. Nonetheless, this study possibly underestimated the effectiveness of reminders. Reminders using either letters or telephone calls are labor- and cost-intensive to some degree. Thus, more effective and efficient methods should be explored for the implementation of reminders.
Medical insurers utilize a reminder method to increase the implementation rate of health checkups and health guidance worldwide. Previous studies regarding the effectiveness of reminders are mainly derived from Western countries such as the United Kingdom. However, as it has been implied that the population’s demographic characteristics may affect the effectiveness of reminders [16], a study should be conducted to clarify the effectiveness of the reminder methods in each context. In addition, there are other methods, such as SMS and e-mail, other than the letter and telephone reminder methods investigated in this study, and more effective methods will probably emerge with the advancement of technology. Such methods also need to be verified using a robust design.
## References
1. **Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018.0) **392** 1736-1788. DOI: 10.1016/S0140-6736(18)32203-7
2. Park Y.W., Zhu S., Palaniappan L., Heshka S., Carnethon M.R., Heymsfield S.B.. **The metabolic syndrome: Prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994**. *Arch. Intern. Med.* (2003.0) **163** 427-436. DOI: 10.1001/archinte.163.4.427
3. Blüher M.. **Obesity: Global epidemiology and pathogenesis**. *Nat. Rev. Endocrinol.* (2019.0) **15** 288-298. DOI: 10.1038/s41574-019-0176-8
4. Guthold R., Stevens G.A., Riley L.M., Bull F.C.. **Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1·9 million participants**. *Lancet Glob. Health.* (2018.0) **6** e1077-e1086. DOI: 10.1016/S2214-109X(18)30357-7
5. Tsushita K., Hosler A.S.S., Miura K., Ito Y., Fukuda T., Kitamura A., Tatara K.. **Rationale and descriptive analysis of specific health guidance: The nationwide lifestyle intervention program targeting metabolic syndrome in Japan**. *J. Atheroscler. Thromb.* (2018.0) **25** 308-322. DOI: 10.5551/jat.42010
6. **Data on Specific Health Checkups and Health Guidance**
7. Khatlani K., Njike V., Costales V.C.. **Effect of lifestyle intervention on cardiometabolic risk factors in overweight and obese women with polycystic ovary syndrome: A systematic review and meta-analysis**. *Metab. Syndr. Relat. Disord.* (2019.0) **17** 473-485. DOI: 10.1089/met.2019.0049
8. Singh N., Stewart R.A.H., Benatar J.R.. **Intensity and duration of lifestyle interventions for long-term weight loss and association with mortality: A meta-analysis of randomised trials**. *BMJ Open* (2019.0) **9** e029966. DOI: 10.1136/bmjopen-2019-029966
9. Sequi-Dominguez I., Alvarez-Bueno C., Martinez-Vizcaino V., Fernandez-Rodriguez R., Del Saz Lara A., Cavero-Redondo I.. **Effectiveness of mobile health interventions promoting physical activity and lifestyle interventions to reduce cardiovascular risk among individuals with metabolic syndrome: Systematic review and meta-analysis**. *J. Med. Internet Res.* (2020.0) **22** e17790. DOI: 10.2196/17790
10. Nieste I., Franssen W.M.A., Spaas J., Bruckers L., Savelberg H.H.C.M., Eijnde B.O.. **Lifestyle interventions to reduce sedentary behaviour in clinical populations: A systematic review and meta-analysis of different strategies and effects on cardiometabolic health**. *Prev. Med.* (2021.0) **148** 106593. DOI: 10.1016/j.ypmed.2021.106593
11. Jørgensen T., Jacobsen R.K., Toft U., Aadahl M., Glümer C., Pisinger C.. **Effect of screening and lifestyle counselling on incidence of ischaemic heart disease in general population: Inter99 randomised trial**. *BMJ* (2014.0) **348** g3617. DOI: 10.1136/bmj.g3617
12. Fukuma S., Iizuka T., Ikenoue T., Tsugawa Y.. **Association of the national health guidance intervention for obesity and cardiovascular risks with health outcomes among Japanese men**. *JAMA Intern. Med.* (2020.0) **180** 1630-1637. DOI: 10.1001/jamainternmed.2020.4334
13. Baron R.C., Melillo S., Rimer B.K., Coates R.J., Kerner J., Habarta N., Chattopadhyay S., Sabatino S.A., Elder R., Leeks K.J.. **Intervention to increase recommendation and delivery of screening for breast, cervical, and colorectal cancers by healthcare providers a systematic review of provider reminders**. *Am. J. Prev. Med.* (2010.0) **38** 110-117. DOI: 10.1016/j.amepre.2009.09.031
14. Sallis A., Sherlock J., Bonus A., Saei A., Gold N., Vlaev I., Chadborn T.. **Pre-notification and reminder SMS text messages with behaviourally informed invitation letters to improve uptake of NHS Health Checks: A factorial randomised controlled trial**. *BMC Public Health* (2019.0) **19**. DOI: 10.1186/s12889-019-7476-8
15. Gidlow C.J., Ellis N.J., Riley V., Chadborn T., Bunten A., Iqbal Z., Ahmed A., Fisher A., Sugden D., Clark-Carter D.. **Randomised controlled trial comparing uptake of NHS Health Check in response to standard letters, risk-personalised letters and telephone invitations**. *BMC Public Health* (2019.0) **19**. DOI: 10.1186/s12889-019-6540-8
16. Cook E.J., Sharp C., Randhawa G., Guppy A., Gangotra R., Cox J.. **Who uses NHS health checks? Investigating the impact of ethnicity and gender and method of invitation on uptake of NHS health checks**. *Int. J. Equity Health* (2016.0) **15** 13. DOI: 10.1186/s12939-016-0303-2
17. Stone T.J., Brangan E., Chappell A., Harrison V., Horwood J.. **Telephone outreach by community workers to improve uptake of NHS Health Checks in more deprived localities and minority ethnic groups: A qualitative investigation of implementation**. *J. Public Health* (2020.0) **42** e198-e206. DOI: 10.1093/pubmed/fdz063
18. Gidlow C., Ellis N., Randall J., Cowap L., Smith G., Iqbal Z., Kumar J.. **Method of invitation and geographical proximity as predictors of NHS Health Check uptake**. *J. Public Health* (2015.0) **37** 195-201. DOI: 10.1093/pubmed/fdu092
19. Ellis N., Gidlow C., Cowap L., Randall J., Iqbal Z., Kumar J.. **A qualitative investigation of non-response in NHS health checks**. *Arch. Public Health* (2015.0) **73** 14. DOI: 10.1186/s13690-015-0064-1
20. Albada A., Ausems M.G.E.M., Bensing J.M., van Dulmen S.. **Tailored information about cancer risk and screening: A systematic review**. *Patient Educ. Couns.* (2009.0) **77** 155-171. DOI: 10.1016/j.pec.2009.03.005
21. Edwards A.G.K., Naik G., Ahmed H., Elwyn G.J., Pickles T., Hood K., Playle R.. **Personalised risk communication for informed decision making about taking screening tests**. *Cochrane Database Syst. Rev.* (2013.0) **2013** CD001865. DOI: 10.1002/14651858.CD001865.pub3
22. Brangan E., Stone T.J., Chappell A., Harrison V., Horwood J.. **Patient experiences of telephone outreach to enhance uptake of NHS Health Checks in more deprived communities and minority ethnic groups: A qualitative interview study**. *Health Expect.* (2019.0) **22** 364-372. DOI: 10.1111/hex.12856
|
---
title: Lactobacillus gasseri LG-G12 Restores Gut Microbiota and Intestinal Health
in Obesity Mice on Ceftriaxone Therapy
authors:
- Mariana de Moura e Dias
- Vinícius da Silva Duarte
- Lúcio Flávio Macedo Mota
- Gabriela de Cássia Ávila Alpino
- Sandra Aparecida dos Reis Louzano
- Lisiane Lopes da Conceição
- Hilário Cuquetto Mantovanie
- Solange Silveira Pereira
- Leandro Licursi Oliveira
- Tiago Antônio de Oliveira Mendes
- Davide Porcellato
- Maria do Carmo Gouveia Peluzio
journal: Foods
year: 2023
pmcid: PMC10001121
doi: 10.3390/foods12051092
license: CC BY 4.0
---
# Lactobacillus gasseri LG-G12 Restores Gut Microbiota and Intestinal Health in Obesity Mice on Ceftriaxone Therapy
## Abstract
Gut microbiota imbalance is associated with the occurrence of metabolic diseases such as obesity. Thus, its modulation is a promising strategy to restore gut microbiota and improve intestinal health in the obese. This paper examines the role of probiotics, antimicrobials, and diet in modulating gut microbiota and improving intestinal health. Accordingly, obesity was induced in C57BL/6J mice, after which they were redistributed and fed with an obesogenic diet (intervention A) or standard AIN-93 diet (intervention B). Concomitantly, all the groups underwent a treatment phase with Lactobacillus gasseri LG-G12, ceftriaxone, or ceftriaxone followed by L. gasseri LG-G12. At the end of the experimental period, the following analysis was conducted: metataxonomic analysis, functional profiling of gut microbiota, intestinal permeability, and caecal concentration of short-chain fatty acids. High-fat diet impaired bacterial diversity/richness, which was counteracted in association with L. gasseri LG-G12 and the AIN-93 diet. Additionally, SCFA-producing bacteria were negatively correlated with high intestinal permeability parameters, which was further confirmed via functional profile prediction of the gut microbiota. A novel perspective on anti-obesity probiotics is presented by these findings based on the improvement of intestinal health irrespective of undergoing antimicrobial therapy or not.
## 1. Introduction
The gut is home to approximately $70\%$ of the microbiota detected in humans, including bacteria, fungi, viruses, and protozoa [1,2]. Modulation of gut microbiota composition and metabolic functions have been proposed as key factors that control obesity development [2,3].
External modulators of gut microbiota include probiotics, antimicrobials, and diet, which acts with speed and high precision in order to impact obesity [4,5]. Probiotics are live microorganisms which provide health benefits to host when consumed in sufficient amounts. They modulate the composition and metabolic functions of the gut microbiota, and contribute to immunological functions through the regulation of cytokines, promotion of oral tolerance to food antigens, and improvement of intestinal barrier functions [5]. In this context, several probiotics, used alone or as synbiotic mixtures, have shown antiobesity effects. For example, Lactobacillus gasseri has beneficial effects on weight loss and body fat reduction in overweight humans and animals [6,7,8].
Alternatively, antimicrobials can contribute to the loss of microbial diversity in the gut over time, impairing metabolic function and leading to impaired metabolism. Therefore, they potentially reduce the colonization resistance against invading pathogens, resulting in dysbiosis [9]. Antimicrobial’s ability to alter the microbiota of the gut varies based on diet, lifestyle, and drug spectrum of action as well as its absorption capacity, which indicates that broader spectrum antimicrobials such as ceftriaxone result in intestinal dysbiosis more frequently [10,11].
Similarly, a high-fat diet (HFD) is capable of promoting intestinal dysbiosis, thus contributing to intestinal barrier dysfunction, immune intolerance to food antigens, activation of pro-inflammatory routes, and circadian cycle disruption that leads to weight gain, abnormal glucose fluxes, and inflammatory response [12]. In contrast, balanced and diversified diets, such as the Mediterranean standard diet, rich in fruits, vegetables, whole grains, and seafood, promotes a diverse gut microbiota, thereby stimulating intestinal barrier function and immunity [13].
Moreover, the production of short-chain fatty acids (SCFAs), metabolites of gut microbiota, and intestinal permeability are also indicative of intestinal and systemic health [14,15]. SCFAs have numerous beneficial effects on the host from increasing mucus and tight junction expressions in the intestinal epithelium to metabolic and appetite modulation [16]. Intestinal permeability is influenced by gut microbiota imbalance and is one of the main factors for low-grade inflammation, making the microbiome a central player as regards inflammatory diseases such as obesity [12].
Given the above, there is a need for insights into mechanisms used to regulate dysbiosis in obese mice. Therefore, this study evaluated how a potential probiotic Lactobacillus gasseri LG-G12 (LG-G12), antimicrobial ceftriaxone, and diet [7,8] act to modulate the intestinal microbiota and subsequently impact intestinal health parameters.
## 2.1. Animals
Animal Ethics Committee of Universidade Federal de Viçosa approved the experiment according to the protocols numbers $\frac{09}{2017}$ and $\frac{33}{2018}$, and the principles established by the National Animal Experimentation Control Council [17].
An experiment was conducted using 72 male C57BL/6J mice [7,8] from the Central Vivarium of the Center for Biological and Health Sciences of Universidade Federal de Viçosa (UFV). The mice were kept in collective cages (two animals per cage) and were submitted to a 12 h light/dark cycle and an average temperature of 22 ± 2 °C. A pair-feeding scheme was used to administer fructose solution and a diet to the animals throughout the experiment. All animal experiments were conducted at UFV’s Experimental Nutrition Laboratory.
## 2.2. Experimental Design and Diet
At 5 weeks, the animals underwent an obesity-induced protocol that lasted 3 months (induction phase). During this initial period, the mice were fed with an HFD where $60\%$ of total calories were derived from lipids (nutritional composition based on diet D12492 of the Research Diets, Inc., New Brunswick, NJ, USA) [18], and a $10\%$ fructose solution (Synth®, Diadema, Brazil) instead of drinking water [19]. The negative control group (G1, $$n = 8$$) received an AIN-93 diet, with $10\%$ of total calories derived from lipids [20] and water from the induction phase until the end of the experiment.
After this period, the treatment phase began and the mice that were fed with an HFD were randomly divided into two intervention groups (A and B) with a subset of four experimental groups each (Figure 1): high-fat diet (HFD) (G2, $$n = 7$$), LG-G12 HFD (G3, $$n = 7$$), cefriatoxane HFD (G4, $$n = 7$$), cefriatoxane + LG-G12 HFD (G5, $$n = 7$$), standard fat diet (SFD) (G6, $$n = 8$$), LG-G12 SFD (G7, $$n = 7$$), cefriatoxane SFD (G8, $$n = 7$$), and cefriatoxane + LG-G12 SFD (G9, $$n = 8$$). Intervention A groups continually received an obesogenic diet during the treatment phase. The obesogenic diet was confirmed by Dias et al. [ 7]. In contrast, intervention B groups initiated an AIN-93 standard diet and water instead of fructose solution during the treatment phase. A gavage treatment was administered every evening at the same time.
The antimicrobial group was treated with 500 mg of ceftriaxone per kg of body mass (Triaxton, Blau Farmacêutica S/A®, Cotia, Brazil) [21]. The potential probiotic group received 109 colony-forming units (CFU) of LG-G12 (Lemma Supply Solutions®, São Paulo, Brazil). L. gasseri LG-G12 was lyophilized and diluted into 200ul of water. Both treatment groups received 500 mg of ceftriaxone per kg of body weight in the first two weeks of the treatment phase and, in the following two weeks, 109 CFU of L. gasseri LG-G12.
At the conclusion of the treatment phase, a total exsanguination was carried out after the animals had been anesthetized with $3\%$ isoflurane (Cristália®, Belo Horizonte, Brazil) and euthanized. This form of euthanasia is recommended for rodents by CONSEA [17]. For future analyses, tissue samples were collected and stored. More information about the experimental design is available at Dias et al. [ 7].
## 2.3. Intestinal Permeability
As previously described by Dias et al. [ 7], after the treatment phase, a lactulose (Daiichi Sankyo®, Barueri, Brazil) and mannitol solution (Synth®, Diadema, Brazil) were supplied to the animals. Subsequently, the 24 h urine of the animals was collected. These sugars were measured by high-performance liquid chromatography (detector model RID 10A, Shimadzu®, Tokyo, Japan).
## 2.4. Quantification of Short-Chain Fatty Acids
By Siegfried et al. [ 22] method, the acetic, propionic, and butyric acids, of the cecal content, were determined, as reported by Dias et al. [ 7], and analyzed by high-performance liquid chromatography (Ultimate 3000, Dionex, Thermo Fisher Scientific®, Waltham, MA, USA).
## 2.5. Composition and Functional Prediction of the Intestinal Microbiota
A pool of stool samples was made as described by Dias et al. [ 7]. This methodology was adopted because the animals are isogenic and live in a controlled environment, and can therefore be considered biological replicates.
Metagenomic DNA was extracted from 200 mg of feces using the method adapted from Zhang et al. [ 23]. Afterward, the quantity of the extracted DNA was evaluated utilizing Qubit (Invitrogen, Thermo Fisher, USA), whereas its integrity and quality were verified through electrophoresis in $1.8\%$ agarose gel. The V3 and V4 regions of 16S rRNA genes were PCR amplified utilizing specific primers (Bakt 341F and Bakt 805R) and sequenced using an Illumina MiSeq desktop sequencer (Illumina, San Diego, CA, USA) at the Macrogen Company (Macrogen Inc®, Seoul, South Korea).
Microbiota data were processed and analyzed with QIIME2 (version 2020.2) [24]. In brief, raw sequence data obtained across the C57BL/6J mice stool samples from group G1 to G9 were imported via the Casava1.8 paired-end pipeline followed by denoising with DADA2 [25] (via q2-dada2). Subsequently, an amplicon sequence variants (ASV) table was constructed to generate a phylogenetic tree by using the align-to-tree-might-fast tree pipeline from the q2-phylogeny plugin [26,27]. When appropriate, samples were rarefied to a sampling depth of 120,326 sequences. Taxonomy was assigned to the 16S data using a Naïve Bayes pre-trained Greengenes 13_8 $99\%$ OTUs classifier [28].
For the functional prediction of the gut microbiota, ASVs (read sequences and read counts) were used as inputs for the PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) pipeline [29]. In brief, ASVs were inserted and aligned into a reference tree composed of 20,000 full 16S rRNA genes from bacterial and archaeal genomes using, respectively, the HMMER (http://www.hmmer.org, accessed on 1 June 2021) and EPA-ng/GAPPA tools [30,31]. Subsequently, the castor R package [32] was used to predict the missing gene families (Enzyme Commission numbers) for each ASV, as well as their respective copy number of 16S rRNA gene sequences, by using the output tree generated in the previous step. Finally, MinPath [33] was adopted to infer MetaCyc pathways based on EC number abundances.
Under accession numbers PRJNA705760 and PRJNA745938, the raw fastq data have been submitted to the Sequence Read Archive (SRA) at NCBI.
## 2.6. Statistical Analysis
The principal component analysis (PCA) was performed using the relative abundance of the most abundant genera (greater than $0.1\%$ in at least one sample). OTU abundance was scaled and then the PCA analysis was performed using the prcomp function of the R program (R Core Team 3.6.2, 2019). Normalization was performed to assure that the PCA results are mathematically independent of the overlap measure. The factorial analysis for OTU information aimed at obtaining the variables which contribute to the highest differentiation across the treatment groups using the FactoMineR R package [34].
Shapiro-Wilk test was used to test the normality of the variables (i.e., Shannon, Chao1, acetate, propionate, butyrate, total SCFA, and lactulose/mannitol ratio). One-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was adopted for parametric data, and Kruskal Wallis complemented by Dunn’s multiple-comparison test was used for non-parametric data. The results were expressed as mean ± standard error of the mean (SEM). Differentially abundant taxa after the treatment phase that most likely explain the differences among the groups (i.e., fecal biomarkers) were assessed using the linear discriminant analysis (LDA) effect size (LEfSe) [35] tool and setting an alpha value of 0.05 and Log LDA threshold of 2.0.
Beta diversity was assessed using the Unweighted and Weighted UniFrac metrics to evaluate bacterial community dissimilarities between the groups. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was used to test whether distances between samples within a certain group are more similar to each other or not. Correlations between continuous variables were determined by Pearson’s (parametric data) or Spearman’s (non-parametric data) correlation with the Paleontological statistics software package for education and data analysis (PAST, v4.06b) [36].
Lastly, the Statistical Analysis of Metagenomic Profile (STAMP) software [37] was used to explore and compare the metabolic potential of the predicted microbial communities across the groups. Functional profiling was built based on the MetaCyc Metabolic Pathway Database [38]. Welch’s t-test (two-sided) was adopted as a statistical hypothesis test. For both analyses, a p-value less than 0.05 was considered a significant difference.
## 3.1. Multidimensional Scaling Analysis (MDS)
MDS analysis based on intestinal microbiota abundance explains around $38.9\%$ of the variation between the groups (G1 to G9) (Figure 2). In intervention “B” (groups G1, G6, G7, G8, and G9) there is microbial homogeneity between G7 and G9. Then it is believed that LG-G12 acted as a positive modulator of the intestinal microbiota (Figure 2A). Moreover, distinct bacterial genera contributed differently to the total intestinal microbial load across the different experimental groups, which suggests different biological and/or metabolic capabilities (Figure 2B).
## 3.2. Alpha and Beta Diversity
The absence of differences in alpha diversity between interventions “A” and “B” was observed (Table 1, Figure S1). However, in the groups that consumed an antimicrobial and high-fat diet (G4 and G5), a tendency towards a reduction in the Shannon and Chao1 indices could be observed, when compared to those who received ceftriaxone and a standard diet (G8 and G9). This result shows a synergy between the antibiotic and high-fat diet which can impair bacterial diversity/richness. As previously described [39,40], the use of antibiotics can harm the intestinal epithelium, as well as favor the growth of specific microbial taxa. When associated with high-calorie diets, both factors can contribute negatively to the development of the gut microbiota [40,41], which justifies our findings on cefriatoxane with the high-fat diet group (G4). Finally, switching to a standard diet (intervention “B”) was capable of increasing intestinal diversity in all groups, which justifies the lack of statistical difference.
Regarding the beta-diversity analysis, there was no difference in terms of gut microbiota dispersion based on UniFrac distance metrics (weighted UniFrac: F-value = 0.77; $$p \leq 0.49$$; unweighted UniFrac: F-value = 1.66; p-value = 0.15). According to PERMANOVA results, a statistically significant difference was observed regarding community dissimilarity considering both UniFrac indices (weighted UniFrac: F-value = 2.68; p-value = 0.003; unweighted UniFrac: F-value = 1.75; p-value = 0.002). Moreover, pairwise comparisons using Qiime beta-group-significance command revealed that the gut composition of the group that received ceftriaxone and a high-fat diet (G4) showed the greatest distance to other groups, especially to G1 (Figure S2). This result indicates that significant differences in gut microbial composition were due to the intervention and not to dispersion effects. Taken together, alpha- and beta-diversity indices evidenced that a synergy between antibiotics and a high-fat diet can impair bacterial community structure and diversity both qualitatively and quantitatively.
## 3.3.1. Phylum Level
Overall, 127 significantly discriminative features (LDA > 2, $p \leq 0.05$) were identified in the LEfSE analysis. The phyla Proteobacteria (LDA > 5) and Spirochaetes (LDA > 2.0) were enriched in group G3, whereas the phylum Tenericutes (LDA > 2.0) can be considered a biomarker for group G4 (Figure 3). The consumption of a processed diet, commonly rich in emulsifiers and artificial sweeteners, can explain the expansion of Proteobacteria, which can increase intestinal permeability and reduce local mucus production [42,43]. Regarding the phylum Spirochaetes, although its identification in fecal samples has not been associated with obesity, Jabbar et al. [ 44] reported the association between Brachyspira and irritable bowel syndrome. Our results indicate a limited impact of the LG-G12 in controlling the two aforementioned taxa. Since a single strain was used in this study, its inclusion in a multiple strain formulation, as suggested by the World Gastroenterology Organisation [5], might represent a more effective strategy in limiting the growth of such undesired phyla, which must be evaluated in further studies.
Nevertheless, Firmicutes and Bacteroidetes have not been identified as biomarkers in any of the interventions, group G4 showed a much higher F/B ratio (Table 2), which is common in the obese population [42]. This means that the combined use of ceftriaxone and a high-fat diet disrupts the intestinal bacterial balance, favoring the growth of a few phyla at the expense of others, which is common in intestinal dysbiosis [45]. Interestingly, following LG-G12 administration, the F/B ratio decreased in the cefriatoxane + LG-G12 A group (G5). This outcome suggests a mechanism of counteraction of LG-G12 to the damage caused by ceftriaxone administration favoring the restoration of intestinal homeostasis [45].
The Gram-positive and Gram-negative genera ratio was also evaluated in each group (Table 2). As expected, the G4 group presented the least proportion of Gram-negative among all groups ($23.50\%$) and the greatest G+/G− ratio (3.24), indicating the selectivity of the antimicrobial ceftriaxone against this specific group of bacteria. Ceftriaxone is a third-generation cephalosporin that targets most Gram-negative bacteria inducing changes in gut microbiota [46]. Our results also reveal that the intervention with LG-G12 alleviated the effects of the synergism between the antimicrobial and the diet offered, restoring intestinal Gram-negative taxa at levels similar to the control groups. Crovesy et al. [ 6], in a systematic review of randomized controlled clinical trials, suggested that the modulatory effect of Lactobacillus in weight loss is strain-dependent and can require its association with calorie restriction, phenolic compounds, or other bacterial strains.
## 3.3.2. Genus Level
Amongst the biomarkers identified by LEfSE analysis, the genera Oscillospira (LDA > 4), Sporosarcina (LDA > 4), Allobaculum (LDA > 3), Jeotgalicoccus (LDA > 3), Bifidobacterium (LDA > 3), and Yaniella (LDA > 3) were assigned as biomarkers for group G1 (Figure 3). The enrichment of the genera *Bifidobacterium is* in agreement with the literature, where an inverse relationship was shown between this genus and obesity. Bifidobacteria deconjugate bile acids, decreasing fat absorption [47]. The higher abundance of Oscillospira in the gut of lean subjects has been addressed in several studies and is positively associated with lower body mass index (BMI) in both children and adults [48]. It is also well reported that a high-fat diet can significantly reduce the intestinal abundance of Allobaculum, a relevant SCFA-producing bacteria, which may display an anti-obesogenic role by reducing intestinal inflammation and improving insulin resistance [49,50,51]. The increase in the relative abundance of the genera Jeotgalicoccus and Sporosarcina, although less described in the literature, are associated with beneficial outcomes in animal models fed high-fat diets [52,53]. To the best of our knowledge, there is no available information regarding the role of Yaniella, a high salt-tolerant microorganism, in healthy or obese subjects. Overall, our results show that low-calorie diets are beneficial to the maintenance of taxa that are negatively associated with obesity.
*The* genera Lactobacillus (LDA > 4), Dehalobacterium (LDA > 2), and Erysipelotrichaceae cc_115 (LDA > 3) were identified as biomarkers in the obese control group (G2). Although most lactobacilli strains can have beneficial and auxiliary effects on weight loss in overweight adults [6], some species, such as Limosilactobacillus reuteri have been associated with weight gain in humans and animals [54,55]. Regarding the genera Dehalobacterium and Erysipelotrichaceae cc-115, little information is available regarding their abundance and role within the intestinal community of obese or overweight subjects. It is reported that the genus Dehalobacterium comprehends microorganisms strictly anaerobic and capable of degrading dichloromethane and was found enriched in both obese and non-obese asthmatic patients [56], whereas Erysipelotrichaceae cc-115 was found depleted in the gut microbiota of community-dwelling physically active older men [57].
Six different genera were enriched in LG-G12 A (G3) after the end of the experimental period: Ruminococcus (LDA > 4), Anaerotruncus (LDA > 4), Bilophila (LDA > 3), Desulfovibrio (LDA > 3), Brachyspira (LDA > 2), and Coprococcus (LDA > 2). We observed that when LG-G12 was offered to animals that were fed a high-fat diet, there was a remarkable enrichment of SCFA-producing bacteria such as Ruminococcus, Anaerotruncus, and Coprococcus which are commonly found in the microbiota of overweight or obese patients [58,59,60]. Intriguingly, we also detected the enrichment of taxa involved in mucus degradation and hydrogen sulfide production such as Brachyspira, Bilophila, and Desulfovibrio, which may indicate a limited action of LG-G12 against these taxa.
*The* genera Enterococcus (LDA > 5), Salinispora (LDA > 3), and Akkermansia (LDA > 4) were identified as biomarkers of the ceftriaxone A group (G4), whereas only Clostridium (LDA > 4) was significantly enriched in the ceftriaxone + LG-G12 A group (G5). Interestingly, Akkermansia, which is a Gram-negative, obligate anaerobe, non-motile, non-spore-forming bacterium, seems to be resilient to the adverse effects of the antimicrobial ceftriaxone. This genus has attracted great interest due to its capability to enhance mucus formation, activate the innate immune system, and promote intestinal homeostasis [61]. As reported by Vesić & Kristich [62], the genus *Enterococcus is* intrinsically resistant to cephalosporins, antibiotics that act on cell wall biosynthesis, which may explain its identification as a biomarker of this group. Additionally, Mishra & Ghosh [63] reports that the E. faecalis AG5 strain mitigates HFD-induced obesity through several mechanisms such as activation of adipocyte apoptosis and the improvement of glucose, insulin, and leptin sensitivity.
The genus Corynebacterium (LDA > 3) was identified as a biomarker of group G1, while the genera Blautia (LDA > 3), Clostridium (LDA > 4), and Akkermansia (LDA > 5) appear enriched in the control group G6 (AIN-93 intake during the treatment phase). The enrichment of the SCFA-producing bacteria Akkermansia, Blautia, and *Clostridium may* contribute to restoring intestinal integrity and the development of intestinal homeostasis. During calorie-restricted diet therapy for overweight or obese individuals, insulin resistance improves and is correlated with an increased abundance of Akkermansia in the gut [61].
Finally, an enrichment of the *Bifidobacterium genus* (LDA > 4) was observed following LG-G12 with a standard diet in the treatment phase (G7). This result indicates a synergism between LG-G12 and endogenous bifidobacteria, which might be strain-specific. Following probiotic intervention with *Latilactobacillus curvatus* and Lactiplantibacillus plantarum, Park et al. [ 64] observed enrichment of *Bifidobacterium pseudolongum* species in HFD-probiotic mice when compared to the HFD-placebo group. Differentially abundant genera were not identified in the ceftriaxone (G8) and ceftriaxone + LG-G12 groups (G9).
## 3.4.1. Groups Treated with LG-G12
*The* genera Enterococcus (r = −0.59, $$p \leq 6.27$$ × 10−3) and Bifidobacterium (r = −0.54, $$p \leq 1.32$$ × 10−2) were negatively correlated with high Lactulose/Mannitol (L/M) ratio (Table 3), in LG-G12 treated groups (G3 and G7) (Figure 4A). Species of the genus Enterococcus can interact with mucosal immune cells, thus activating intestinal immune response [64]. Wu et al. [ 65] report that the probiotic E. faecium NCIMB 11181 can ameliorate necrotic enteritis by improving intestinal mucosal barrier function and modulating gut microbiota. Bifidobacterium, which is indicative of microbial diversity [66], can protect against obesity and diabetes, as well as improve intestinal integrity and control metabolic endotoxemia, important parameters for the assessment of intestinal balance and health [41,67].
In terms of SCFA production (Figure 4B, Table 3), the genera Enterococcus ($r = 0.48$, $$p \leq 3.12$$ × 10−2), Allobaculum ($r = 0.76$, $$p \leq 8.96$$ × 10−5), Sporosarcina ($r = 0.83$, $$p \leq 4.73$$ × 10−6), Jeotgalicoccus ($r = 0.87$, $$p \leq 7.67$$ × 10−7), Staphylococcus ($r = 0.91$, $$p \leq 2.50$$ × 10−8), Bifidobacterium ($r = 0.60$, $$p \leq 4.79$$ × 10−3), Blautia ($r = 0.59$, $$p \leq 6.11$$ × 10−3) were positively correlated with the total amount of SCFA, whereas Prevotella ($r = 0.50$, $$p \leq 2.33$$ × 10−2) was only positively correlated with the production of butyrate.
Kong et al. [ 41] reported that the consumption of high-fat and high-sucrose diets reduces the abundance of Prevotella and, consequently, butyrate levels. In addition, the probiotic administration (Lactobacillus acidophilus, Bifidobacterium longum, and Enterococcus faecalis) can restore the intestinal microbiota, increasing microorganisms such as Lactobacillus, Bifidobacterium, and Akkermansia. Based on our results, we believe that LG-G12 positively modulated SCFA-producing bacteria such as Allobaculum, Bifidobacterium, and Prevotella.
Overall, the bacterial genera that negatively correlated with the L/M ratio were positively correlated with the production of SCFA, indicating a relevant role of such organic acids with the integrity of the intestinal barrier, which is in line with previous studies [41,68,69].
## 3.4.2. Groups Treated with Ceftriaxone
The correlation analysis performed considering groups G4, and G8 (Figure S3A, Table 3) revealed that Staphylococcus (r = −0.81, $$p \leq 7.49$$ × 10−4) was negatively correlated with lactulose, whereas Clostridium ($r = 0.48$, $$p \leq 3.79$$ × 10−2) was positively correlated with L/M ratio. Zeng et al. [ 70] reported that lactulose inhibited the effect of *Staphylococcus aureus* due to the production of sialyllactulose, an antimicrobial enzyme capable to cause damage to the S. aureus cell membrane, which can be good for intestinal health. The positive correlation between *Clostridium and* a high L/M ratio is not news as some species such as *Clostridium difficile* can secrete toxins with cytotoxic effects on the intestinal epithelium [71].
In terms of SCFA (Figure S3B, Table 3), the genera Allobaculum ($r = 0.67$, $$p \leq 1.59$$ × 10−3) and Bifidobacterium ($r = 0.54$, $$p \leq 1.72$$ × 10−2), were positively correlated with total SCFA production, whereas only the genus Stenotrophomonas (r = −0.48, $$p \leq 3.76$$ × 10−2) showed a negative correlation. Even following antimicrobial treatment, the genera Allobaculum and Bifidobacterium were negatively correlated with the L/M ratio and positively correlated with the production of SCFA, which indicate that these genera may act in the maintenance of intestinal integrity and homeostasis as previously described by Kong et al. [ 41]. Regarding the genus Stenotrophomonas, some species belonging to this genus, such as Stenotrophomonas maltophilia, are considered pathogenic bacteria [72] and highly resistant to antibiotics [73], which may justify its presence among the ceftriaxone-treated groups.
## 3.4.3. Groups Treated with Ceftriaxone Followed by LG-G12
Correlation analyses encompassing groups G5 and G9 (Figure S4A) revealed that the genus Desulfovibrio ($r = 0.48$, $$p \leq 3.29$$ × 10−2) was positively correlated with L/M ratio and, consequently, loss of intestinal integrity. Desulfovibrio members are frequently elevated in intestinal dysbiosis, causing intestinal permeability and inflammation [74], which is consistent with our findings.
Concerning SCFAs (Figure S4B), the genera Prevotella ($r = 0.49$, $$p \leq 2.98$$ × 10−2) and Faecalibacterium ($r = 0.50$, $$p \leq 2.44$$ × 10−2) were positively correlated with their total amount (represented here by the sum of acetate, propionate, and butyrate), whereas some genera were positively correlated with only certain compounds, but not with total production, as follows: Allobaculum (Acetic: $r = 0.88$, $$p \leq 2.95$$ × 10−3; Propionic, $r = 0.73$, $$p \leq 2.70$$ × 10−4; Butyric, $r = 0.64$, $$p \leq 2.62$$ × 10−3) and Bifidobacterium (Acetic, $r = 0.81$, $$p \leq 1.71$$ × 10−2; Propionic, $r = 0.67$, $$p \leq 1.11$$ × 10−3; Butyric, $r = 0.74$, $$p \leq 1.87$$ × 10−4). Acetate and lactate are among the SCFAs produced by Bifidobacterium [75], whereas butyrate production in particular is more related to prebiotics, as discussed previously [68]. The identification of Faecalibacterium and Allobaculum, both genera described as producers of SCFA [41,69]. This association may also improve intestinal health by promoting SCFA-producing genera and, consequently, enhancing the gut microbiota.
## 3.5. Functional Predictions of the Gut Microbiota
During the different interventions evaluated in the current study, we aimed to identify whether microbial metabolic pathways were enriched in the gut microbiota, which could be associated with the effects of a high-fat diet or not. In addition, we focused on identifying metabolic pathways related to SCFA production that can directly impact intestinal health. Taking into account the comparison between groups G7 and G3, 58 functional pathways differed significantly between the groups (Table S1), and the great majority (approximately $69.0\%$) were enriched in group G7. Regarding the MetaCyc pathways associated with SCFA production, six metabolic pathways were identified (Figure 5), and four of them were present in group G7 (Bifidobacterium shunt, heterolactic fermentation, hexitol fermentation to lactate, formate, ethanol, and acetate). The enrichment of Bifidobacterium shunt, which is a classic pathway of carbohydrate metabolisms such as fructose and glucose, can generate compounds serving as an energy source for intestinal epithelial cells [68,75], which explains the greater intestinal integrity noticed in this group. Only the acetyl-Coa fermentation to butanoate II and L-lysine fermentation to acetate and butanoate pathways were enriched in the G3 group.
In the groups that underwent ceftriaxone treatment followed by LG-G12 (groups G5 and G9), only 14 functional pathways differed significantly between both groups (Table S2) with approximately $64.0\%$ of the features enriched in the G9 group. The enrichment of the super pathway of D-glucarate and D-galactarate degradation in group G5 also demonstrates the use of alternative carbon sources for growth. The use of dicarboxylic acid sugars as growth substrate occurs in many different bacteria but is especially found in Gram-negative bacteria such as members of the Enterobacteriaceae, Moraxellaceae, and Pseudomonadaceae families [76]. Metabolic pathways, Bifidobacterium shunt, and heterolactic fermentation, associated with SCFA production were enriched only in group G9 (Figure S5). Similarly, in the G9 group, we also observed the enrichment of the Bifidobacterium shunt and heterolactic fermentation pathways, both associated with carbohydrate metabolism [77], which evidence an important role of low-calorie diets in the enrichment of these functions. Interestingly, the super pathway of D-glucarate and D-galactarate degradation and the pathway of purine nucleotide degradation II (aerobic) were enriched in the G5 group. This indicates that this group of microbes is using alternative carbon sources.
Finally, 12 functional pathways differed significantly between groups G4 and G8 (ceftriaxone administration only) (Table S3), with the vast majority of pathways (approximately $66.0\%$) being enriched in the G4 group. The main metabolic pathways enriched in the G4 group were associated with menaquinol biosynthesis and de novo nucleotide biosynthesis. The enrichment of menaquinol biosynthesis might be related to the energy processes of bacteria since menaquinones are relevant growth factors for gut microbiota [78,79] Traditionally, the gut microbiota is an important source of purines, which are used in different functions related to the intestinal barrier and innate immunity, being necessary for intestinal protection and health [80]. Since dysbiosis was observed in group G4, it is believed that the enrichment of nucleotide biosynthesis pathways confirms an expansion of specific microbial taxa in this group.
## 4. Conclusions
Consumption of a high-fat diet associated with ceftriaxone was able to reduce microbial diversity. It was observed that LG-G12 had the best effects when it was combined with a low-calorie diet in restoring gut homeostasis. Higher caecal SCFA contributed to increased intestinal integrity. Also, the genera that presented a negative correlation with a high L/M ratio were similar to those that had a positive correlation with total SCFA production. This trend was further confirmed by metagenomic predictions of the gut microbiota. LG-G12 is presented in this study as a novel adjuvant treatment for overweight or obese individuals through gut microbiota modulation and improvement of intestinal health in models undergoing antimicrobial therapy or not.
## References
1. Ley R.E., Turnbaugh P.J., Klein S., Gordon J.I.. **Human gut microbial ecology linked to obesity**. *Nature* (2006.0) **444** 1022-1023. DOI: 10.1038/4441022a
2. Sokol H.. **Definition and roles of the gut microbiota**. *Rev. Prat.* (2019.0) **69** 776-782. PMID: 32233323
3. Abenavoli L., Scarpellini E., Colica C., Boccuto L., Salehi B., Sharifi-Rad J., Aiello V., Romano B., De Lorenzo A., Izzo A.A.. **Gut Microbiota and Obesity: A Role for Probiotics**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11112690
4. Maruvada P., Leone V., Kaplan L.M., Chang E.B.. **The Human Microbiome and Obesity: Moving beyond Associations**. *Cell Host Microbe* (2017.0) **22** 589-599. DOI: 10.1016/j.chom.2017.10.005
5. Hunt R., Armstrong D., Katelaris P., Afihene M., Bane A., Bhatia S., Chen M.H., Choi M.G., Melo A.C., Fock K.M.. **World Gastroenterology Organisation Global Guidelines**. *J. Clin. Gastroenterol.* (2017.0) **51** 467-478. DOI: 10.1097/MCG.0000000000000854
6. Crovesy L., Ostrowski M., Ferreira D.M.T.P., Rosado E.L., Soares-Mota M.. **Effect of**. *Int. J. Obes.* (2017.0) **41** 1607-1614. DOI: 10.1038/ijo.2017.161
7. Dias M.M., Louzano S.A.R., Conceição L.L., Conceição R.F., Mendes T.A.O., Pereira S.S., Oliveira L.L., Peluzio M.C.G.. **Antibiotic Followed by a Potential Probiotic Increases Brown Adipose Tissue, Reduces Biometric Measurements, and Changes Intestinal Microbiota Phyla in Obesity**. *Probiotics Antimicrob. Proteins* (2021.0) **13** 1621-1631. DOI: 10.1007/s12602-021-09760-0
8. Louzano S.A.R., Dias M.M., Conceição L.L., Mendes T.A.O., Peluzio M.C.G.. **Ceftriaxone causes dysbiosis and changes intestinal structure in adjuvant obesity treatment**. *Pharmacol. Rep.* (2022.0) **74** 111-123. DOI: 10.1007/s43440-021-00336-x
9. Lange K., Buerger M., Stallmach A., Bruns T.. **Effects of antibiotics on gut microbiota**. *Dig. Dis.* (2016.0) **34** 260-268. DOI: 10.1159/000443360
10. Kim S., Covington A., Pamer E.G.. **The intestinal microbiota: Antibiotics, colonization resistance, and enteric pathogens**. *Immunol. Rev.* (2017.0) **279** 90-105. DOI: 10.1111/imr.12563
11. Richards D.M., Heel R.C., Brogden R.N., Speight T.M., Avery G.S.. **Ceftriaxone**. *Drugs* (1984.0) **27** 469-527. DOI: 10.2165/00003495-198427060-00001
12. Dabke K., Hendrick G., Devkota S.. **The gut microbiome and metabolic syndrome**. *J. Clin. Investig.* (2019.0) **129** 4050-4057. DOI: 10.1172/JCI129194
13. Khalili H., Chan S.S.M., Lochhead P., Ananthakrishnan A.N., Hart A.R., Chan A.T.. **The role of diet in the aetiopathogenesis of inflammatory bowel disease**. *Nat. Rev. Gastroenterol. Hepatol.* (2018.0) **15** 525-535. DOI: 10.1038/s41575-018-0022-9
14. Ríos-Covián D., Ruas-Madiedo P., Margolles A., Gueimonde M., de Los Reyes-Gavilán C.G., Salazar N.. **Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health**. *Front. Microbiol.* (2016.0) **7** 185. DOI: 10.3389/fmicb.2016.00185
15. Khoshbin K., Camilleri M.. **Effects of dietary components on intestinal permeability in health and disease**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2020.0) **319** G589-G608. DOI: 10.1152/ajpgi.00245.2020
16. Alpino G.C.Á., Pereira-Sol G.A., Dias M.M.E., Aguiar A.S., Peluzio M.D.C.G.. **Beneficial effects of butyrate on brain functions: A view of epigenetic**. *Crit. Rev. Food Sci. Nutr.* (2022.0) 1-10. DOI: 10.1080/10408398.2022.2137776
17. **Brasil. Conselho Nacional de Controle de Experimentação Animal (CONSEA) (2008) Lei no 11794, de 8 de outubro de 2008**
18. Membrez M., Blancher F., Jaquet M., Bibiloni R., Cani P.D., Burcelin R.G., Corthesy I., Macé K., Chou C.J.. **Gut microbiota modulation with norfloxacin and ampicillin enhances glucose tolerance in mice**. *FASEB J.* (2008.0) **22** 2416-2426. DOI: 10.1096/fj.07-102723
19. Della Vedova M.C., Muñoz M.D., Santillan L.D., Plateo-Pignatari M.G., Germanó M.J., Rinaldi Tosi M.E., Garcia S., Gomez N.N., Fornes M.W., Gomez Mejiba S.E.. **A Mouse Model of Diet-Induced Obesity Resembling Most Features of Human Metabolic Syndrome**. *Nutr. Metab. Insights* (2016.0) **9** NMI-S32907. DOI: 10.4137/NMI.S32907
20. Reeves P.G., Nielsen F.H., Fahey G.C.. **AIN-93 purified diets for laboratory rodents: Final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN-76A rodent diet**. *J. Nutr.* (1993.0) **123** 1939-1951. DOI: 10.1093/jn/123.11.1939
21. Rajpal D.K., Klein J.L., Mayhew D., Boucheron J., Spivak A.T., Kumar V., Ingraham K., Paulik M., Chen L., Van Horn S.. **Selective Spectrum Antibiotic Modulation of the Gut Microbiome in Obesity and Diabetes Rodent Models**. *PLoS ONE* (2015.0) **10**. DOI: 10.1371/journal.pone.0145499
22. Siegfried V.R., Ruckermann H., Stumpf G., Siegfried B.D., Ruckemann H., Siegfried R., Siegfried M.R.. **Method for the determination of organic acids in silage by high performance liquid chromatography**. *Landwirtsch. Forsch.* (1984.0) **37** 298-304
23. Zhang B.W., Li M., Ma L.C., Wei F.W.. **A widely applicable protocol for DNA isolation from fecal samples**. *Biochem. Genet.* (2006.0) **44** 494-503. DOI: 10.1007/s10528-006-9050-1
24. Bolyen E., Rideout J.R., Dillon M.R., Bokulich N.A., Abnet C.C., Al-Ghalith G.A., Alexander H., Alm E.J., Arumugam M., Asnicar F.. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. *Nat. Biotechnol.* (2019.0) **37** 852-857. DOI: 10.1038/s41587-019-0209-9
25. Callahan B.J., McMurdie P.J., Rosen M.J., Han A.W., Johnson A.J., Holmes S.P.. **DADA2: High-resolution sample inference from Illumina amplicon data**. *Nat. Methods* (2016.0) **13** 581-583. DOI: 10.1038/nmeth.3869
26. Katoh K., Misawa K., Kuma K., Miyata T.. **MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform**. *Nucleic Acids Res.* (2002.0) **30** 3059-3066. DOI: 10.1093/nar/gkf436
27. Price M.N., Dehal P.S., Arkin A.P.. **FastTree 2-approximately maximum-likelihood trees for large alignments**. *PLoS ONE* (2010.0) **5**. DOI: 10.1371/journal.pone.0009490
28. DeSantis T.Z., Hugenholtz P., Larsen N., Rojas M., Brodie E.L., Keller K., Huber T., Dalevi D., Hu P., Andersen G.L.. **Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB**. *Appl. Environ. Microbiol.* (2006.0) **72** 5069-5072. DOI: 10.1128/AEM.03006-05
29. Douglas G.M., Maffei V.J., Zaneveld J.R., Yurgel S.N., Brown J.R., Taylor C.M., Huttenhower C., Langille M.G.I.. **PICRUSt2 for prediction of metagenome functions**. *Nat. Biotechnol.* (2020.0) **38** 685-688. DOI: 10.1038/s41587-020-0548-6
30. Barbera P., Kozlov A.M., Czech L., Morel B., Darriba D., Flouri T., Stamatakis A.. **EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences**. *Syst. Biol.* (2019.0) **68** 365-369. DOI: 10.1093/sysbio/syy054
31. Czech L., Stamatakis A.. **Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0217050
32. Louca S., Doebeli M.. **Efficient comparative phylogenetics on large trees**. *Bioinformatics* (2018.0) **34** 1053-1055. DOI: 10.1093/bioinformatics/btx701
33. Ye Y., Doak T.G.. **A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes**. *PLoS Comput. Biol.* (2009.0) **5**. DOI: 10.1371/journal.pcbi.1000465
34. Lê S., Josse J., Husson F.. **FactoMineR: An R package for multivariate analysis**. *J. Stat. Softw.* (2008.0) **25** 1-18. DOI: 10.18637/jss.v025.i01
35. Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S., Huttenhower C.. **Metagenomic biomarker discovery and explanation**. *Genome Biol.* (2011.0) **12** R60. DOI: 10.1186/gb-2011-12-6-r60
36. Hammer Ø., Harper D.A., Ryan P.D.. **PAST: Paleontological statistics software package for education and data analysis**. *Palaeontol. Electron.* (2001.0) **4** 1-9
37. Parks D.H., Tyson G.W., Hugenholtz P., Beiko R.G.. **STAMP: Statistical analysis of taxonomic and functional profiles**. *Bioinformatics* (2014.0) **30** 3123-3124. DOI: 10.1093/bioinformatics/btu494
38. Caspi R., Billington R., Keseler I.M., Kothari A., Krummenacker M., Midford P.E., Ong W.K., Paley S., Subhraveti P., Karp P.D.. **The MetaCyc database of metabolic pathways and enzymes—A 2019 update**. *Nucleic Acids Res.* (2020.0) **48** D445-D453. DOI: 10.1093/nar/gkz862
39. Cheng R., Liang H., Zhang Y., Guo J., Miao Z., Shen X., Chen G., Cheng G., Li M., He F.. **Contributions of**. *Benef. Microbes* (2020.0) **11** 489-509. DOI: 10.3920/BM2019.0191
40. Ianiro G., Tilg H., Gasbarrini A.. **Antibiotics as deep modulators of gut microbiota: Between good and evil**. *Gut* (2016.0) **11** 1906-1915. DOI: 10.1136/gutjnl-2016-312297
41. Kong C., Gao R., Yan X., Huang L., Qin H.. **Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet**. *Nutrition* (2019.0) **60** 175-184. DOI: 10.1016/j.nut.2018.10.002
42. Crovesy L., Masterson D., Rosado E.L.. **Profile of the gut microbiota of adults with obesity: A systematic review**. *Eur. J. Clin. Nutr.* (2020.0) **74** 1251-1262. DOI: 10.1038/s41430-020-0607-6
43. Shin N.R., Whon T.W., Bae J.-W.. **Proteobacteria: Microbial signature of dysbiosis in gut microbiota**. *Trends Biotechnol.* (2015.0) **33** 496-503. DOI: 10.1016/j.tibtech.2015.06.011
44. Jabbar K.S., Dolan B., Eklund L., Wising C., Ermund A., Johansson Å., Törnblom H., Simren M., Hansson G.C.. **Association between**. *Gut* (2021.0) **70** 1117-1129. DOI: 10.1136/gutjnl-2020-321466
45. Stojanov S., Berlec A., Štrukelj B.. **The Influence of Probiotics on the**. *Microorganisms* (2020.0) **8**. DOI: 10.3390/microorganisms8111715
46. Zhao Z., Wang B., Mu L., Wang H., Luo J., Yang Y., Yang H., Li M., Zhou L., Tao C.. **Long-Term Exposure to Ceftriaxone Sodium Induces Alteration of Gut Microbiota Accompanied by Abnormal Behaviors in Mice**. *Front. Cell. Infect. Microbiol.* (2020.0) **10** 258. DOI: 10.3389/fcimb.2020.00258
47. Gomes A.C., Hoffmann C., Mota J.F.. **The human gut microbiota: Metabolism and perspective in obesity**. *Gut Microbes* (2018.0) **9** 308-325. DOI: 10.1080/19490976.2018.1465157
48. Konikoff T., Gophna U.. *Trends Microbiol.* (2016.0) **24** 523-524. DOI: 10.1016/j.tim.2016.02.015
49. Guo S., Zhao H., Ma Z., Zhang S., Li M., Zheng Z., Ren X., Ho C.T., Bai N.. **Anti-Obesity and Gut Microbiota Modulation Effect of Secoiridoid-Enriched Extract from**. *Molecules* (2020.0) **25**. DOI: 10.3390/molecules25174001
50. Wang J., Wang P., Li D., Hu X., Chen F.. **Beneficial effects of ginger on prevention of obesity through modulation of gut microbiota in mice**. *Eur. J. Nutr.* (2020.0) **59** 699-718. DOI: 10.1007/s00394-019-01938-1
51. Zhou L., Xiao X., Zhang Q., Zheng J., Li M., Wang X., Deng M., Zhai X., Liu J.. **Gut microbiota might be a crucial factor in deciphering the metabolic benefits of perinatal genistein consumption in dams and adult female offspring**. *Food Funct.* (2019.0) **10** 4505-4521. DOI: 10.1039/C9FO01046G
52. Machate D.J., Figueiredo P.S., Marcelino G., Guimarães R.C.A., Hiane P.A., Bogo D., Pinheiro V.A.Z., Oliveira L.C.S., Pott A.. **Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21114093
53. Song H., Shen X., Deng R., Zhang Y., Zheng X.. **Dietary anthocyanin-rich extract of açai protects from diet-induced obesity, liver steatosis, and insulin resistance with modulation of gut microbiota in mice**. *Nutrition* (2021.0) **86** 111176. DOI: 10.1016/j.nut.2021.111176
54. Armougom F., Henry M., Vialettes B., Raccah D., Raoult D.. **Monitoring bacterial community of human gut microbiota reveals an increase in**. *PLoS ONE* (2009.0) **4**. DOI: 10.1371/journal.pone.0007125
55. Million M., Maraninchi M., Henry M., Armougom F., Richet H., Carrieri P., Valero R., Raccah D., Vialettes B., Raoult D.. **Obesity-associated gut microbiota is enriched in**. *Int. J. Obes.* (2012.0) **36** 817-825. DOI: 10.1038/ijo.2011.153
56. Michalovich D., Rodriguez-Perez N., Smolinska S., Pirozynski M., Mayhew D., Uddin S., Van Horn S., Sokolowska M., Altunbulakli C., Eljaszewicz A.. **Obesity and disease severity magnify disturbed microbiome-immune interactions in asthma patients**. *Nat. Commun.* (2019.0) **10** 5711. DOI: 10.1038/s41467-019-13751-9
57. Langsetmo L., Johnson A., Demmer R.T., Fino N., Orwoll E.S., Ensrud K.E., Hoffman A.R., Cauley J.A., Shmagel A., Meyer K.. **The Association between Objectively Measured Physical Activity and the Gut Microbiome among Older Community Dwelling Men**. *J. Nutr. Health Aging* (2019.0) **23** 538-546. DOI: 10.1007/s12603-019-1194-x
58. Bailén M., Bressa C., Martínez-López S., González-Soltero R., Montalvo Lominchar M.G., San Juan C., Larrosa M.. **Microbiota Features Associated with a High-Fat/Low-Fiber Diet in Healthy Adults**. *Front. Nutr.* (2020.0) **7** 583608. DOI: 10.3389/fnut.2020.583608
59. Castaner O., Goday A., Park Y.M., Lee S.H., Magkos F., Shiow S.T.E., Schröder H.. **The Gut Microbiome Profile in Obesity: A Systematic Review**. *Int. J. Endocrinol.* (2018.0) **2018** 4095789. DOI: 10.1155/2018/4095789
60. Palmas V., Pisanu S., Madau V., Casula E., Deledda A., Cusano R., Uva P., Vascellari S., Loviselli A., Manzin A.. **Gut microbiota markers associated with obesity and overweight in Italian adults**. *Sci. Rep.* (2021.0) **11** 5532. DOI: 10.1038/s41598-021-84928-w
61. Naito Y., Uchiyama K., Takagi T.. **A next-generation beneficial microbe:**. *J. Clin. Biochem. Nutr.* (2018.0) **63** 33-35. DOI: 10.3164/jcbn.18-57
62. Vesić D., Kristich C.J.. **MurAA is required for intrinsic cephalosporin resistance of**. *Antimicrob. Agents Chemother.* (2012.0) **56** 2443-2451. DOI: 10.1128/AAC.05984-11
63. Park D.Y., Ahn Y.T., Park S.H., Huh C.S., Yoo S.R., Yu R., Sung M.K., McGregor R.A., Choi M.S.. **Supplementation of**. *PLoS ONE* (2013.0) **8**. DOI: 10.1371/journal.pone.0059470
64. Fine R.L., Manfredo Vieira S., Gilmore M.S., Kriegel M.A.. **Mechanisms and consequences of gut commensal translocation in chronic diseases**. *Gut Microbes* (2020.0) **11** 217-230. DOI: 10.1080/19490976.2019.1629236
65. Wu Y., Zhen W., Geng Y., Wang Z., Guo Y.. **Effects of dietary**. *Poult. Sci.* (2019.0) **98** 150-163. DOI: 10.3382/ps/pey368
66. Liu D., Jiang X.Y., Zhou L.S., Song J.H., Zhang X.. **Effects of Probiotics on Intestinal Mucosa Barrier in Patients With Colorectal Cancer after Operation: Meta-Analysis of Randomized Controlled Trials**. *Medicine* (2016.0) **95** e3342. DOI: 10.1097/MD.0000000000003342
67. Cani P.D., Neyrinck A.M., Fava F., Knauf C., Burcelin R.G., Tuohy K.M., Gibson G.R., Delzenne N.M.. **Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia**. *Diabetologia* (2007.0) **50** 2374-2383. DOI: 10.1007/s00125-007-0791-0
68. De Vuyst L., Moens F., Selak M., Rivière A., Leroy F.. **Summer Meeting 2013: Growth and physiology of bifidobacteria**. *J. Appl. Microbiol.* (2014.0) **116** 477-4791. DOI: 10.1111/jam.12415
69. Huang C.C., Shen M.H., Chen S.K., Yang S.H., Liu C.Y., Guo J.W., Chang K.W., Huang C.J.. **Gut butyrate-producing organisms correlate to Placenta Specific 8 protein: Importance to colorectal cancer progression**. *J. Adv. Res.* (2019.0) **22** 7-20. DOI: 10.1016/j.jare.2019.11.005
70. Zeng J., Hu Y., Jia T., Zhang R., Su T., Sun J., Gao H., Li G., Cao M., Song M.. **Chemoenzymatic synthesis of sialylated lactuloses and their inhibitory effects on**. *PLoS ONE* (2018.0) **13**. DOI: 10.1371/journal.pone.0199334
71. Buccigrossi V., Lo Vecchio A., Marano A., Guarino A.. **Differential effects of**. *Pediatr. Res.* (2019.0) **85** 1048-1054. DOI: 10.1038/s41390-019-0365-0
72. Looney W.J., Narita M., Mühlemann K.. *Lancet Infect. Dis.* (2009.0) **9** 312-323. DOI: 10.1016/S1473-3099(09)70083-0
73. Kalidasan V., Joseph N., Kumar S., Awang Hamat R., Neela V.K.. **Iron and Virulence in**. *Front. Cell. Infect. Microbiol.* (2018.0) **8** 401. DOI: 10.3389/fcimb.2018.00401
74. Chen M., Hui S., Lang H., Zhou M., Zhang Y., Kang C., Zeng X., Zhang Q., Yi L., Mi M.. **SIRT3 Deficiency Promotes High-Fat Diet-Induced Nonalcoholic Fatty Liver Disease in Correlation with Impaired Intestinal Permeability through Gut Microbial Dysbiosis**. *Mol. Nutr. Food Res.* (2019.0) **63** e1800612. DOI: 10.1002/mnfr.201800612
75. Manome A., Abiko Y., Kawashima J., Washio J., Fukumoto S., Takahashi N.. **Acidogenic Potential of Oral**. *Front. Microbiol.* (2019.0) **10** 1099. DOI: 10.3389/fmicb.2019.01099
76. Aghaie A., Lechaplais C., Sirven P., Tricot S., Besnard-Gonnet M., Muselet D., de Berardinis V., Kreimeyer A., Gyapay G., Salanoubat M.. **New insights into the alternative D-glucarate degradation pathway**. *J. Biol. Chem.* (2008.0) **283** 15638-15646. DOI: 10.1074/jbc.M800487200
77. Verce M., De Vuyst L., Weckx S.. **Comparative genomics of**. *Food Microbiol.* (2020.0) **89** 103448. DOI: 10.1016/j.fm.2020.103448
78. Aussel L., Pierrel F., Loiseau L., Lombard M., Fontecave M., Barras F.. **Biosynthesis and physiology of coenzyme Q in bacteria**. *Biochim. Biophys. Acta* (2014.0) **1837** 1004-1011. DOI: 10.1016/j.bbabio.2014.01.015
79. Fenn K., Strandwitz P., Stewart E.J., Dimise E., Rubin S., Gurubacharya S., Clardy J., Lewis K.. **Quinones are growth factors for the human gut microbiota**. *Microbiome* (2017.0) **5** 161. DOI: 10.1186/s40168-017-0380-5
80. Lee J.S., Wang R.X., Goldberg M.S., Clifford G.P., Kao D.J., Colgan S.P.. **Microbiota-Sourced Purines Support Wound Healing and Mucous Barrier Function**. *iScience* (2020.0) **23** 101226. DOI: 10.1016/j.isci.2020.101226
|
---
title: A Novel Mix of Polyphenols and Micronutrients Reduces Adipogenesis and Promotes
White Adipose Tissue Browning via UCP1 Expression and AMPK Activation
authors:
- Francesca Pacifici
- Gina Malatesta
- Caterina Mammi
- Donatella Pastore
- Vincenzo Marzolla
- Camillo Ricordi
- Francesca Chiereghin
- Marco Infante
- Giulia Donadel
- Francesco Curcio
- Annalisa Noce
- Valentina Rovella
- Davide Lauro
- Manfredi Tesauro
- Nicola Di Daniele
- Enrico Garaci
- Massimiliano Caprio
- David Della-Morte
journal: Cells
year: 2023
pmcid: PMC10001138
doi: 10.3390/cells12050714
license: CC BY 4.0
---
# A Novel Mix of Polyphenols and Micronutrients Reduces Adipogenesis and Promotes White Adipose Tissue Browning via UCP1 Expression and AMPK Activation
## Abstract
Background: *Obesity is* a pandemic disease characterized by excessive severe body comorbidities. Reduction in fat accumulation represents a mechanism of prevention, and the replacement of white adipose tissue (WAT) with brown adipose tissue (BAT) has been proposed as one promising strategy against obesity. In the present study, we sought to investigate the ability of a natural mixture of polyphenols and micronutrients (A5+) to counteract white adipogenesis by promoting WAT browning. Methods: *For this* study, we employed a murine 3T3-L1 fibroblast cell line treated with A5+, or DMSO as control, during the differentiation in mature adipocytes for 10 days. Cell cycle analysis was performed using propidium iodide staining and cytofluorimetric analysis. Intracellular lipid contents were detected by Oil Red O staining. Inflammation Array, along with qRT-PCR and Western Blot analyses, served to measure the expression of the analyzed markers, such as pro-inflammatory cytokines. Results: A5+ administration significantly reduced lipids’ accumulation in adipocytes when compared to control cells ($p \leq 0.005$). Similarly, A5+ inhibited cellular proliferation during the mitotic clonal expansion (MCE), the most relevant stage in adipocytes differentiation ($p \leq 0.0001$). We also found that A5+ significantly reduced the release of pro-inflammatory cytokines, such as IL-6 and Leptin ($p \leq 0.005$), and promoted fat browning and fatty acid oxidation through increasing expression levels of genes related to BAT, such as UCP1 ($p \leq 0.05$). This thermogenic process is mediated via AMPK-ATGL pathway activation. Conclusion: Overall, these results demonstrated that the synergistic effect of compounds contained in A5+ may be able to counteract adipogenesis and then obesity by inducing fat browning.
## 1. Introduction
Obesity is a pandemic health problem [1]. In 2016, the World Health Organization (WHO) estimated that 650 million adults, 340 million adolescents and 39 million children were affected by obesity, and these numbers are growing fast [2]. This condition has been worsened by increased junk food consumption, highly enriched with sugar and fat, that contributes to the development of visceral adiposity, which is strongly associated with cardiovascular diseases (CVD) [3]. Visceral adiposity is primarily composed of white adipose tissue (WAT) and is the main type of adipose tissue serving as energy storage. WAT also acts as an endocrine organ, secreting several pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-α, Interleukin (IL)-6, and leptin, among others [4]. In a state of obesity, the significant increase in WAT and in cytokine levels led to the onset of a pro-inflammatory state typical of this pathological condition and its related disorders (insulin resistance, diabetes mellitus, and CVD).
Recently, it has been proposed that WAT transdifferentiation into brown adipose tissue (BAT), a phenomenon known as browning, may be a novel approach to counteract obesity [5]. BAT activation enhances energy expenditure and promotes a negative energy balance reducing weight gain in animal models [6,7]. BAT uncouples fatty acid oxidation from adenosine triphosphate (ATP) production, dissipating energy as heat [8]. This beneficial process is primarily mediated by AMP-activated protein kinase (AMPK) that, when triggered by specific impulses, such as cold and/or fasting, induces phosphorylation and activation of adipose triglyceride lipase (ATGL), leading to an increase in lipolysis and fatty acids (FA) release [7,9]. These FA, in turn, bind to the uncoupling protein 1 (UCP1), a protein located in the inner mitochondrial membrane, promoting the dissipation of an electrochemical gradient as heat [9].
Based on these known mechanisms, several pharmacological and nutritional approaches have been proposed to counteract obesity and fat accumulation [10]. Among nutritional compounds, polyphenols showed a significant anti-obesity effect by regulating lipid metabolism [11]. Resveratrol, the most studied among polyphenols, promotes BAT metabolism by increasing expression of UCP1 in rodents [12]. However, the major limitation in the clinical application of polyphenols, especially resveratrol, is their low bioavailability [13]. To avoid this problem, several resveratrol derivatives with enhanced bioavailability have been proposed and investigated, such as the glycosylated derivate polydatin and the methoxylated derivative pterostilbene [14]. Chronic pterostilbene administration in mice fed with a high fat diet has already been reported to improve lipid metabolism and to promote expression of UCP1 and other factors related to BAT [15]. Recently, we demonstrated that a novel mix of polyphenols and micronutrients, called A5+, was able to protect against inflammation by reducing cytokines-mediated processes in different in vitro experimental models [16,17].
Based on these findings, the present study aimed to evaluate the effects of A5+ in counteracting adipogenesis by promoting WAT browning in a model of 3T3-L1 murine fibroblasts.
## 2.1. Cell Culture, Differentiation and Treatments
A murine 3T3-L1 fibroblast cell line was provided by Prof. Massimiliano Caprio (San Raffaele Open University) and cultured in Dulbecco’s Modified Eagle’s Medium (DMEM, 4.5 g/L glucose) (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), supplemented with $10\%$ Fetal Calf Serum and $1\%$ penicillin/streptomycin (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) at 37 °C in a humidified, $5\%$ CO2 atmosphere.
To induce differentiation, as previously reported [18], cells were seeded at the desired concentration in the culture medium. When they reached confluence, the medium was changed. The new differentiation medium was composed of DMEM 4.5 g/L glucose supplemented with $10\%$ Fetal Bovine Serum (FBS, Corning, NY, USA), $1\%$ penicillin/streptomycin, 1 µg/mL insulin, 0.5 mM isobutylmethylxanthine (IBMX), and 1 µM dexamethasone, 50 µM A5+ (SirtLIfe srl, Rome), or DMSO (for control cells) (Sigma Aldrich, Saint Louis, MO, USA) for 2 days. On day 2, the differentiation medium was replaced with DMEM (4.5 g/L glucose) containing $10\%$ FBS, 1 µg/mL insulin, and 50 µM A5+, or DMSO (for control cells), until day 10. The medium was changed every 2 days until day 10.
A5+ is composed of ellagic acid ($20\%$), polydatin ($98\%$), pterostilbene ($20\%$), and honokiol ($20\%$), mixed with recommended doses of zinc, selenium, and chromium. It is dissolved in DMSO at 1 mg/mL, as reported by Pacifici et al. [ 17].
## 2.2. Oil Red O Staining
Oil Red O staining was performed to quantify the intracellular lipid content as previously described [18]. Briefly, 1 × 105 cells were seeded in a 6-multiwell plate and differentiated as reported in Section 2.1. Then, the cells were washed and fixed with $4\%$ formalin (Sigma Aldrich, Saint Louis, MO, USA). Subsequently, the cells were incubated with $60\%$ isopropanol (Sigma Aldrich, Saint Louis, MO, USA) and then stained with Oil Red O solution (0.5 g/L, Sigma Aldrich, Saint Louis, MO, USA). The dye solution maintained by the cells was dissolved in pure isopropanol and quantified at 490 nm by using the Multiskan FC microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).
## 2.3. Proliferation Assay
For cell proliferation, 2 × 104 cells were plated in a 24-multiwell plate and differentiated as previously reported. At time 0 and at 48 h, the cells were detached using trypsin solution $0.05\%$ (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), then they were centrifuged and the pellet was resuspended in culture medium. Then, 10 µL o cell resuspension was added to 10 µL of trypan blue (Sigma Aldrich, Saint Louis, MO, USA) and analyzed with a Countess Automated Cell Counter (Thermo Fisher Scientific, Waltham, MA, USA).
## 2.4. Cell Cycle Analysis
Cell cycle analysis was performed using Propidium Iodide staining as reported in Pacifici et al. [ 17]. Briefly, the cells were seeded at 1 × 105 in a 6-multiwell plate and differentiated as reported in Section 2.1. Then, both the supernatants and cells were collected in a FACS collection tube and centrifuged at 1600 rpm for 5 min. Subsequently, the supernatant was discarded, and the pellet was fixed with $70\%$ ethanol for 45 min [19]. Finally, the cells were washed with PBS, stained with PI solution, and analyzed using cytofluorimetric analysis.
## 2.5. Inflammatory Array
Cytokines profile was analyzed in the supernatants of differentiated cells using the Mouse Inflammation Array C1 (Ray-Biotech, Inc., Norcross, GA, USA), as previously reported [17]. Briefly, the cells were treated as described in Section 2.1; at day 10, the supernatants were collected, centrifuged to remove cell debris, and used for the assay. Membranes with 40 spotted cytokine antibodies were blocked with the supplied blocking buffer and then incubated overnight at +4 °C with the supernatants. The next day, the membranes were washed and incubated overnight at +4 °C with a biotinylated antibody cocktail. The next day, the membranes were washed, and HRP-Streptavidin solution was added over night at +4 °C. The following day, the membranes were washed and detected by chemiluminescence. The membranes map is reported in Table 1.
## 2.6. Gene Expression Analysis
*For* gene expression analysis, total RNA was isolated by using TRIzol reagent (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. Then, 2.5 µg of total RNA was reverse transcribed into cDNA by using the High-Capacity cDNA Archive Kit (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). qRT-PCR was performed using the ABI Prism 7500 instrument (Applied Biosystem, Thermo Fisher Scientific, Waltham, MA, USA). cDNA amplification was assessed using a specific primer reported by Marzolla et al. [ 20] (UCP1, Adbr3, Cidea, DIO2, Cpt1beta, Cpt2, Crat, ACADM, ACADL, Hadha, Aco2, Idh3a Sdhac, Cs), and PowerUp SYBR green dye (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. All samples were normalized using TATA-box binding protein (TBP) as an internal control; the relative quantification was calculated using the comparative ΔΔCT method, and the values were expressed as 2−ΔΔCT.
## 2.7. Western Blot Analysis
The 3T3-L1 cell pellets were lysed at 4 °C in an HNTG lysis buffer ($1\%$ Triton X-100, 50 mM HEPES, $10\%$glycerol, 150 mM NaCl, $1\%$ sodium deoxycholate) supplemented with Phosphatase Inhibitor Cocktail 2 and 3 (Sigma Aldrich, Milan, Italy) and protease inhibitor cocktail (Sigma Aldrich, Milan, Italy). A clear supernatant was obtained by centrifugation of lysates at 13,000× g for 15 min at 4 °C. Protein concentration was determined using a BCA protein assay kit (Pierce; Thermo Fisher Scientific, Milan, Italy). Protein samples were subjected to sodium dodecylsulfate polyacrilamide gel electrophoresis (SDS-PAGE) using Miniprotean precast gels (BioRad; Segrate, Italy) and electroblotted onto nitrocellulose membranes (Bio-Rad, Segrate, Italy). Membranes were blocked for 1 h at room temperature (RT) with $5\%$ non-fat milk in Tris-Buffered Saline with $0.05\%$ Tween 20 (TBS-T). Incubation with primary specific antibodies was performed in the blocking solution ($5\%$ milk or bovine serum albumin in TBS-T) overnight at 4 °C and horseradish peroxidase-conjugated secondary antibodies (in blocking solution) for 1 h at RT. We used antibodies against AMPK-α 1:1000 (Cell Signaling, Danvers, MA, USA), phospho-AMPK-α (Thr172) 1:1000 (Cell Signaling, Danvers, MA), ATGL 1:1000 (Cell Signaling, Danvers, MA), phospho-ATGL (Ser406) 1:1000 (Abcam Cambridge, MA, USA), and UCP1 1:1000 (Abcam Cambridge, MA, USA). The appropriate secondary horseradish peroxidase-conjugated antibodies from Jackson Immunoresearch were used in the blocking solution (1:5000). Immunoreactive bands were visualized by Luminata Forte Western Chemiluminescent HRP substrate (Millipore (Merk); Milan, Italy) using an ImageQuant LAS 4000 (GE Healthcare). Equal samples loading was confirmed using GAPDH 1:30,000 (Sigma Aldrich, Milan, Italy) and bands quantified by densitometry using the ImageQuant TL software from GE Healthcare Life Sciences.
## 2.8. Statistical Analysis
All data were analyzed using GraphPad Prism 9 (La Jolla, CA, USA). An unpaired two-tailed Student’s test was used for statistical analysis and significance. All data were expressed as mean ± SEM. Values of $p \leq 0.05$ were considered statistically significant.
## 3.1. A5+ Blunts Intracellular Lipid Accumulation
In order to test whether A5+ was able to reduce intracellular lipid accumulation, we induced 3T3-L1 differentiation into a mature adipocyte phenotype. Then, we stained the differentiated cells with an Oil Red O solution that recognized triglycerides and lipids. As reported in Figure 1, A5+ administration significantly reduced lipid accumulation when compared to control cells, as confirmed by the Oil Red O absorbance at 490 nm ($p \leq 0.005$). These results indicated a direct effect of this compound on the mechanisms associated with fat storage. To further validate a reduction in adipogenesis, we also analyzed the mRNA expression of some adipogenic factors (Figure 1, Panels b–d). Accordingly, we observed a significant increase in FAB4 ($p \leq 0.001$) and adiponectin expression ($p \leq 0.05$) in the A5+-treated cells. Moreover, PPARγ levels were increased following A5+ administration, in agreement with its ability to promote adipogenesis in both white in brown adipose tissue, and to boost the brown-fat characteristics in white adipose tissue [21]. Taken together, these data suggest an involvement of A5+ in reducing white adipocytes maturation.
## 3.2. A5+ Inhibits Cell Proliferation by Arresting the Cell Cycle in G2-M Phase
Mitotic clonal expansion (MCE) is one of the most relevant stages in adipocytes differentiation. MCE is the moment where the cells reentered the cell cycle and promoted the transcription of several genes involved in 3T3-L1 adipocytes differentiation [22]. Based on the importance of MCE, we tested whether A5+ could act at this stage by reducing cell proliferation and thus, the differentiation driving force. Cells were plated at 1 × 105 cells/well in a 6-multiweel plate and differentiation was induced as previously reported. Then, at day 2, cell number and cell cycle were assessed. As expected, while physiological proliferation occurred in control cells, A5+ administration significantly reduced cell proliferation ($p \leq 0.005$) (Figure 2, Panel a). We also evaluated the cycle to confirm the cell growth arrest mediated by the selected compound. As reported in Figure 2, Panel b, cells treated with A5+ showed a cell cycle arrest in G2-M phase compared to control cells ($p \leq 0.05$). These results were further confirmed by the G2-M cell cycle arrest observed during the follow-up of this process with a peak at day 10 ($p \leq 0.0001$) (Figure 2, Panel c).
## 3.3. A5+ Administration Blunts Inflammatory Cytokines Release in Adipocytes
It is well known that mature adipocytes secrete several pro-inflammatory cytokines, thereby contributing to systemic inflammation and complications in obese subjects [23]. In order to evaluate whether this novel compound may impact on inflammation, we tested the secretion levels of several cytokines directly involved in adipocytes maturation and lipid accumulation in a differentiated mature 3T3-L1 adipocytes medium. As shown in Figure 3, A5+ administration significantly reduced the release of BLC, Eotaxin 1, IL-6, Leptin ($p \leq 0.005$), the chemokin CXCL9 ($p \leq 0.05$), RANTES ($p \leq 0.001$), and TIMP1 ($p \leq 0.05$) when compared to control cells. These data further highlight the relevant anti-inflammatory effect of polyphenols in general, and A5+ in particular. These findings are also in agreement with our previous data [17].
## 3.4. A5+ Promotes Fat Browning
Recently, a novel strategy to counteract obesity has been reported: it is based on the increase in activity and/or amount of brown adipose tissue (BAT), which, as opposed to WAT, dissipates energy by generating heat and leading to a negative energy balance and weight loss [6]. Based on our previous results, we tested whether reduction in lipid content after A5+ treatment may be attributed to fat browning. Therefore, we differentiated cells and isolated RNA to evaluate gene expression levels of important genes related to BAT. As reported in Figure 4, cells treated with A5+ displayed significantly increased levels of UCP1 ($p \leq 0.05$), Adrb3 ($p \leq 0.0001$), and Cidea ($p \leq 0.05$). A positive but non-significant trend was also shown for DIO2. These data demonstrated that this natural compound was able to promote fat browning, suggesting a potential role in blunting fat accumulation and obesity by triggering the switch from WAT to BAT.
## 3.5. A5+ Regulates Lipid Metabolism
Fatty acid (FA) oxidation is essential to induce UCP1 expression and, thus, to maintain and develop fat browning [24]. Based on our results showing the up-regulation of browning-related genes, we decided to analyze expression levels of genes involved in FA oxidation (Figure 5). As expected, genes involved in mitochondrial FA uptake, in particular Cpt2, significantly increased in A5+-treated cells when compared to control (ctr) cells ($p \leq 0.05$) (Figure 5, Panel a). Moreover, following A5+ administration, all analyzed components linked to FA oxidation increased when compared to ctr cells (ACADM: $p \leq 0.05$; ACADL: $p \leq 0.005$; Hadha: $p \leq 0.05$) (Figure 5, Panel b). The Acetyl-Coa derived from FA, metabolized by FAO, enters the TCA cycle to produce the most relevant cofactors essential for mitochondrial respiration [25]. According to the previously shown results genes involved in the TCA cycle were upregulated after treatment (Aco2 and Idh3a: $p \leq 0.005$; Sdhac and Cs: $p \leq 0.05$) (Figure 5, Panel c). Taken together, these data suggest that A5+ regulates brown fat thermogenesis.
## 3.6. A5+ Regulates Cellular Lipid Metabolism in 3T3-L1 via AMPK-ATGL Pathway
The observation that A5+ treatment increases the expression of thermogenesis-related markers prompted us to investigate the molecular mechanisms underlying the browning of 3T3-L1 adipocytes. 3T3-L1 pre-adipocytes were differentiated, in complete medium, in the presence or absence of A5+ for 10 days. A5+ effects on 3T3-L1 cells were assessed using western blot analysis of UCP-1 protein expression in terminally differentiated 3T3-L1 cells (day 10). A significant increase of UCP-1 protein expression was observed in A5+-treated 3T3-L1 cells when compared with control cells ($p \leq 0.05$) (Figure 6, Panel b). Given the well-known role of AMP-activated protein kinase (AMPK) as a sensor of intracellular energy state by regulating FA metabolism and thermogenesis in adipose tissue [26], we investigated whether A5+ was able to activate AMPK. We observed that A5+ administration induced a significant increase of AMPK-α phosphorylation at threonine-172 (Thr172) at day 10 of 3T3-L1 cell differentiation, indicating its capacity to induce AMPK activation ($p \leq 0.001$) (Figure 6, Panel b). Adipose triglyceride lipase (ATGL) can be phosphorylated at serine-406 (Ser406) by AMPK to increase its catalytic activity and, in turn, lipolysis in adipocytes [27].Therefore, we examined ATGL phosphorylation at Ser406 in A5+-treated 3T3-L1 cells and observed that it was significantly increased in A5+-treated cells when compared to control cells ($p \leq 0.05$) (Figure 6, Panel b).
## 4. Discussion
In the present study, by using a model of a 3T3-L1 fibroblast cell line differentiated into mature adipocytes, we reported, for the first time, that a mix of polyphenols and micronutrients (A5+) may be useful in preventing obesity and its related complications. A5+ administration reduced the accumulation of intracellular lipids and inhibited adipocytes differentiation during MCE, therefore blunting fat accumulation. Moreover, as reported in our previous studies [16,17], A5+ significantly reduced the release of pro-inflammatory cytokines, including leptin. All these beneficial properties of A5+ were primarily linked to its ability to triggering fat browning, or rather switching white adipose tissue to brown adipose tissue, as demonstrated by an increase in the genes linked with this mechanism and with fatty acid oxidation. At a molecular level, overexpression of UCP1 and the activation of AMPK represented the main thermogenic pathways involved.
Recently, we showed that A5+ significantly blunted inflammation in an in vitro model of Parkinson’s disease [17]. This relevant effect was explained, at least in part, by the synergistic and integrative effect of its components that act in different phases of cellular rescue mechanisms against damage and/or cellular stress. Similarly, in obesity, where a low grade of inflammation plays a pivotal role [28], the components of A5+ may induce a preventive and protective effect. The efficacy of the different polyphenols against obesity has been already largely explored and reported [29]. We previously demonstrated that tyrosol, a major polyphenol found in extra virgin olive oil, inhibited adipogenesis by downregulating several adipogenic factors (leptin and aP2) and transcription factors (C/EBPα, PPARγ, SREBP1c, and Glut4) and by modulating the histone deacetylase sirtuin 1 [18]. A study using the same in vitro model of the present research, showed that phenolic acids, including ellagic acid, inhibited lipid accumulation throughout the whole process of adipogenesis differentiation [30]. However, in this study it was remarked that, despite the similar structure of these compounds, they show interactions with different targets when compared to those reported in the previous study; they also exert distinct effects in adipogenesis [30]. Moreover, polydatin, pterostilbene, and honokiol were not tested. Polydatin was shown to reduce body weight in high fat diet (HFD)-fed mice and to downregulate serum levels of triglyceride, low density lipoprotein (LDL), aspartate aminotransferase (AST), and alanine aminotransferase (ALT), and to upregulate high-density lipoprotein (HDL) [31]. In association with the loss of weight, polydatin also reduced levels of pro-inflammatory factors such as IL-6 [31]. On the other hand, pterostilbene significantly ameliorated free fatty acids (FFA)-induced lipid accumulation in HepG2 cells and activated FA β-oxidation to inhibit FA synthesis in HFD-fed mice via AMPK activation [32]. Again, honokiol supplementation promoted the browning of WAT by upregulation of UCP1 and AMPK expression in HFD mice [33]. All these findings are completely in line with the results of the present study and with our hypothesis of the concomitant and interactive effect of the A5+ compounds on the adipogenesis mechanisms.
After A5+ treatment, we found a cell cycle arrest in the G2-M phase during adipogenesis, which may be the main cause of the following cascade effect, including reduction in cytokine cellular secretion. Among all pro-inflammatory factors, a significant decrease in leptin release was found. This may have an important consequence since leptin is a primary adipokine linked to mechanisms leading to obesity and its complications regulating body mass via negative feedback between adipose tissue and hypothalamus. [ 28,34]. In turn, the reduction of IL-6 and CXCL9, that increase the concentration of FFA [35], may drive the regulation of mitochondrial FA metabolism.
The ultimate protective step of induced by A5+ is the promotion of fat browning. This is a complex process in which gut microbiota also plays an important role [36]. BAT includes several cells, such as pre-adipocytes, stem progenitor cells, and immune cells, and has anti-inflammatory action through the ability to dissipate energy in the form of heat, primarily mediated by UCP1 [8]. In obesity, BAT function is negatively affected by inflammatory mediators, such as high levels of cytokines. For this reason, anti-inflammatory supplementation, even natural, has already been proposed to preserve it [5]. Here we found that treatment with A5+ increases the expression of the main genes involved in fat browning and in FA oxidation. These processes control adipose tissue thermogenesis [8]. UCP1 generates a heat dissipating energy proton gradient from the electron transport chain in mitochondrial respiration [37]. The increase in cellular respiration has favorable effects on other cellular pathways such as AMPK-ATGL, which, in turn, are pivotal to activate central and peripheral beneficial effects of BAT [9]. Here, we demonstrated either an increase of UCP1 and AMPK-ATGL expression after A5+ treatment. Interestingly, AMPK has already been shown to be positively modulated by other polyphenols, such as resveratrol [9].
The beneficial effect of minerals dissolved in A5+ (zinc, selenium, and chromium) against obesity has been largely demonstrated. Recently, the levels of these elements were found to be significantly reduced when measured in blood serum, hair, and urine of obese adult patients, demonstrating their predictive role in obesity and the helpful impact of their adequate replacement therapy [38].
## 5. Conclusions
In conclusion, in the present article we found that a natural product composed of highly bioavailable polyphenols and minerals may help in preventing some cellular processes associated with obesity, primarily by reducing cellular lipid accumulation and by increasing fat browning through enhancement of mitochondrial respiration and fatty acid oxidation (Figure 7). Further studies in this important field are necessary to understand how to counteract this pandemic disease.
## References
1. Safaei M., Sundararajan E.A., Driss M., Boulila W., Shapi’i A.. **A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity**. *Comput. Biol. Med.* (2021.0) **136** 104754. DOI: 10.1016/j.compbiomed.2021.104754
2. **Uited Nations News: Over One billion Obese People Globally, Health Crisis Must Be Reversed–WHO**
3. Singh S.A., Dhanasekaran D., Ganamurali N., Preethi L., Sabarathinam S.. **Junk food-induced obesity- a growing threat to youngsters during the pandemic**. *Obes. Med.* (2021.0) **26** 100364. DOI: 10.1016/j.obmed.2021.100364
4. Coppack S.W.. **Pro-inflammatory cytokines and adipose tissue**. *Proc. Nutr. Soc.* (2001.0) **60** 349-356. DOI: 10.1079/PNS2001110
5. Scarano F., Gliozzi M., Zito M.C., Guarnieri L., Carresi C., Macri R., Nucera S., Scicchitano M., Bosco F., Ruga S.. **Potential of Nutraceutical Supplementation in the Modulation of White and Brown Fat Tissues in Obesity-Associated Disorders: Role of Inflammatory Signalling**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22073351
6. Kim S.H., Plutzky J.. **Brown Fat and Browning for the Treatment of Obesity and Related Metabolic Disorders**. *Diabetes Metab. J.* (2016.0) **40** 12-21. DOI: 10.4093/dmj.2016.40.1.12
7. Kurylowicz A., Puzianowska-Kuznicka M.. **Induction of Adipose Tissue Browning as a Strategy to Combat Obesity**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21176241
8. Cannon B., Nedergaard J.. **Brown adipose tissue: Function and physiological significance**. *Physiol. Rev.* (2004.0) **84** 277-359. DOI: 10.1152/physrev.00015.2003
9. Van der Vaart J.I., Boon M.R., Houtkooper R.H.. **The Role of AMPK Signaling in Brown Adipose Tissue Activation**. *Cells* (2021.0) **10**. DOI: 10.3390/cells10051122
10. Silvester A.J., Aseer K.R., Yun J.W.. **Dietary polyphenols and their roles in fat browning**. *J. Nutr. Biochem.* (2019.0) **64** 1-12. DOI: 10.1016/j.jnutbio.2018.09.028
11. Wang S., Moustaid-Moussa N., Chen L., Mo H., Shastri A., Su R., Bapat P., Kwun I., Shen C.L.. **Novel insights of dietary polyphenols and obesity**. *J. Nutr. Biochem.* (2014.0) **25** 1-18. DOI: 10.1016/j.jnutbio.2013.09.001
12. Andrade J.M., Frade A.C., Guimaraes J.B., Freitas K.M., Lopes M.T., Guimaraes A.L., de Paula A.M., Coimbra C.C., Santos S.H.. **Resveratrol increases brown adipose tissue thermogenesis markers by increasing SIRT1 and energy expenditure and decreasing fat accumulation in adipose tissue of mice fed a standard diet**. *Eur. J. Nutr.* (2014.0) **53** 1503-1510. DOI: 10.1007/s00394-014-0655-6
13. Gambini J., Ingles M., Olaso G., Lopez-Grueso R., Bonet-Costa V., Gimeno-Mallench L., Mas-Bargues C., Abdelaziz K.M., Gomez-Cabrera M.C., Vina J.. **Properties of Resveratrol: In Vitro and In Vivo Studies about Metabolism, Bioavailability, and Biological Effects in Animal Models and Humans**. *Oxid. Med. Cell Longev.* (2015.0) **2015** 837042. DOI: 10.1155/2015/837042
14. Arbo B.D., Andre-Miral C., Nasre-Nasser R.G., Schimith L.E., Santos M.G., Costa-Silva D., Muccillo-Baisch A.L., Hort M.A.. **Resveratrol Derivatives as Potential Treatments for Alzheimer’s and Parkinson’s Disease**. *Front. Aging Neurosci.* (2020.0) **12** 103. DOI: 10.3389/fnagi.2020.00103
15. La Spina M., Galletta E., Azzolini M., Gomez Zorita S., Parrasia S., Salvalaio M., Salmaso A., Biasutto L.. **Browning Effects of a Chronic Pterostilbene Supplementation in Mice Fed a High-Fat Diet**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20215377
16. De Angelis M., Della-Morte D., Buttinelli G., Di Martino A., Pacifici F., Checconi P., Ambrosio L., Stefanelli P., Palamara A.T., Garaci E.. **Protective Role of Combined Polyphenols and Micronutrients against Influenza A Virus and SARS-CoV-2 Infection In Vitro**. *Biomedicines* (2021.0) **9**. DOI: 10.3390/biomedicines9111721
17. Pacifici F., Salimei C., Pastore D., Malatesta G., Ricordi C., Donadel G., Bellia A., Rovella V., Tafani M., Garaci E.. **The Protective Effect of a Unique Mix of Polyphenols and Micronutrients against Neurodegeneration Induced by an In Vitro Model of Parkinson’s Disease**. *Int. J. Mol. Sci.* (2022.0) **23**. DOI: 10.3390/ijms23063110
18. Pacifici F., Farias C.L.A., Rea S., Capuani B., Feraco A., Coppola A., Mammi C., Pastore D., Abete P., Rovella V.. **Tyrosol May Prevent Obesity by Inhibiting Adipogenesis in 3T3-L1 Preadipocytes**. *Oxid. Med. Cell Longev.* (2020.0) **2020** 4794780. DOI: 10.1155/2020/4794780
19. Rea S., Della-Morte D., Pacifici F., Capuani B., Pastore D., Coppola A., Arriga R., Andreadi A., Donadel G., Di Daniele N.. **Insulin and Exendin-4 Reduced Mutated Huntingtin Accumulation in Neuronal Cells**. *Front. Pharmacol.* (2020.0) **11** 779. DOI: 10.3389/fphar.2020.00779
20. Marzolla V., Feraco A., Gorini S., Mammi C., Marrese C., Mularoni V., Boitani C., Lombes M., Kolkhof P., Ciriolo M.R.. **The novel non-steroidal MR antagonist finerenone improves metabolic parameters in high-fat diet-fed mice and activates brown adipose tissue via AMPK-ATGL pathway**. *FASEB J.* (2020.0) **34** 12450-12465. DOI: 10.1096/fj.202000164R
21. Nedergaard J., Petrovic N., Lindgren E.M., Jacobsson A., Cannon B.. **PPARgamma in the control of brown adipocyte differentiation**. *Biochim. Biophys. Acta* (2005.0) **1740** 293-304. DOI: 10.1016/j.bbadis.2005.02.003
22. Tang Q.Q., Otto T.C., Lane M.D.. **Mitotic clonal expansion: A synchronous process required for adipogenesis**. *Proc. Natl. Acad. Sci. USA* (2003.0) **100** 44-49. DOI: 10.1073/pnas.0137044100
23. Kawai T., Autieri M.V., Scalia R.. **Adipose tissue inflammation and metabolic dysfunction in obesity**. *Am. J. Physiol. Cell Physiol.* (2021.0) **320** C375-C391. DOI: 10.1152/ajpcell.00379.2020
24. Gonzalez-Hurtado E., Lee J., Choi J., Wolfgang M.J.. **Fatty acid oxidation is required for active and quiescent brown adipose tissue maintenance and thermogenic programing**. *Mol. Metab.* (2018.0) **7** 45-56. DOI: 10.1016/j.molmet.2017.11.004
25. Hankir M.K., Klingenspor M.. **Brown adipocyte glucose metabolism: A heated subject**. *EMBO Rep.* (2018.0) **19** e46404. DOI: 10.15252/embr.201846404
26. Wu L., Zhang L., Li B., Jiang H., Duan Y., Xie Z., Shuai L., Li J., Li J.. **AMP-Activated Protein Kinase (AMPK) Regulates Energy Metabolism through Modulating Thermogenesis in Adipose Tissue**. *Front. Physiol.* (2018.0) **9** 122. DOI: 10.3389/fphys.2018.00122
27. Ahmadian M., Abbott M.J., Tang T., Hudak C.S., Kim Y., Bruss M., Hellerstein M.K., Lee H.Y., Samuel V.T., Shulman G.I.. **Desnutrin/ATGL is regulated by AMPK and is required for a brown adipose phenotype**. *Cell. Metab.* (2011.0) **13** 739-748. DOI: 10.1016/j.cmet.2011.05.002
28. Riondino S., Roselli M., Palmirotta R., Della-Morte D., Ferroni P., Guadagni F.. **Obesity and colorectal cancer: Role of adipokines in tumor initiation and progression**. *World J. Gastroenterol.* (2014.0) **20** 5177-5190. DOI: 10.3748/wjg.v20.i18.5177
29. Ramirez-Moreno E., Arias-Rico J., Jimenez-Sanchez R.C., Estrada-Luna D., Jimenez-Osorio A.S., Zafra-Rojas Q.Y., Ariza-Ortega J.A., Flores-Chavez O.R., Morales-Castillejos L., Sandoval-Gallegos E.M.. **Role of Bioactive Compounds in Obesity: Metabolic Mechanism Focused on Inflammation**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11091232
30. Aranaz P., Navarro-Herrera D., Zabala M., Migueliz I., Romo-Hualde A., Lopez-Yoldi M., Martinez J.A., Vizmanos J.L., Milagro F.I., Gonzalez-Navarro C.J.. **Phenolic Compounds Inhibit 3T3-L1 Adipogenesis Depending on the Stage of Differentiation and Their Binding Affinity to PPARgamma**. *Molecules* (2019.0) **24**. DOI: 10.3390/molecules24061045
31. Mo J.F., Wu J.Y., Zheng L., Yu Y.W., Zhang T.X., Guo L., Bao Y.. **Therapeutic efficacy of polydatin for nonalcoholic fatty liver disease via regulating inflammatory response in obese mice**. *RSC Adv.* (2018.0) **8** 31194-31200. DOI: 10.1039/C8RA05915B
32. Tsai H.Y., Shih Y.Y., Yeh Y.T., Huang C.H., Liao C.A., Hu C.Y., Nagabhushanam K., Ho C.T., Chen Y.K.. **Pterostilbene and Its Derivative 3’-Hydroxypterostilbene Ameliorated Nonalcoholic Fatty Liver Disease Through Synergistic Modulation of the Gut Microbiota and SIRT1/AMPK Signaling Pathway**. *J. Agric. Food Chem.* (2022.0) **70** 4966-4980. DOI: 10.1021/acs.jafc.2c00641
33. Ding Y., Zhang L., Yao X., Zhang H., He X., Fan Z., Song Z.. **Honokiol Alleviates High-Fat Diet-Induced Obesity of Mice by Inhibiting Adipogenesis and Promoting White Adipose Tissue Browning**. *Animals* (2021.0) **11**. DOI: 10.3390/ani11061493
34. Sainz N., Barrenetxe J., Moreno-Aliaga M.J., Martinez J.A.. **Leptin resistance and diet-induced obesity: Central and peripheral actions of leptin**. *Metabolism* (2015.0) **64** 35-46. DOI: 10.1016/j.metabol.2014.10.015
35. Eder K., Baffy N., Falus A., Fulop A.K.. **The major inflammatory mediator interleukin-6 and obesity**. *Inflamm. Res.* (2009.0) **58** 727-736. DOI: 10.1007/s00011-009-0060-4
36. Rovella V., Rodia G., Di Daniele F., Cardillo C., Campia U., Noce A., Candi E., Della-Morte D., Tesauro M.. **Association of Gut Hormones and Microbiota with Vascular Dysfunction in Obesity**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13020613
37. Ikeda K., Yamada T.. **UCP1 Dependent and Independent Thermogenesis in Brown and Beige Adipocytes**. *Front. Endocrinol.* (2020.0) **11** 498. DOI: 10.3389/fendo.2020.00498
38. Tinkov A.A., Skalnaya M.G., Ajsuvakova O.P., Serebryansky E.P., Chao J.C., Aschner M., Skalny A.V.. **Selenium, Zinc, Chromium, and Vanadium Levels in Serum, Hair, and Urine Samples of Obese Adults Assessed by Inductively Coupled Plasma Mass Spectrometry**. *Biol. Trace Elem. Res.* (2021.0) **199** 490-499. DOI: 10.1007/s12011-020-02177-w
|
---
title: A Comparative Analysis of Treatment-Related Changes in the Diagnostic Biomarker
Active Metalloproteinase-8 Levels in Patients with Periodontitis
authors:
- Mutlu Keskin
- Juulia Rintamarttunen
- Emre Gülçiçek
- Ismo T. Räisänen
- Shipra Gupta
- Taina Tervahartiala
- Tommi Pätilä
- Timo Sorsa
journal: Diagnostics
year: 2023
pmcid: PMC10001139
doi: 10.3390/diagnostics13050903
license: CC BY 4.0
---
# A Comparative Analysis of Treatment-Related Changes in the Diagnostic Biomarker Active Metalloproteinase-8 Levels in Patients with Periodontitis
## Abstract
Background: Previous studies have revealed the potential diagnostic utility of aMMP-8, an active form of MMP-8, in periodontal and peri-implant diseases. While non-invasive point-of-care (PoC) chairside aMMP-8 tests have shown promise in this regard, there is a dearth of literature on the evaluation of treatment response using these tests. The present study aimed to investigate treatment-related changes in aMMP-8 levels in individuals with Stage III/IV—Grade C periodontitis compared to a healthy control group, using a quantitative chairside PoC aMMP-8 test, and to determine its correlation with clinical parameters. Methods: The study included 27 adult patients (13 smoker, 14 non-smoker) with stage III/IV-grade C periodontitis and 25 healthy adult subjects. Clinical periodontal measurements, real-time PoC aMMP-8, IFMA aMMP-8, and Western immunoblot analyses were performed before and 1 month after anti-infective scaling and root planing periodontal treatment. Time 0 measurements were taken from the healthy control group to test the consistency of the diagnostic test. Results: Both PoC aMMP-8 and IFMA aMMP-8 tests showed a statistically significant decrease in aMMP-8 levels and improvement in periodontal clinical parameters following treatment ($p \leq 0.05$). The PoC aMMP-8 test had high diagnostic sensitivity ($85.2\%$) and specificity ($100.0\%$) for periodontitis and was not affected by smoking ($p \leq 0.05$). Treatment also reduced MMP-8 immunoreactivity and activation as demonstrated by Western immunoblot analysis. Conclusion: The PoC aMMP-8 test shows promise as a useful tool for the real-time diagnosis and monitoring of periodontal therapy.
## 1. Introduction
Periodontitis is a chronic inflammatory disease that affects the tissues that support the teeth and is extremely prevalent in the community [1,2]. The pathogenic evolution of the dysbiotic microbial structure in the dental biofilm is among the most crucial factors in the onset and progression of periodontal disease. This process then leads to the continuation of tissue destruction as a result of the host response’s non-physiologic overreaction [2]. Periodontitis, one of the most common causes of tooth loss, is not only limited to local tissues but has been linked to a variety of systemic diseases, including diabetes, cardiovascular disease, cancer, and Alzheimer’s disease [3,4,5,6,7,8]. As a result, early diagnosis of the inflammatory periodontal disease process is critical in preventing tissue destruction [9,10].
Matrix metalloproteinases (MMPs), a family of genetically distinct but structurally related proteases that can degrade almost all extracellular matrix (ECM) structures, play an important role in tissue destruction caused by degenerative periodontal diseases [11]. MMPs can also process non-matrix bioactive molecules affecting immune responses [12]. These non-matrix bioactive molecules include, but are not limited to serpins, pro- and anti-inflammatory cytokines and chemokines, growth factors, complement components and insulin-receptor, and thereby MMPs can modify immune responses and systemic diseases [10,12]. Currently, 23 MMPs have been found to be expressed (released) in humans. MMP-8, also known as collagenase-2, is a pro-enzyme that is primarily derived from neutrophils [10]. It can be activated by microbial virulence factors, proinflammatory cytokines, and reactive oxygen species. Numerous studies have focused on MMP-8 as a diagnostic biomarker for periodontal diseases, and it has been found in oral fluids, such as mouth rinse, saliva, gingival crevicular fluid (GCF), and peri-implantitis sulcular fluid (PISF) [10]. MMP-8 levels in these fluids have been shown to correlate with the severity of periodontal and peri-implant diseases [10,13,14,15,16,17].
MMP-8 is produced and expressed during the neutrophils’ development and maturation in the bone marrow and is stored in subcellular neutrophilic granules in a latent state. When infection-induced inflammatory periodontal and peri-implant diseases appear, the process of selective degranulation and extracellular proMMP-8 release and activation begins [12,18,19]. MMP-8 has been found to be the most common collagenolytic protease in the diseased periodontium and peri-implantium [10,20,21,22,23].
The active form of MMP-8 which is called the active MMP-8, or aMMP-8, is the main mediator of the active tissue destruction process in inflammatory periodontal and peri-implant diseases [10,22]. The aMMP-8 levels in intraoral fluids (mouth rinse, saliva, i.e., GCF and PISF) have been found to rise in inflammatory periodontal and peri-implant diseases, i.e., [23,24,25]; aMMP-8 is regarded to be among the key biomarkers that play an important role in the diagnosis of periodontal and peri-implant diseases and has been implemented as a biomarker into the new classification of these diseases [10,12,17,21,26].
Traditional methods for diagnosing periodontal diseases include bleeding on probing, clinical attachment level measurement, probing depth, and radiographic findings [2,27]. Classical periodontal examination methods can be painful for the patient, and they are time-consuming procedures that must be repeated in all follow-up processes after periodontal treatment, which adds to bacteremia [2]. Furthermore, probing related evaluations such as bleeding on probing, pocket depth, and so on may not yield objective results due to a variety of factors, such as the force applied by the examiner and the characteristics of the periodontal probe, etc. Hence, the classical clinical assessments have been regarded to be at least partially erroneous [2,18,27,28]. On the other hand, radiographic examination methods can only provide information about the destructive effects of periodontal disease which have occurred in the past [2,29].
When considered a stand-alone evaluation criterion, bleeding on probing (BOP) values, which are considered as the gold standard for assessing periodontal disease activity, may thus be ineffective in diagnosing active periodontitis [30]. A number of longitudinal studies have also shown that BOP alone is not a good predictor of periodontal tissue destruction in treated cases [31,32]. Several studies have shown that a chairside PoC aMMP-8 test could be more effective in the diagnosis of subclinical periodontal diseases compared to BOP [33,34,35,36,37].
There are a few studies characterizing the periodontal treatment-related changes of aMMP-8, which give promising results about periodontal disease activity and evaluating its correlation with other oral biomarkers in the literature [23,24,38].
The present study aimed to investigate treatment-related changes in aMMP-8 levels in individuals with periodontitis using quantitative a chairside PoC aMMP-8 test and its on-line and real-time quantitative correlation with the studied clinical periodontal parameters. Consistency characteristics of the diagnostic tests were evaluated. The aMMP-8 levels and molecular forms were also assessed by IFMA and Western immunoblotting analysis, respectively.
## 2.1. Study Population and Design
The study design is presented in Figure 1. A total of 27 patients visiting a private clinic “Özel Fulya Ağız ve Diş Sağlığı Kliniği” in Tekirdağ, Turkey for their periodontal problems were recruited in the present study. The study was approved by the Biruni University Ethics Committee (2015-KAEK-71-22-06) and was carried out according to the principles of the Declaration of Helsinki. Oral and written consent was obtained from all recruited subjects. The inclusion criteria for the study were: interdental clinical attachment loss: ≥5 mm (at the site of greatest loss), detection of radiographic bone loss extending beyond $33\%$ of the root, tooth loss due to periodontitis: ≤4 teeth (Stage III Periodontitis), ≥5 teeth (Stage IV Periodontitis). Patients with Acquired Immune Deficiency Syndrome (AIDS), uncontrolled diabetes (HbA1c > 7), and other immune-system-related chronic diseases (Crohn’s disease, etc.) were excluded from the study. Pregnant or lactating females and individuals who had received periodontal treatment within the last year were also excluded. A total of 25 systemically and periodontally healthy dental students from the University of Helsinki, Finland served as healthy controls.
## 2.2. Periodontal Examination Procedure
Comprehensive periodontal examination was performed at baseline and 1 month following periodontal treatment by a single periodontist (M.K.). Probing depths (PD) were measured at six sites of each tooth with a Williams color-coded Michigan probe. Plaque index was recorded by assigning a score of 0–3 to each surface, and average oral plaque score was calculated for each patient [39]. The percentage of bleeding on probing (BoP) was determined after probing depth measurements. Gingival margin levels (GML) were determined by taking the enamel–cement junction (ECJ) into account during probing depth measurements. The areas where the free gingival margin ended at the apical of the EJC were recorded as positive values, and the areas where the free gingival margin terminated at the coronal point were recorded as negative values. Clinical attachment levels for each site were determined as the sum of GML and PD.
## 2.3. Periodontal Treatment Procedure
Periodontal treatment was carried out by a specialist periodontist (M.K.). Initially, cause-related therapy, including full-mouth scaling and root planing procedures, were performed along with oral hygiene instructions. At 2 weeks following the non-surgical phase of the periodontal therapy, periodontal sites associated with irregular bony contours, angular defects, or pockets in which a complete access with non-surgical periodontal therapy was not possible, such as grade II–III furcation defects, were treated with open flap debridement. Patients who underwent the surgical phase of treatment were prescribed amoxicillin plus clavulanic acid (1gr/day) and chlorhexidine mouth rinse ($0.12\%$) twice a day for 7 days and recalled thereafter for suture removal. All patients were re-evaluated clinically 1 month following treatment.
## 2.4. Quantitative Chairside PoC aMMP-8 Analyses
Levels of aMMP-8 were measured quantitatively using rapid PoC chairside aMMP-8 kits (Periosafe®, Dentognostics GmbH, Solingen, Germany) and a quantitative spectrometer analyzer (Oralyzer®, Dentognostics GmbH, Solingen, Germany) on mouth rinse samples collected before treatment and 1 month following periodontal treatment. To perform a comparative analysis with the periodontitis patient group, analysis of aMMP-8 was also conducted on the healthy control group at T0 (baseline). PoC chairside aMMP-8 analyses were performed prior to clinical measurements, and manufacturer’s instructions were followed. It was recommended that patients and controls not eat for 1 h before analyses. First, the patients and controls were instructed to rinse their mouths with clean water (drinking or distilled water) for 30 s and spit it out. After a waiting period of 1 min, they were told to rinse their mouths for 30 s with 5 mL of distilled water in the aMMP-8 kit (Periosafe®) and spit it back into the container. Then, 3–4 drops were taken from the container with a sterile syringe and poured into the well on the test cassette provided in the aMMP-8 kit. Immediately after that, the cassette was transferred to the digital spectrometer device (Oralyzer®) and quantitative results were obtained after 5 min. The remaining liquid in the container was transferred to Eppendorf tubes and stored at −70 °C for further laboratory analysis.
## 2.5. Measurement of the aMMP-8 Levels by Immunofluorometric Assay (IFMA)
The aMMP-8 level from mouth rinse samples was determined by a time-resolved immunofluorescence assay (IFMA) as described by Öztürk et al. [ 40]. Briefly, aMMP-8-specific monoclonal antibodies 8708 and 8706 (Actim Oy, Espoo, Finland) were used in the analysis as a catching antibody and a tracer antibody, respectively. In this protocol, the diluted samples were allowed to incubate for 1 h with the Europium labelled tracer antibody. The fluorescence was measured using an EnVision 2015 multimode reader (PerkinElmer, Turku, Finland).
## 2.6. Western-Immunoblotting Testing Procedure
The molecular forms of MMP-8 were detected from mouth rinse samples by a modified enhanced chemiluminescence (ECL) Western blotting kit according to protocols recommended by the manufacturer (GE Healthcare, Amersham, UK) as described earlier by Rautava et al. [ 41]. Briefly, the proteins of mouth rinse samples were first separated by electrophoresis and then electro-transferred onto nitrocellulose membranes Protran (Whatman GmbH, Dassel, Germany). The membranes were incubated overnight with monoclonal primary antibodies anti-MMP-8 [42] and then with horseradish peroxidase-linked secondary antibody (GE Healthcare, Buckinghamshire, UK) for 1 h. The membranes were washed 4 times in TBST between each step for 15 min. The proteins were visualized using the ECL system according to protocol. The recombinant human MMP-8 (100 ng, Calbiochem, Darnstadt, Germany) was used as a positive control.
## 2.7. Statistical Analysis
All periodontal parameters, including probing depth, bleeding on probing, plaque index, and clinical attachment level were examined before periodontal treatment and 1 month following anti-infective periodontal treatment. Normality tests were performed to test the normality of the data before calculating paired samples t-tests. Paired-samples t-test was used to analyze the statistically significant differences between these two phases. The effect of smoking on aMMP-8 levels was tested with repeated measures ANOVA. A $p \leq 0.05$ was accepted as statistically significant value.
Receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC) were used to examine the diagnostic accuracy of aMMP-8 to classify periodontitis and periodontally healthy subjects. In order to identify optimal cut-offs from the ROC curves, the Youden Index was used for calculating diagnostic sensitivity and specificity (Se and Sp).
## 3.1. Study Population
A total of 27 periodontitis patients (4 = Stage III, 23 = Stage IV, 27 = Grade C) and 25 healthy control subjects were enrolled in the study. Ages of periodontitis patients ranged between 30 and 70 years. All healthy subjects were younger (age range 23 to 25 years) than the study group ($p \leq 0.01$). Demographic characteristics of periodontitis patients and healthy control subjects are shown in Table 1.
## 3.2. Clinical Periodontal Parameters
All periodontitis patients were subjected to non-surgical periodontal therapy followed by open flap debridement in seven of them. Statistically significant improvements following anti-infective treatment were observed for all periodontal parameters ($p \leq 0.001$) (Table 2). Scatter plot diagrams of the relationship between probing depths, bleeding on probing, clinical attachment level, and plaque indices before periodontal treatment and after anti-infective periodontal treatment are presented in Figure 2. The clinical parameters as well as aMMP-8 levels of the periodontitis patients reduced to levels close to that of healthy subjects following periodontal therapy (Table 2).
Both non-smoker and smoker subjects showed statistically significant decreases in terms of inflammatory clinical parameters ($p \leq 0.001$). A similar clinical healing pattern was observed in both groups (Figure 3).
## 3.3. aMMP-8 Results
A statistically significant decrease in oral rinse aMMP-8 levels following anti-infective periodontal treatment was observed regarding both Oralyzer® and IFMA results and in correlation with bleeding on probing ($p \leq 0.05$) (Table 2 and Figure 4). Both Oralyzer® and IFMA results indicated a similar pattern of decrease in terms of oral rinse aMMP-8 levels, and it was also observed that smoking did not have a significant effect on aMMP-8 PoC testing (Figure 4 and Figure 5) ($p \leq 0.05$).
An ROC analysis was used for analyzing the diagnostic ability of aMMP-8 PoC and IFMA tests to discriminate patients with periodontitis (before treatment) from healthy controls (Figure 6).
AUC was also calculated and showed excellent discrimination ability between periodontitis and periodontally healthy groups (aMMP-8 POC test = 0.963; $95\%$ CI: 0.904–1.000; $p \leq 0.001$ and aMMP-8 IFMA test = 0.975; $95\%$ CI: 0.941–1.000; $p \leq 0.001$). Optimal cut-offs for aMMP-8 POC and IFMA tests were estimated by Youden’s Index (aMMP-8 POC test: 20.0 ng/mL; sensitivity: 0.852; specificity: 1.000; aMMP-8 IFMA test: 43.20 ng/mL; sensitivity: 0.926; specificity: 0.920).
With the cut-off set at 20 ng/mL, pretreatment sensitivity was $85.2\%$ and post-treatment sensitivity was $81.5\%$; $85.2\%$ (23 out of 27) of study subjects were aMMP-8 positives (>20 ng/mL), and $78.3\%$ (18 out of 23) of aMMP-8 positive patients were converted to aMMP-8 negatives (<20 ng/mL) following periodontal therapy.
With the cut-off set at 10 ng/mL, pretreatment sensitivity was $100\%$. All (27 out of 27) study subjects were aMMP-8 positives (>10 ng/mL), and $43.4\%$ (10 out of 23) aMMP-8 positive subjects converted to aMMP-8 negatives (<10 ng/mL) following therapy(Table 3).
## 3.4. Western Immunoblotting Analysis Results
Representative Western immunoblot analysis and aMMP-8 POC-test outcomes of MMP-8 in the studied mouth rinse samples from orally and systemically healthy and diseased study subjects are shown in Figure 7. MMP-8 was in latent form in the healthy sample (Figure 7A, Lane 2), and in the diseased samples it was converted to active and fragmented forms (Lane 3) as analyzed by monoclonal anti-MMP-8 antibody (Figure 7A). Negative (−, <20 ng/mL) and positive (+, ≥20 ng/mL) aMMP-8 POC-test outcomes are shown in Figure 7B.
## 4. Discussion
Periodontal diseases are chronic inflammatory conditions affecting the supporting tissues of the teeth [1]. One of the key enzymes involved in the breakdown of these tissues is matrix metalloproteinase-8 (MMP-8). While MMP-8 is important for normal tissue remodeling and repair, excessive or uncontrolled production of this enzyme can lead to tissue destruction and the progression of periodontal diseases. Recent studies have focused on the use of aMMP-8 as a biomarker for periodontal diseases; aMMP-8 refers to the active form of MMP-8, which is produced by neutrophils and other inflammatory cells in response to bacterial infection. Elevated levels of aMMP-8 have been linked to increased tissue destruction and disease progression in periodontal diseases, making it a valuable diagnostic and prognostic tool for these conditions [12]. Furthermore, aMMP-8 has been shown to be a more specific marker for active periodontal disease than total MMP-8, which can be found in both active and inactive forms [12,21].
The present study aimed to evaluate treatment-related changes of mouth rinse aMMP-8 levels by using PoC aMMP-8 kits and Oralyser-reader, which is a non-invasive method that rapidly and quantitatively produces chairside on-line and real-time results. Both chairside PoC aMMP-8 tests and IFMA aMMP-8 laboratory analysis confirmed that pre-treatment mouth rinse aMMP-8 levels were clearly higher than mouth rinse levels of patients after 1 month following periodontal treatment.
Our study provides valuable insights into the potential use of PoC chairside aMMP-8 tests and IFMA aMMP-8 laboratory analysis in the diagnosis and post-treatment follow-up of periodontal diseases. However, there are several limitations that should be considered when interpreting the results. Firstly, the small sample size could limit the generalizability of our findings. Secondly, the short follow-up period of only 1 month limits the assessment of the effectiveness of these techniques over time. On the other hand, the absence of periodontally healthy smokers in our study groups can be considered a limitation in comparative evaluations. Despite these limitations, our study provides important insights into the potential use of PoC chairside aMMP-8 tests and IFMA aMMP-8 laboratory analysis in the diagnosis and post-treatment follow-up of periodontal diseases.
This study utilized both the aMMP-8 PoC chairside aMMP-8 test and the aMMP-8 IFMA measurements that utilize the same monoclonal antibodies (Sorsa T et al., US patent no: US10488415B2). These techniques utilize two monoclonal, i.e., primary or catching antibody and secondary or detection [9,17,23,43,44]. Despite that, they correlate with each other; the techniques produced different values evidencing that both techniques can independently diagnose and differentiate periodontal health and disease. Both techniques can also be applied to monitor the treatment of the disease [12,24,35]. This study thus confirms and further extends the results of several previous studies demonstrating the potential benefits of POC chairside aMMP-8 and IFMA aMMP-8 laboratory analysis in terms of diagnostic distinction between periodontal health and disease [34,36,37,40,45,46,47,48]. Furthermore, our present findings are in accordance with numerous studies linking elevated oral aMMP-8, but not total MMP-8, to active and progressive stages of periodontal and peri-implant diseases [20,23,43,49,50,51,52,53,54,55].
It was previously shown that smokers had significantly higher levels of aMMP-8 in their saliva compared to ex-smokers or non-smokers [17,54]. When the pre-periodontal treatment results were evaluated from a diagnostic point of view, smoking was not found to significantly affect the aMMP-8 PoC testing being in agreement with previous studies on aMMP-8 in oral fluids (Mäntylä et al., 2006). The sensitivity of the test was found to be $85.2\%$ when the cut-off value was determined to be 20 ng/mL. According to a recently published study of Öztürk VÖ et al. [ 40], in which they included Stage III and IV periodontitis patients, diagnostic sensitivity of PoC aMMP-8 was observed as $83.9\%$ [40]. In other studies in which periodontitis and peri-implantitis patients were included and the cut-off value was determined to be 20 ng/mL, it was observed that the aMMP-8 PoC test’s sensitivity ranged between 76–$90\%$ [21].
Clinical periodontal parameters of pre-treatment and 1 month following periodontal treatment revealed statistically significant improvement as predicted and consistent with the literature. [ 56,57]. The quantitative chairside PoC aMMP-8 and IFMA aMMP-8 laboratory results both demonstrated a statistically significant decrease, correlating with and reflecting well with the clinical findings. There are many studies in the literature reporting a decrease in aMMP-8 levels following periodontal treatment [10,12,24,25,48,49,58]. While MMP-8 in its latent form was detected more frequently in the healthy state [53], the release of degranulated aMMP-8, its activated form, increases with periodontal and peri-implant inflammation and disease severity [12,23,54,55]. The statistical decrease in aMMP-8 levels post-periodontal treatment suggests that active tissue destruction, along with clinical disease activity, is reduced, confirming the role of MMP-8 in periodontitis pathogenesis [10,12,59].
When analyzing the clinical results, it becomes clear that factors, such as deep periodontal pockets, bleeding on probing (BOP), and oral hygiene, are strongly linked. However, despite treatment, not all patients were able to achieve complete oral health status as these parameters did not return to normal levels in all cases. Furthermore, it was observed that the post-treatment mouth rinse aMMP-8 levels (in both IFMA and PoC chairside aMMP-8 Tests) were higher than health-associated levels. In the study of Umeizudike et al., it was found that in the sixth month post-periodontal treatment, the aMMP-8 levels did not reach close to that of the healthy control group [48]. Literature data further suggests that individuals with gingivitis may have elevated aMMP-8 IFMA levels and aMMP-8 release may persist in the periodontal sites that respond poorly to treatment [25,35,36]. Periodontally and systemically healthy dental students without any periodontal disease experience and activity all had negative ([-], <20 ng/mL) aMMP-8 levels. This finding was also compatible with the literature which further affirms that Periosafe PoC aMMP-8 test negativity can be regarded as a biomarker of periodontal and peri-implant health [40]. Since this study includes a 1-month follow-up, the clinical and biochemical findings might not have reached the level of complete health due to persistent gingival inflammation and residual periodontal pockets. There are studies in the literature that suggest that the post-treatment re-evaluation period ranges from 2 weeks to 6 months [59, 60]. Morrison et al. state that the severity of periodontitis can be significantly reduced during the 1-month post-periodontal treatment follow-up process. However, the oral hygiene process must be fully ensured to determine the ongoing treatment need [60].
When comparing aMMP-8 cut-off 20 ng/mL vs. 10 ng/mL [34,36], we found that especially after treatment, the periodontal health targeting was reduced with a 10 ng/mL cut-off. Deng et al. [ 36] used 10 ng/mL as the diagnostic cut-off value, but it should be remembered that it is not recommended by the manufacturers [21,35]. Our present results provide further support for the use of 20 ng/mL as the diagnostic cut-off value for aMMP-8 PoC tests [23,24,34,35].
Laboratory analysis of immunological inflammatory factors is considered to be the gold standard [61,62,63]. The results of PoC chairside aMMP-8 tests were consistent with those of IFMA aMMP-8 analyses, indicating that non-invasive PoC aMMP-8 analysis [12,21,34,45,64] can make a potential contribution regarding the diagnosis and periodontal therapy follow-up. However, there is a need for more longitudinal studies on the functionality of PoC chairside aMMP-8 analyses in periodontal treatment and its follow-up.
## 5. Conclusions
Observation of alarmingly high mouth rinse aMMP-8 levels in individuals with periodontitis through both point-of-care aMMP-8 and IFMA aMMP-8 analyses, and their significant decrease after anti-infective periodontal treatment, highlights the practical utility of the point-of-care aMMP-8 test for real-time diagnosis and monitoring of periodontal treatment progress.
## 6. Patents
TS is the inventor of U.S. patents 1,274,416, 5,652,223, 5,736,341, 5,864,632, 6,143,476 and US $\frac{2017}{0023571}$A1 (issued 6 June 2019), WO $\frac{2018}{060553}$ A1 (issued 31 May 2018), 10,488,415 B2, and US $\frac{2017}{0023671}$A1, Japanese Patent 2016-554676 and South Korean Patent No. 10-2016-7025378.
## References
1. Könönen E., Gursoy M., Gursoy U.. **Periodontitis: A Multifaceted Disease of Tooth-Supporting Tissues**. *J. Clin. Med.* (2019) **8**. DOI: 10.3390/jcm8081135
2. Kinane D.F., Stathopoulou P.G., Papapanou P.N.. **Periodontal diseases**. *Nat. Rev. Dis. Prim.* (2017) **3** 17038. DOI: 10.1038/nrdp.2017.38
3. Heikkilä P., But A., Sorsa T., Haukka J.. **Periodontitis and cancer mortality: Register-based cohort study of 68,273 adults in 10-year follow-up**. *Int. J. Cancer* (2018) **142** 2244-2253. DOI: 10.1002/ijc.31254
4. Heikkilä P., Niskanen L., But A., Sorsa T., Haukka J.. **Oral health associated with incident diabetes but not other chronic diseases: A register-based cohort study**. *Front. Oral Health* (2022) **3** 82. DOI: 10.3389/froh.2022.956072
5. Baeza M., Morales A., Cisterna C., Cavalla F., Jara G., Isamitt Y., Pino P., Gamonal J.. **Effect of periodontal treatment in patients with periodontitis and diabetes: Systematic review and meta-analysis**. *J. Appl. Oral Sci.* (2020) **28** e20190248. DOI: 10.1590/1678-7757-2019-0248
6. Sanz M., Del Castillo A.M., Jepsen S., Juanatey J.R.G., D’Aiuto F., Bouchard P., Chapple I., Dietrich T., Gotsman I., Graziani F.. **Periodontitis and cardiovascular diseases: Consensus report**. *J. Clin. Periodontol.* (2020) **47** 268-288. DOI: 10.1111/jcpe.13189
7. Gasparoni L., Alves F., Holzhausen M., Pannuti C., Serpa M.. **Periodontitis as a risk factor for head and neck cancer**. *Med. Oral Patol. Oral Cir. Bucal.* (2021) **26** e430-e436. DOI: 10.4317/medoral.24270
8. Sadrameli M., Bathini P., Alberi L.. **Linking mechanisms of periodontitis to Alzheimer’s disease**. *Curr. Opin. Neurol.* (2020) **33** 230-238. DOI: 10.1097/WCO.0000000000000797
9. Mäntylä P., Stenman M., Kinane D.F., Tikanoja S., Luoto H., Salo T., Sorsa T.. **Gingival crevicular fluid collagenase-2 (MMP-8) test stick for chair-side monitoring of periodontitis**. *J. Periodontal Res.* (2003) **38** 436-439. DOI: 10.1034/j.1600-0765.2003.00677.x
10. Sorsa T., Gursoy U.K., Nwhator S., Hernández M., Tervahartiala T., Leppilahti J., Gürsoy M., Könönen E., Emingil G., Pussinen P.J.. **Analysis of matrix metalloproteinases, especially MMP-8, in gingival crevicular fluid, mouthrinse and saliva for monitoring periodontal diseases**. *Periodontology 2000* (2016) **70** 142-163. DOI: 10.1111/prd.12101
11. Laronha H., Caldeira J.. **Structure and Function of Human Matrix Metalloproteinases**. *Cells* (2020) **9**. DOI: 10.3390/cells9051076
12. Sorsa T., Tjäderhane L., Konttinen Y.T., Lauhio A., Salo T., Lee H., Golub L.M., Brown D.L., Mäntylä P.. **Matrix metalloproteinases: Contribution to pathogenesis, diagnosis and treatment of periodontal inflammation**. *Ann. Med.* (2006) **38** 306-321. DOI: 10.1080/07853890600800103
13. Rai B., Kharb S., Jain R., Anand S.C.. **Biomarkers of periodontitis in oral fluids**. *J. Oral Sci.* (2008) **50** 53-56. DOI: 10.2334/josnusd.50.53
14. Choi D.-H., Moon I.-S., Choi B.-K., Paik J.-W., Kim Y.-S., Choi S.-H., Kim C.-K.. **Effects of sub-antimicrobial dose doxycycline therapy on crevicular fluid MMP-8, and gingival tissue MMP-9, TIMP-1 and IL-6 levels in chronic periodontitis**. *J. Periodontal Res.* (2004) **39** 20-26. DOI: 10.1111/j.1600-0765.2004.00696.x
15. Gonçalves P.F., Huang H., McAninley S., Alfant B., Harrison P., Aukhil I., Walker C., Shaddox L.M.. **Periodontal treatment reduces matrix metalloproteinase levels in localized aggressive periodontitis**. *J. Periodontol.* (2013) **84** 1801-1808. DOI: 10.1902/jop.2013.130002
16. Skurska A., Dolinska E., Pietruska M., Pietruski J.K., Dymicka V., Kemona H., Arweiler N.B., Milewski R., Sculean A.. **Effect of nonsurgical periodontal treatment in conjunction with either systemic administration of amoxicillin and metronidazole or additional photodynamic therapy on the concentration of matrix metalloproteinases 8 and 9 in gingival crevicular fluid in patients with aggressive periodontitis**. *BMC Oral Health* (2015) **15**. DOI: 10.1186/s12903-015-0048-0
17. Mäntylä P., Stenman M., Kinane D., Salo T., Suomalainen K., Tikanoja S., Sorsa T.. **Monitoring periodontal disease status in smokers and nonsmokers using a gingival crevicular fluid matrix metalloproteinase-8-specific chair-side test**. *J. Periodontal Res.* (2006) **41** 503-512. DOI: 10.1111/j.1600-0765.2006.00897.x
18. Weiss S.J., Peppin G., Ortiz X., Ragsdale C., Test S.T.. **Oxidative Autoactivation of Latent Collagenase by Human Neutrophils**. *Science* (1985) **227** 747-749. DOI: 10.1126/science.2982211
19. Epstein F.H., Weiss S.J.. **Tissue Destruction by Neutrophils**. *N. Engl. J. Med.* (1989) **320** 365-376. DOI: 10.1056/NEJM198902093200606
20. Sorsa T., Uitto V.-J., Suomalainen K., Vauhkonen M., Lindy S.. **Comparison of interstitial collagenases from human gingiva, sulcular fluid and polymorphonuclear leukocytes**. *J. Periodontal Res.* (1988) **23** 386-393. DOI: 10.1111/j.1600-0765.1988.tb01618.x
21. Sorsa T., Nwhator S.O., Sakellari D., Grigoriadis A., Umeizudike K.A., Brandt E., Keskin M., Tervahartiala T., Pärnänen P., Gupta S.. **aMMP-8 Oral Fluid PoC Test in Relation to Oral and Systemic Diseases**. *Front. Oral Health* (2022) **3** 897115. DOI: 10.3389/froh.2022.897115
22. Luchian I., Goriuc A., Sandu D., Covasa M.. **The Role of Matrix Metalloproteinases (MMP-8, MMP-9, MMP-13) in Periodontal and Peri-Implant Pathological Processes**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23031806
23. Lähteenmäki H., Tervahartiala T., Räisänen I.T., Pärnänen P., Mauramo M., Gupta S., Sampson V., Rathnayake N., Heikkinen A., Alassiri S.. **Active MMP-8 point-of-care (PoC)/chairside enzyme-test as an adjunctive tool for early and real-time diagnosis of peri-implantitis**. *Clin. Exp. Dent. Res.* (2022) **8** 485-496. DOI: 10.1002/cre2.537
24. Alassiri S., Parnanen P., Rathnayake N., Johannsen G., Heikkinen A.-M., Lazzara R., Van Der Schoor P., Van Der Schoor J.G., Tervahartiala T., Gieselmann D.. **The Ability of Quantitative, Specific, and Sensitive Point-of-Care/Chair-Side Oral Fluid Immunotests for aMMP-8 to Detect Periodontal and Peri-Implant Diseases**. *Dis. Markers* (2018) **2018** 1306396. DOI: 10.1155/2018/1306396
25. Sorsa T., Gieselmann D., Arweiler N.B., Hernández M.. **A quantitative point-of-care test for periodontal and dental peri-implant diseases**. *Nat. Rev. Dis. Prim.* (2017) **3** 17069. DOI: 10.1038/nrdp.2017.69
26. Keskin M., Lähteenmäki H., Rathnayake N., Räisänen I.T., Tervahartiala T., Pärnänen P., Şenışık A.M., Karaçetin D., Balkanay A.Y., Heikkilä P.. **Active matrix metalloproteinase-8 and interleukin-6 detect periodontal degeneration caused by radiotherapy of head and neck cancer: A pilot study**. *Expert Rev. Proteom.* (2020) **17** 777-784. DOI: 10.1080/14789450.2020.1858056
27. Papapanou P.N., Sanz M., Buduneli N., Dietrich T., Feres M., Fine D.H., Flemmig T.F., Garcia R., Giannobile W.V., Graziani F.. **Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions**. *J. Periodontol.* (2018) **89** S173-S182. DOI: 10.1002/JPER.17-0721
28. Dukka H., Saleh M.H.A., Ravidà A., Greenwell H., Wang H.L.. **Is bleeding on probing a reliable clinical indicator of peri-implant diseases?**. *J. Periodontol.* (2021) **92** 1669-1674. DOI: 10.1002/JPER.20-0890
29. Gul S.S., Abdulkareem A.A., Sha A.M., Rawlinson A.. **Diagnostic Accuracy of Oral Fluids Biomarker Profile to Determine the Current and Future Status of Periodontal and Peri-Implant Diseases**. *Diagnostics* (2020) **10**. DOI: 10.3390/diagnostics10100838
30. Leppilahti J.M., Harjunmaa U., Järnstedt J., Mangani C., Hernández M., Tervahartiala T., Lopez R., Ashorn U., Ashorn P., Gieselmann D.-R.. **Diagnosis of Newly Delivered Mothers for Periodontitis with a Novel Oral-Rinse aMMP-8 Point-of-Care Test in a Rural Malawian Population**. *Diagnostics* (2018) **8**. DOI: 10.3390/diagnostics8030067
31. Chaves E.S., Caffesse R.G., Morrison E.C., Stults D.L.. **Diagnostic discrimination of bleeding on probing during maintenance periodontal therapy**. *Am. J. Dent.* (1990) **3** 167-170. PMID: 2076243
32. Kaldahl W.B., Kalkwarf K.L., Patil K.D., Molvar M.P.. **Relationship of Gingival Bleeding, Gingival Suppuration, and Supragingival Plaque to Attachment Loss**. *J. Periodontol.* (1990) **61** 347-351. DOI: 10.1902/jop.1990.61.6.347
33. Räisänen I.T., Sorsa T., van der Schoor G.-J., Tervahartiala T., van der Schoor P., Gieselmann D.-R., Heikkinen A.M.. **Active Matrix Metalloproteinase-8 Point-of-Care (PoC)/Chairside Mouthrinse Test vs. Bleeding on Probing in Diagnosing Subclinical Periodontitis in Adolescents**. *Diagnostics* (2019) **9**. DOI: 10.3390/diagnostics9010034
34. Sorsa T., Alassiri S., Grigoriadis A., Räisänen I.T., Pärnänen P., Nwhator S.O., Gieselmann D.-R., Sakellari D.. **Active MMP-8 (aMMP-8) as a Grading and Staging Biomarker in the Periodontitis Classification**. *Diagnostics* (2020) **10**. DOI: 10.3390/diagnostics10020061
35. Sorsa T., Grigoriadis A., Sakellari D., Gupta S., Sahni V., Tervahartiala T., Räisänen I.T.. **On the accuracy, sensitivity, and grading of mouthrinse active matrix metalloproteinase-8 (aMMP-8) point-of-care testing (POCT)**. *J. Clin. Periodontol.* (2021) **48** 1495-1498. DOI: 10.1111/jcpe.13521
36. Deng K., Pelekos G., Jin L., Tonetti M.S.. **Diagnostic accuracy of a point-of-care aMMP-8 test in the discrimination of periodontal health and disease**. *J. Clin. Periodontol.* (2021) **48** 1051-1065. DOI: 10.1111/jcpe.13485
37. Deng K., Wei S., Xu M., Shi J., Lai H., Tonetti M.S.. **Diagnostic accuracy of active matrix metalloproteinase-8 point-of-care test for the discrimination of periodontal health status: Comparison of saliva and oral rinse samples**. *J. Periodontal Res.* (2022) **57** 768-779. DOI: 10.1111/jre.12999
38. Schmalz G., Kummer M.K., Kottmann T., Rinke S., Haak R., Krause F., Schmidt J., Ziebolz D.. **Association of chairside salivary aMMP-8 findings with periodontal risk assessment parameters in patients receiving supportive periodontal therapy**. *J. Periodontal Implant. Sci.* (2018) **48** 251-260. DOI: 10.5051/jpis.2018.48.4.251
39. Mercado F., Marshall R.I., Klestov A.C., Bartold P.. **Is there a relationship between rheumatoid arthritis and periodontal disease?**. *J. Clin. Periodontol.* (2000) **27** 267-272. DOI: 10.1034/j.1600-051x.2000.027004267.x
40. Öztürk V., Emingil G., Umeizudike K., Tervahartiala T., Gieselmann D.-R., Maier K., Köse T., Sorsa T., Alassiri S.. **Evaluation of active matrix metalloproteinase-8 (aMMP-8) chair-side test as a diagnostic biomarker in the staging of periodontal diseases**. *Arch. Oral Biol.* (2021) **124** 104955. DOI: 10.1016/j.archoralbio.2020.104955
41. Rautava J., Gürsoy U.K., Kullström A., Könönen E., Sorsa T., Tervahartiala T., Gürsoy M.. **An Oral Rinse Active Matrix Metalloproteinase-8 Point-of-Care Immunotest May Be Less Accurate in Patients with Crohn’s Disease**. *Biomolecules* (2020) **10**. DOI: 10.3390/biom10030395
42. Hanemaaijer R., Sorsa T., Konttinen Y.T., Ding Y., Sutinen M., Visser H., van Hinsbergh V.W., Helaakoski T., Kainulainen T., Rönkä H.. **Matrix metalloproteinase-8 is expressed in rheumatoid synovial fibroblasts and endothelial cells. Regulation by tumor necrosis factor-alpha and doxycycline**. *J. Biol. Chem.* (1997) **272** 31504-31509. DOI: 10.1074/jbc.272.50.31504
43. Sorsa T., Mäntylä P., Tervahartiala T., Pussinen P.J., Gamonal J., Hernandez M.. **MMP activation in diagnostics of periodontitis and systemic inflammation**. *J. Clin. Periodontol.* (2011) **38** 817-819. DOI: 10.1111/j.1600-051X.2011.01753.x
44. Umeizudike K.A., Lähteenmäki H., Räisänen I.T., Taylor J.J., Preshaw P.M., Bissett S.M., Tervahartiala T., Nwhator S., Pärnänen P., Sorsa T.. **Ability of matrix metalloproteinase-8 biosensor, IFMA, and ELISA immunoassays to differentiate between periodontal health, gingivitis, and periodontitis**. *J. Periodontal Res.* (2022) **57** 558-567. DOI: 10.1111/jre.12985
45. Sorsa T., Mäntylä P., Ronka H., Kallio P., Kallis G.-B., Lundqvist C., Kinane D.F., Salo T., Golub L.M., Teronen O.. **Scientific basis of a matrix metalloproteinase-8 specific chair-side test for monitoring periodontal and peri-implant health and disease**. *Ann. N. Y. Acad. Sci.* (1999) **878** 130-140. DOI: 10.1111/j.1749-6632.1999.tb07679.x
46. Sorsa T., Tjäderhane L., Salo T.. **Matrix metalloproteinases (MMPs) in oral diseases**. *Oral Dis.* (2004) **10** 311-318. DOI: 10.1111/j.1601-0825.2004.01038.x
47. Uitto V.-J., Suomalainen K., Sorsa T.. **Salivary collagenase. Origin, characteristics and relationship to periodontal health**. *J. Periodontal Res.* (1990) **25** 135-142. DOI: 10.1111/j.1600-0765.1990.tb01035.x
48. Gangbar S., Overall C.M., McCulloch C.A., Sodek J.. **Identification of polymorphonuclear leukocyte collagenase and gelatinase activities in mouthrinse samples: Correlation with periodontal disease activity in adult and juvenile periodontitis**. *J. Periodontal Res.* (1990) **25** 257-267. DOI: 10.1111/j.1600-0765.1990.tb00914.x
49. Lee W., Aitken S., Sodek J., McCulloch C.A.. **Evidence of a direct relationship between neutrophil collagenase activity and periodontal tissue destruction in vivo: Role of active enzyme in human periodontitis**. *J. Periodontal Res.* (1995) **30** 23-33. DOI: 10.1111/j.1600-0765.1995.tb01249.x
50. Mancini S., Romanelli R., Laschinger C.A., Overall C.M., Sodek J., McCulloch C.A.. **Assessment of a Novel Screening Test for Neutrophil Collagenase Activity in the Diagnosis of Periodontal Diseases**. *J. Periodontol.* (1999) **70** 1292-1302. DOI: 10.1902/jop.1999.70.11.1292
51. Romanelli R., Mancini S., Laschinger C., Overall C.M., Sodek J., McCulloch C.A.G.. **Activation of neutrophil collagenase in periodontitis**. *Infect. Immun.* (1999) **67** 2319-2326. DOI: 10.1128/IAI.67.5.2319-2326.1999
52. Kiili M., Cox S.W., Chen H.W., Wahlgren J., Maisi P., Eley B.M., Salo T., Sorsa T.. **Collagenase-2 (MMP-8) and collagenase-3 (MMP-13) in adult periodontitis: Molecular forms and levels in gingival crevicular fluid and immunolocalisation in gingival tissue**. *J. Clin. Periodontol.* (2002) **29** 224-232. DOI: 10.1034/j.1600-051x.2002.290308.x
53. Mc Crudden M.T.C., Irwin C.R., El Karim I., Linden G.J., Lundy F.T.. **Matrix metalloproteinase-8 activity in gingival crevicular fluid: Development of a novel assay**. *J. Periodontal Res.* (2017) **52** 556-561. DOI: 10.1111/jre.12423
54. Liede K.E., Haukka J.K., Hietanen J.H., Mattila M.H., Rönkö H., Sorsa T.. **The association between smoking cessation and periodontal status and salivary proteinase levels**. *J. Periodontol.* (1999) **70** 1361-1368. DOI: 10.1902/jop.1999.70.11.1361
55. Haffajee A.D., Cugini M.A., Dibart S., Smith C., Kent R.L., Socransky S.S.. **The effect of SRP on the clinical and microbiological parameters of periodontal diseases**. *J. Clin. Periodontol.* (1997) **24** 324-334. DOI: 10.1111/j.1600-051X.1997.tb00765.x
56. Suvan J.E.. **Effectiveness of mechanical nonsurgical pocket therapy**. *Periodontology 2000* (2005) **37** 48-71. DOI: 10.1111/j.1600-0757.2004.03794.x
57. Hernández M., Gamonal J., Tervahartiala T., Mäntylä P., Rivera O., Dezerega A., Dutzan N., Sorsa T.. **Associations between matrix metalloproteinase-8 and -14 and myeloperoxidase in gingival crevicular fluid from subjects with progressive chronic periodontitis: A longitudinal study**. *J. Periodontol.* (2010) **81** 1644-1652. DOI: 10.1902/jop.2010.100196
58. Teles R., Sakellari D., Teles F., Konstantinidis A., Kent R., Socransky S., Haffajee A.. **Relationships among gingival crevicular fluid biomarkers, clinical parameters of periodontal disease, and the subgingival microbiota**. *J. Periodontol.* (2010) **81** 89-98. DOI: 10.1902/jop.2009.090397
59. Pattison G.L., Pattison A.M., Carranza F.A., Newman M.G.. **Principles of periodontal instrumentation**. *Clinical Periodontology* (1996) 451-465
60. Segelnick S.L., Weinberg M.A.. **Reevaluation of initial therapy: When is the appropriate time?**. *J. Periodontol.* (2006) **77** 1598-1601. DOI: 10.1902/jop.2006.050358
61. Morrison E.C., Ramfjord S.P., Hill R.W.. **Short-term effects of initial, nonsurgical periodontal treatment (hygienic phase)**. *J. Clin. Periodontol.* (1980) **7** 199-211. DOI: 10.1111/j.1600-051X.1980.tb01963.x
62. Rathnayake N., Åkerman S., Klinge B., Lundegren N., Jansson H., Tryselius Y., Sorsa T., Gustafsson A.. **Salivary biomarkers for detection of systemic diseases**. *PLoS ONE* (2013) **8**. DOI: 10.1371/journal.pone.0061356
63. Gursoy U.K., Könönen E., Huumonen S., Tervahartiala T., Pussinen P., Suominen A.L., Sorsa T.. **Salivary type I collagen degradation end-products and related matrix metalloproteinases in periodontitis**. *J. Clin. Periodontol.* (2013) **40** 18-25. DOI: 10.1111/jcpe.12020
64. Thomas M.V., Branscum A., Miller C.S., Ebersole J., Al-Sabbagh M., Schuster J.L.. **Within-subject variability in repeated measures of salivary analytes in healthy adults**. *J. Periodontol.* (2009) **80** 1146-1153. DOI: 10.1902/jop.2009.080654
|
---
title: Models and Algorithms for the Refinement of Therapeutic Approaches for Retinal
Diseases
authors:
- Elfriede Friedmann
- Simon Dörsam
- Gerd U. Auffarth
journal: Diagnostics
year: 2023
pmcid: PMC10001150
doi: 10.3390/diagnostics13050975
license: CC BY 4.0
---
# Models and Algorithms for the Refinement of Therapeutic Approaches for Retinal Diseases
## Abstract
We are developing a Virtual Eye for in silico therapies to accelerate research and drug development. In this paper, we present a model for drug distribution in the vitreous body that enables personalized therapy in ophthalmology. The standard treatment for age-related macular degeneration is anti-vascular endothelial growth factor (VEGF) drugs administered by repeated injections. The treatment is risky, unpopular with patients, and some of them are unresponsive with no alternative treatment. Much attention is paid to the efficacy of these drugs, and many efforts are being made to improve them. We are designing a mathematical model and performing long-term three-dimensional Finite Element simulations for drug distribution in the human eye to gain new insights in the underlying processes using computational experiments. The underlying model consists of a time-dependent convection-diffusion equation for the drug coupled with a steady-state Darcy equation describing the flow of aqueous humor through the vitreous medium. The influence of collagen fibers in the vitreous on drug distribution is included by anisotropic diffusion and the gravity via an additional transport term. The resulting coupled model was solved in a decoupled way: first the Darcy equation with mixed finite elements, then the convection-diffusion equation with trilinear Lagrange elements. Krylov subspace methods are used to solve the resulting algebraic system. To cope with the large time steps resulting from the simulations over 30 days (operation time of 1 anti-VEGF injection), we apply the strong A-stable fractional step theta scheme. Using this strategy, we compute a good approximation to the solution that converges quadratically in both time and space. The developed simulations were used for the therapy optimization, for which specific output functionals are evaluated. We show that the effect of gravity on drug distribution is negligible, that the optimal pair of injection angles is (50∘,50∘), that larger angles can result in $38\%$ less drug at the macula, and that in the best case only $40\%$ of the drug reaches the macula while the rest escapes, e.g., through the retina, that by using heavier drug molecules, more of the drug concentration reaches the macula in an average of 30 days. As a refined therapy, we have found that for longer-acting drugs, the injection should be made in the center of the vitreous, and for more intensive initial treatment, the drug should be injected even closer to the macula. In this way, we can perform accurate and efficient treatment testing, calculate the optimal injection position, perform drug comparison, and quantify the effectiveness of the therapy using the developed functionals. We describe the first steps towards virtual exploration and improvement of therapy for retinal diseases such as age-related macular degeneration.
## 1. Introduction
Retinal diseases are unfortunately the most common cause of blindness in wealthy countries and already the most common cause of childhood blindness worldwide [1,2,3,4,5,6,7,8,9,10]. In people with retinal diseases, the light-sensitive neural cells in the retina are damaged and, in the worst case, die. For a long time, little attention was paid to these diseases, partly because they came to the fore late, and partly because in the past there were no treatment options. This has changed drastically today and although there are still forms of retinal degeneration for which there is no cure, it has been shown for many age-related eye diseases that prevention is possible and successful treatment can also be available. Accurate diagnostics and the resulting treatment require modern and expensive equipment. Extraordinary progress has been made in research by integrating information from animal and tissue culture models with clinical observations and with retinal biochemistry and physiology [11,12,13,14,15,16,17,18]. Artificial Intelligence research has shown great promise, especially in the classification of diagnostic images in this medical area [19,20].
Age-related macular degeneration (AMD) is the retinal disease that is the most common cause of vision loss in industrialized countries. It is a disease of the macula that results from late-onset neurodegeneration of the pigment epithelium photoreceptor complex [21]. The disease affects $10\%$ of those over 65 years of age and $25\%$ of those over 75 years of age. The characteristic sign of AMD is drusen in the macula. The exact pathology is not yet fully understood, but it is thought to be a complex interaction of many factors. The formation of drusen promotes loss of the retinal pigment epithelium (RPE), dysfunction of Bruch’s membrane and further photoreceptor death. Progressive damage of Bruch’s membrane along with upregulation of vascular endothelial growth factor (VEGF), a biomarker for AMD, leads to uncontrolled growth of abnormal choroidal vessels under the RPE [21]. There is no standard treatment for dry AMD, and several innovative treatments are in progress. For wet AMD significant advances has been made in recent years. Prior to the introduction of the first anti-VEGF agent (Macugen) around 2004, laser photocoagulation and photodynamic therapy were used. Since then, other more effective anti-VEGF agents have been developed: Lucentis (ranibizumab), Avastin (bevacizumab), Eylea (aflibercept), and the latest approved agents Beovu (brolucizumab, 2020) [22] and Vabysmo (faricimab, 2022) [23]. Treatment is not fixed; there are four dosing regimens in clinical use [21]. Monthly or bimonthly injections lead to long-term success, but do not take into account individual disease progression. A pro-re-nata strategy requires many check-ups and does not show good improvement, as reactivation of the disease is often detected too late, which can lead to irreversible visual loss. A treat-and-extend regimen is a personalized treatment in which the next injection is optimally planned [24,25]. An observe-and-plan regimen is also a personalized treatment that includes a personalized treatment plan with multiple injections [26]. Of course, the newly developed drugs are more effective and therefore require fewer injections. There are still many unresolved issues regarding frequency of treatment, proper dose, location of injection, effectiveness of therapy, choice of drug and more. Nevertheless, due to the high cost of the drugs and the inconvenience of the injections, as well as the increasing burden on patients due to the many consultations and inconvenient injections, there is an urgent need for a personalized, long-lasting optimized therapy solution for patients with AMD. One promising approach is treatment with a Port Delivery System filled with a drug that can be refilled [27] or with drug loaded hydrogels for sustained delivery [28,29].
In this article, we use mathematical modeling and numerical simulation to refine the results of the therapeutic approach for AMD. These so-called in silico or virtual experiments cost less than laboratory experiments, are not limited in performance, and do not have to follow ethical rules. Once developed and implemented, they can be used as an additional source of information in combination with experiments for parameter identification or validation. They can help accelerate and improve insights for therapeutic approaches. In addition, we have developed a model and algorithms that can also be used for various retinal or other diseases or other mass transfer problems in the eye.
Initial studies on the treatment of age-related macular degeneration based on computer simulations can be found in [30,31,32,33]. In [30], the influence of the diffusion coefficient, retinal permeability, and vitreous humor flow on drug distribution was analyzed. The results showed that for rapidly diffusing drugs (drugs with high diffusion coefficients), aqueous humor convection plays a minor role in drug transport. For slow diffusing drugs (drugs with small diffusion coefficients) and low viscosity vitreous fluid, convection plays a greater role and may result in higher drug concentrations reaching the retina. These higher drug concentrations in one location can generally be potentially toxic. Each drug must be evaluated in this regard. Delayed injection of the drug has been shown to avoid conditions of retinal toxicity and allow lower drug concentrations with a longer residence time on the retinal surface. Drugs with high diffusivity and retinal permeability cause uniform distribution of the drug along the retinal surface, whereas drugs with low diffusivity and retinal permeability localize the drug concentration along the posterior retinal surface. The mathematical model uses the Navier Stokes equations for aqueous humor flow coupled to the convection diffusion equation used for drug distribution. Here, an additional term was used for release of the drug from the injection site at a specific rate. The simulations were performed using Ansys Fluent [34]. In [32], the concentration distribution of the drug around the macula of a rabbit eye was analyzed. For this purpose, the geometrical model and some of the parameters of the mathematical model were calibrated with respect to experimental results with rabbit eyes. Running a model and simulations together with experiments has the advantage that the model is calibrated against the experiments and in a sense validated. Two substances were considered in the experiment: Fluorescein and FITC-Dextran. Fluorescein has a larger diffusion coefficient, DF=6×10−10 m2/s, than FITC-Dextran, DD=3.9×10−11 m2/s, and therefore diffuses much faster, and higher fluorescein concentrations are found around the macula. In the experiments and simulations the drug distribution was observed for one day. Three simulation results are shown: the drug distribution after 5, 15 and 24 h. The results were that fluorescein reaches the macula earlier and in higher concentration, towards the end of the day nothing arrives at the macula because fluorescein is depleted. The behavior of FITC-Dextran concentration is different, due to the slower diffusion, the concentration around the macula builds up slowly but steadily. The diffusion coefficient of FITC-Dextran has a similar magnitude to the diffusion coefficient of bevacizumab [35] measured in a rabbit eye. Simulations were performed using a finite volume method. In [33], the effects of injection time, needle gauge, and needle angle are analyzed using an older, simpler mathematical model. Here, simulations in three dimensions (3D) are presented for the first time in a simple idealized geometry. Previously, only two-dimensional simulations had been performed. The distribution of fluorescein in the vitreous is also analyzed. Two injection angles are compared in the plane of the optical axes. The result was that more drug reaches the macula when the injection is in the direction towards the macula. Depending on the injection, both the toxicity risk and the drug effect may increase or decrease. The 3D geometry is roughly estimated and complex to construct. We will present a solution for personalized geometry construction that can be easily adapted to patient data (OCT and US).
In this paper, we present an extended model for drug distribution in the human eye. Our mathematical model is based on the approach in [36]. There, a transport-diffusion equation was used to describe drug distribution in the vitreous, and it was coupled with the Darcy equation for vitreous humor flow. We use the same equations in 3D (large computational cost and higher effort for grid construction) with a different inflow condition for a better physical representation of the flow, we add gravity to discuss its effect on drug distribution, we add anisotropic diffusion by including collagen fibres and we add nonlinear diffusion for certain substances. First, we present a realistic geometry of the vitreous body. For the geometry description in three dimensions, we use mathematical functions fitted to our own experimental data. This is an advantage for the numerical simulations, the grid can be constructed for the desired fineness. All previous models in the cited papers have not taken gravity into account, with the exception of [37]. Here, experiments have shown that gravity should play a role in drug distribution. An unusually strong inflow profile was also used there. We systematically analyze the influence of gravity on drug distribution in the vitreous and complete the model with the correct inflow conditions in the ciliary body region. First results of our simulations in two dimensions are presented in [31]. In the following, we discuss the differences in the simulation methods and in the obtained results compared to previous models. In addition, the success of the therapy is investigated with respect to various optimization parameters.
## 2.1. Mathematical Models
We have developed a method to construct a patient-specific vitreous body from an ultrasound scan. To describe the shape of the vitreous body, we have developed a mathematical formula that describes a modified Limacon. The formula contains parameters that are adjusted to the ultrasound data (see Figure 1). For ultrasound data, B-scans were generated from left to right and from right to left. From every image 164 data points were extracted. The data set tables can be found in Appendix A, Table A1,Table A2,Table A3,Table A4. All data points are used to perform the parameter estimation to determine the shape of the vitreous body. Three of the parameters of the mathematical model can be adjusted to personalize the vitreous shape. As proof of concept we used all quantitative data sets from 12 patients, each with 164 data points, to describe an average vitreous shape that we use for our “Virtual Eye” in the computer. The maximum deviation of the data is 0.34 mm. More ultrasound data from a large number of patients may be included in a future study. The methods developed can be easily applied to more data. Due to rotational symmetry, it is sufficient to describe the two-dimensional profile of the vitreous:[1]x=R(q,ϕ)cos(ϕ)+mx,y=R(q,ϕ)sin(ϕ),R(q,ϕ)=q1+q2cos(ϕ^)+q3cos(ϕ^)3, where q=(q1,q2,q3)∈R3, (mx,0) is the center of the Limacon, mx∈R and ϕ∈[0,2π]. With ϕ^ we denote the dependency on the parameter mx after converting the data into polar coordinates: ϕ(mx)=arccos(x−mxr) with r=(x−mx)2+y2. For more information, see [38,39].
Next, we describe the physiology in the vitreous body of the human eye. We begin with the flow of the aqueous humor produced in the ciliary body. Most of this fluid circulates in the anterior chamber of the eye and is drained through the trabecular meshwork into Schlemm’s canal. A smaller portion flows through the vitreous body, enters the retina, and is flushed out through the blood system. We describe the flow of aqueous humor with the Darcy model [36,40]:[2]v=−κμ∇p,divv=0, where κ is the hydraulic conductivity, μ the viscosity of the fluid, v its velocity and p the pressure. The Darcy model describes flow in porous media and represents a first approximation to the physiology in the human vitreous, since the flow penetrates a viscoelastic medium containing a collagen network. In terms of boundary conditions, we know that the vitreous is bounded by the retina and the lens. Between the lens and the retina, the aqueous humor has room to enter through the hyaloid membrane. We denote the retinal boundary by ΓR, the lens boundary by ΓL and the inflow boundary by ΓH. Thus, the boundary of Ω is ∂Ω=ΓR∪ΓL∪ΓH. We assume that the lens is an impermeable organ so that aqueous humor cannot penetrate. Thus, we obtain homogeneous Neumann boundary conditions at the lens:[3]n·$v = 0$onΓL, where n is the normal vector. In the literature, the boundary conditions for the Darcy equations are generally Dirichlet conditions for pressure. Our simulations have shown that the flow near the inflow looks unphysical due to our mixed boundary conditions, so we impose a Poiseuille velocity profile for the inflow:[4]n·v(x,y,z)=cpflow(Rpflow2−rpflow2(x,y,z))=:vpflowonΓH, where cpflow is a parameter that regulates the strength of the inflow, *Rpflow is* half the distance from the lens to the retina, and rpflow is the distance from a velocity particle to the central circular ring between the lens and retina.
The epiretinal membrane is the outer layer of the retina connected to the vitreous body. The flow of a fluid through this membrane is described with a permeability condition and is a Robin-type boundary condition:[5]n·v=KRCSp−PvLonΓR, where *Pv is* the episcleral pressure, KRCS the total hydraulic conductivity and L the thickness of the retina.
Now we present the model of the anti-VEGF treatment of age related macular degeneration. We skip the injection process and start with long-term simulations for the drug distribution when the drug is already in the vitreous. The drug distribution is given by the following transport-diffusion equation:[6]∂tC+(v·∇)C−DΔC=0 where C is the drug concentration, which here depends on space and time t∈[0,T] and D is the diffusion parameter of the drug in the vitreous. We use $D = 4$×10−11 m2/s for bevacizumab in a rabbit eye from [35] in our model for the human eye model. In our simulations, we consider only one injection, i.e., we simulate the drug distribution during one month. At the lens we assume homogeneous boundary conditions, the drug does not penetrate the lens:[7]∂nC=0onΓL×[0,T].
At the retina we have a Robin type boundary condition [8]PC+(n·v)kC=−D∂nC+(n·v)ConΓR×[0,T], where k is a partition coefficient for the drug describing the fraction in the vitreous and retina, and P is the permeability of the retina. At the hyaloid membrane, we assume a homogeneous Dirichlet boundary condition $C = 0$ in this model, which means that the drug will not diffuse against the flow. In another project involving pharmacology in the model [41], the concentrations of the different complexes may leak into the anterior part of the eye through the boundary at the hyaloid membrane between the anterior chamber and the vitreous.
## 2.2. Numerical Methods
The presented model [2]–[8] is solved numerically by the Finite Element (FE) method using the C++ software library deal.ii [42,43]. In our model, the Darcy equation is time independent and can be decoupled from the system. It is first solved using the mixed Finite Element method, then the computed velocity can be substituted into the time-dependent convection-diffusion equation, which is discretized using the Rothe method [44]. In the spatial discretization, we use trilinear Lagrange elements for the drug concentration, Raviart-Thomas elements for the velocity, and discontinuous piecewise constant elements for the pressure. To solve the time dependent model with sufficient accuracy, the time-stepping method should be at least second order and stable, so the fractional step theta scheme is used. A detailed description can be found in [45].
The resulting system of algebraic equations is transformed into a saddle point problem in the Darcy case:[9]MBBT0VP=F0, where M is the mass matrix, B is the matrix resulting from the discretization of the divergence, V is the vector with the degrees of freedom of the velocity, P is the vector with the degrees of freedom of the pressure, and F is the vector of the right hand side. A detailed derivation of this linear system of equations is described in [39]. The reason of this transformation into a saddle point problem is that often the initial problem is ill-conditioned or very poorly conditioned, and cannot be solved directly. Therefore, the problem is represented as a saddle point problem. This form of representation does not change the properties of the system like invertibility, spectral properties, and conditioning, these are maintained, but there is a well developed solution theory for this representation. Exact knowledge of the system properties is important for the development of solution algorithms. In some cases, the special structure of the saddle point problem can be exploited to avoid or mitigate the ill-conditioning. The structure of the right-hand side also plays a role here. A common solution method for [9] is the Schur complement technique, a segregated approach, which is also very well suited for our problem. The difficulty here is that there is no so-called best method for all kinds of problems, but different efficient solvers, which were developed for certain model equations. Some solution methods are presented in [46]. With the Schur Complement method one obtains the following system of linear equations:[10]MhVh=Fh−BhPh,BhTMh−1BhPh=BhTMh−1Fh.
The matrix BTM−1B is here the Schur complement, and is symmetric and positive definite. The Conjugate Gradient (CG) method [47] can be used to solve the system of linear Equations [10].
The fully discretized convection-diffusion equations for each time step are as follows:[11](MhC+θΔtAh)Chm(t)=(Mh−(1−θ)ΔtAh)Chm−1(t), where MhC is the mass matrix and Ah the stiffness matrix, C is the vector with the degrees of freedom for the concentration, θ and Δt are parameters given by the fractional step theta scheme. In this case, the resulting linear system has a nonsymmetric matrix. Iterative methods such as GMRES with ILU preconditioning work well with a large sparse matrix [48], i.e., when a solution is computed on a fine grid, and are also generally not affected by a single zero eigenvalue. Without preconditioning, the iterative Krylov subspace method converges poorly. Preconditioning is a simple transformation of the linear system and leads to a coefficient matrix with the required spectral property that all eigenvalues are contained in the half-plane Re(z)>0, z∈C, i.e., it is non-singular.
For the numerical implementation of our model, more than 6000 lines of code were implemented in the existing deal.ii environment. The most time-consuming part of the solution process is setting up and solving the algebraic system of equations. The code was validated and a convergence analysis was performed.
## 2.3. Mathematical Functionals for Drug Comparison
With the methods developed, we are studying the effect of injection position in terms of how much drug remains in the vitreous and how much drug operates in a specific region. To quantify these effects, output functionals are developed and included in [38].
The functional JΩ(t,C):R+×H01(Ω)→R+ denotes the relative amount of drug C remaining in the vitreous at the current time, [12]JΩ(t,C):=∫ΩC(t,x)dx∫ΩC(0,x)dx.
The functional JM(t,C):R+×H01(Ω)→R+ denotes the amount of drug C present in a specific region M at the current time, [13]JM(t,C):=∫MC(t,x)dx,M=Br(m)∩Ω, where Br(m) is a sphere with center m at the macula and radius r. The drug is one of the antibody therapies and blocks VEGF by binding to it and washing it out, preventing the formation and growth of vessels around the macula. In our model, we focus on the drug distribution by diffusion and convection. The kinetics of the drug is the subject of a separate project and will be presented in a future paper. Here, we consider only the drug transport to the macula and estimate the amount of drug that reaches this area without reactions in the vitreous. Depending on the stage of the disease, VEGF may already be distributed in the vitreous. In this case, the drug is already acting there.
Finally, we estimate the total amount of drug that can react with VEGF molecules in a period [0,T]: JM,Ω(T,C):R+×H01(Ω)→R+ [14]JM,Ω(T,C):=∫0T∫MC(t,x)dx∫ΩC(0,x)dxdt,M=Br(m)∩Ω, where Br(m) is a sphere with the center m at the macula and radius r.
Due to the integration over time, the amount of drug reaching the macula is overestimated because it takes time for the drug to diffuse into this area as well. In addition, the amount depends on the choice of the size of Br(m). In this paper, we choose $r = 2$ mm. Furthermore, we can measure how much of the drug does not reach this area and is lost by diffusion through the retina during the period [0,T]: JR(T,C):R+×H01(Ω)→R+ [15]JR(T,C):=1−JΩ(T,C)+JM,Ω(T,C).
These functionals are used for the drug comparison study.
## 3. Results
We presented long-term finite element simulations of drug distribution in the vitreous that include a period of 30 days. This period corresponds to the typical time from the first to the second injection with, for example, ranibizumab. In Figure 2 we visualize the 3D drug distribution in our Virtual Eye and the aqueous humor flow. The flow is produced at the ciliary body, enters in the vitreous via the zonules and leaves the vitreous through the retina via permeability. The drug is distributed through diffusion and convection. At the initial time there is a ball shaped concentration distribution near the lens (the injected drug) and at the presented time already some diffusion and convection occured which is shown in the Figure.
## 3.1. The Influence of Gravity on Drug Distribution
It is assumed that the patient’s head position after injection has a relevant influence on drug distribution in the eye. The experiments in [37] confirmed the effect of gravity on the distribution of bevacizumab in an undisturbed balanced salt solution in vitro. Bevacizumab did not immediately dissolve and distribute evenly in the solution as expected, but rather settled in the lower part of the tube than in the upper part due to gravity. This effect was still observed after 7 days. Thus, whether the patient is standing or lying down may matter when a rapid local effect is needed.
In our simulations we consider a patient with age-related macular degeneration in the left eye. The drug is injected from the left side. For simplicity, we assume that the patient has the usual head orientation, i.e., the face is directed forward. The drug distribution is computed over 30 days for the following cases:The patient stands (over the total time).The patient lies on the back. The patient lies sideways on the left side. The patient lies sideways on the right side. The patient stands half the day and lies on the back for the rest of the day.
The results of our simulations are shown in Figure 3. The influence of gravity turns out to be small. When the patient is lying on the right side, $38.4\%$ of the injected drug reaches the macula, and we calculate the highest concentration of 0.287kgm3 around the macula at 6 days after injection. In all other cases, slightly less drug reaches the macula. The worst case, if any, is when the patient is lying on the left side where the drug was injected. Then $37.6\%$ of the injected drug reaches the macula. The highest concentration is 0.281kgm3. To deliver more drug to the macula, it is advantageous if gravity points in the direction of injection. In the other three cases, we observe a positive effect when gravity is directed toward the macula. We measure $38.3\%$ of the injected drug on the macula when the patient is lying on its back, $38\%$ when the patient is standing, and $38.2\%$ when the patient stands half the day and lies on its back the rest of the day.
In summary, patients lying on their back or on the injection side showed the most successful therapy, but in our models and simulations gravity does not play a significant role. The results show a maximum difference of less than $1\%$ in the concentration of injected drug at the macula. However, we have considered a healthy homogeneous vitreous here. In a heterogeneous vitreous with a more complex consistency, the situation may change.
## 3.2. Optimal Injection Position
The goal of an optimal treatment of age-related macular degeneration is the local effect of the drug as a VEGF blocker at the macula. The drug should act in this way for as long as possible to achieve the best results. The therapy is expensive and the injection is uncomfortable, so the drug should be used effectively. Furthermore, too high drug concentrations can lead to toxicity. Thus, only certain doses of the drug are injected over several weeks.
In this section, we will analyze whether the healing process, as measured by the amount of drug reaching the macula, depends on the injection position. Regardless of a feasible initial concentration we will investigate which location is optimal for the injection. Four different injection positions (see Figure 4) are considered as toy problems (virtual experiments) to find out the relevance of the injection position. We do not discuss whether the chosen positions are realistically reasonable. The first position we consider is the standard position for an injection into the left eye: 3.5 mm from limbus across the pars plana toward the center of the posterior pole (in geometry: 3.5 mm from the limbus and 5.5 mm to the left of the pupil axis). The needle depth is 5 mm. The second position we consider is 10 mm from the limbus via the pars plana direction to the center of the posterior pole. Here the needle penetrates to the center of the vitreous (∼10 mm). The third injection position is 8 mm from the limbus via the pars plana direction laterally to the posterior pole. The optic nerve serves as a point of orientation. The needle penetrates ∼7–8 mm. The final injection position we will analyze is chosen 10 mm from the limbus via the pars plana direction toward the center of the posterior pole. Here, the needle penetrates in the direction of the optic nerve (∼18 mm).
A period of 30 days is used for all four simulations. The results are shown in Figure 5. The standard injection always results in the lowest concentration on the macula. This indicates that the therapy can be optimized. To achieve the highest drug concentration around the macula, the most obvious option is the injection directly near the macula. This goal is achieved only in the first eight of 30 days. After nine days, injection into the central vitreous leads to the highest drug concentration at the macula. At the second injection position, the drug remains around the macula the longest. At the fourth injection position, the highest amount of drug (10 times higher) reaches the macula.
The pathology and permeability of the vitreous as well as of the retina, and the injection site have a major impact on drug distribution. Injection in the second position is the best choice for longer acting drugs, e.g., aflibercept: these drugs work best when injected into the center of the vitreous, they must be effective for several weeks. Injection in the fourth position is the best choice for intensified initial treatment, such as the treatment of a thick edema: In the first 50 h, we have the highest amount of drug in the macula area (more than 10× higher), but after 50 h, the drug concentration is lower than with the injection at the second position, which is close to the vitreous border and the retina, where part of the drug escapes. The standard position also has the disadvantage of allowing some of the drug to escape through the zonule into the anterior chamber, resulting in a relatively smaller amount of drug reaching the macula. However, exactly how much reaches the macula depends on the completeness of the model and the accuracy of the parameters used.
## 3.3. Optimal Injection Angle
This subsection analyzes the influence of the injection angle on the amount of drug reaching the macula, the site of action. Although there are precise instructions for the injection procedure, position and needle length, in practice there are slight differences in position and penetration angle for each individual injection. In this section, we will perform some virtual (toy) experiments to gain insights into the sensitivity of the amount of drug at the macula with respect to the angle of injection. We will discuss injection positions that may not be appropriate from a medical perspective, but help provide detailed information about the physics and physiology behind the injection process and whole treatment.
We introduce a coordinate system to define two penetration angles ψxy and ψz for orientation in space, see Figure 6. In our geometry, the x-axis is the optical axis and points from the lens to the retina (in Figure 6, it points upward), the y-axis is the horizontal line perpendicular to the optical axis, running from left to right in Figure 6, and the z-axis is the vertical line perpendicular to the optical axis, pointing in Figure 6. We define the penetration angle ψxy between the needle direction and the optical (x-) axis on the xy-plane and the penetration angle ψz between the needle direction and the z-axis.
In [33], the standard injection was defined as ψxy=50∘ and compared to the penetration angle ψ˜xy=75∘. The penetration angle ψz was set to 90∘ in both cases. In our simulations, we analyze ψxy=50∘, ψ˜xy=75∘ and additionally ψ^xy=25∘ and we include several other possibilities with ψz≠90∘, i.e., the needle is not injected along the xy-plane. For comparison we calculate the functionals JM, JR, JM,Ω, JΩ from Section 2.3 for each simulation configuration. First, we analyze the injections with ψz=90∘ following [33]. Our simulations confirm the results in [33], a penetration angle of 75∘ results in much less drug reaching the macula, see Figure 7. During the 30 days, we calculated about $31\%$ more drug on the macula at a penetration angle of 50∘. In [33], the differences in the amount of drug reaching the macula between an injection angle of 75∘ and of 50∘ are even around $50\%$, because there an injection with a fast injection speed was considered. We neglect the injection velocity in our model, since this would lead to a different type of equations and thus to different solution methods. With our model, we only analyze the effect of drug diffusion and not drug convection through the injection.
In all cases considered, the concentration of the drug at the macula increases in the first days. The highest concentration is reached after about 6 days, and after that we measure a monotonic decrease of the concentration. The maximum is reached at 0.282kgm3 for a penetration angle of 50∘ and 0.214kgm3 for 75∘. The penetration angle of 25∘ leads to a similar result as that for 50∘. We obtained about $2\%$ more drug on the macula for a penetration angle of 25∘ and the highest concentration is 0.291kgm3. At both 25∘ and 50∘ angles, the injection needle is almost exactly aligned with the macula, which explains the positive results. In contrast, at an angle of 75∘, the drug is injected farther from the macula and closer to the lens. This results in more drug being cleared through the retina before it can reach the macula. This effect is confirmed by analyzing the functionals JR and JM,Ω, see Figure 8.
We calculate that at a penetration angle of 75∘, about $30\%$ of the injected drug reaches the macula and that about $70\%$ diffuses through the retina and is washed out by the blood vessels. The other two angles 25∘ and 50∘ show that $40\%$ of the drug reaches the macula and $60\%$ of the drug diffuses through the retina. Furthermore, the functional JΩ shows us that more drug remains in the vitreous body for these two penetration angles for all times than at 75∘. For 25∘, we measure slightly higher concentration of drug in the vitreous than for 50∘.
In summary, an injection angle ψxy between 25∘ and 50∘ gives similar good results and demonstrates that its influence on drug distribution is small. With an injection angle in this range, a greater amount of drug reaches the macula than with larger injection angles. However, an injection with ψxy=75∘ leads to changes in the amount of drug around the macula that are likely to have a negative effect on the therapy; therapy will be less effective in this case.
When examining the penetration angle ψz, the simulations show that the larger the angle, the less drug is around the macula. This result can be explained by the fact that a larger amount of the drug escapes through the retina and cannot reach the site of action. In Figure 9, the penetration angle ψxy is chosen to be 50∘. At a penetration angle ψz=115∘, we measure $10\%$ less drug on the macula than at ψz=90∘ and as much as $38\%$ less at ψz=140∘. In addition, a penetration angle of ψz=90∘ results in a loss of about $60\%$ of the injected drug through the retina, an angle of ψz=115∘ of about $65\%$, and an angle of ψz=140∘ of about $75\%$. This suggests that changes in the penetration angle ψz may limit the local effect of the drug as a VEGF blocker at the macula.
## 3.4. The Influence of the Diffusion Coefficient on Drug Distribution
In age-related macular degeneration, an anti-VEGF drug is injected as standard. The choice of drug determines the course of therapy. Currently, the most commonly used anti-VEGF drugs are ranibizumab (Lucentis, Genentech, San Francisco, CA, USA and Novartis Ophthalmics, Basel, Switzerland), bevacizumab (Avastin, Genentech, San Francisco, CA, USA) and aflibercept (Eylea, Regeneron, Tarrytown, NY, USA). There are a large number of studies and comparisons of these drugs for the treatment of age-related macular degeneration, diabetic retinopathy, diabetic macular edema and retinal vein occlusions. For example, in [49,50,51] all three anti-VEGF drugs are compared in patients with diabetic macular edema. All three drugs result in improvement in visual acuity. However, it is unclear which drug is best suited for which patient. In age-related macular degeneration, roughly equivalent effects on the healing process are observed for the drugs bevacizumab and ranibizumab in [52]. Aflibercept is a newer drug used to treat age-related macular degeneration. In [53], it is reported that longer intervals between injections can be achieved with aflibercept. The most important process for the effectiveness of therapy is pharmacology. We consider this in our models and simulations in another project. In this paper, we analyze the effect of drug diffusion in the three-dimensional vitreous, i.e., how much drug reaches the site of action. Our virtual experiments are designed with fixed parameters for eye geometry and for injection. With the mathematical functionals we have developed, we can evaluate the amount of drug used in the vitreous and around the macula over time, so we can perform a systematic analysis to study the influence of certain parameters on drug distribution. One very relevant parameter is the diffusion coefficient, as already recognized in [30]. The molecular weight of bevacizumab is 148 kDa, so it is a large molecule with a half-life twice as long as ranibizumab [50,51]. The molecular weight of ranibizumab is 48 kDa [54] and of aflibercept 115 kDA, which also has higher affinity than ranibizumab or bevacizumab [55,56,57].
In the following, we perform simulations for the diffusion parameter $D = 4$×10−11m2s from [35] and for some fictive diffusion parameters, one smaller, $D = 2$×10−11m2s, and one larger, $D = 8$×10−11m2s, to quantify the differences. This gives us insight into drug diffusion and conclusions can be drawn for other diffusion coefficients as well. First, we consider the comparison of drug concentration in specific regions of the eye as illustrated in Figure 10.
The larger the diffusion coefficient, the faster the drug diffuses and reaches the macula more quickly. Heavier molecules have smaller diffusion coefficients. However, after a few days, only a small amount of the drug is observed around the macula. At the macula, a concentration greater than 0.1kgm3 can be achieved in the first days only with the larger diffusion coefficient, $D = 8$×10−11m2s. The time period is about 10 days, then the concentration at the macula decreases. For molecules with a slower diffusion coefficient, $D = 2$×10−11m2s, this period is about 22 days and about 15 days for $D = 4$×10−11m2s. In contrast, the highest concentration is obtained for the largest diffusion coefficient. The maximum concentration 2.9kgm3 is reached for $D = 8$×10−11m2s, 2.8kgm3 for $D = 4$×10−11m2s and 2.7kgm3 for $D = 2$×10−11m2s. Furthermore, a greater amount of drug reaches the macula over the 30 days when the diffusion coefficient is smaller. After 30 days, for $D = 2$×10−11m2s about $50\%$ of the drug escapes through the retina, about $40\%$ can act on the macula and $10\%$ is still in the vitreous body. For $D = 8$×10−11m2s about $80\%$ of the drug escapes through the retina and only $20\%$ acts on the macula, leaving nothing in the vitreous. The results for $D = 4$×10−11m2s are in between the values obtained for the previously mentioned cases. Overall, a higher diffusion coefficient results in a significant loss of drug through the retina and less drug can reach the macula.
## 4. Discussion
In this paper, we discuss the efficacy of anti-VEGF therapy against wet age-related macular degeneration. For this purpose, we model the drug distribution in the vitreous with a convection-diffusion equation. The convection is caused by the vitreous humor flow, which is modeled with the Darcy equation, taking into account the consistency of the vitreous, including the collagen network present within. Mathematically challenging are the mixed boundary conditions describing realistic physiology in the complex geometry of the eye, so even the Darcy equation here takes on a new face not found in the literature, an inflow is prescribed by a Poiseuille velocity profile. The derived model is solved numerically using the Finite Element method. For this purpose, over 6000 lines of code were implemented in the existing deal.ii environment. Trilinear Lagrangian elements are used to discretize the drug concentration, Raviart-Thomas elements are used for the velocity of the vitreous humor, and discontinuous piecewise constant elements are used for the associated pressure. The system is solved in a decoupled way, first solving the Darcy equation using the Schur complement method and the CG method, and then solving the time-dependent convection-diffusion equation using the Rothe method and the GMRES method with an ILU preconditioner. To quantitatively evaluate the efficacy of therapy, we introduce functionals, the relative amount of drug remaining in the vitreous at a given time point, the amount of drug localized around the macula, and the total amount of drug available to react with VEGF molecules over a given time. We perform long-term simulations covering the period of one injection, i.e., 30 days.
We discuss the influence of gravity on drug distribution and found that unlike the experiments performed in [37], where the gravity causes a higher drug concentration at the bottom of the test tube than at the top, even after 7 days, that gravity does not play a significant role in our models and simulations. Furthermore, we compared 4 injection positions and found that the standard position used in the treatment performed worst, namely then the least amount of drug arrived at the macula. An injection into the center of the vitreous, 10 mm from the limbus via pars plana toward the center of the posterior pole with needle depth about 10 mm, seems to be optimal, so that the highest drug amount reaches the macula in 30 days. Our results are purely theoretical; if the injection positions considered are reasonable from a medical point of view is another task that cannot be discussed here. The optimal injection angles are 25∘ between the needle direction and the optical axis in the xy-plane, and 90∘ between the needle direction and the z-axis. The differences between 25∘ and 50∘ are small, while the differences to 75∘ are large. The highest drug concentration at the macula occurs 6 days after injection. The simulations are also useful to try different diffusion constants and quantify the difference in the functionals. The following values were compared: 2×10−11 m2/s, 4×10−11 m2/s and 8×10−11 m2/s, where 4×10−11 m2/s corresponds to bevacizumab. A smaller diffusion coefficient, corresponding to heavier molecules, appears to be beneficial for treatment. The drug would not diffuse through the vitreous as quickly, allowing more of it to be transported to the macula by the vitreous humor flow.
Our model is certainly already very complex and realistic, but there are further possibilities for extension. In a current project, we are analyzing different physical boundary conditions for the drug at the hyaloid membrane to also see the distribution in the anterior chamber. We know that a large fraction of the drug is washed out through Schlemm’s canal. In [41], we consider a viscoelastic vitreous model and thus include the collagen fibers and the consistency of the vitreous. A fluid structure interaction model is also created to include the elastic sclera and lens. A recently completed dissertation [58] covers the current pharmacology of anti-VEGF therapy, including the life span of the drug. It would also be useful to calibrate and validate the models with experimental data. This will require close collaboration with experimentalists. It should also be noted that simulations of the different models require different numerical methods that take time to be implemented. However, once developed, numerous tests are readily available to provide insights into the physical and chemical processes that can improve therapy. The in silico experiments proposed here can be used together with studies, in vitro and animal experiments. However, they can drastically reduce the number and thus the cost of these experiments, since numerous test can be performed and preselected by the computer and its user.
## 5. Conclusions
It has been demonstrated that VEGF is only produced locally and also only acts locally [59]. It is therefore important that the drug reaches the site of action, the macula. It is also important that enough molecules reach this area so that the VEGF is completely blocked. Since VEGF is also produced continuously, it is important that a sufficient concentration of drug reaches the macula over a long period of time. The drug distribution and amount of drug on the macula can be influenced by the choice of drug and the way of administration. Based on our models and simulation results, we can recommend a refined AMD therapy. Whether the findings are useful from a medical point of view and feasible in practice remains an open question at present and can be communicated at a later time.
Refined Therapy: When the drug is injected centrally into the vitreous, a certain amount of the drug reaches the macula the longest. This can be interesting for longer acting drugs, e.g., aflibercept. Otherwise, a large portion of the drug escapes through the retina. If the drug is injected closer to the macula, a higher concentration arrives there, but for a shorter period of time. This can be interesting for an intensified initial treatment, e.g., for the treatment of a thick edema. The needle should be oriented in the direction of the macula. An unfavorable insertion angle can lead to a loss of up to $38\%$ of the drug at the macula. A larger diffusion coefficient for the drug, a lighter molecule, results in a higher drug concentration at the macula, but on average over 30 days, it results in a lower drug concentration at the macula because more drug also escapes through the retina more quickly.
## 6. Patents
E. Friedmann, S. Dörsam, and V. Olkhovskiy, Test and optimization of medical treatments for the human eye. European patent application ep 18000349, 2018.
## References
1. Gilbert C.E., Canovas R., Hagan M., Rao S., Foster A.. **Causes of childhood blindness: Results from west Africa, south India and Chile**. *Eye* (1993.0) **77** 184-188. DOI: 10.1038/eye.1993.39
2. Steinkuller P.G., Du L., Gilbert C., Foster A., Collins M.L., Coats D.K.. **Childhood blindness**. *J. Am. Assoc. Pediatr. Ophthalmol. Strabismus* (1993.0) **3** 26-32. DOI: 10.1016/S1091-8531(99)70091-1
3. Gilbert C., Foster A.. **Childhood blindness in the context of VISION 2020: The right to sight**. *Bull. World Health Organ.* (2001.0) **79** 227-232. PMID: 11285667
4. Taylor H.R., Keeffe J.E.. **World blindness: A 21st century perspective**. *Br. J. Ophthalmol.* (2001.0) **85** 261-266. DOI: 10.1136/bjo.85.3.261
5. Yorston D.. **Retinal diseases and vision 2020**. *Community Eye Health* (2003.0) **16** 19-20. PMID: 17491850
6. Rahi J.S.. **Childhood blindness: A UK epidemiological perspective**. *Eye* (2007.0) **21** 1249-1253. DOI: 10.1038/sj.eye.6702837
7. Nazimul H., Rohit K., Anjli H.. **Trend of retinal diseases in developing countries**. *Expert Rev. Ophthalmol.* (2008.0) **3** 43-50. DOI: 10.1586/17469899.3.1.43
8. Kong L., Fry M., Al-Samarraie M., Gilbert C., Steinkuller P.G.. **An update on progress and the changing epidemiology of causes of childhood blindness worldwide**. *J. Am. Assoc. Pediatr. Ophthalmol. Strabismus* (2012.0) **16** 501-507. DOI: 10.1016/j.jaapos.2012.09.004
9. Gabrielle P.H., Nguyen V., Wolff B., Essex R., Young S., Hunt A., Cheung C.M.G., Arnold J.J., Barthelmes D., Creuzot- Garcher C.. **Intraocular pressure changes and vascular endothelial growth factor inhibitor use in various retinal diseases: Long-term outcomes in routine clinical practice: Data from the Fight Retinal Blindness! Registry**. *Ophthalmol. Retin.* (2020.0) **4** 861-870. DOI: 10.1016/j.oret.2020.06.020
10. Heath Jeffery R.C., Mukhtar S.A., McAllister I.L., Morgan W.H., Mackey D.A., Chen F.K.. **Inherited retinal diseases are the most common cause of blindness in the working-age population in Australia**. *Ophthalmic Genet.* (2021.0) **42** 431-439. DOI: 10.1080/13816810.2021.1913610
11. Rattner A., Sun H., Nathans J.. **Molecular genetics of human retinal diseases**. *Annu. Rev. Genet.* (1999.0) **33** 89-131. DOI: 10.1146/annurev.genet.33.1.89
12. Bok D.. **Contributions of genetics to our understanding of inherited monogenic retinal diseases and age-related macular degeneration**. *Arch. Ophthalmol.* (2007.0) **125** 160-164. DOI: 10.1001/archopht.125.2.160
13. Kiser P.D., Palczewski K.. **Retinoids and retinal diseases**. *Annu. Rev. Vis. Sci.* (2016.0) **2** 197-234. DOI: 10.1146/annurev-vision-111815-114407
14. Kanow M.A., Giarmarco M.M., Jankowski C.S., Tsantilas K., Engel A.L., Du J., Linton J.D., Farnsworth C.C., Sloat S.R., Rountree A.. **Biochemical adaptations of the retina and retinal pigment epithelium support a metabolic ecosystem in the vertebrate eye**. *elife* (2017.0) **6** e28899. DOI: 10.7554/eLife.28899
15. Amin S.V., Khanna S., Parvar S.P., Shaw L.T., Dao D., Hariprasad S.M., Skondra D.. **Metformin and retinal diseases in preclinical and clinical studies: Insights and review of literature**. *Exp. Biol. Med.* (2022.0) **247** 317-329. DOI: 10.1177/15353702211069986
16. Barnstable C.J.. **Epigenetics and degenerative retinal diseases: Prospects for new therapeutic approaches**. *The Asia-Pac. J. Ophthalmol.* (2022.0) **11** 328-334. DOI: 10.1097/APO.0000000000000520
17. Chen Y., Coorey N.J., Zhang M., Zeng S., Madigan M.C., Zhang X., Gillies M.C., Zhu L., Zhang T.. **Metabolism dysregulation in retinal diseases and related therapies**. *Antioxidants* (2022.0) **11**. DOI: 10.3390/antiox11050942
18. Platania C.B.M., Drago F., Bucolo C.. **The P2X7 receptor as a new pharmacological target for retinal diseases**. *Biochem. Pharmacol.* (2022.0) 114942. DOI: 10.1016/j.bcp.2022.114942
19. De Fauw F., Ledsam J.R., Romera-Paredes B., Nikolov S., Tomasev N., Blackwell S., Askham H., Glorot X., O’Donoghue B., Visentin D.. **Clinically applicable deep learning for diagnosis and referral in retinal diseases**. *Nat. Med.* (2018.0) **24** 1342-1350. DOI: 10.1038/s41591-018-0107-6
20. Jin K., Yan Y., Chen M., Wang J., Pan X., Liu X., Liu M., Lou L., Wang Y., Ye J.. **Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration**. *Acta Ophthalmol.* (2022.0) **100** e512-e520. DOI: 10.1111/aos.14928
21. Al-Zamil W.N., Yassin S.A.. **Recent developments in age-related macular degeneration: A review**. *Clin. Interv. Aging* (2017.0) **12** 1313. DOI: 10.2147/CIA.S143508
22. Tadayoni R., Sararols L., Weissgerber G., Verma R., Clemens A., Holz F.G.. **Brolucizumab: A newly developed anti-VEGF molecule for the treatment of neovascular age-related macular degeneration**. *Ophthalmologica* (2021.0) **244** 93-101. DOI: 10.1159/000513048
23. Heier J.S., Khanani A.M., Ruiz C.Q., Basu K., Ferrone P.J., Brittain C., Figueroa M.S., Lin H., Holz F.G., BPharm V.P.. **Efficacy, durability, and safety of intravitreal faricimab up to every 16 weeks for neovascular age-related macular degeneration (TENAYA and LUCERNE): Two randomised, double-masked, phase 3, non-inferiority trials**. *Lancet* (2022.0) **399** 729-740. DOI: 10.1016/S0140-6736(22)00010-1
24. Hufendiek K., Pielen A., Framme C.. **Strategies of Intravitreal Injections with Anti-VEGF: “Pro re Nata versus Treat and Extend”**. *Klin. Monatsblatter Augenheilkd.* (2017.0) **235** 930-939
25. Augsburger M., Sarra G.M., Imesch P.. **Treat and extend versus pro re nata regimens of ranibizumab and aflibercept in neovascular age-related macular degeneration: A comparative study**. *Graefe’s Arch. Clin. Exp. Ophthalmol.* (2019.0) **257** 1889-1895. DOI: 10.1007/s00417-019-04404-0
26. Mantel I., Zola M., De Massougnes S., Dirani A., Bergin C.. **Factors influencing macular atrophy growth rates in neovascular age-related macular degeneration treated with ranibizumab or aflibercept according to an observe-and-plan regimen**. *Br. J. Ophthalmol.* (2019.0) **103** 900-905. DOI: 10.1136/bjophthalmol-2018-312430
27. Sarkar A., Sodha S.J., Junnuthula V., Kolimi P., Dyawanapelly S.. **Novel and investigational therapies for wet and dry age-related macular degeneration**. *Drug Discov. Today* (2022.0) **27** 2322-2332. DOI: 10.1016/j.drudis.2022.04.013
28. Herlihy K.P., Williams S., Owens G., Savage J., Gardner L., Robeson R., Maynor B., Navratil T., Gilger B.C., Yerxa B.R.. **Extended release of microfabricated protein particles from biodegradable hydrogel implants for the treatment of age related macular degeneration**. *Investig. Ophthalmol. Vis. Sci.* (2014.0) **55** 1960
29. Seah I., Zhao X., Lin Q., Liu Z., Su S.Z.Z., Yuen Y.S., Hunziker W., Lingam G., Loh X.J., Su X.. **Use of biomaterials for sustained delivery of anti-VEGF to treat retinal diseases**. *Eye* (2020.0) **34** 1341-1356. DOI: 10.1038/s41433-020-0770-y
30. Kathawate J., Acharya S.. **Computational modeling of intravitreal drug delivery in the vitreous chamber with different vitreous substitutes**. *J. Abbr.* (2008.0) **51** 5598-5609. DOI: 10.1016/j.ijheatmasstransfer.2008.04.053
31. Dörsam S., Friedmann E., Stein J.. **Modeling and Simulations of Drug Distribution in the Human Vitreous**. *Top. Probl. Fluid Mech.* (2017.0) 95-102. DOI: 10.14311/TPFM.2017.013
32. Haghjou N., Abdekhodaie M.J., Cheng Y.L., Saadatmand M.. **Computer Modeling of Drug Distribution after Intravitreal Administration**. *World Acad. Sci. Eng. Technol.* (2011.0) **5** 194-204
33. Jooybar E., Abdekhodaiea M.J., Farhadia F., Cheng Y.L.. **Computational modeling of drug distribution in the posterior segment of the eye: Effects of device variables and positions**. *Math. Biosci.* (2014.0) **255** 11-20. DOI: 10.1016/j.mbs.2014.06.008
34. **Southpointe 2600 Ansys Drive Canonsburg**
35. Penkova A.N., Martinez J.C., Humayun M., Tadle A., Galesic A., Calle A., Thompson M., Pratt M., Sadhal S.S.. **Bevacizumab diffusion coefficient in vivo measurement of rabbit vitreous humor with flourescein**. *Investig. Ophthalmol. Vis. Sci..* (2019.0) **60** 1552-5783
36. Friedrich S., Cheng Y.L., Saville, B.. **FE modeling of drug distribution in the vitreous humor of the rabbit eye**. *Ann. Biomed. Eng.* (1997.0) **25** 303-314. DOI: 10.1007/BF02648045
37. Kim R.Y., Kwon S., Ra H.. **Gravity influences bevacizumab distribution in an undisturbed balanced salt solution in vitro**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0223418
38. Friedmann E., Dörsam S., Olkhovskiy V.. **Test and optimization of medical treatments for the human eye**. (2018.0)
39. Dörsam S.. **Finite Element Simulations for the Design of Therapeutical Approaches for Retinal Diseases**. *Ph.D. Thesis* (2022.0)
40. Stein J.. **Modeling of Drug Distribution in the Human Vitreous for the Treatment of Retinal Diseases**. *Ph.D. Thesis* (2021.0)
41. Drobny A., Friedmann E.. **Numerical simulation of viscoelastic fluid-structure interaction problems and drug therapy in the eye**. *Proc. Appl. Math. Mech.* (2021.0) **20** e202000260. DOI: 10.1002/pamm.202000260
42. Bangerth W., Heister T., Heltai L., Kanschat G., Kronbichler M., Maier M., Turcksin B., Young T.. **The textttdeal.II Library, Version 8.2**. *Archive of Numerical Software* (2015.0) **Volume 3** 1-8. DOI: 10.11588/ans.2015.100.18031
43. Bangerth W., Hartmann R., Kanschat G.. **Deal.II—A General Purpose Object Oriented Finite Element Library**. *ACM Trans. Math. Softw.* (2007.0) **33** 24/1-24/27. DOI: 10.1145/1268776.1268779
44. Großmann C., Roos H.G., Stynes M.. *Numerical Treatment of Partial Differential Equations* (2007.0)
45. Meidner D., Richter T.. **Goal-oriented error estimation for the fractional step theta scheme**. *Comput. Methods Appl. Math.* (2014.0) **288** 45-59
46. Benzi M., Golub G.H., Liesen J.. **Numerical solution of saddle point problems**. *Acta Numer.* (2005.0) **14** 1-137. DOI: 10.1017/S0962492904000212
47. Hestenes M.R., Stiefel E.. **Methods of conjugate gradients for solving linear systems**. *J. Res. Natl. Bur. Stand.* (1952.0) **49** 409-436. DOI: 10.6028/jres.049.044
48. Saad Y., Schultz M.H.. **A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems**. *SIAM J. Sci. Stat. Comput.* (1986.0) **7** 856-869. DOI: 10.1137/0907058
49. Heier J.S., Bressler N.M., Avery R.L., Bakri S.J., Boyer D.S., Brown D.M., Dugel P.U., Freund K.B., Glassman A.R., Kim J.E.. **Comparison of Aflibercept, Bevacizumab, and Ranibizumab for Treatment of Diabetic Macular Edema: Extrapolation of Data to Clinical Practice**. *JAMA Ophthalmol.* (2016.0) **134** 95-99. DOI: 10.1001/jamaophthalmol.2015.4110
50. Wells J.A., Glassman A.R., Ayala A.R., Jampol L.M., Bressler N.M., Bressler S.B., Brucker A.J., Ferris F.L., Hampton G.R., Jhaveri C.. **Aflibercept, Bevacizumab, or Ranibizumab for Diabetic Macular Edema: Two-Year Results from a Comparative Effectiveness Randomized Clinical Trial**. *Ophthalmology* (2016.0) **123** 1351-1359. DOI: 10.1016/j.ophtha.2016.02.022
51. Cai S., Bressler N.M.. **Aflibercept, bevacizumab or ranibizumab for diabetic macular oedema**. *Curr. Opin. Ophthalmol.* (2017.0) **28** 636-643. DOI: 10.1097/ICU.0000000000000424
52. **Ranibizumab and Bevacizumab for Neovascular Age-Related Macular Degeneration**. *N. Engl. J. Med.* (2011.0) **364** 1897-1908. DOI: 10.1056/NEJMoa1102673
53. Sarwar S., Clearfield E., Soliman M.K., Sadiq M.A., Baldwin A.J., Hanout M., Agarwal A., Sepah Y.J., Do D.V., Nguyen Q.D.. **Aflibercept for neovascular age-related macular degeneration**. *Cochrane Database of Systematic Reviews* (2016.0) **2** 1465-1858
54. Park S.C., Su D., Tello C.. **Anti-VEGF therapy for the treatment of glaucoma: A focus on ranibizumab and bevacizumab**. *Expert. Opin. Biol. Ther.* (2012.0) **12** 1641-1647. DOI: 10.1517/14712598.2012.721772
55. Andreoli C.M., Miller J.W.. **Anti-vascular endothelial growth factor therapy for ocular neovascular diseaser**. *Curr. Opin. Ophthalmol.* (2007.0) **18** 502-508. DOI: 10.1097/ICU.0b013e3282f0ca54
56. Al-Latayfeh M., Silva P.S., Sun J.K., Aiello L.P.. **Antiangiogenic therapy for ischemic retinopathies**. *Cold Spring Harb. Perspect. Med.* (2012.0) **2** a006411. DOI: 10.1101/cshperspect.a006411
57. Chang J.H., Garg N.K., Lunde E., Han K.Y., Jain S., Azar D.T.. **Corneal neovascularization: An anti-VEGF therapy review**. *Surv. Ophthalmol.* (2012.0) **57** 415-429. DOI: 10.1016/j.survophthal.2012.01.007
58. Drobny A.. **Mathematical Modeling and Adaptive Finite Element Simulation of Viscoelastic Fluid-Structure Interaction Systems and Chemical Processes with Applications to Ophthalmology**. *Ph.D. Thesis* (2022.0)
59. Pożarowska D., Pożarowski P.. **The era of anti-vascular endothelial growth factor (VEGF) drugs in ophthalmology, VEGF and anti-VEGF therapy**. *Cent. Eur. J. Immunol.* (2016.0) **41** 311-316. DOI: 10.5114/ceji.2016.63132
|
---
title: Groove Pancreatitis—Tumor-like Lesion of the Pancreas
authors:
- Gabriella Gábos
- Carmen Nicolau
- Alexandra Martin
- Ofelia Moșteanu
journal: Diagnostics
year: 2023
pmcid: PMC10001155
doi: 10.3390/diagnostics13050866
license: CC BY 4.0
---
# Groove Pancreatitis—Tumor-like Lesion of the Pancreas
## Body
A 45-year-old male smoker with a past history of severe chronic alcoholism presented to our gastroenterology department with a 2-month history of intermittent episodes of upper abdominal pain radiating to the back, nausea, postprandial vomiting, and poor appetite that persisted for 3 months, followed by a 10 kg weight loss. The patient reported no history of hypertension, previous abdominal surgery or diabetes mellitus. His family and drug history were unremarkable. Physical exams were unremarkable except for bilateral upper quadrant abdominal tenderness and hypoactive bowel sounds, while no abdominal mass was identified. Laboratory results showed that hemogram, amylase, lipase, albumin, renal and liver function tests were within normal limits. A tumor marker test found a slightly increased level of carbohydrate antigen 19-9 (CA 19-9) at 40 U/mL (normal range ≤ 30 U/mL). Carcioembryonic antigen (CEA) and alpha-fetoprotein (AFP) levels were both normal. US demonstrated mild hepatic steatosis with no cholelithiasis or acute cholecystitis but revealed general thickening of the second part of the duodenum and voluminous pancreas head. Esophagogastroduodenoscopy showed a narrowed second part of the duodenum due to an irregular, edematous, “reddish” polypoidal-appearance mass rising at the D1–D2 junction with intact extending mucosa (Figure 1). Additionally, histological examination of the pseudo-polypoid biopsy specimen revealed chronic and active mucosal inflammation and edema in the mesenchyme and was negative for malignancy. A computed tomography (CT) of the abdomen, pre-contrast phase (Figure 2), arterial phase (Figure 3) and portal venous phase (Figure 4) revealed duodenal wall thickening with luminal narrowing. It was noted that the contrast intake was parenchymal in nature and relatively homogeneous without any accompanying cystic forms. The periduodenal adipose tissue and the area of the duodenal–pancreatic groove were infiltrated with minimal adjacent fluid. Minimum densification of the right anterior pararenal fascia was observed. The cephalic pancreatic area was slightly swollen but with a relatively homogeneous acinar structure. No parenchymal calcifications or cysts were visible. The body and tail of the pancreas were healthy. The Wirsung duct and biliary system were normal as well. The pancreaticoduodenal artery was permeable and interposed between the head of the pancreas and the thickened duodenal wall. Ascites were not reported, but some pericephalic pancreatic and periduodenal lymphadenopathy, likely inflammatory, was seen.
Nonetheless, there was still a concern of malignancy due to the position of the mass. Endoscopic ultrasound (EUS) was performed (Figure 5), which described mass-like growth of the pancreatic head, narrowed duodenal wall and associated stenosis, but revealed no common bile duct (CBD) stricture or dilatation of the pancreatic duct system. EUS-FNA was performed from the exceptionally thickened duodenal wall and the groove area, which showed exclusively inflammatory changes, but no malignant or dysplastic cells. The imaging appearances (US, CT, and EUS), clinical presentation, medical history of alcohol abuse, laboratory markers and cytology results of EUS FNA were highly suggestive of GP, so major unneeded surgery was avoided in this early phase of the disease. In the absence of extreme complications (biliary obstruction or crucial gastric outlet obstruction), our patient was treated by conservative medical measures (proton pump inhibitors (PPI) and pancreatic enzyme supplement, as well as avoidance of alcohol). After being released from the hospital, the patient stopped drinking, followed a low-fat diet, and experienced no further symptoms for six months.
First described by Becker and Mischke in 1973, GP is an infrequent and still under-recognized type of recurrent or chronic pancreatitis that involves the anatomic space between the head of the pancreas, the common bile duct (CBD) and the duodenum, the so-called groove area [1]. Becker defined two forms of GP: “segmental” and “pure”. The first affects the pancreatic head with development of scar tissue within the groove, while the second involves exclusively the groove itself, sparing the pancreatic head [1].
Diagnosis is frequently challenging, and many physicians are not familiar with the disorder, which possibly contributes to its low incidence [1,2]. The accurate cause of this disorder has yet to be determined. Blockage of the minor papilla is one of the discussed aspects. Brunner gland hyperplasia is similarly thought to be a source, with stasis of pancreatic enzymes in the dorsal pancreas. Heterotopic pancreatic alterations undergoing fibrosis and inflammation in the groove area have been implicated. The most essential association is described to be an extended history of alcohol consumption. Continuous alcohol intake intensifies protein volume, which causes an escalation in pancreatic fluid thickness, provoking the inflammatory response [1,2].
In various studies, no difference was found in age and gender dispersion among GP and common chronic pancreatitis [1,2]. GP is generally recognized in middle-aged men with a history of significant alcohol abuse [2,3]. Clinically, patients present with chronic intermittent post-prandial abdominal pain similar to chronic pancreatitis. Some of them may present recurrent nausea, postprandial vomiting, frequently severe weight loss from impaired intestinal mobility, and duodenal stenosis [2,4]. Jaundice is infrequent in GP, contrary to pancreatic carcinoma, which presents with progressive jaundice. The duration of the clinical symptoms fluctuates from a few weeks to more than one year. The course of the GP is often chronic and debilitating [2,3,4,5].
Laboratory data often show little elevation of serum pancreatic enzymes and periodically of serum hepatic enzymes. Bilirubin levels can be high if the CBD is obstructed, and alkaline phosphatase levels can also be elevated despite the nonappearance of ductal reduction. CEA, AFP and CA 19-9 tumour markers are barely elevated [2,6,7].
Imaging plays a fundamental aspect in recognizing this entity. The literature on sonography has barely reported the appearance of GP. US commonly reveals a hypoechoic mass with thickening of the duodenal wall [5,6].
CT scans generally show mural thickening of the duodenal wall or a hypodense, insufficiently enhanced mass between the head of the pancreas and a thickened wall of the duodenum. Supplementary data include distension of the head of the pancreas and irregular calcifications. CBD may be narrowed with a smooth, tapered, and constant stenosis [8,9,10].
The most typical finding on magnetic resonance (MR) is a sheet-like mass corresponding to the fibrous scar in the groove among the head of the pancreas and the duodenum. MR imaging generally presents a hypointense mass on T1-weighed MR images in comparison with the pancreatic parenchyma and is iso- or slightly hyperintense on T2-weighed MR images. By contrast, administration enhancement is principally postponed due to the presence of fibrous tissues. Cystic lesions of the groove or duodenal wall may be noticed, especially on T2-weighted images. Duodenal wall thickening and duodenal wall stenosis are also commonly observed [11]. Irie et al. and Ferreira et al. reported the MRI features of patients with GP with the above MRI findings, and histological analysis revealed that these imaging features correlated with fibrous scarring in each patients [12,13].
Magnetic resonance cholangiopancreatography (MRCP) helps separate GP from CBD carcinoma, as GP shows smooth CBD tapering and shouldering is uncommon [5,8].
Esophagogastroduodenoscopy is also necessary as it can identify a congested and polypoid mucosa of the duodenum, with narrowing of its lumen or buldging of duodenal bulb [5,8,9]. Biopsies of the duodenal mucosa mostly report an incomplete result or an active inflammatory reaction without any evidence of neoplastic lesions [5,9]. Valentini et al. reported the gastrointestinal endoscopy features of their patient with GP with the above-mentioned findings [14].
The probability of accumulating samples from suspicious lesions using EUS-FNA makes EUS an ideal procedure to distinguish pancreatic adenocarcinoma from GP, allowing a diagnosis by cytopathology in approximately $90\%$ of cases [5,9,15]. EUS can reveal narrowing and thickening of the second portion of the duodenum with intramural cysts, mild thickening of the CBD, heterogeneous hypoechoic mass and enlargement of the pancreatic head, with calcifications or pseudocysts. Regular narrowing of the CBD is seen in GP, while intermittent ductal narrowing with obstructive jaundice is seen in pancreatic adenocarcinoma. EUS FNA biopsy demonstrates enormous variability depending on the area sampled, and the presence of cytological features related to reactive cellular atypia resulting from pancreatitis may simulate malignancy [5,9,15]. To our knowledge, no studies compare EUS-FNA to FNB, specifically in GP. However, currently, Wong et al. [ 15] analyzed the diagnostic performance of EUS-guided tissue acquisition by EUS-guided FNA vs. EUS-guided FNB for solid pancreatic mass, and they established that the diagnostic yield of the solid pancreatic mass was higher in FNB than in FNA (94.6 vs. $89.6\%$).
Radiologically, inflammatory modification in the groove between the duodenum and the pancreatic head can look indistinguishable from a malignancy. Nevertheless, it is crucial to recognize the integral clinical picture and the patient’s symptoms. A significant characteristic is the absence of major vessel encasement in GP, although some displacement may be noticed. Graziani et al. [ 16] described that the gastroduodenal artery is luxated leftward in GP while, in carcinoma, it is situated between the lesion and the duodenum [4,16]. Pancreatic adenocarcinoma spreading to the peripancreatic tissue or the duodenum is anticipated to penetrate and occlude peripancreatic vessels [4,17]. Ishigami et al. [ 18] described that patchy central enhancement in the portal venous phase is most evocative of GP, occurring in $93\%$ of patients. Patchy central enhancement reveals pancreatic tissue in the inflammatory mass. In the same report, peripheral enhancement was only noticed in GP carcinomas. Cystic lesions in the groove are more frequent in GP than in pancreatic carcinoma [19]. A younger age is also more suggestive of GP [1,5].
Pancreatic adenocarcinomas are much more likely to invade the retroperitoneum and involve the vasculature, which was not the case with our patient. A unique finding of GP is a thickening of the medial duodenal wall, as opposed to pancreatic adenocarcinoma [2,4,5]. In our case, the diagnosis was established on clinical suspicion after a biopsy with EUS suggested an inflammatory growth. The findings that cemented the diagnosis were the lesion’s position, the luminal narrowing of the duodenum, and the minimal post-contrast enhancement of this lesion. The pancreatic duct and CBD were not enlarged, suggesting a benign nature.
When the diagnosis is obvious, GP can be treated by conservative medical measures, including endoscopic therapy as the first line of intervention. Abstinence from alcohol, pancreatic rest, and opioid analgesics are the most commonly used conservative measures. While conservative management is preferred, resection is the gold standard in the appearance of obstructing manifestations or any suspicion of malignancy [4,5]. Therefore, it becomes essential to consider this entity as a potential and close the second differential to pancreatic carcinoma. Currently, in a review article, seven patients received endoscopic therapy, which was considered a reasonable treatment method [20]. In some studies, the primary line of management was pain management, which was mandatory in relatively half of the subjects [21]. These outcomes were identical to those found in other articles, which also revealed that conservative management was successful in half of the patients [22]. In one large retrospective case series using the endoscopic approach, linked with medical treatment, total clinical success in approximately $70\%$ of patients was obtained in five years [23]. Still, prospective, controlled studies are needed to confirm these findings.
GP should routinely be considered in the differential diagnosis for patients presenting with pancreatic head enlargements with no cholestatic jaundice, mainly when a duodenal obstruction is present and neither duodenal biopsies nor pancreatic head FNA establishes adenocarcinoma.
It is fundamental for physicians to become more acquainted with clinical, paraclinical and imaging findings that are evocative of GP because it can imitate pancreatic malignancy, whose prognosis and management are entirely different. Therefore, this report aims to make this entity and hidden anatomical area more recognizable to clinicians, creating a conclusive imaging diagnosis and decreasing further diagnostic work-up such as unnecessary surgeries and delayed diagnosis.
## Abstract
Groove pancreatitis (GP) is an uncommon appearance of pancreatitis represented by fibrous inflammation and a pseudo-tumor in the area over the head of the pancreas. The underlying etiology is unidentified but is firmly associated with alcohol abuse. We report the case of a 45-year-old male patient with chronic alcohol abuse who was admitted to our hospital with upper abdominal pain radiating to the back and weight loss. Laboratory data were within normal limits, except for the level of carbohydrate antigen (CA) 19-9. An abdominal ultrasound and computed tomography (CT) scan revealed swelling of the pancreatic head and duodenal wall thickening with luminal narrowing. We performed an endoscopic ultrasound (EUS) with fine needle aspiration (FNA) from the markedly thickened duodenal wall and the groove area, which revealed only inflammatory changes. The patient improved and was discharged. The principal objective in managing GP is to exclude a diagnosis of malignancy, whilst a conservative approach might be more acceptable for patients instead of extensive surgery.
## References
1. Becker V., Mischke U.. **Groove pancreatitis**. *Int. J. Pancreatol.* (1991) **10** 173-182. DOI: 10.1007/BF02924155
2. Pallisera-Lloveras A., Ramia-Ángel J.M., Vicens-Arbona C., Cifuentes-Rodenas A.. **Groove pancreatitis**. *Rev. Esp. Enferm. Dig.* (2015) **107** 280-288. PMID: 25952803
3. Stolte M., Weiss W., Volkholz H., Rosch W.. **A special form of segmental pancreatitis: “Groove pancreatitis”**. *Hepatogastroenterology* (1982) **29** 198-208. PMID: 7173808
4. Triantopoulou C., Dervenis C., Giannakou N., Papailiou J., Prassopoulos P.. **Groove pancreatitis: A diagnostic challenge**. *Eur. Radiol.* (2009) **19** 1736-1743. DOI: 10.1007/s00330-009-1332-7
5. Parga-Bermúdez J.E., Gómez-Zuleta M.A.. **Groove pancreatitis mimicking pancreatic cancer: Case report and literature review**. *Rev. Colomb. Gastroenterol.* (2021) **36** 19-25
6. Goransky J., Alvarez F.A., Picco P., Spina J.C., Santibañes Md Mazza O.. **Groove pancreatitis vs groove pancreatic adenocarcinoma. Report of two cases and review of the literature**. *Acta Gastroenterol. Latinoam.* (2013) **43** 248-253. PMID: 24303693
7. Fujita N., Shirai Y., Tsukada K., Kurosaki I., Iiai T., Hatakeyama K.. **Groove pancreatitis with recurrent duodenal obstruction. Report of a case successfully treated with pylorus-preserving pancreaticoduodenectomy**. *Int. J. Pancreatol.* (1997) **21** 85-88. DOI: 10.1007/BF02822390
8. Raman S.P., Salaria S.N., Hruban R.H., Fishman E.K.. **Groove pancreatitis: Spectrum of imaging findings and radiology-pathology correlation**. *Am. J. Roentgenol.* (2013) **201** 29-39. DOI: 10.2214/AJR.12.9956
9. Patel B.N., Brooke Jeffrey R., Olcott E.W., Zaheer A.. **Groove pancreatitis: A clinical and imaging overview**. *Abdom. Radiol.* (2020) **45** 1439-4146. DOI: 10.1007/s00261-019-02239-1
10. Zaheer A., Haider M., Kawamoto S., Hruban R.H., Fishman E.K.. **Dual-phase CT findings of groove pancreatitis**. *Eur. J. Radiol.* (2014) **83** 1337-1343. DOI: 10.1016/j.ejrad.2014.05.019
11. Blasbalg R., Baroni R., Costa D., Machado M.C.C.. **MRI features of groove pancreatitis**. *Am. J. Roentgenol.* (2007) **189** 73-80. DOI: 10.2214/AJR.06.1244
12. Irie H., Honda H., Kuroiwa T., Hanada K., Yoshimitsu K., Tajima T., Jimi M., Yamaguchi K., Masuda K.. **MRI of groove pancreatitis**. *J. Comput. Assist. Tomogr.* (1998) **22** 651-655. DOI: 10.1097/00004728-199807000-00027
13. Ferreira A., Ramalho M., Herédia V., de Campos R., Marques P.. **Groove pancreatitis: A Case Report and Review of the Literature**. *J. Radiol. Case Rep.* (2010) **4** 9-17. DOI: 10.3941/jrcr.v4i11.588
14. Valentini G., Surace M., Grosso S., Vernetto A., Serra A.M., Andria I., Dario M.. **Paraduodenal Pancreatitis: Many faces of the Same Diagnostic Challenge**. *Arch. Gastroenterol. Res.* (2020) **1** 73-82
15. Wong T., Pattarapuntakul T., Netinatsunton N., Ovartlarnporn B., Sottisuporn J., Chamroonkul N., Sripongpun P., Jandee S., Kaewdech A., Attasaranya S.. **Diagnostic performance of endoscopic ultrasound—Guided tissue acquisition by EUS–FNA versus EUS–FNB for solid pancreatic mass without ROSE: A retrospective study**. *World J. Surg Oncol.* (2022) **20** 215. DOI: 10.1186/s12957-022-02682-3
16. Graziani R., Tapparelli M., Malago R., Girardi V., Fruolloni L., Cavallini G., Mucelli R.P.. **The various imaging aspects of chronic pancreatitis**. *JOP* (2005) **6** 73-88. PMID: 15650290
17. Shanbhogue A.K., Fasih N., Surabhi V.R., Doherty G.P., Shanbhogue D.K., Sethi S.K.. **A clinical and radiologic review of uncommon types and causes of pancreatitis**. *RadioGraphics* (2009) **29** 1003-1026. DOI: 10.1148/rg.294085748
18. Ishigami K., Tajima T., Nishie A., Kakihara D., Fujita M., Asayama Y., Ushijima Y., Irie H., Nakamura M., Takahata S.. **Differential diagnosis of groove pancreatic carcinomas vs. groove pancreatitis: Usefulness of the portal venous phase**. *Eur. J. Radiol.* (2010) **74** 95-100. DOI: 10.1016/j.ejrad.2009.04.026
19. Gabata T., Kadoya M., Terayama N., Sanada J., Kobayashi S., Matsui O.. **Groove pancreatic carcinomas: Radiological and pathological findings**. *Eur. Radiol.* (2003) **13** 1679-1684. DOI: 10.1007/s00330-002-1743-1
20. Chantarojanasiri T., Isayama H., Nakai Y., Matsubara S., Yamamoto N., Takahara N., Mizuno S., Hamada T., Kogure H., Koike K.. **Groove pancreatitis: Endoscopic treatment via the minor papilla and duct of Santorini morphology**. *Gut Liver* (2018) **12** 208-213. DOI: 10.5009/gnl17170
21. Tarvainen T., Nykänen T., Parviainen H., Kuronen J., Kylänpää L., Sirén J., Kokkola A., Sallinen V.. **Diagnosis, natural course and treatment outcomes of groove pancreatitis**. *HPB* (2021) **23** 1244-1252. DOI: 10.1016/j.hpb.2020.12.004
22. Lekkerkerker S.J., Nio C.Y., Issa Y., Fockens P., Verheij J., Busch O.R., van Gulik T.M., Rauws E.A., Boermeester M.A., van Hooft J.E.. **Clinical outcomes and prevalence of cancer in patients with possible groove pancreatitis**. *J. Gastroenterol. Hepatol.* (2016) **31** 1895-1900. DOI: 10.1111/jgh.13376
23. Arvanitakis M., Rigaux J., Toussaint E., Eisendrath P., Bali M.A.. **Endotherapy for paraduodenal pancreatitis: A large retrospective case series**. *Endoscopy* (2014) **46** 580-587. DOI: 10.1055/s-0034-1365719
|
---
title: Evaluation, Description and Magnitude of Readmission Phenomenon in Azienda
Ospedaliero Universitaria Pisana (AOUP) for Chronic-Degenerative Diseases in the
Period 2018–2021
authors:
- Matteo Filippi
- Erika Del Prete
- Ferruccio Aquilini
- Michele Totaro
- Francesca Di Serafino
- Sara Civitelli
- Giulia Geminale
- David Rocchi
- Nunzio Zotti
- Angelo Baggiani
journal: Healthcare
year: 2023
pmcid: PMC10001156
doi: 10.3390/healthcare11050651
license: CC BY 4.0
---
# Evaluation, Description and Magnitude of Readmission Phenomenon in Azienda Ospedaliero Universitaria Pisana (AOUP) for Chronic-Degenerative Diseases in the Period 2018–2021
## Abstract
Background: Readmissions are hospitalizations following a previous hospitalization (called index hospitalization) of the same patient that occurred in the same facility or nursing home. They may be a consequence of the progression of the natural history of a disease, but they may also reveal a previous suboptimal stay, or ineffective management of the underlying clinical condition. Preventing avoidable readmissions has the potential to improve both a patient’s quality of life, by avoiding exposure to the risks of re-hospitalization, and the financial well-being of health care systems. Methods: We investigated the magnitude of 30 day repeat hospitalizations for the same Major Diagnostic Category (MDC) in the Azienda Ospedaliero Universitaria Pisana (AOUP) over the period from 2018 to 2021. Records were divided into only admissions, index admissions and repeated admission. The length of the stay of all groups was compared using analysis of variance and subsequent multi-comparison tests. Results: Results showed a reduction in readmissions over the period examined (from $5.36\%$ in 2018 to $4.46\%$ in 2021), likely due to reduced access to care during the COVID-19 pandemic. We also observed that readmissions predominantly affect the male sex, older age groups, and patients with medical Diagnosis Related Groups (DRGs). The length of stay of readmissions was longer than that of index hospitalization (difference of 1.57 days, $95\%$ CI 1.36–1.78 days, $p \leq 0.001$). The length of stay of index hospitalization is longer than that of single hospitalization (difference of 0.62 days, $95\%$ CI 0.52–0.72 days, $p \leq 0.001$). Conclusions: A patient who goes for readmission thus has an overall hospitalization duration of almost two and a half times the length of the stay of a patient with single hospitalization, considering both index hospitalization and readmission. This represents a heavy use of hospital resources, about 10,200 more inpatient days than single hospitalizations, corresponding to a 30-bed ward working with an occupancy rate of $95\%$. Knowledge of readmissions is an important piece of information in health planning and a useful tool for monitoring the quality of models of patient care.
## 1. Introduction
In the last decade, facing an increasing complexity of care and the development of healthcare systems, we have witnessed a “patient readmission” phenomenon. With these words, we refer to a new admission of a patient discharged before from another hospital or healthcare setting [1]. Time cut-off and inclusion/exclusion standards are different among countries. In Italy, for example, we can talk of readmission when the same patient stays in a ward more than 1 day, after a previous admission (called “index hospitalization”) in the same healthcare setting. Readmissions are acceptable in some cases, such as frequent admission for chemotherapy treatments, but in other cases they could be a warning of some clinical issues. A premature discharge could not permit adequate patient management, increasing infectious exposure and death risk. Simultaneously, hospital spending and resources are compromised, as shown by the Management and Health (Management e Sanità, MeS) Laboratory of Sant’Anna School of Advanced Studies [2].
In the United States of America (USA), readmission has been a public health concern since the early 1980s, mainly for older adults related to their engagement in the Medicare program [3]. A few studies examined whether some interventions could prevent the readmission of those patients with chronic conditions such as diabetes [4,5] or chronic obstructive pulmonary disease (COPD) [6]. The main interest of readmission analysis was its use as a quality index, as well as the evaluation of rates and costs. In the late 1980s, the Prospective Payment System (PPS) began to be a concern for a likely increase in premature discharges, since health structures are paid by the Diagnosis Related Group (DRG) [7]. The same concern arose when the DRG system spread in Europe [8]. However, the impact of PPS on readmission rates was not supported by empirical evidence [9]. With the development of predictive models in public health, we can assume that readmissions are preventable by 10–$50\%$ of the total amount [10] and we can evaluate even disease-specific readmission rates [11]: heart failure, ischemic cardiac disease, myocardial infarction, COPD and pneumonia are the main focus for health management. The Centers for Medicare and Medicaid Services (CMS) in 2009 showed that pneumonia, heart failure and myocardial infarction were $20\%$ of total admission in healthcare settings. In 2012, the US Government approved the Hospital Readmission Reduction Program (HRRP), which aimed to empower patients and caregivers in reducing readmission. The payment of healthcare structures is now based on the quality of care, rewarding hospitals with a lower 30-day readmission rate [12].
In Italy, the National Health Service (Servizio Sanitario Nazionale, SSN) is a separate case when we consider the Prospective Payment System (PPS) effects on health services [13]. DRGs were introduced in Italy in 1994 and the Tuscany Region has collaborated with the MeS Laboratory of the Sant’Anna School of Advanced Studies in the “System of performance evaluation” since 2006 [14,15,16,17,18]. This system was adopted by the other 12 Italian regions, founding the “Network of Regions” in 2008, where 27 University Hospitals are included. In this way, an annual report is shared with each region’s results, based on common indexes. Since 2011, this report has been available to all the stakeholders and the general public.
Furthermore, since 1991 in Italy there is a wide use of the Hospital Discharge Register (Scheda di Dimissione Ospedaliera, SDO), based on UHDDS (Uniform Hospital Discharge Data Set), which is a tool for collecting discharge information from healthcare settings [19]. In 2000, SDO was updated with ICD-9-CM classification [20]; it contains patients’ personal data, hospitalization features and clinical information. The importance of SDO concerns a uniform classification of hospital discharges, feeding a huge information flow that allows economic, management, clinic and epidemiologic evaluations.
Furthermore, by analyzing SDO information flow, we can evaluate different DRG-specific indicators, such as performance indicators, appropriateness indicators and control indicators. Among appropriateness indicators, we can find “Patient readmission in 30 days by same Major Diagnostic Category (MDC)”, which evaluates premature readmission in 30 days since the previous admission for the same (or similar) diagnosis. This indicator evaluates patient safety and the efficacy of resource employment.
In the USA, after local ventures [21], the national dedication to performance evaluation began with a pilot program of the “Joint Commission on Accreditation of Healthcare Organization” (JCAHO, later named “The Joint Commission”, JCI) [22]. In 2001, JCI, Centers for Medicare and Medicaid Services (CMS), American Hospital Association and others founded the Hospital Quality Alliance (HQA) as a means to sending performance data to CMS [23,24], with voluntary membership. In 2012, after the 2003 Medicare Modernization Act, quality indicators were integrated in CMS payment rates and published on the “Hospital Compare” website.
In Italy, quality indicators are regulated by Legislative Decree $\frac{502}{1992}$, for accreditation purposes. Afterwards, the D.Lgs. $\frac{56}{2000}$ introduced monitoring processes in health assistance, as a quality assurance system. Moreover, in 2001, the indicators and parameters to monitor the adherence of assistance essential levels by the regions were clarified. Among these parameters, we can find short and long stay rates, dismissal rates, hospitalization rates by specific surgical procedures, and others. The importance of quality evaluation in the SSN is now strengthened and in 2019 the Ministry of Health published the “New Guarantee System for monitoring health assistance” (Nuovo Sistema di Garanzia per il monitoraggio dell’assistenza sanitaria, NSG).
Concerning the heavy costs of patient readmission, about USD 15–20 billion [25] is spent yearly in the USA. In 2003, about $20\%$ of Medicare patients were readmitted within 30 days. Readmission of HRPP target pathologies (myocardial infarction, cardiac failure and pneumonia) went from $21.5\%$ in 2007 to $17.8\%$ in 2015. Other pathologies went from $15.3\%$ to $13.1\%$ [26].
In the United Kingdom (UK), readmission rates in emergencies went from $8\%$ in 1998 to $10\%$ in 2006, being stable from 2006 to 2012 and decreasing in the next period [27]. However, there is variability among clinical areas, witnessing an increasing readmission rate for pneumonia, diabetes, cholecystectomy and hysterectomy. National Health System (NHS) data show a small increase in the national readmission rate, from $12.5\%$ in $\frac{2013}{2014}$ to $13.8\%$ in $\frac{2017}{2018}$ [28]. Moreover, readmission rates reached $14.9\%$ in disadvantaged areas, but $12.7\%$ in more advantaged areas.
Considering the readmissions, we can distinguish avoidable from unavoidable ones and our efforts concern the first sort [29]. A systematic review of 34 studies [30] shows that avoidable readmissions are $27.1\%$, even if they range from $5\%$ to $79\%$. An observational study, conducted on 1000 patients of General Medicine readmitted in 30 days, ascertained that $27\%$ are potentially preventable [31,32].
Many studies proposed clinical and demographic parameters to evaluate the risk of readmission and “high-risk patients” [33], as shown in Table 1.
With all that said, many efforts are spent on the research of screening tools to identify high-risk patients, but the results are not satisfying and predictive models have a low discriminating function. The literature asserts that it is not possible to predict readmissions and even most recent reviews show that predictive models, created for specific populations and conditions, are not standardizable [34].
For example, a screening tool is LACE index, which identifies patients with a readmission risk, but not avoidable readmissions [34,35]. This tool has been improved, creating LACE+, which contains more clinical parameters. Besides LACE/LACE+, another tool is HOSPITAL Score, specifically modeled to identify avoidable readmissions in 30 days with a computer algorithm [35] and it has a quite high discriminant capacity. Moreover, 8Ps is a tool created by the Society of Hospital Medicine to identify and stratify each risk and pair it with a risk-specific intervention.
Many factors increase readmission risk, and many factor are potentially avoidable or improvable [36], such as premature discharges, inadequate post-discharge support, delayed or missing follow-up, therapeutical mistakes, adverse drug reactions, and inefficient continuity of care.
Given this, as shown in a systematic review of 2011 [37], many efforts aim to reduce readmissions, improving predischarge and postdischarge interventions. In predischarge interventions, we can find patient education, medication reconciliation, discharge planning and scheduling of adequate follow-up. In postdischarge interventions are included all the follow-up measures such as telephone calls, home visits and ambulatory visits.
Other studies [38,39] underline how the combination of interventions can improve clinical outcomes and reduce readmission rates. Most efficient interventions are complex ones, aimed to give the patient a self-care capability.
The main goal of this paper is to evaluate readmission rates within 30 days, for the same MDC in a 4-year period—including data from the COVID-19 pandemic period—in Azienda Ospedaliero Universitaria Pisana (AOUP). The other purpose is the evaluation of the length of stay of readmitted patients to quantify the hospital burden. After an overall analysis, we focused on three clinical conditions: COPD, arrhythmia, and hypertension.
## 2. Materials and Methods
The study was performed in the Azienda Ospedaliero Universitaria Pisana (AOUP) (i.e., Pisa University Hospital), which represents the tertiary referral center for the “Toscana Nord-Ovest” local health unit. The administrative Hospital Discharge Records database of the AOUP represents the study data source. All records were coded with a medical DRG. We examined 30-day readmission rates with the same MDC diagnosis in AOUP, between 1 December 2017 and 31 January 2022.
The informed consent was applied for patients and healthcare personnel.
Variables included in the analysis were gender, age at the time of discharge, type of DRG (medical or surgical), length of stay, primary and secondary diagnosis codes according to the ICD-9-CM (International Classification of Diseases—ninth revision—Clinical Modification).
Descriptive statistics were used to describe the basic features of the data in the study. The variables used in the model were chosen according to availability in the HDR database.
The records were divided into three groups: only admissions (OA), index admissions (IA) to indicate the first admission of patients that were readmitted for the same MDC within 30 days after discharge, and repeated admissions (RA) to indicate admissions that followed the first one within 30 days from the previous discharge.
The length of stay of the three groups was compared using analysis of variance and subsequent multi-comparison tests. Outliers above the 95th percentile (corresponding to 20 days) were excluded from the analysis regarding the length of stay. The paired t-test was used to test for differences in the length of stay between IA and RA. The t-test for independent samples was used to test for differences in the length of stay between OA and RA.
One-way analysis of variance was used to evaluate differences between age groups in the mean length of stay of IA and OA. The same test has been performed to analyze differences in the readmission phenomenon between the pre-COVID-19 pandemic period (years 2018–2019) in comparison to the COVID-19 pandemic phase (years 2020–2021).
Association between categorical variables was assessed through the Chi-square test;, differences between continuous variables were compared using t-test and analysis of variance.
Multivariate logistic regression analysis was performed evaluating which variables influence readmissions. Dependent variables were obtained for IA versus OA groups.
We considered some binomial variables such as sex (male or female), type of DRG (medical, surgical), year groups (2018–2019, 2020–2021), and a multinomial variable (age groups: <36 years; 37–54 years; 55–67 years; 68–77 years; >77 years).
Three pathology groups were included in the analysis: chronic obstructive pulmonary disease (COPD), cardiac arrhythmias, hypertension.
Afterwards, the multicollinearity was evaluated in order to establish if variables are near perfect linear combinations of one another. We used the Variance Inflation Factor (VIF) command after the regression to check for multicollinearity. Variables whose VIF values are greater than 10 may merit further investigation. Tolerance, defined as 1/VIF, is used to check the collinearity degree.
A p-value ≤ 0.05 is regarded as evidence of a statistically significant result.
The software STATA 15.1 (StataCorp 4905 Lakeway Drive College Station, Texas 77845 USA) and MS Excel were used for all statistical analyses.
## 3. Results
Table 2 shows the general features of admissions in AOUP between 2018 and 2021.
During the study period, there were 167.792 hospital admissions, 8346 of which were repeated ($4.97\%$). Outliers cover $5.6\%$ (9345 out of 167.792) of the whole sample. It includes both male and female with different ages and pathologies. Splitting the study period in years, we could see that from 2018 to 2019 there was a slight reduction in readmission rates (from $5.36\%$ in 2018 to $5.33\%$ in 2019), whereas in 2020 and 2021 there was a more consistent reduction ($4.57\%$ in 2020 and $4.46\%$ in 2021) (Figure 1).
A statistically significant reduction in readmission was observed in the COVID-19 pandemic period (years 2020–2021) in comparison to the pre-COVID-19 pandemic years (2018–2019) ($$p \leq 0.001$$).
From an overall evaluation of the four years, we could see that females were prevalent in only admissions ($53.28\%$ females versus $46.72\%$ males); males were prevalent in index admissions and repeated admissions ($45.46\%$ females versus $54.54\%$ males in RA and $46.47\%$ females versus $53.53\%$ males in IA).
The average age of patients with OA was 55.9 years (SD = ±24.3 years), while the average age of patients with IA was significantly higher, 61.7 years (SD = ±20.9 years). Dividing into quintiles by age group, we could see that OA had a similar subdivision in age groups; IA and RA were disproportionately older ages.
Analyzing the distribution in medical DRG and surgical DRG, OA had a majority of surgical DRG ($55.60\%$ surgical DRG versus $44.40\%$ medical DRG); in contrast, IA and RA had a majority of medical DRG, especially in IA ($74.57\%$ medical DRG versus $25.43\%$ surgical DRG).
We selected three clinical conditions according to the greater frequency of DRG in IA: COPD (DRG 4912x, 492xx, 4932x), cardiac arrhythmias (DRG 427), and hypertension (DRG 401). This choice was based on the fact that among IA the most frequent diagnoses were DRG 49121 chronic obstructive bronchitis, with acute exacerbation; DRG 42731 atrial fibrillation; DRG 4011 hypertension.
These diagnoses belong to the previously mentioned three clinical conditions.
To evaluate the length of hospital stays, first, we analyzed the distribution of the length of stays to eliminate admissions that were above the 95th percentile (corresponding to 20 days), because these outliers determined distortions of the remaining sample. From a global evaluation, we found that IA means a hospital stay was longer than an OA mean hospital stay (5.3 ± 4.41 days versus 4.68 ± 4.01 days). We conducted a longitudinal comparison between IA and RA of the same patients through a paired t-test. Excluding the outliers above 20 days, mean RA hospitalization (6.82 ± 8.22 days) was longer than mean IA hospitalization (5.25 ± 4.40 days). The difference, statistically significant, was 1.57 days ($95\%$ CI 1,36–1.78; $p \leq 0.001$).
Using a t-test for independent samples, we compared the lengths of the IA and OA stays. The mean IA length of stay (5.30 ± 4.41 days) was longer than the mean OA length of stay (4.68 ± 4.01 days). The difference, statistically significant, was 0.62 days ($95\%$ CI 0.52–0.72; $p \leq 0.001$). Moreover, IA hospitalization was longer than OA hospitalization both for males (5.36 days versus 4.98 days) and females (5.23 days versus 4.42 days).
To better understand the length of stay between patients that later underwent readmission (IA) and patients that have an only admission (OA), we analyzed the age breakdown. Even in this case, we excluded the outliers above 20 days. The results are presented in Figure 2. We can observe that the mean length of stay is longer for IA than OA for each age group.
Statistically significant differences between age groups in the mean length of stay of IA and OA were observed ($$p \leq 0.008$$).
After analyzing the overall sample, we decided to focus on three clinical conditions that were more frequent among index admissions: COPD (DRG 4912x, 492xx, 4932x), cardiac arrhythmias (DRG 427), and hypertension (DRG 401). We analyzed the mean length of stay and the age breakdown for each condition.
## 3.1. Chronic Obstructive Pulmonary Disease
We conducted a longitudinal comparison between IA and RA of the same patients with DRG 4912x, 492xx, 4932x, through a paired t-test. Excluding the outliers above 20 days, mean RA hospitalization (8.5 ± 8.70 days) was longer than mean IA hospitalization (6.88 ± 4.54 days). The difference, statistically significant, was 1.62 days ($95\%$ CI 0.64–2.60; $p \leq 0.001$).
Using a t-test for independent samples, we compared the length of stay of IA and OA for admission with DRG 4912x, 492xx, 4932x. The mean IA length of stay (7.01 ± 4.24 days) was longer than the mean OA length of stay (6.72 ± 4.33 days). Nevertheless, the difference was not statistically significant ($$p \leq 0.1$$). Even if the difference was not statistically significant, IA hospitalization was longer than OA hospitalization both for males (6.85 days versus 6.58 days) and females (7.29 days versus 6.98 days).
Considering the length of stay, we analyzed the age breakdown for patients with DRG 4912x, 492xx, 4932x. The results are presented in Figure 3. We can observe that the mean length of stay is longer for IA than OA for age groups above 68 years, but not for the 55–67 years group.
## 3.2. Arrhythmias
We conducted a longitudinal comparison between IA and RA of the same patients with DRG 427, through a paired t-test. Excluding the outliers above 20 days, mean RA hospitalization (7.89 ± 7.17 days) was longer than mean IA hospitalization (6.49 ± 4.21 days). The difference, statistically significant, was 1.39 days ($95\%$ CI 0.77–2.01; $p \leq 0.001$).
Using a t-test for independent samples, we compared the length of stay of IA and OA for admission with DRG 427. The mean IA length of stay (7.00 ± 4.32 days) was longer than the mean OA length of stay (6.10 ± 4.15 days). The difference, statistically significant, was 0.91 days ($95\%$ CI 0.57–1.25; $p \leq 0.001$). Moreover, IA hospitalization was longer than OA hospitalization both for males (6.80 days versus 6.07 days) and females (7.27 days versus 6.12 days).
Considering the length of stay, we analyzed the age breakdown for patients with DRG 427. The results are presented in Figure 4. We can observe that the mean length of stay is longer for IA than OA for all age groups except for the group <36 years.
## 3.3. Hypertension
We conducted a longitudinal comparison between IA and RA of the same patients with DRG 401, through a paired t-test. Excluding the outliers above 20 days, mean RA hospitalization (7.32 ± 6.77 days) was longer than mean IA hospitalization (5.24 ± 4.19 days). The difference, statistically significant, was 2.08 days ($95\%$ CI 1.58–2.57; $p \leq 0.001$).
Using a t-test for independent samples, we compared the length of stay of IA and OA for admission with DRG 401. The mean IA length of stay (5.99 ± 4.43 days) was longer than the mean OA length of stay (5.71 ± 4.20 days). The difference, statistically significant, was of 0.28 days ($95\%$ CI −0.01–1.25; $p \leq 0.05$). Moreover, IA hospitalization was longer than OA hospitalization for females (6.67 days IA versus 5.73 days OA), and for males OA was slightly longer than IA (5.58 days IA versus 5.70 days OA).
Concerning the length of stay, we analyzed the age breakdown for patients with DRG 401. The results are presented in Figure 5. We can observe that the mean length of stay is longer for IA than OA for all age groups except for the group <36 years.
## 3.4. Multivariate Logistic Regression Results
Multivariate logistic regression data highlight that all variables are statistically significant (p ≤ 0.001) and represent a risk factor for readmissions (Odds Ratio >1). Considering the type of pathology, a positive association between COPD and readmissions ($p \leq 0.001$; Odds Ratio 1.20) was detected. The same result was not obtained for cardiac arrythmias where a not statistically significant negative association was shown ($$p \leq 0.05$$; Odds Ratio 0.89). Moreover, considering the hypertension disease, a statistically significant negative association was detected ($$p \leq 0.01$$, Odds Ratio 0.77).
The variables that most influence the readmissions are the presence of COPD pathologies; the 2018–2019 two-year period; the male sex; the presence of a medical DRG; and the age range between 55 and 77 years.
These data were supported by multicollinearity evaluation, where a mean VIF of 1.75 was detected. Considering VIF values ranging from 1.07 to 3.56, low collinearities were always observed.
All data are shown in Table 3.
## 4. Discussion
Results show a decrease in repeated admissions from $5.36\%$ in 2018 to $4.46\%$ in 2021. The reduction was particularly evident in 2020 ($4.57\%$) and 2021 ($4.46\%$), probably due to the reduced health care access during the COVID-19 pandemic. However, there was also a slight decrease before the pandemic, from 2018 ($5.36\%$) to 2019 ($5.33\%$).
Readmitted patients were more frequently male, older, and admitted with a medical DRG. Otherwise, single admitted patients were more frequently female, younger, and admitted with a surgical DRG.
Concerning the pathology groups, a positive association between COPD and readmission was detected. This phenomenon was not shown for cardiovascular diseases.
Hypertension and arrhythmia are common conditions that can be effectively managed at home with adequate compliance and adherence to treatment, maintaining a proactive attitude to prevent complications. By constantly monitoring blood pressure and heart rate, for example, individuals with hypertension and arrhythmia can live almost serenely with the condition and autonomously prevent exacerbations, maintaining optimal health without having to frequently visit healthcare facilities. This goal can be achieved by modifying lifestyle, including regular exercise, a healthy diet, stress reduction, and of course taking prescribed medications as directed. The causes leading patients with hypertension/arrhythmia to hospitalization are generally secondary causes due to poor management of the underlying condition (e.g., heart attack, coronary artery disease, organ damage, etc.) and to a lesser extent due to the main pathology.
Even in the case of COPD, patients can take measures to reduce the risk of exacerbations, such as quitting smoking, avoiding air pollution, and keeping up to date with vaccinations against respiratory viruses. With adequate education and self-care, patients with COPD can minimize the severity and frequency of exacerbations and improve overall quality of life. However, COPD presents a greater challenge in terms of territorial management of the patient. Although there are various therapeutic options for COPD, it can still lead to sudden exacerbations that can cause severe breathing difficulties and can even be life-threatening, requiring immediate treatment in a hospital setting to carefully monitor the patient, sometimes repeatedly.
We found that RA mean hospital stay was longer than IA ones (difference of 1.57 days, $95\%$ CI 1.36–1.78 days, $p \leq 0.001$), and IA mean hospital stay was longer than OA ones (difference of 0,62 days, $95\%$ CI 0.52–0.72 days, $p \leq 0.001$). A readmitted patient stays, on average, almost two and a half times as long compared to a single admitted patient, considering both index admission and repeated admission. This represents a heavy hospital resource utilization, about 10.200 hospital stay days more than single admissions, considering only 2021, which is the equivalent of a department of 30 beds with $95\%$ bed occupancy. Tuscany Region classifies the optimal use of an ordinary bed in a ward as being occupied $95\%$ of the time (equal to 346 hospital days/year). Data are obtained dividing the 10,200 days of hospitalization by the 346 days developed for a single bed.
It should be noted that we excluded outliers from the analysis because they were not representative of the overall sample, but they determined an additional bed occupation.
The analysis of the selected clinical conditions demonstrates similar results regarding IA and RA length of stay. For patients with COPD (DRG 4912x, 492xx, 4932x), the difference was not statistically significant, whereas the difference was statistically significant for arrhythmias (DRG 427) and hypertension (DRG 401). The fact that RA mean length of stay was longer than IA mean length of stay may suggest that patients’ conditions increase in complexity from the IA to the RA.
Readmissions are an important issue in healthcare. It is possible to consult 30-day readmission rates by connecting to the site of Management e Sanità (MeS) by Sant’Anna School of Pisa [2]. In 2021, Tuscany Region had the highest readmission rate among the adhering regions.
The reduction in readmission in 2020 and 2021 is visible in all the regions. The cause of this reduction may be the reduced health care access during the COVID-19 pandemic; therefore, it will be interesting to see the readmission rates in the coming years.
Focusing on Tuscany, in 2021 the AOUP readmission rate was slightly above Tuscany’s mean value.
The trend in Tuscany shows a decrease in readmissions for all the healthcare facilities in the last three years. Before the pandemic, AOUP was the facility with the highest number of readmissions ($5.92\%$).
Analyzing Tuscany’s healthcare facilities overall, we can see that readmissions were stable before the COVID-19 pandemic.
Reducing avoidable readmissions is one of the strongly pursued goals of health policies, since it would improve quality and reduce health care expenditures. However, a direct link between readmissions and quality of healthcare has not been clearly demonstrated, despite numerous studies conducted for several decades already. However, the knowledge of readmissions constitutes important information in the perspective of healthcare planning, so the monitoring is a useful tool that can allow one to find hidden dynamics in the mechanisms of admission and discharge of patients from hospital facilities.
In this study, readmissions were considered in their generality, but these may show only gross changes in the hospital’s inpatient activity. Next, we analyzed which principal diagnoses are most associated with readmissions. Within the same category of principal diagnoses, we selected the three most representative diagnoses and investigated the characteristics that differentiate patients who encounter readmission from patients with the same diagnosis who do not encounter readmission. These features are interesting for two reasons. In fact, they allow us to characterize the type of patients who are admitted to the AOUP, improving healthcare planning. On the other hand, we could hypothesize targeting interventions toward these patients to reduce the incidence of repeat hospitalizations.
Regarding interventions to reduce readmissions, the literature shows that there are significant effects when several interventions are combined simultaneously, which should be part of a prevention strategy, especially for those categories of patients most at risk. However, it must be pointed out that most of these studies are conducted in the United States, so there is no certainty about their validity in our country as well, which has a radically different health care system.
In this study, we may highlight some advantages and disadvantages.
Thanks to our data, the AOUP may apply strategies, based on the activation of post-discharge emergency medicine clinics also from Emergency Department access (verification of therapeutic adherence). This may limit the use of new access in the presence of non-acute symptoms.
On the other hand, the organizational structure of the regional healthcare system clearly separates hospital assistance from the territorial sort. In this way, hospitals cannot regulate personal services (home assistance, territorial general medicine) in and out of the hospital.
## 5. Conclusions
The challenge in recent years has been to strengthen the continuity of care between hospitals and communities. The aging of the population and the increase in chronic conditions and life expectancy are important changes that the National Health System has to deal with. Health services have been organized over the years with “hospital-centered” logic, which is very efficient in providing timely and quality treatment in acute conditions, but often inadequate to ensure care to patients with many chronicities, who are often elderly and frail.
In January 2022, after the approval of the National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR), the Italian government allocated EUR 8.42 billion to regions and autonomous provinces for the implementation of interventions in the health sector (strengthening of proximity networks, facilities, telemedicine for territorial health care and digitalization of the National Health Service).
In this way, in the coming years, we will witness the strengthening of territorial facilities and the transition to a new model of healthcare, made up of processes and pathways in which the hospital is an integral part of the system. Bearing this in mind, quality indicators, such as readmissions, can be used in monitoring the performance of new models of care.
## References
1. **Patient Readmission**
2. **Sistema di Valutazione Delle Performance, Pisa**. (2022.0)
3. Burgess J.F., Hockenberry J.. **Can all cause readmission policy improve quality or lower expenditures? A historical perspective on current initiatives**. *Health Econ. Policy Law* (2014.0) **9** 193-213. PMID: 23987089
4. Graham H., Livesley B.. **Can readmissions to a geriatric medical unit be prevented?**. *Lancet* (1983.0) **1** 404-406. PMID: 6130389
5. Fishbein H.A., Faich G., Ellis S.. **Incidence and hospitalization patterns of insulin- dependent diabetes mellitus**. *Diabetes Care* (1982.0) **5** 630-633. DOI: 10.2337/diacare.5.6.630
6. Roselle S., D’Amico F.. **The effect of home respiratory therapy on hospital readmission rates of patients with chronic obstructive pulmonary disease**. *Respir. Care* (1982.0) **27** 1194-1199. PMID: 10315311
7. Malatestinic W., Braun L., Jorgenson J.A., Eskew J.. **Components of Medicare reimbursement**. *Am. J. Health Syst. Pharm.* (2003.0) **60** S3-S7. DOI: 10.1093/ajhp/60.suppl_6.S3
8. Busse R., Geissler A., Aaviksoo A., Cots F., Häkkinen U., Kobel C., Mateus C., Or Z., O’Reilly J., Serdén L.. **Diagnosis related groups in Europe: Moving towards transparency, efficiency, and quality in hospitals?**. *Br. Med. J.* (2013.0) **346** f3197. DOI: 10.1136/bmj.f3197
9. DesHarnais S., Kobrinski E., Chesney J., Long M., Ament R., Fleming S.. **The early effects of the prospective payment system on inpatient utilization and the quality of care**. *Inquiry* (1987.0) **24** 7-16. PMID: 2951337
10. Clarke A.. **Are readmissions avoidable?**. *Br. Med. J.* (1990.0) **301** 1136-1138. DOI: 10.1136/bmj.301.6761.1136
11. Gooding J., Jette A.. **Hospital readmissions among the elderly**. *J. Am. Geriatr. Soc.* (1985.0) **33** 595-601. DOI: 10.1111/j.1532-5415.1985.tb06315.x
12. **Readmissions Reduction Program**
13. Meloni C., Pelissero G., Angelillo I.. *Igiene* (2007.0)
14. **Regione Toscana. Delibera n.1202 del 30 October 2017. Protocollo di Intesa tra Regione Toscana e Università Degli Studi di Firenze, Pisa e Siena, ex Art. 13 LR 40/2005, Florence**. (2017.0)
15. **Sistema di Valutazione del Network Delle Regioni**. (2022.0) **Volume 119** 264-273
16. **Ministro della Sanità, Decreto 28 dicembre 1991, Istituzione della Scheda di Dimissione Ospedaliera, Ministero della Sanità, Editor. Rome**. (1991.0)
17. **La Scheda di Dimissione Ospedaliera (SDO). 7 May 2010**
18. Rosenthal G.E., Hammar P.J., Way L.E., Shipley S.A., Doner D., Wojtala B., Miller J., Harper D.L.. **Using hospital performance data in quality improvement: The Cleveland Health Quality Choice experience**. *Jt. Comm. J. Qual. Improv.* (1998.0) **24** 347-360. PMID: 9689568
19. Williams S.C., Schmaltz S.P., Morton D.J., Koss R.G., Loeb J.M.. **Quality of care in USA hospitals as reflected by standardized measures, 2002–2004**. *N. Engl. J. Med.* (2005.0) **353** 255-264. DOI: 10.1056/NEJMsa043778
20. Landon B.E., Normand S.L., Lessler A., O’Malley A.J., Schmaltz S., Loeb J.M., McNeil B.J.. **Quality of care for the treatment of acute medical conditions in US hospitals**. *Arch Intern. Med.* (2006.0) **166** 2511-2517. DOI: 10.1001/archinte.166.22.2511
21. Jha A.K., Li Z., Orav E.J., Epstein A.M.. **Care in USA hospitals—The Hospital Quality Alliance program**. *N. Engl. J. Med.* (2005.0) **353** 265-274. DOI: 10.1056/NEJMsa051249
22. Jencks S.F., Williams M., Coleman E.. **Rehospitalizations among patients in the Medicare fee-for-service program**. *N. Engl. J. Med.* (2009.0) **360** 1418-1428. DOI: 10.1056/NEJMsa0803563
23. Zuckerman R.B., Sheingold S.H., Orav E.J., Ruhter J., Epstein A.M.. **Readmissions, Observation, and the Hospital Readmissions Reduction Program**. *N. Engl. J. Med.* (2016.0) **374** 1543-1551. DOI: 10.1056/NEJMsa1513024
24. Friebel R., Hauck K., Aylin P., Steventon A.. **National trends in emergency readmission rates: A longitudinal analysis of administrative data for England between 2006 and 2016**. *BMJ Open* (2018.0) **8** e020325. DOI: 10.1136/bmjopen-2017-020325
25. **NHS Digital. Emergency Readmissions Published for First Time in Five Yearsitle**. (2019.0)
26. Benbassat J., Taragin M.. **Hospital readmissions as a measure of quality of health care: Advantages and limitations**. *Arch Intern. Med.* (2000.0) **160** 1074-1081. DOI: 10.1001/archinte.160.8.1074
27. van Walraven C., Bennett C., Jennings A., Austin P.C., Forster A.J.. **Proportion of hospital readmissions deemed avoidable: A systematic review**. *Can. Med Assoc. J.* (2011.0) **183** E391-E402. DOI: 10.1503/cmaj.101860
28. Auerbach A.D., Kripalani S., Vasilevskis E.E., Sehgal N., Lindenauer P.K., Metlay J.P., Fletcher G., Ruhnke G.W., Flanders S.A., Kim C.. **Preventability and Causes of Readmissions in a National Cohort of General Medicine Patients**. *JAMA Intern. Med.* (2016.0) **176** 484-493. DOI: 10.1001/jamainternmed.2015.7863
29. Alper E., O’Malley T., Greenwald J., Auerbach A.. **Hospital discharge and readmission**. *Primary Care* (2021.0)
30. Kansagara D., Englander H., Salanitro A., Kagen D., Theobald C., Freeman M., Kripalani S.. **Risk prediction models for hospital readmission: A systematic review**. *J. Am. Med. Assoc.* (2011.0) **306** 1688-1698
31. Gatt M.L., Cassar M., Buttigieg S.. **A review of literature on risk prediction tools for hospital readmissions in older adults**. *J. Health Organ. Manag.* (2022.0)
32. van Walraven C., Dhalla I.A., Bell C., Etchells E., Stiell I.G., Zarnke K., Austin P.C., Forster A.J.. **Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community**. *Can. Med Assoc. J.* (2010.0) **182** 551-557. DOI: 10.1503/cmaj.091117
33. Rajaguru V., Han W., Kim T.H., Shin J., Lee S.G.. **LACE Index to Predict the High Risk of 30- Day Readmission: A Systematic Review and Meta-Analysis**. *J. Pers. Med.* (2022.0) **12**. DOI: 10.3390/jpm12040545
34. Van Walraven C., Wong J., Forster A.J.. **LACE+ index: Extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data**. *Open Med.* (2012.0) **6** e80-e90. PMID: 23696773
35. Donzé J.D., Williams M.V., Robinson E.J., Zimlichman E., Aujesky D., Vasilevskis E.E., Kripalani S., Metlay J.P., Wallington T., Fletcher G.S.. **International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions**. *JAMA Intern. Med.* (2016.0) **176** 496-502. DOI: 10.1001/jamainternmed.2015.8462
36. Hansen L.O., Greenwald J.L., Budnitz T., Howell E., Halasyamani L., Maynard G., Vidyarthi A., Coleman E.A., Williams M.V.. **Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization**. *J. Hosp. Med.* (2013.0) **8** 421-427. DOI: 10.1002/jhm.2054
37. Hansen L.O., Young R.S., Hinami K., Leung A., Williams M.V.. **Interventions to reduce 30-day rehospitalization: A systematic review**. *Ann. Intern. Med.* (2011.0) **155** 520-528. DOI: 10.7326/0003-4819-155-8-201110180-00008
38. Hesselink G., Schoonhoven L., Barach P., Spijker A., Gademan P., Kalkman C., Liefers J., Vernooij-Dassen M., Wollersheim H.. **Improving patient handovers from hospital to primary care: A systematic review**. *Ann. Intern. Med.* (2012.0) **157** 417-428. DOI: 10.7326/0003-4819-157-6-201209180-00006
39. Leppin A.L., Gionfriddo M.R., Kessler M., Brito J.P., Mair F.S., Gallacher K., Wang Z., Erwin P.J., Sylvester T., Boehmer K.. **Preventing 30-day hospital readmissions: A systematic review and meta-analysis of randomized trials**. *JAMA Intern. Med.* (2014.0) **174** 1095-1107. DOI: 10.1001/jamainternmed.2014.1608
|
---
title: Serious Clinical Outcomes of COVID-19 Related to Acetaminophen or NSAIDs from
a Nationwide Population-Based Cohort Study
authors:
- Jin-Woo Kim
- Siyeong Yoon
- Jongheon Lee
- Soonchul Lee
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001174
doi: 10.3390/ijerph20053832
license: CC BY 4.0
---
# Serious Clinical Outcomes of COVID-19 Related to Acetaminophen or NSAIDs from a Nationwide Population-Based Cohort Study
## Abstract
Acetaminophen and non-steroidal anti-inflammatory drugs (NSAIDs) have been widely prescribed to infected patients; however, the safety of them has not been investigated in patients with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our objective was to evaluate the association between the previous use of acetaminophen or NSAIDs and the clinical outcomes of SARS-CoV-2 infection. A nationwide population-based cohort study was conducted using the Korean Health Insurance Review and Assessment Database through propensity score matching (PSM). A total of 25,739 patients aged 20 years and older who tested for SARS-CoV-2 were included from 1 January 2015 to 15 May 2020. The primary endpoint was a positive result for a SARS-CoV-2 test, and the secondary endpoint was serious clinical outcomes of SARS-CoV-2 infection, such as conventional oxygen therapy, admission to the intensive care unit, need for invasive ventilation care, or death. Of 1058 patients, after propensity score matching, 176 acetaminophen users and 162 NSAIDs users were diagnosed with coronavirus disease 2019. After PSM, 162 paired data sets were generated, and the clinical outcomes of the acetaminophen group were not significantly different from those of the NSAIDs group. This suggests that acetaminophen and NSAIDs can be used safely to control symptoms in patients suspected of having SARS-CoV-2.
## 1. Introduction
As of December 2019, a new coronavirus, serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a global health threat. In January 2020, the World Health Organization named the syndrome coronavirus disease 2019 (COVID-19). About $5\%$ of patients with COVID-19 experience acute respiratory distress syndrome (ARDS), septic shock, or multiple organ failure requiring hospitalization in intensive care unit (ICU) [1]. There are several risk factors for mortality from COVID-19, including older age, smoking, cardiovascular disease, chronic kidney disease, diabetes and obesity, malignancy, chronic HIV infection, and treatment with dexamethasone [2,3,4,5,6,7]. Concerns have been raised about drug use related the risk of COVID-19. However, these concerns have not been fully identified.
Acetaminophen (AAP) is a safe analgesic considered as a treatment to reduce fever and chills, which are the first symptoms of COVID-19. AAP and non-steroidal anti-inflammatory drugs (NSAIDs) have been widely prescribed to infected patients to control fever, pain, and inflammation [8]. Both are inexpensive, widely available, and have well-described risk profiles. The main mechanism of NSAIDs is the inhibition of cyclooxygenase enzymes by the formation of prostaglandins derivatives from arachidonic acid [9]. Conversely, NSAIDs treatment for community-acquired pneumonia has been known to be related to an increased risk of pleuropulmonary complications [10]. However, their safety in SARS-CoV-2 patients has not yet been investigated.
Our objective was to evaluate the association between prior use of acetaminophen or NSAIDs and the potential influence on susceptibility to SARS-CoV-2 infection and worsening of serious clinical outcomes of COVID-19 by using nationwide COVID-19 data from the Korean National Health Insurance System (NHIS).
## 2.1. Data Sources and Study Subjects
Data were obtained from the Korean Health Insurance Review and Assessment Service (HIRA). This large-scale cohort provided data on all individuals tested for SARS-CoV-2 in South Korea through services co-operating with the HIRA, the Prevention and Ministry of Health and Welfare, and the Korean Centers for Disease Control (CDC) from 1 January 2015 to 15 May 2020, and referral to the Korean CDC (excluding self-referral) ($$n = 25$$,739). During the COVID-19 pandemic, the Korean government has provided complementary and compulsory health insurance for all patients with COVID-19. Thus, access to information consisting of personal data, patients’ medical records (including medical visits, prescriptions, diagnoses, and procedures) within 6 years, hospital visits, outcomes related to COVID-19, and death records has been provided in this database of COVID-19. The medical records of all patients were anonymized.
## 2.2. Study Population
We defined the first SARS-CoV-2 test data set for each patient as the cohort entry date (individual index date). Of the 25,739 patients tested for SARS-CoV-2, those under the age of 20, with no history of AAP or NSAIDs treatment, or with a history of prescribing AAP and NSAIDs within 2 weeks of the index date were excluded ($$n = 24$$,508).
The SARS-CoV-2 infection was defined as a positive real-time reverse transcriptase-PCR (RT-PCR) assay using nasal and pharyngeal swabs according to the World Health Organization (WHO) guidelines [11]. Between 1 January 2015 and 15 May 2020, information on age, sex, and region of residence was extracted from the insurance eligibility data by combining the claims-based data of the National Health Insurance Service. A history of underlying diseases (hypertension—HTN, chronic kidney disease—CKD, cerebrovascular disease—CVA, diabetes mellitus—DM, chronic obstructive pulmonary diseases—COPD, and asthma) was confirmed by submitting at least two claims within one year using the appropriate International Classification of Diseases, 10th revision (ICD-10) code [12]. The Charlson Comorbidity Index (CCI) scores were calculated from the ICD-10 codes using previous methods [12]. The residential region was classified as Seoul, Gyeonggi, Gyeongbuk, Daegu, or other [13]. Drugs used within 30 days prior to the index date included systemic steroids [14].
The final sample included patients who tested for SARS-CoV-2 and were prescribed AAP or NSAIDs, and comprised 1231 individuals, of whom 338 tested positive for SARS-CoV-2 (Figure 1).
## 2.3. Exposure
All prescription AAP or NSAIDs were identified within two weeks of the index date. A non-treatment user was defined as a patient who had not been prescribed AAP or NSAIDs within 2 weeks prior to the index date.
## 2.4. Outcomes
The primary outcome was defined as a positive result for SARS-CoV-2 test [15]. The secondary outcomes were serious clinical outcomes, including composite endpoint 1 (conventional oxygen therapy, admission to the intensive care unit—ICU, mechanical ventilation, or death). In addition, except conventional oxygen therapy, composite endpoint 2 (ICU admission, mechanical ventilation, or death) was analyzed [3].
We analyzed the period from taking the study medication to clinical outcome in patients with COVID-19.
## 2.5. Ethics Approval
This study was approved by the Institutional Review Board of the corresponding author’s hospital. The anonymized data were provided to the authors by NHIS.
## 2.6. Statistical Analysis
A logistic regression model was used to adjust for age, gender, and region of residence (Seoul, Gyeonggi, Gyeongbuk, Daegu, or other) by performing two rounds of propensity score matching (PSM) to balance the baseline characteristics of both groups and decline potential confounding factors; history of HTN, CKD, CVA, DM, COPD, or asthma; CCI (0, 1, or ≥2); and current systemic steroids use. We evaluated the PSM of both groups in a 1:1 ratio using a ‘greedy nearest-neighbor’ algorithm and calculated the predicted probability of AAP versus NSAIDs in all patients for SARS-CoV-2 test ($$n = 25$$,739) and AAP versus NSAIDs users among patients with confirmed COVID-19 ($$n = 338$$). Matching adequacy in the absence of major imbalances for each baseline covariate was assessed by comparing the standardized mean difference (SMD) with the distribution of PSM scores, which was more useful than calculating the p-values of the t-tests [12]. The primary endpoint was positive results of the SARS-CoV-2 test. The secondary endpoint was the composite endpoint and serious clinical outcomes of COVID-19 patients. Data were analyzed using a logistic regression model and expressed as adjusted ORs (aOR) with $95\%$ confidence intervals (CI) for both groups after adjusting for potential confounding factors; age, sex, region of residence, history of HTN, CKD, CVA, DM, COPD, or asthma; CCI, and current use of systemic steroids. Further analyses were conducted to establish the robustness of the results; AAP or NSAIDs use was stratified by duration of use.
## 3. Results
Of the 25,739 patients who underwent SARS-CoV-2 tests, 1231 patients prescribed either AAP ($$n = 643$$) or NSAIDs ($$n = 588$$) were defined in the complete unmatched cohort. In addition, the baseline characteristics in the entire cohort are shown in Table 1. The mean age of the entire cohort was 55.8 years (±19.7 years), and there were 681 females ($55.3\%$).
In the two cohorts, patients taking AAP or NSAIDs were matched in equal numbers ($$n = 529$$). There are no major imbalances in demographic and no clinical characteristics were observed in SMD within groups of the PSM-matched cohorts. The SARS-CoV-2 test positivity rate in patients using AAP was $33.3\%$ ($\frac{176}{529}$) compared to $31.0\%$ ($\frac{162}{529}$) in those using NSAIDs (Table 2).
Table 3 shows the baseline characteristics of COVID-19 confirmed patients.
We performed a PSM-matched analysis of positive SARS-CoV-2 patients. COVID-19 patients had a concordant history of AAP ($$n = 176$$) and NSAIDs ($$n = 162$$) use. The baseline characteristics of patients with a diagnosis of COVID-19 treated with AAP or NSAIDs are described in Table 3.
No major imbalances in demographics and clinical characteristics were noted when evaluated using SMD within the PSM-matched cohort groups in Table 4.
The use of AAP was not related to an increased risk of composite endpoint 1 of COVID-19 compared to the use of NSAIDs (Table 5a).
The use of AAP and NSAIDs was not significantly associated with an increased risk of serious COVID-19 outcomes (composite endpoint 1) (Table 5b).
As shown in Table 6, there were no significant differences in the period from taking the medication to clinical outcomes between the AAP and NSAIDs groups.
## 4. Discussion
The present study using a nationwide Korean cohort investigated whether AAP or NSAIDs use increased susceptibility to SARS-CoV-2 infection among 25,739 patients who tested for SARS-CoV-2. This study found that 338 of 1058 patients previously prescribed AAP or NSAIDs had a positive test for SARS-CoV-2. In addition, the study found no significant differences in mortality or serious clinical outcomes in patients receiving AAP or NSAIDs within 2 weeks prior to diagnosis of COVID-19. Our results suggest that the use of AAP or NSAIDs may be a safe option for symptom relief, even when COVID-19 is suspected.
The effect of NSAIDs in patients with COVID-19 has been controversial in previous studies. Prada et al. have demonstrated that exposure to NSAIDs does not increase the risk of SARS-CoV-2 infection or the severity of the COVID-19 [16]. A prospective, multicenter cohort study in the United Kingdom based on the ISARIC Clinical Characterization Protocol [17] demonstrated that NSAIDs use was not associated with worse in-hospital mortality (matched OR 0.95, $95\%$ CI 0.84–1.07; $$p \leq 0.35$$), critical care admission (1.01, 0.87–1.17; $$p \leq 0.89$$), requirement for invasive ventilation (0.96, 0.80–1.17; $$p \leq 0.69$$), or oxygen requirement (1.00, 0.89–1.12; $$p \leq 0.97$$). In addition, in a recent systematic review, Zhao et al. [ 18] also demonstrated that prior use of NSAIDs was not associated with mechanical ventilation, but with a decrease in mortality (aOR), 0.68; $95\%$ confidence interval (CI), 0.52–0.89). Huh et al. revealed that NSAIDs were not related to the diagnosis of COVID-19 (adjusted OR—aOR, 1.04; $95\%$ confidence interval—CI, 0.97–1.12), but were associated with severe disease (aOR, 1.53; $95\%$ CI, 1.25–1.86) using the Korean HIRA database [19].
There have been several studies that revealed the efficacy of different types of NSAIDs. In a double-blinded randomized control study, 500 mg of naproxen every 12 h could improve cough and shortness of breath in COVID-19 patients [20]. In an in vitro study [21], compared to paracetamol or the COX-2 inhibitor celecoxib, naproxen has direct antiviral activity against SARS-CoV-2 replication and protects the lung epithelium from damage caused by the pandemic virus, combining antiviral and anti-inflammatory effects. Another study reported the effectiveness of an in vitro study according to the dose of indomethacin; the treatment with sustained-release formulation at a dose of 75 mg twice daily is expected to achieve a complete response within 3 days for the SARS-CoV-2 infection. [ 22] Moreover, Kiani et al. [ 23] investigated the effectiveness of ketotifen, naproxen, and indomethacin, alone or in combination, in reducing SARS-CoV-2 replication. They found that the combination of ketotifen with indomethacin or naproxen all increased in percentage the inhibition of SARS-CoV-2 replication, and no cytotoxic effects were observed. Although this study did not analyze whether different types of NSAIDs affect serious clinical outcomes, it was found that NSAIDs had a positive effect on COVID-19 infection when reviewing previous studies.
Acetaminophen, compared to other over-the-counter drugs, is a safe and commonly recommended analgesic. Micallef et al. [ 24] demonstrated that symptomatic treatment with NSAIDs for uncomplicated symptoms (fever, pain, or myalgia) deriving from COVID-19 is not recommended due to an increased risk of severe bacterial complications, and treatment with AAP as a safer drug alternative is recommended.
Although not discussed in this study, another study revealed that patients with acute liver injury usually have undetectable levels of AAP; thus, acute liver injury or failure should be considered in patients with COVID-19 when chronic AAP ingestion is reported and is very high [25].
This study using the Korean HIRA database demonstrated that NSAIDs compared to AAP could be an alternative option for the relief of COVID-19 symptoms. NSAIDs can lead to misdiagnosis by masking the fever and worsening the prognosis of COVID-19. It is also possible that ibuprofen may upregulate angiotensin-converting enzyme-2 (ACE-2) expression and allow the SARS-CoV-2 virus to enter easily through epithelial cells.
This study has several advantages. Above all, the data source of the HIRA database consisted of a very large sample data set, and the effects of confounding factors associated with NSAIDs were ruled out using PSM. Furthermore, this was the first study to evaluate the effectiveness of AAP and NSAIDs in Asian COVID-19 patients using PSM and a unique analysis.
There were several limitations in this study. First, patients were included according to prescription medications; therefore, the use of a drug listed on electronic health records may not demonstrate exhaustive exposure to drugs. Second, this was a retrospective study, and, despite efforts to adjust for all confounders by PSM, additional unmeasured confounding factors might have influenced the outcomes. Third, in this study, the total amount of AAP or NSAIDs and different types of NSAIDs were not considered. Despite these limitations, this study revealed evidence based on cohort data of the safety of AAP or NSAIDs prior to the diagnosis of COVID-19.
## 5. Conclusions
The use of AAP or NSAIDs prior to the diagnosis of COVID-19 was not associated with worse outcomes of COVID-19 in a nationwide Korean cohort study with a PSM. Therefore, AAP or NSAIDs can be safely prescribed to COVID-19 patients.
## References
1. Cao X.. **COVID-19: Immunopathology and its implications for therapy**. *Nat. Rev. Immunol.* (2020) **20** 269-270. DOI: 10.1038/s41577-020-0308-3
2. Grasselli G., Zangrillo A., Zanella A., Antonelli M., Cabrini L., Castelli A., Cereda D., Coluccello A., Foti G., Fumagalli R.. **Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy**. *JAMA* (2020) **323** 1574-1581. DOI: 10.1001/jama.2020.5394
3. Guan W.J., Liang W.H., Zhao Y., Liang H.R., Chen Z.S., Li Y.M., Liu X.Q., Chen R.C., Tang C.L., Wang T.. **Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis**. *Eur. Respir. J.* (2020) **55** 2000547. DOI: 10.1183/13993003.00547-2020
4. Lighter J., Phillips M., Hochman S., Sterling S., Johnson D., Francois F., Stachel A.. **Obesity in Patients Younger Than 60 Years Is a Risk Factor for COVID-19 Hospital Admission**. *Clin. Infect. Dis.* (2020) **71** 896-897. DOI: 10.1093/cid/ciaa415
5. Dai M., Liu D., Liu M., Zhou F., Li G., Chen Z., Zhang Z., You H., Wu M., Zheng Q.. **Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak**. *Cancer Discov.* (2020) **10** 783-791. DOI: 10.1158/2159-8290.CD-20-0422
6. Blanco J.L., Ambrosioni J., Garcia F., Martínez E., Soriano A., Mallolas J., Miro J.M.. **COVID-19 in patients with HIV: Clinical case series**. *Lancet HIV* (2020) **7** e314-e316. DOI: 10.1016/S2352-3018(20)30111-9
7. Horby P., Lim W.S., Emberson J.R., Mafham M., Bell J.L., Linsell L., Staplin N., Brightling C., Ustianowski A., Elmahi E.. **Dexamethasone in Hospitalized Patients with COVID-19**. *N. Engl. J. Med.* (2021) **384** 693-704. DOI: 10.1056/NEJMoa2021436
8. Bhala N., Emberson J., Merhi A., Abramson S., Arber N., Baron J.A., Bombardier C., Cannon C., Farkouh M.E., FitzGerald G.A.. **Vascular and upper gastrointestinal effects of non-steroidal anti-inflammatory drugs: Meta-analyses of individual participant data from randomised trials**. *Lancet* (2013) **382** 769-779. DOI: 10.1016/s0140-6736(13)60900-9
9. Bruno A., Tacconelli S., Patrignani P.. **Variability in the response to non-steroidal anti-inflammatory drugs: Mechanisms and perspectives**. *Basic Clin. Pharmacol. Toxicol.* (2014) **114** 56-63. DOI: 10.1111/bcpt.12117
10. Basille D., Thomsen R.W., Madsen M., Duhaut P., Andrejak C., Jounieaux V., Sørensen H.T.. **Nonsteroidal Antiinflammatory Drug Use and Clinical Outcomes of Community-acquired Pneumonia**. *Am. J. Respir. Crit. Care Med.* (2018) **198** 128-131. DOI: 10.1164/rccm.201802-0229LE
11. Park J., Lee S.H., You S.C., Kim J., Yang K.. **Non-steroidal anti-inflammatory agent use may not be associated with mortality of coronavirus disease 19**. *Sci. Rep.* (2021) **11** 5087. DOI: 10.1038/s41598-021-84539-5
12. Woo A., Lee S.W., Koh H.Y., Kim M.A., Han M.Y., Yon D.K.. **Incidence of cancer after asthma development: 2 independent population-based cohort studies**. *J. Allergy Clin. Immunol.* (2021) **147** 135-143. DOI: 10.1016/j.jaci.2020.04.041
13. Yon D.K., Lee S.W., Woo A., Koh H.Y., Jee H.M., Ha E.K., Lee K.J., Shin Y.H., Han M.Y.. **Exposure to humidifier disinfectants is associated with upper and lower airway diseases**. *Pediatr. Allergy Immunol.* (2020) **31** 578-582. DOI: 10.1111/pai.13233
14. Jung S.Y., Choi J.C., You S.H., Kim W.Y.. **Association of Renin-angiotensin-aldosterone System Inhibitors with Coronavirus Disease 2019 (COVID-19)- Related Outcomes in Korea: A Nationwide Population-based Cohort Study**. *Clin. Infect. Dis.* (2020) **71** 2121-2128. DOI: 10.1093/cid/ciaa624
15. Mehta N., Kalra A., Nowacki A.S., Anjewierden S., Han Z., Bhat P., Carmona-Rubio A.E., Jacob M., Procop G.W., Harrington S.. **Association of Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers with Testing Positive for Coronavirus Disease 2019 (COVID-19)**. *JAMA Cardiol.* (2020) **5** 1020-1026. DOI: 10.1001/jamacardio.2020.1855
16. Prada L., Santos C.D., Baião R.A., Costa J., Ferreira J.J., Caldeira D.. **The Risk of SARS-COV-2 Infection and COVID-19 Severity Associated with The Exposure to Non-Steroidal Anti-Inflammatory Drugs: Systematic Review and Meta-Analysis**. *J. Clin. Pharmacol.* (2021) **61** 1521-1533. DOI: 10.1002/jcph.1949
17. Drake T.M., Fairfield C.J., Pius R., Knight S.R., Norman L., Girvan M., Hardwick H.E., Docherty A.B., Thwaites R.S., Openshaw P.J.M.. **Non-steroidal anti-inflammatory drug use and outcomes of COVID-19 in the ISARIC Clinical Characterisation Protocol UK cohort: A matched, prospective cohort study**. *Lancet Rheumatol.* (2021) **3** e498-e506. DOI: 10.1016/S2665-9913(21)00104-1
18. Zhao H., Huang S., Huang S., Liu F., Shao W., Mei K., Ma J., Jiang Y., Wan J., Zhu W.. **Prevalence of NSAID use among people with COVID-19 and the association with COVID-19-related outcomes: Systematic review and meta-analysis**. *Br. J. Clin. Pharmacol.* (2022) **88** 5113-5127. DOI: 10.1111/bcp.15512
19. Huh K., Ji W., Kang M., Hong J., Bae G.H., Lee R., Na Y., Jung J.. **Association of prescribed medications with the risk of COVID-19 infection and severity among adults in South Korea**. *Int. J. Infect. Dis.* (2021) **104** 7-14. DOI: 10.1016/j.ijid.2020.12.041
20. Amponsah S.K., Tagoe B., Adams I., Bugyei K.A.. **Efficacy and safety profile of corticosteroids and non-steroidal anti-inflammatory drugs in COVID-19 management: A narrative review**. *Front. Pharmacol.* (2022) **13** 1063246. DOI: 10.3389/fphar.2022.1063246
21. Terrier O., Dilly S., Pizzorno A., Chalupska D., Humpolickova J., Boura E., Berenbaum F., Quideau S., Lina B., Feve B.. **Antiviral Properties of the NSAID Drug Naproxen Targeting the Nucleoprotein of SARS-CoV-2 Coronavirus**. *Molecules* (2021) **26**. DOI: 10.3390/molecules26092593
22. Gomeni R., Xu T., Gao X., Bressolle-Gomeni F.. **Model based approach for estimating the dosage regimen of indomethacin a potential antiviral treatment of patients infected with SARS CoV-2**. *J. Pharmacokinet. Pharmacodyn.* (2020) **47** 189-198. DOI: 10.1007/s10928-020-09690-4
23. Kiani P., Scholey A., Dahl T.A., McMann L., Iversen J.M., Verster J.C.. **In Vitro Assessment of the Antiviral Activity of Ketotifen, Indomethacin and Naproxen, Alone and in Combination, against SARS-CoV-2**. *Viruses* (2021) **13**. DOI: 10.3390/v13040558
24. Micallef J., Soeiro T., Jonville-Béra A.P.. **Non-steroidal anti-inflammatory drugs, pharmacology, and COVID-19 infection**. *Therapie* (2020) **75** 355-362. DOI: 10.1016/j.therap.2020.05.003
25. Rodríguez-Morales A.J., Cardona-Ospina J.A., Murillo-Muñoz M.M.. **Gastroenterologists, Hepatologists, COVID-19 and the Use of Acetaminophen**. *Clin. Gastroenterol. Hepatol.* (2020) **18** 2142-2143. DOI: 10.1016/j.cgh.2020.04.025
|
---
title: Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques
Applied across Hospitals in England
authors:
- Laure de Preux
- Dheeya Rizmie
- Daniela Fecht
- John Gulliver
- Weiyi Wang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001179
doi: 10.3390/ijerph20053852
license: CC BY 4.0
---
# Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England
## Abstract
Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.
## 1. Introduction
There is clear empirical evidence that links short-term exposure to ambient air pollution with a wide range of societal and economic impacts, including on health (e.g., [1,2,3]), productivity (e.g., [4,5,6]), and learning (e.g., [7,8]). However, as pollutants tend to vary spatially and temporally, studies are often challenged by imprecise air pollution estimates to establish such impacts. Air pollutants, such as nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM), originate from different sources, disperse differentially, and can uniquely interact with other environmental factors, such as temperature and humidity, over time. These complexities are further compounded by the computational demands required to model air pollution concentrations at high spatial and temporal resolutions, which makes precise or accurate exposure assessment challenging. Instead, studies often rely on sparse air pollution measurements from monitoring stations and simple assumptions when assigning air pollution exposure to individuals or geographical locations (e.g., schools, factories, hospitals, etc.). As a result, studies may be biased due to measurement error as robust, local, and frequent air pollution levels continue to be difficult to estimate. Thus, the use of air pollution exposures estimated with biases hinders the identification of the air pollution impact on individual outcomes.
In an ideal research setting, individuals would be equipped with personal portable monitors to collect precise and accurate estimates of their exposure as they move across space and time. While this is the most-accurate way of tracking personal exposure, it is extremely costly, cumbersome, mainly available for small samples over a limited amount of time, and not necessarily informative for policy design. In a similar vain, low-cost sensors are suggested as an alternative; but their reliability, availability, and precision are still an issue, and they currently do not support the development of national models. To circumvent these limitations, various exposure assessment methods have been developed to assign air pollution concentrations to a given location (e.g., residential address or hospital). The most-simplistic approach is proximity-based assessments, which are based on the proximity of a location to an emission source or monitoring station to assess changes in ambient air quality [9]. Another approach is spatial interpolation, which generates estimates for unsampled locations using the covariance and distance between the unsampled location and sampled location (e.g., from air monitoring stations). This rests on the principle that near things are more related than distant things [10]. The most-commonly used spatial interpolation techniques is inverse distance weighting (IDW). Although IDW may seem to be an acceptable approach that produces high-frequency time series datasets, it lacks sensitivity to topological variation and atmospheric conditions that may influence some air pollutants. These methods heavily rely on the availability of monitoring data and may produce overly smoothed concentration surfaces, in cases of a limited number of monitoring stations [11]. IDW also has the potential to lead to systematic estimation bias, especially with sparse monitoring networks and topological complexity. On the other hand, dispersion models are mathematical simulations of how air pollutants disperse in the atmosphere. Dispersion models estimate the concentration of pollutants as they travel away from an emission source, how they interact with other pollutants in the atmosphere, and how they are dispersed due to meteorological conditions [12]. Dispersion models are capable of modelling concentrations for short-term (e.g., hourly) and long-term (e.g., annually) averaging periods. The drawback is that dispersion models are demanding both in terms of input data and computational power. In places with no, or limited, air pollution monitoring stations, economists have also explored the use of satellite data (e.g., [13,14]). Satellite information on air pollution can occasionally be obtained at high temporal frequency; however, its use requires expensive pre-processing, and data are not often available at the required spatial resolution [15]. Its accuracy is dependent on the spatiotemporal characteristics of the air pollutant considered. The availability of satellite sensors is disproportionately spread globally, increasing the difficulty in studying low- and middle-income settings. Therefore, such estimates are often not readily available nor easily accessible to social science researchers.
Economists interested in the impact of air quality on societal outcomes often develop economic models using natural experiments or simple exposure assessment methods. Natural experiments rely on an exogenous change in emission sources (e.g., the closure of factories or a change in government policies) to overcome measurement challenges and avoid the need to accurately quantify changes in air pollution concentrations. While this method might be able to uncover causal relationships, it is not suitable to establish concentration–response relationships. Therefore, to assess concentration–response relationships, economists tend to employ simple exposure assessment methods (e.g., nearest-neighbour matching or IDW). These methods sacrifice either the temporal frequency by relying on annual averages or the geographical precision by assigning the same air pollution level to a large number of locations.
In this paper, we propose a simplified exposure assessment approach to produce temporally and spatially highly resolved estimates for the main regulated air pollutants: nitrogen dioxide (NO2), ozone (O3), and particulate matter with a diameter smaller than 10 µm (PM10) and smaller than 2.5 µm (PM2.5). Our method relies on land use regression (LUR) models to derive robust estimates of local air pollution levels. Compared to dispersion models, LUR models are less challenging in terms of input data and computational processing and can account for high spatial variability. With their relatively low demand on the input data, LUR models have the potential to provide an improved, yet accessible, robust alternative to weighted averages whilst capturing the spatial heterogeneity of air pollution. Traditional LUR models are widely used in predicting long-term (e.g., annual) air pollution estimates. However, since typical land use input variables (e.g., road distribution, population density, etc.) are fairly constant over time, their application to estimate short-term (e.g., daily) exposures is limited. ( Therefore, they are commonly used to develop annual models as the variables (e.g., land use, road length) are time-invariant. It is, in principle, possible to develop daily LUR models, but the lack of daily data required to build the model is generally a restriction. Over the last decade, there has been increasing interest in combining different modelling techniques to overcome their respective limitations, so-called “hybrid models”. Our methodology accounts for both environmental characteristics that may influence emission and dispersion patterns and daily variability. This approach relies on LUR and allows for the derivation of estimates at a fine geographical scale, as well as at a high time frequency, which increases the accuracy compared to the standard IDW.
To illustrate the effectiveness of this approach, we developed daily air pollution estimates (daily LUR) across England. We validated the models in space and time using an independent subset of data from the monitoring stations. Similarly, we estimated the weighted averages of pollution measurements using IDW. We assigned both our daily LUR and IDW estimates to hospitals in the National Health Service (NHS) in England and assessed the impact of the daily variation of air pollution on accident and emergency (A&E) visits between 2010 and 2011 using a flexible multiple fixed effects distributed lag model [6,16]. This allowed us to quantify the impact of different air pollution exposure assessment models on health outcomes. The differences incurred by exposure assessments may subsequently influence policy perspectives.
Our results varied by pollutant. NO2, PM2.5, and PM10 demonstrated notable discrepancies between the two exposure assessment approaches—with daily LUR estimates resulting in statistically significant effects, while IDW estimates suggesting no impact of air pollution on A&E visits. The effect sizes using IDW were half those estimated by daily LUR. Conversely, health estimates from O3 were similar when using IDW measurements and daily LUR estimates.
Our findings suggest that the use of IDW risks the introduction of a substantial downward bias, which has the potential to limit the ability of uncovering potential economic estimates and underestimate the potential effects of air pollution. This paper proposes a simpler methodology to improve the accuracy of assigning air pollution exposure across space for studies that require temporally high-resolution information (e.g., daily or weekly). It should be clear that the daily LUR is not the panacea to pollution exposure and that there are more complex methods to assign pollution exposure, for example using machine learning (e.g., random forest, XGBoost, neural networks). However, they require more data and are computationally intensive. Therefore, the daily LUR represents a user-friendly improvement over the IDW method.
The remainder of the paper is structured as follows: The next section presents a brief background of air pollution assessment techniques (Section 2). Section 3 presents our proposed exposure assessment technique. Section 4 applies this technique to a case study, outlining our health setting and empirical approach. Findings from this case study are reported in Section 5 and compare estimates between the techniques. Finally, Section 6 discusses the implications of our findings and concludes.
## 2. Background on Air Pollution Exposure Assignment
Air pollution is one of the most-serious environmental concerns of our generation: not only is it closely linked with anthropogenic activities related to climate change, it also directly affects individuals’ health and well-being. Air pollution has given rise to extensive research documenting increased mortality (e.g., [1,2,3]) and morbidity (e.g., [17,18]) and decreased productivity and human capital (e.g., [4,5,6]) and school performance (e.g., [7,8]). Given the complexity in accurately estimating air pollution levels, economic studies often have to make trade-offs between temporal and spatial precision in estimating air pollution or circumvent estimating air quality altogether. Approaches can be broadly categorised into four types of air pollution studies on economic outcomes: studies using (i) specific sources of air pollution (e.g., emissions from a factory), (ii) natural experiments that provide a rapid exogenous change in the ambient air quality (e.g., policy changes), (iii) air pollution modelling (e.g., modelling air quality with satellite-based products in [19]), and (iv) monitoring stations capturing specific pollutants at a specific location.
The first approach utilises variation in specific sources of air pollution, such as emissions from traffic (e.g., [20,21,22]) or manufacturing sites (e.g., [23]). In these studies, the impact of ambient air quality is only indirectly captured by a relative change of activity at the source. The main issues with this approach are that it only captures the effect of a unique variation in a local source, often without knowledge of its impact on the overall air quality, and it assumes that the emissions from other sources (e.g., manufacturing sector) remain constant over the period of the evaluation. Additional assumptions on the spatial extent of impacts are also required. While it may serve to demonstrate that a change in air pollution is beneficial or detrimental to the outcome of interest, this approach cannot inform dose–response relationships and peaks of air pollution (e.g., [21,24,25]).
Secondly, natural experiments or quasi-experimental approaches (e.g., [3,24,26]) are commonly adopted and focus on abrupt, and often unanticipated, changes in ambient air pollution levels. These research designs typically come from changes in environmental policy, such as the introduction of the 1970 Clean Air Act (e.g., [3,24,27]) or the closure of power plants (e.g., [28]). The advantage of this approach is that it controls for the issues of residential sorting, as well as acclimatisation. The former refers to the possible bias from individuals choosing their residential location as a function of ambient air quality and their individual susceptibility to, or preference for, air quality. A natural experiment typically offers an abrupt change in air quality, and the observed effect is more likely to be a result that can be attributed to the change in air quality as opposed to behavioural changes, such as avoidance behaviour (see [17,29,30] for a discussion on avoidance behaviour). Due to this, it could be argued any detected effects are close to causal. However, it only relies on a single and local variation in average air quality across a specific population and often does not capture daily changes in air pollution concentrations.
The third approach, using annual air pollution models, predicts spatially granular estimates, via data-intensive and complex models, but is only feasible for annual estimates due to the complexity of the models [31]. This presents an attractive option to researchers due to its ability to provide air pollution at a fine granular scale that captures the heterogeneity of a pollutant’s geographical distribution (e.g., [32]). However, as estimates are often limited to long-term annual averages, they fail to account for the burdens imposed by short-lived pollutants (e.g., ozone) and prevent one from obtaining short-term variations that may have separate effects on the outcomes of interest (e.g., health and academic performance [33]).
Finally, economists also use direct measurements from ground monitoring stations (e.g., [6,16,34]) or satellites (e.g., [35,36]). Monitoring stations are becoming increasingly prevalent, particularly in urban locations. They represent a valuable source of information, often on an hourly basis, that captures temporal changes at their location (e.g., [37] with SO2 and black smoke, [1,38,39,40] with CO, PM10, and O3,1] with PM10 and O3, and [2] with PM2.5 and O3). Such time series data are easy to obtain and straightforward to analyse; however, air pollution monitoring networks often remain sparse, and assumptions are required to obtain proxies of local air pollution concentrations in areas without monitoring stations. A common approach has been to assign a point of interest, an area, or an individual to their nearest monitoring station (nearest neighbour matching). However, this has been shown to be a poor marker in spatial assessments of air pollution exposures [41] as it disregards the various essential dispersion characteristics of each pollutant. Another naive practice has been to average the stations’ values across the neighbourhood of the points of interest [42,43,44]. While this offers frequent estimates, its geographical aggregation, similar to nearest neighbour matching, is likely to introduce a large bias as it does not account for the regional characteristics that affect air pollution sources and dispersion.
There is growing interest in exploring the effects of, and accounting for, short- and long-term variation in air pollution without sacrificing spatial granularity. On the one hand, advanced modelling, in principle, could achieve higher temporal frequency, but requires high computational power and more input data than are often available. These models are often challenging to implement across a large geographical area (e.g., across a country) or over a long time period. On the other hand, daily or hourly air pollution measurements are easily accessible given the wide availability of monitoring stations, but suffer from systematic biases when the pollutant’s dispersion characteristics and local topography are not accounted for. Therefore, these limitations present a need for an approach in air pollution exposure assignment that is both [1] accessible to social scientists, amongst other disciplines, and [2] considerate to the range of influencing factors of each pollutant, which enables a more accurate air pollution assignment to provide robust evidence of the impacts of air pollution concentrations on a wide range of outcomes.
## 3. Methodology
Our modelling approach was based on an LUR model, which is a widely used air pollution exposure assessment method to estimate annual average air pollution concentrations for environmental epidemiology [45,46,47]. LUR models have been developed for cities in North America [48], Europe [49], Asia [50], Australia [51], South Africa [52], and larger geographic areas including North America [53,54], Europe [55,56], Australia [57], and Asia [58]. The spatial resolution of LUR models provides the opportunity for estimates on a fine geographical scale, depending on their land use variables—typically ranging from 100 m by 100 m to 1 km by 1 km. In order to obtain air pollution estimates of more frequent temporal variation (i.e., daily), we propose temporal scaling of the traditional LUR (daily LUR). This approach offers a more accessible and reliable way of estimating daily ambient air pollution in various geographical settings as opposed to predictions derived purely from empirical relationships.
We modelled the annual LUR model using a standard methodology and detail its steps in Appendix A. This begins by gathering air pollution measurements at monitoring stations, then identifying variables that can (a) predict the measured air pollution concentrations from various sources (e.g., road traffic and industrial plants) and sinks (e.g., forests) and (b) estimate the direction of their effects using a regression model. We combined the traditional land use input variables with a chemical transport model (CTM). The CTM estimates simulate the physical and chemical processes of pollutant transport based on emission inventories (location, strength, size) and meteorological inputs (e.g., temperature, relative humidity, wind speed, and wind direction). The model was then calibrated and validated against data from monitoring stations, before the production of concentration surfaces.
The daily LUR estimates proposed in this paper were derived as follows: once validated annual surfaces were obtained, the annual estimates were scaled to obtain temporal variation using measurements from air pollution monitoring stations. The granularity depends on the requirements of the study. For example, one may have individual health data geocoded at the level of a geographic unit p. Supposing we have LUR estimates for N geographic units, p, we take the centroid of each unit and assume the centroid pc to be representative of the entire unit. Modelled annual air pollution concentrations Cannual′ are extracted from the LUR surface at all geographic unit centroids. Daily exposure for each p, DailyExpopc, is calculated by scaling monitored daily concentrations to annual concentrations such as [1]DailyExpopc=CdailyCannual×Cannual′ where Cdaily and Cannual are the measured daily and annual concentration from the nearest background monitoring station of the geographic unit centroid pc, respectively; Cannual′ is the estimated annual concentration of each geographic unit extracted from the LUR surface.
In practice, it is unlikely that the outcome data and LUR estimates are at the same geographical scale. If outcome data are geocoded for an aggregated area, q, which is larger than p, the annual concentration for each q, AnnualExpoq, is calculated by averaging all modelled annual air pollution concentrations Cannual′ for each p within each q (Equation [2]), again assuming the centroid qc to be representative of the entire area q. Daily exposure for each qc, DailyExpoqc, is then calculated using the aggregated annual exposure (Equation [3]). [ 2]AnnualExpoq=∑Cannual′N [3]DailyExpoqc=CdailyCannual×AnnualExpoq where q is the geographic area, Cannual′ is the estimated annual concentration of each geographic unit extracted from the LUR surface, N is the number of geographic units, p, within each aggregated area q, and Cdaily and Cannual are the measured daily and annual concentration from the nearest background monitoring station of geographic unit centroid qc, respectively.
## 4. Case Study: A&E Visits to the English National Health Service
To assess the performance of the daily LUR model, compared to IDW, we applied the air pollution exposure assignment approach described in Section 3 and IDW to a healthcare setting in England. We modelled A&E visits to hospitals in the NHS across England from 1 April 2010 to 31 March 2011 as a function of air pollution assigned to the neighbourhood of the hospital, controlling for various confounders. A&E visits do not require a diagnosis, and therefore, the majority of the visits are unclassified in terms of disease or visit purpose. We began by quantifying the differences between air pollution concentrations from different exposure assignment techniques. We subsequently quantified the differences in the estimated air-pollution-associated A&E visits using the two different exposure assignment techniques. This allowed us to illustrate how the use of daily LUR estimates performs against IDW estimates when identifying its impact on social outcomes.
## 4.1. Study Population and Data Sources
All observations were unique at the day and hospital level with the sum of A&E visits to the hospital on that day. All observations were then assigned an air pollution concentration using daily LUR and IDW to the centroid of the hospital postcode district (PCD) level. Further, they were also assigned meteorological characteristics measured from the nearest monitoring station, including important confounders, temperature, and relative humidity. We matched all data at the hospital visit date level between 1 April and 31 March 2011. Summary statistics describing our air pollution data can be found in Appendix B. Below, the emphasis is on further detailing each dataset implored in our empirical illustration.
The Automatic Urban and Rural Monitoring Network (AURN) [59] provides ratified daily mean measured concentrations of four major health-relevant pollutants: NO2, O3, PM2.5, and PM10. The AURN classifies monitoring stations as background urban, background suburban, background rural, traffic urban, industrial urban, and industrial suburban. We only included background sites to avoid the influence of road traffic and industrial emissions, which can result in biased exposure assignment (i.e., overprediction). The completeness of the data was checked for each pollutant and each monitoring station based on a $75\%$ completeness site selection rule. A monitoring station was included if it had more than $75\%$ daily mean measurements over (a) an entire year and (b) within each month. This site selection rule ensures that the available daily data of each site have a good representativeness of a year when they are averaged for the annual mean. For PM2.5 and PM10, this selection rule resulted in too few sites; therefore, a less-stringent criterion of $50\%$ data completeness applied for these two pollutants. After applying the above criterion, the number of selected monitoring stations used in Equation (A1) (Appendix A) over this study year was 59 for NO2, 63 for O3, 38 for PM2.5, and 25 for PM10.
The meteorological data, which are used as confounders in Section 4.5, came from the Met Office Integrated Data Archive System (MIDAS) database. It provides meteorological characteristics collected by the Met Office. The meteorological conditions aspects are captured by irregularly spaced stations across England. The dataset contains daily and hourly meteorological measurements, such as daily air temperature and relative humidity, provided by 106 stations.
A&E visits came from the Hospital Episode Statistics (HES) database from NHS Digital across 220 hospitals in England. These are the universe of visits over that period. The mean number of daily A&E visits was 200 (SD 124) per hospital. The mean age of all visits was 37.9 (SD 6.6) years old, with $48.8\%$ of patients being female. The data provide information on hospital utilisation. Each observation includes details on visit type (e.g., treatment, diagnosis type), socioeconomic status (Index of Multiple Deprivation), patient characteristics (e.g., age group and gender), and hospital specifics (e.g., postcode). Data are collected during a patient’s visit to the provider for multiple administrative and financial purposes. Due to a high rate of missing values in the classification of diagnoses or treatments, we used all-cause A&E visits.
## 4.2. Pollution Assignment Methods
We began by estimating an LUR model for England and then applying the methodology outlined in Section 3. For our LUR model, we obtained six types of Geographic Information System (GIS)-derived land use data including: land cover, population/household, road network, traffic, topography, and building. The predictors were chosen mostly based on the ones used in the European Study of Cohorts for Air Pollution Effects (ESCAPE) study [60], with one predictor on building volume as a proxy for street ventilation [61]. When estimating our annual air pollution surface (Stage 3 in Appendix A) to derive daily LUR estimates, we used a resolution of 25 m by 25 m because it is the smallest resolution of the datasets (i.e., land cover). At this resolution, the spatial variation of the variables is not aggregated. However, as previously mentioned, the resolution can be adjusted according to the study needs. Given the size of the selected monitoring stations, we used a 5-fold cross-validation. This allowed an adequate amount of sample data to be included in each fold and used in the validation. Models are summarised by several measurements including the adjusted R2, root-mean-squared error (RMSE), and coefficient (β) in Appendix B Table A1.
We then implemented the strategy outlined in Section 3 to obtain air pollution estimates by daily LUR. We applied the scaling in Equation [3] to an LUR model (described in Appendix A) for 2778 PCDs across England over the same period. For our application, we used the centroid of each PCD. Air pollution estimates were assigned to each NHS hospital using their PCD. Air pollution was assigned at the hospital level as this analysis looked at the contemporaneous impact on A&E visits. A&E visits capture the immediate effects of deviations in air pollution levels. As the average distance of a patient’s residential postcode district to his/her A&E hospital is 13.3 km (SD 10.9), with a maximum distance of 223 km, the assignment of air pollution at the hospital level reduces the risk of inaccurately assigning location exposure.
As a comparison, a spatial interpolation of monitoring data using IDW is included. IDW does not involve statistical modelling: it is based on the distance weighting of nearest monitoring stations to a location. Air pollution exposure at each PCD j on day t, DailyExptj, equals the average values of daily measurements from k monitoring stations within a 50 km radius, with weights proportional to the inverse of the square of the distance between their residence and the monitoring station (Equation [4]). [ 4]DailyExptj=∑$k = 1$nPkt×1dkj2∑$k = 1$n1dkj2 where *Pkt is* the measured daily concentration at each monitoring station k on day t; d is the distance between postcode centroid j and monitoring station k. We used a maximum radius of 50 km to include a moderate number (n) of monitoring stations. For instances where a PCD has no monitoring stations within 50 km, we used the nearest monitoring station ($$n = 1$$).
## 4.3. Defining Air Pollution Bins
The primary exposure variables of interest in this analysis were seven 5 μgm−3 daily air pollution bins, constructed for PM2.5 and PM10, ranging from values under 5 μgm−3 to over 30 μgm−3. For NO2, six 10 μgm−3 daily air pollution bins were constructed ranging from values under 10 μgm−3 to over 50 μgm−3. Finally, seven 10 μgm−3 daily air pollution bins were constructed for O3 ranging from values under 10 μgm−3 to over 60 μgm−3. These thresholds were used to ease the comparison of the air pollution assessment methods. These variables indicate whether air pollution measured at a given NHS hospital falls in the specified air pollution range. As daily air pollution is defined at the NHS hospital level (hospital-day), we preserved the spatial variation in air pollution to allow for the identification of its effects. The 0–10 μgm−3 bin (NO2 and O3) and 0–5 μgm−3 bin (PM2.5 and PM10) were the reference categories and omitted in all regressions; consequently, all estimates were interpreted as the impact of a day in the given air pollution range relative to a day in either the 0–5 μgm−3 or 0–10 μgm−3 range.
## 4.4. Quantifying the Differences in Exposure Assessment Approaches
In this section, we compare air pollution estimates derived from different air pollution exposure assignment methods (daily LUR and IDW) and any differences that may be subsequently introduced in air pollution–health impact analysis. We first compared annual air pollution concentrations spatially through maps to illustrate the geographical variation in air pollution concentrations. Second, we compared the correlations of daily estimated air pollution concentrations with observed values (i.e., air pollution measurements) at monitoring stations that we used as a benchmark to quantify the potential bias introduced by daily LUR and IDW. Our third comparison was similar to the second, but using annual levels, we randomly omitted some air pollution monitoring stations to derive the air pollution estimates at the monitoring station and compared the derived estimates to actual measurements taken at these locations. Finally, we describe how air pollution exposure assigned to hospitals was classified into different treatment bins—potentially creating different treatment intensities and, thus, impacting the overall conclusion.
The spatial distribution of the monitoring stations for NO2, O3, PM2.5, and PM10 is shown in Figure 1. We included stations from Wales and Scotland to “borrow” measurements from monitoring stations, within 50 km of England. We produced air pollution surfaces at the PCD level to visually compare the spatial pattern generated from the two approaches (daily LUR and IDW). The comparison of the maps provides an insight into the spatial heterogeneity of the different methods. Greater spatial granularity enables the identification of hot spots of air pollution, which are often in densely populated areas (e.g., London, Birmingham) and, therefore, essential to assess the impact of air pollution on individuals.
Whilst the comparisons of daily air pollution estimates are insightful, they are not representative of the precision of IDW in other locations, as the comparisons occurred at monitoring stations where the IDW estimates were calculated from. In order to assess the precision of IDW, we further explored the magnitude of any discrepancies through a “bench-marking” approach. The measurements at monitoring stations (MONs) were considered true observations of air pollution exposure that we can use to compare against air pollution estimates derived from the other exposure assignment methods. Specifically, we were interested in the difference between these true observations and the concentrations derived using assignment methods. We applied the daily LUR model and IDW to obtain daily air pollution estimates, for all four pollutants, at each monitoring station, while excluding the station in question from its own measurement/estimation. For example, for IDW estimate at monitoring station A, we deliberately excluded measurements from A and used measurements from the second-nearest station, B. This was to mimic situations where a location for estimation is not near a monitoring station. We then calculated the absolute difference (Deviationmt) at the monitoring station, m, following [5]|Deviationm,d|=|Pollutionm,di−Pollutionm,dMON| where Pollutantm,di is the air pollution concentration at the monitoring station, m, on day d, estimated through air pollution technique i. i can be from: IDW or daily LUR. Pollutantm,dMON is the average daily air pollution concentration reported at monitoring station m on day d. The same bench-marking calculations were conducted using IDW, daily LUR, and satellite monitors (SAT) at an annual level for NO2 and PM2.5. Annual calculations were estimated as this was the most granular temporal scale available using satellite monitors. These results mirror the results that are presented in the paper with a larger deviation observed for SAT for NO2.
To compare daily estimates from the daily LUR and IDW, as described in Section 4.2, we produced pairwise scatter plots that compare daily estimates against measurements recorded from monitoring stations. We used the Pearson correlation coefficient (Pearson’ r) to indicate the strength of the linear relationship between the two sets of data.
In addition, the accuracy and precision of the models were quantified by regressing daily predictions (from the “bench-marking” approach, where values from the station in question were excluded) against daily measurements and summarised in terms of the coefficient of determination (R2), root-mean-squared error (RMSE), beta, and intercept.
It could be argued that the conventional modelling approach in social sciences, by flexibly modelling air pollution impacts through the use of indicator bins, small deviations in concentrations from different air pollution exposure methods are unimportant should observations be properly classified into correct indicator bins. However, mismeasurement of an individual’s or unit’s air pollution exposure risks misclassification of individuals to indicator bins. To assess how the classification varies across the different methods, we compared any deviations between the assigned bins from daily LUR and the IDW for each observation by calculating the percentage of observations that did not fall into the same indicator bin category.
## 4.5. Identification Strategy
To identify the effects of each pollutant, we exploited the panel structure of our data and built on the panel approach used in [6,16,62]. We introduced NHS hospital fixed effects (FE), which account for local air quality baselines and allowed us to identify the impact of short-term air pollution variation around the local average air quality. Implicitly, NHS hospitals without high peaks of air pollution throughout the year form a counterfactual for NHS hospitals that do have peaks in that same year, after accounting for fixed differences between the NHS hospitals and for common time effects. Naturally, many hospitals had multiple events over the period of the analyses. An attractive feature of this approach is that it builds in placebo tests that should identify likely violations of this assumption. Furthermore, this identification strategy relies on the unpredictable and presumably random daily local variation in air pollution.
Using a panel dataset, we employed a distributed lag Poisson regression model with multiple fixed effects to estimate the effect of air pollution on daily A&E visits and for the three days following a day in which air pollution falls into an extreme air pollution bin. Equation [6] denotes the reduced form relationship between air pollution and A&E visits. The total net effect of air pollution on A&E visits was flexibly modelled by including a series of indicator variables for air pollution.
The goal was to estimate the net effect of air pollution on day d on the number of A&E visits (Yjd) per NHS hospital, j, per day, d, and for three days following day d:[6]log(Yjd)=α+∑p∈(<b,…>u)βpPollutantjdp+βMeanTempjd+ιHumidityjd++∑$l = 13$πp1lPollutantjlp1+∑$l = 13$πp2lPollutantjlp2+∑$l = 13$πp3lPollutantjlp3+ζkDayofWeekk+ρrHolidaysr+σmMonthm+τjHospitalj+ϵjd Pollutantjdp are a series of regressors that equal 1 if the daily air pollution at NHS hospital j falls into a predefined air pollution bin and zero otherwise. For each pollutant, regressions were run separately using the air pollution bins described above. Consequently, these coefficients βh semi-parametrically describe the pollution–visits relationship, the net of seasonal influences and relative to the lowest air pollution bin (i.e., 0 μgm−3 to 5 μgm−3 or 0 μgm−3 to 10 μgm−3) that is omitted in all regressions.
Pollutantjlp1, Pollutantjlp2, and Pollutantjlp3 are indicator variables for up to 3 days following a day in a predefined air pollution bin of extreme air pollution exposure and zero otherwise. Therefore, the extreme air pollution lag effect was estimated for 30 days following a day of extreme air pollution.
A&E visits, health, and air pollution vary seasonally. A series of time-fixed effects for day of the week (DayofWeekk), school and bank holidays (Holidaysr), and month (Monthm) intended to control for the seasonal effects of cyclical variation. The use of time-fixed effects makes no assumptions on seasonal form, does not constrain the model, and avoids specification errors. Additionally, as seasonality is measured at a relatively fine scale, the flexibility inherited from such granular fixed effects also accounts for health changes that are driven by long-term behavioural changes. In addition, fixed effects for NHS hospitals were also included for 220 NHS hospitals over our study period (Hospitalj). As our observed geographical unit was the NHS hospital, the inclusion of these fixed effects also captures population grouping effects, such as residential sorting. Overall, these variables account for the influence of unobserved confounding factors.
MeanTempjd and Humidityjd represent the daily mean temperature (in Celsius) and daily relative humidity on day d at NHS hospital j and were included as potential confounders of the effect of air pollution on A&E visits. Finally, ϵjd represents the standard idiosyncratic disturbance term.
We used clustered and robust standard errors to allow for arbitrary within-group correlations at the hospital level. All analyses were conducted with Stata MP v15 [63].
## 5.1. Quantifying the Differences in Exposure Assessment Approaches
The spatial distribution of air pollution estimates at the PCD level is illustrated in Appendix C Figure A1. ( Appendix C Figure A2 reports the surfaces of annual estimates, as daily maps demonstrate wide variation depending on the day chosen for representation.) The air pollution surfaces were produced at the annual level (by averaging daily estimates) to demonstrate the spatial distribution of the estimated concentrations from daily LUR and IDW. Although having the same spatial resolution (i.e., PCD centroids), the surfaces generated from LUR models capture more spatial heterogeneity compared to the surfaces from IDW models. In rural areas, where there are fewer monitoring stations, the spatial variation is almost uniform when using IDW models.
Figure 2 compares daily air pollution concentrations using estimates from the daily LUR and IDW models with measurements. Overall, both modelling approaches were in high agreement with the measurements from monitoring stations—with the highest correlations observed for PM10, PM2.5, and O3. IDW appeared more precise than, or equally as good as, daily LUR in this comparison. This was expected, as IDW models are completely informed by measurements at monitoring stations, and the comparison does not reflect the accuracy of estimates in out-of-sample areas (i.e., any locations other than monitoring stations). Furthermore, the IDW estimates of several locations used only one nearest monitoring station, which resulted in perfect fitting between measurements and estimates.
Table 1 shows summary statistics that quantify the daily average difference across all four pollutants between the average daily LUR or IDW estimates and the average daily air pollution concentration observed at the different air pollution monitoring stations that were omitted in the calculation of the estimates. The magnitude of the deviation varies by pollutant, but on average, daily LUR provides more accurate estimates than IDW: the averages of the difference are smaller using daily LUR, for all pollutants, than IDW. The standard deviations of the difference are also smaller for daily LUR, suggesting greater accuracy of the estimates. In addition, IDW tends to provide maximum values that are larger than the maximum values obtained using daily LUR, which is true for all pollutants, except for NO2. This is further supported by looking at the distributions of the differences reported in Appendix D Figure A3 for each pollutant. Daily LUR appears to be more accurate than IDW.
Table 2 shows the model performance from the validation, where daily estimates were regressed against daily measurements at monitoring sites. Daily LUR outperformed IDW for all pollutants across all parameters. Briefly, daily LUR accounted for $13\%$ to $39\%$ more variation in the measured daily concentrations compared to IDW. Daily LUR also had lower RMSE values, which suggests smaller errors (RMSE ranging from 6.87 to 14.50 μgm−3 for daily LUR; 8.21 to 22.80 μgm−3 for IDW).
When assessing exposure by indicator bins, we found large discrepancies in the classification. Figure 3 illustrates the differences between the daily LUR and IDW bins for each pollutant. In our dataset, only $66.7\%$ of observations fall into the same bin for PM2.5 across both exposure assignment techniques. This match decreases to $56.5\%$ for PM10, $54.2\%$ for O3, and $40.2\%$ for NO2. This suggests that, depending on the pollutant, at least $33.3\%$ of the observations were inconsistently assigned to a pollutant bin. In some extreme cases, hospitals were assigned to high air pollution exposure bins in one method, but to low air pollution exposure bins in the other. While such exposure misclassification occurs, it appears that IDW is more likely to classify hospitals in higher exposure bins, relative to the daily LUR groups. This was expected as IDW relies on air pollution monitoring stations that are often located in areas of concern, where pollution concentrations are more likely to be high. Therefore, IDW extrapolates over extreme values over distances and, therefore, potentially overestimates air pollution exposure at hospitals.
## 5.2. Regression Results
In the previous section, we demonstrated that the classification of air pollution exposure using traditional indicator bins corresponded to, at best, $66\%$ of observations being allocated to the same indicator bin when using daily LUR and IDW. Specifically, we observed that IDW measurements appeared to generally classify observations to higher exposure bins. In this section, we assess how this difference in classification impacts the estimated effects of air pollution on A&E visits using Equation [6].
In all cases, we found different point estimates when using daily LUR and IDW across comparable exposure bins. We found that the estimated changes, associated with air pollution exposure, to A&E utilisation rates varied depending on the air pollution exposure assessment approach applied. This variation differed across pollutants.
When comparing the point estimates between exposure assessment approaches for NO2 (Figure 4), we obtained estimates of similar sizes. However, these estimates varied in statistical significance between exposure assignment techniques. We observed no statistically significant effect of air pollution on hospital visits across the pollutant when using IDW as the exposure assessment technique. This implies that there is no relationship between NO2 and A&E visits. Conversely, we saw a steady increase in hospital visits associated with NO2 exposure when using daily LUR as the exposure assessment approach. Statistical significance was observed for the more extreme air pollution bins, 40–50 μgm−3 ($p \leq 0.01$) and >50 μgm−3 ($p \leq 0.001$). The estimates for both methods are illustrated in Appendix E Table A3.
When comparing the point estimates between exposure assessment approaches for O3 (Figure 4), we obtained estimates of similar sizes. Both exposure assessment techniques had estimates of statistical significance. In both instances, we observed a decrease in hospital visits as air pollution exposure increases. The estimates for both methods are illustrated in Appendix E Table A4.
PM10 and PM2.5 display different relationships across exposure assessment approaches (Figure 4). For PM10, we saw a steady increase in visits as air pollution increases. However, this effect was statistically non-significant when using IDW. Conversely, there appeared to be statistically significant effects for values above 25 μgm−3 using daily LUR. For PM2.5, point estimates varied in size between daily LUR and IDW, with only statistically significant results when using daily LUR for values above 15 μgm−3. The regression results are outlined in Appendix E Table A5.
These estimates are plain correlations and by no means causal in these models. The nature of A&E visits are non-specific in our data, and therefore, the effects observed are likely masked by the aggregation to all-cause visits. The value of the different regressions only lies in the comparison of the coefficients using the different pollution assignment methods.
## 6. Conclusions
Ambient air pollution is an environmental factor with wide-ranging effects on human health and well-being. The assessment of air pollution exposure on social outcomes requires the estimation of air pollution, which has been performed in the economic literature in several ways. We illustrated how a widely used method in the social sciences, IDW, misclassifies air pollution concentrations, particularly in areas with sparse monitoring networks. We proposed a simpler computational approach, based on land use regression (LUR), that increases the geographical precision and accuracy compared to IDW, while still offering estimates of high temporal frequency. Our LUR outperformed IDW in our cross-validation study using various indicators of performance.
The difference in parameter estimates for the IDW approach and the daily LUR model was likely due to the inability of the IDW approach to account for different emission sources (such as road traffic, industrial activities) and topographies. We observed that, on average, air pollution concentrations derived from daily LUR showed smaller prediction errors than IDW and, thus, a higher accuracy. The instability of the IDW approach was also documented by [32], who compared this with a dispersion model to find the latter outperforming the inverse distance approach, when using annual air pollution concentrations. Whilst the use of dispersion models provides reliable air pollution estimates, their use is computationally demanding and generally inaccessible for wider contexts.
Our findings showed that the IDW approach, which has been the convention to measure air pollution in previous economic studies, is likely to exacerbate measurement error in exposure assignment due to its lower accuracy and precision. The level of these varies by pollutant. For PM, which comprises atmospheric aerosol particles that fluctuate less geographically compared to NO2 and travel long distances, both PM10 and PM2.5 displayed small discrepancies in their assigned air pollution exposure and, therefore, negligible differences in the estimated health impacts. For both pollutants, we failed to identify health impacts using IDW, otherwise observed with daily LUR. Contrastingly, NO2 is a pollutant that diffuses rapidly and, therefore, exhibits a higher degree of spatial variation. In this case, the two concentrations assigned using the two different methods were largely different, being in agreement for less than half of our observations. Although this resulted in similar point estimates of the impact of air pollution concentrations on the health outcome, the variability observed was much smaller under the daily LUR approach, which resulted in statistically significant health impacts. Finally, health estimates associated with O3 were relatively unresponsive to exposure assessment approaches. Overall, the daily LUR model approach was able to account for some of the spatio-temporal variation associated with each pollutant, resulting in (i) the assignment of a more accurate and precise air pollution concentration and (ii) a more precise estimate of associated health impacts.
It is important to acknowledge that the economic significance of any variation created by the choice of pollution exposure method will vary with the pollution–outcome dose–response function related to the outcome of interest. In our illustration, the use of IDW resulted in an overestimation of air pollution effects on hospital utilisation, compared to the daily LUR. However, as other outcomes (e.g., mortality, obesity, productivity, etc.) carry their own unique relationship with air pollution, the associated sensitivity to exposure assignment may be of different magnitudes. In instances where large changes in air pollution are required to identify an impact on the outcome (e.g., obesity), the consequence of this difference in pollution exposure assignment may be smaller than in studies where small changes in air pollution are meaningful (e.g., mortality).
This paper illustrated how LUR models can be adapted to construct a reliable and frequent measure of local air pollution exposure. The daily LUR has several important advantages over other exposure assignment techniques, including less stringent data requirements, low computational costs, and the consideration of environmental characteristics, topological variation, and atmospheric conditions. Still, some of the emission sources and process characteristics used in the daily LUR model could be subject to imprecise measurement. While this approach is not devoid of measurement error, we have begun to bridge the gaps in accurate air pollution modelling for economic assessment. Most importantly, the availability of accessible LUR models for various cities and countries allows for this technique to be used in less-studied contexts (e.g., low- and middle-income countries with poorer and sparse monitoring networks).
These findings emphasise the need to be mindful of the exposure assessment technique utilised in economic studies as, depending on the pollutant, conventional approaches may introduce degrees of measurement error and variability that have the potential to bias the analysis and underestimate the impacts of air pollution. Our results may contribute to a more accurate evaluation of air pollution impacts and, subsequently, inform future environmental policies.
## References
1. Evans M.F., Smith V.K.. **Do new health conditions support mortality–air pollution effects?**. *J. Environ. Econ. Manag.* (2005.0) **50** 496-518. DOI: 10.1016/j.jeem.2005.04.002
2. Deryugina T., Heutel G., Miller N.H., Molitor D., Reif J.. **The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction**. *Am. Econ. Rev.* (2019.0) **109** 4178-4219. DOI: 10.1257/aer.20180279
3. Chay K., Greenstone M.. **The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession**. *Q. J. Econ.* (2003.0) **118** 1121-1167. DOI: 10.1162/00335530360698513
4. Lavy V., Ebenstein A., Roth S.. **The Impact of Air Pollution on Cognitive Performance and Human Capital Formation**
5. Kahn M.E., Li P.. *The Effect of Pollution and Heat on High Skill Public Sector Worker Productivity in China* (2019.0)
6. Chang T., Zivin J.G., Gross T., Neidell M.. **Particulate pollution and the productivity of pear packers**. *Am. Econ. J. Econ. Policy* (2016.0) **8** 141-169. DOI: 10.1257/pol.20150085
7. Currie J., Hanushek E.A., Kahn E.M., Neidell M., Rivkin S.G.. **Does Pollution Increase School Absences?**. *Rev. Econ. Stat.* (2009.0) **91** 682-694. DOI: 10.1162/rest.91.4.682
8. Heissel J., Persico C., Simon D.. *Does Pollution Drive Achievement? The Effect of Traffic Pollution on Academic Performance* (2019.0). DOI: 10.3386/w25489
9. Jerrett M., Arain A., Kanaroglou P., Beckerman B., Potoglou D., Sahsuvaroglu T., Morrison J., Giovis C.. **A review and evaluation of intraurban air pollution exposure models**. *J. Expo. Anal. Environ. Epidemiol.* (2005.0) **15** 185-204. DOI: 10.1038/sj.jea.7500388
10. Tobler W.R.. **A Computer Movie Simulating Urban Growth in the Detroit Region**. *Econ. Geogr.* (1970.0) **46** 234-240. DOI: 10.2307/143141
11. Akita Y., Baldasano J.M., Beelen R., Cirach M., de Hoogh K., Hoek G., Nieuwenhuijsen M., Serre M.L., de Nazelle A.. **A large scale air pollution estimation method combining Land Use Regression and Chemical Transport Modeling in a geostatistical framework**. *Environ. Sci. Technol.* (2014.0) **48** 4452-4459. DOI: 10.1021/es405390e
12. Beevers S.D., Kitwiroon N., Williams M.L., Kelly F.J., Ross Anderson H., Carslaw D.C.. **Air pollution dispersion models for human exposure predictions in London**. *J. Expo. Sci. Environ. Epidemiol.* (2013.0) **23** 647-653. DOI: 10.1038/jes.2013.6
13. Deschenes O., Wang H., Wang S., Zhang P.. **The effect of air pollution on body weight and obesity: Evidence from China**. *J. Dev. Econ.* (2020.0) **145** 1-58. DOI: 10.1016/j.jdeveco.2020.102461
14. Foster A., Gutierrez E., Kumar N.. **Voluntary Compliance, Pollution Levels, and Infant Mortality in Mexico**. *Am. Econ. Rev.* (2009.0) **99** 191-197. DOI: 10.1257/aer.99.2.191
15. Donaldson D., Storeygard A.. **The View from Above: Applications of Satellite Data in Economics**. *J. Econ. Perspect.* (2016.0) **30** 171-198. DOI: 10.1257/jep.30.4.171
16. Chang T., Zivin J.G., Gross T., Neidell M.. **The effect of pollution on worker productivity: Evidence from call center workers in China**. *Am. Econ. J. Appl. Econ.* (2019.0) **11** 151-172. DOI: 10.1257/app.20160436
17. Neidell M.. **Information, Avoidance Behavior, and Health: The Effect of Ozone on Asthma Hospitalizations**. *J. Hum. Resour.* (2009.0) **44** 450-478. DOI: 10.3368/jhr.44.2.450
18. Ward C.J.. **It’s an ill wind: The effect of fine particulate air pollution on respiratory hospitalizations**. *Can. J. Econ.* (2019.0) **48** 1694-1732. DOI: 10.1111/caje.12177
19. Sorek-Hamer M., Chatfield R., Liu Y.. **Review: Strategies for using satellite-based products in modeling PM**. *Environ. Int.* (2020.0) **144** 106057. DOI: 10.1016/j.envint.2020.106057
20. Holland S.P., Mansur E.T., Muller N.Z., Yates A.J.. **Distributional Effects of Air Pollution from Electric Vehicle Adoption**. *J. Assoc. Environ. Resour. Econ.* (2019.0) **6** S65-S94. DOI: 10.1086/701188
21. Currie J., Walker R.. **Traffic Congestion and Infant Health: Evidence from E-ZPass**. *Am. Econ. J. Appl. Econ.* (2011.0) **3** 65-90. DOI: 10.1257/app.3.1.65
22. Bauernschuster S., Hener T., Rainer H.. **When labor disputes bring cities to a standstill: The impact of public transit strikes on traffic, accidents, air pollution, and health**. *Am. Econ. J. Econ. Policy* (2017.0) **9** 1-37. DOI: 10.1257/pol.20150414
23. Hanna B.G.. **House values, incomes, and industrial pollution**. *J. Environ. Econ. Manag.* (2007.0) **54** 100-112. DOI: 10.1016/j.jeem.2006.11.003
24. Chay K., Dobkin C., Greenstone M.. **The Clean Air Act of 1970 and Adult Mortality**. *J. Risk Uncertain.* (2003.0) **27** 279-300. DOI: 10.1023/A:1025897327639
25. Barreca A.I., Neidell M., Sanders N.J.. *Long Run Pollution Exposure and Adult Mortality: Evidence from the Acid Rain Program* (2017.0)
26. Isen A., Rossin-Slater M., Walker W.R.. **Every Breath You Take—Every Dollar You’ll Make: The Long-Term Consequences of the Clean Air Act of 1970**. *J. Political Econ.* (2017.0) **125** 848-902. DOI: 10.1086/691465
27. Walker W.R.. **Environmental Regulation and Labor Reallocation: Evidence from the Clean Air Act**. *Am. Econ. Rev.* (2011.0) **101** 442-447. DOI: 10.1257/aer.101.3.442
28. Luechinger S.. **Air pollution and infant mortality: A natural experiment from power plant desulfurization**. *J. Health Econ.* (2014.0) **37** 219-231. DOI: 10.1016/j.jhealeco.2014.06.009
29. Moretti E., Neidell M.. **Pollution, Health, and Avoidance Behavior: Evidence from the Ports of Los Angeles**. *J. Hum. Resour.* (2011.0) **46** 154-175. DOI: 10.3368/jhr.46.1.154
30. Janke K.. **Air pollution, avoidance behaviour and children’s respiratory health: Evidence from England**. *J. Health Econ.* (2014.0) **38** 23-42. DOI: 10.1016/j.jhealeco.2014.07.002
31. 31.
Committee on the Medical Effects of Air Pollutants
The Mortality Effects of Long-Term Exposure to Particulate Air Pollution in the United Kingdom2010108. *The Mortality Effects of Long-Term Exposure to Particulate Air Pollution in the United Kingdom* (2010.0) 108
32. Filippini M., Masiero G., Steinbach S.. **The impact of ambient air pollution on hospital admissions**. *Eur. J. Health Econ.* (2019.0) **20** 919-931. DOI: 10.1007/s10198-019-01049-y
33. Austin W., Heutel G., Kreisman D.. **School bus emissions, student health and academic performance**. *Econ. Educ. Rev.* (2019.0) **70** 109-126. DOI: 10.1016/j.econedurev.2019.03.002
34. Zivin J.G., Neidell M., Sanders N.J., Singer G.. *When Externalities Collide: Influenza and Pollution* (2020.0) 22
35. Wu M., Cao X.. **Greening the career incentive structure for local officials in China: Does less pollution increase the chances of promotion for Chinese local leaders?**. *J. Environ. Econ. Manag.* (2021.0) **107** 102440. DOI: 10.1016/j.jeem.2021.102440
36. Giaccherini M., Kopinska J., Palma A.. **When particulate matter strikes cities: Social disparities and health costs of air pollution**. *J. Health Econ.* (2021.0) **78** 102478. DOI: 10.1016/j.jhealeco.2021.102478
37. Maddison D.. **Dose response functions and the harvesting effect**. *Resour. Energy Econ.* (2006.0) **28** 313-332. DOI: 10.1016/j.reseneeco.2005.12.001
38. Neidell M.. **Air pollution, health, and socio-economic status: The effect of outdoor air quality on childhood asthma**. *J. Health Econ.* (2004.0) **23** 1209-1236. DOI: 10.1016/j.jhealeco.2004.05.002
39. Janke K., Propper C., Henderson J.. **Do current levels of air pollution kill? The impact of air pollution on population mortality in England**. *Health Econ.* (2009.0) **18** 1031-1055. DOI: 10.1002/hec.1475
40. Currie J., Neidell M.. **Air pollution and infant health: What can we learn from California’s recent experience?**. *Q. J. Econ.* (2005.0) **120** 1003-1030. DOI: 10.1162/003355305774268219
41. Gulliver J., de Hoogh K., Fecht D., Vienneau D., Briggs D.. **Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution**. *Atmos. Environ.* (2011.0) **45** 7072-7080. DOI: 10.1016/j.atmosenv.2011.09.042
42. White C.. **The Dynamic Relationship between Temperature and Morbidity**. *J. Assoc. Environ. Resour. Econ.* (2017.0) **4** 1155-1198. DOI: 10.1086/692098
43. Mullins J.T., White C.. **Temperature and mental health: Evidence from the spectrum of mental health outcomes**. *J. Health Econ.* (2019.0) **68** 102240. DOI: 10.1016/j.jhealeco.2019.102240
44. Deschênes O., Greenstone M.. **Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the US**. *Am. Econ. J. Appl. Econ.* (2011.0) **3** 152-185. DOI: 10.1257/app.3.4.152
45. Bechle M.J., Millet D.B., Marshall J.D.. **National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000–2010**. *Environ. Sci. Technol.* (2015.0) **49** 12297-12305. DOI: 10.1021/acs.est.5b02882
46. Di Q., Amini H., Shi L., Kloog I., Silvern R., Kelly J., Sabath M.B., Choirat C., Koutrakis P., Lyapustin A.. **Assessing no2 concentration and model uncertainty with high spatiotemporal resolution across the contiguous united states using ensemble model averaging**. *Environ. Sci. Technol.* (2020.0) **54** 1372-1384. DOI: 10.1021/acs.est.9b03358
47. Wang M., Sampson P.D., Hu J., Kleeman M., Keller J.P., Olives C., Szpiro A.A., Vedal S., Kaufman J.D.. **Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM**. *Environ. Sci. Technol.* (2016.0) **50** 5111-5118. DOI: 10.1021/acs.est.5b06001
48. Clougherty J.E., Wright R.J., Baxter L.K., Levy J.I.. **Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants**. *Environ. Health* (2008.0) **7** 17. DOI: 10.1186/1476-069X-7-17
49. Beelen R., Hoek G., Vienneau D., Eeftens M., Dimakopoulou K., Pedeli X., Tsai M.Y., Künzli N., Schikowski T., Marcon A.. **Development of NO**. *Atmos. Environ.* (2013.0) **72** 10-23. DOI: 10.1016/j.atmosenv.2013.02.037
50. Saraswat A., Apte J.S., Kandlikar M., Brauer M., Henderson S.B., Marshall J.D.. **Spatiotemporal land use regression models of fine, ultrafine, and black carbon particulate matter in New Delhi, India**. *Environ. Sci. Technol.* (2013.0) **47** 12903-12911. DOI: 10.1021/es401489h
51. Dirgawati M., Barnes R., Wheeler A.J., Arnold A.L., McCaul K.A., Stuart A.L., Blake D., Hinwood A., Yeap B.B., Heyworth J.S.. **Development of Land Use Regression models for predicting exposure to NO**. *Environ. Model. Softw.* (2015.0) **74** 258-267. DOI: 10.1016/j.envsoft.2015.07.008
52. Muttoo S., Ramsay L., Brunekreef B., Beelen R., Meliefste K., Naidoo R.N.. **Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa**. *Sci. Total Environ.* (2018.0) **610–611** 1439-1447. DOI: 10.1016/j.scitotenv.2017.07.278
53. Hystad P., Setton E., Cervantes A., Poplawski K., Deschenes S., Brauer M., van Donkelaar A., Lamsa L., Martin R., Jerrett M.. **Creating national air pollution models for population exposure assessment in Canada**. *Environ. Health Perspect.* (2011.0) **119** 1123-1129. DOI: 10.1289/ehp.1002976
54. Novotny E.V., Bechle M.J., Millet D.B., Marshall J.D.. **National satellite-based land use regression: NO**. *Environ. Sci. Technol.* (2011.0) **45** 4407-4414. DOI: 10.1021/es103578x
55. Vienneau D., de Hoogh K., Bechle M.J., Beelen R., van Donkelaar A., Martin R.V., Millet D.B., Hoek G., Marshall J.D.. **Western European Land Use Regression Incorporating Satellite-and Ground-Based Measurements of NO**. *Environ. Sci. Technol.* (2013.0) **47** 13555-13564. DOI: 10.1021/es403089q
56. Kerckhoffs J., Wang M., Meliefste K., Malmqvist E., Fischer P., Janssen N.A.H., Beelen R., Hoek G.. **A national fine spatial scale land use regression model for ozone**. *Environ. Res.* (2015.0) **140** 440-448. DOI: 10.1016/j.envres.2015.04.014
57. Knibbs L.D., Hewson M.G., Bechle M.J., Marshall J.D., Barnett A.G.. **A national satellite-based land use regression model for air pollution exposure assessment in Australia**. *Environ. Res.* (2014.0) **135** 204-211. DOI: 10.1016/j.envres.2014.09.011
58. Xu H., Bechle M.J., Wang M., Szpiro A.A., Vedal S., Bai Y., Marshall J.D.. **National PM**. *Sci. Total Environ.* (2019.0) **655** 423-433. DOI: 10.1016/j.scitotenv.2018.11.125
59. **DEFRA. Automatic Urban and Rural Monitoring Network, 2016**
60. Cyrys J., Eeftens M., Heinrich J., Ampe C., Armengaud A., Beelen R., Bellander T., Beregszaszi T., Birk M., Cesaroni G.. **Variation of NO**. *Atmos. Environ.* (2012.0) **62** 374-390. DOI: 10.1016/j.atmosenv.2012.07.080
61. Tang R., Blangiardo M., Gulliver J.. **Using building heights and street configuration to enhance intraurban PM**. *Environ. Sci. Technol.* (2013.0) **47** 11643-11650. DOI: 10.1021/es402156g
62. Wei Y., Yazdi M.D., Di Q., Requia W.J., Dominici F., Zanobetti A., Schwartz J.. **Emulating causal dose–response relations between air pollutants and mortality in the Medicare population**. *Environ. Health* (2021.0) **20** 53. DOI: 10.1186/s12940-021-00742-x
63. 63.
StataCorp
Stata Statistical Software: Release 15StataCorp.College Station, TX, USA2017. *Stata Statistical Software: Release 15* (2017.0)
64. Eeftens M., Beelen R., De Hoogh K., Bellander T., Cesaroni G., Cirach M., Declercq C., Dedele A., Dons E., De Nazelle A.. **Development of land use regression models for PM**. *Environ. Sci. Technol.* (2012.0) **46** 11195-11205. DOI: 10.1021/es301948k
65. 65.
IBM Corp
IBM SPSS Statistics for WindowsVersion 24.0IBM Corp.Armonk, NY, USA2016. *IBM SPSS Statistics for Windows* (2016.0)
66. 66.
ESRI Ltd
ArcGIS: Release 10.4ESRI Ltd.Singapore2016. *ArcGIS: Release 10.4* (2016.0)
|
---
title: Smoking Habits and Attitudes toward Smoking in Patients with Severe Mental
Illness in Residential Facilities in Insular Greece
authors:
- Ioanna Botsari
- Georgia Marouli
- Aikaterini Arvanitaki
- Vaios Peritogiannis
journal: Healthcare
year: 2023
pmcid: PMC10001183
doi: 10.3390/healthcare11050642
license: CC BY 4.0
---
# Smoking Habits and Attitudes toward Smoking in Patients with Severe Mental Illness in Residential Facilities in Insular Greece
## Abstract
Smoking may contribute to increased cardiovascular morbidity and mortality in patients with schizophrenia spectrum disorders. The objective of the present study is to explore the attitudes toward smoking in patients with severe mental illness in residential rehabilitation facilities in insular Greece. The patients ($$n = 103$$) were studied with the use of a questionnaire based on a semi-structured interview. Most of the participants ($68.3\%$) were current regular smokers, had been smoking for 29 years and started smoking at an early age. The majority ($64.8\%$) reported having tried to quit smoking in the past, and only half had been advised by a physician to quit. The patients agreed on the rules for smoking and believed that the staff should avoid smoking in the facility. The years of smoking were statistically significantly correlated to the educational level and the treatment with antidepressant medication. A statistical analysis showed that longer stay period in the facilities correlates with current smoking, an effort to quit and increased belief that smoking causes harm to health. Further research on the attitudes of patients in residential facilities toward smoking is needed, which could guide interventions for smoking cessation and should be assumed by all health professionals who are involved in the care of those patients.
## 1. Introduction
Schizophrenia and related syndromes are still the most devastating severe mental illnesses (SMI), according to the most recent Global Burden of Disease Study [1]. The course of schizophrenia is chronic, the prognosis is generally modest, and the long-term outcome is often poor, with high rates of disability and poor functioning [2]. Notably, life expectancy in patients with schizophrenia spectrum disorders has been reported to be 15–20 years shorter than the general population, and the gap is increasing over the decades, despite the substantial progress in the treatment of these disorders [3,4]. Despite concerns regarding the relatively high suicide rates in schizophrenia, most deaths are attributable to preventable diseases, such as cardiovascular events [5]. Cardiovascular morbidity and mortality are highly prevalent in SMI and are the result of a complex interplay of factors that act synergistically [6,7]. Lifestyle factors are widely recognized as playing an important role in the physical morbidity of patients. These include a sedentary lifestyle and poor physical activity, poor nutrition, alcohol/substance abuse and smoking [5]. The association between smoking and schizophrenia is well-established. In a recent review of the literature, the reported prevalence range of nicotine use during the prodromal phase of schizophrenia across studies was 16.6–$46\%$. Interestingly, several studies reported an increased risk for psychosis in heavy smokers [8]. Smoking rates in patients with established schizophrenia are high, with negligible changes in smoking prevalence over time, and are associated with well-known adverse effects on the patient’s health [9]. It is therefore relevant to elucidate the habits and attitudes of patients with SMI toward smoking and plan interventions for smoking cessation accordingly.
In Greece, there are only a few studies with regard to smoking habits in patients with SMI and their views and attitudes toward smoking [10,11,12]. These studies comprise mixed patient populations, who are inpatients and outpatients in rehabilitation facilities, residing in metropolitan locations, namely, the capital of Greece, Athens. Less is known with regard to smoking habits in patients with SMI in rural or insular regions in Greece. The objective of the present study was, therefore, to explore the attitudes toward smoking in patients with schizophrenia and other SMI in residential rehabilitation facilities in insular Greece.
## 2. Materials and Methods
This is a cross-sectional, quantitative, descriptive, correlation study with closed, structured questions, based on a Likert scale. Attitudes towards smoking are measurable concepts and can be measured and presented objectively using specific questions and numerical data. The study objective is to highlight the correlation between variables, and quantitative research was most suitable in this regard.
## 2.1. The Study Setting
The present study was conducted in nine residential facilities run by the General Hospital of Corfu, Northwest Greece. A total of 153 patients are residents in those facilities. In line with international practice [13,14], patients who are eligible for such residential care suffer an SMI, mostly a psychotic or severe affective disorder, have poor psychosocial functioning, more and longer hospitalizations, worse clinical courses and outcomes; and need high levels of support. Such facilities in Greece are staffed on-site by interdisciplinary teams, which mostly comprise psychologists, social workers and nursing staff, who are recovery-oriented and enable psychosocial rehabilitation [15]. All the patients receive medication and adherence is ensured with direct supervision [16].
## 2.2. Recruitment/Exclusion Criteria
To participate in the study, all the individuals had to be adults, reside in a facility for psychosocial rehabilitation and have a chronic and severe mental disorder diagnosis (F20–29 and F31, respectively, according to the International Classification of Diseases, 10th revision [ICD-10]) and agree to the aims of the study. The exclusion criteria were mental retardation comorbidity, severe cognitive disturbance, not filling out the consent form, or not completing the questionnaire.
## 2.3. Participants
All 153 residents in the rehabilitation facilities were initially considered eligible for participation in the study, but 23 met the exclusion criteria (10 had co-morbid mental retardation and 13 were rated by the treating teams as severely cognitively disturbed). The remaining 130 patients were all approached for participation; 11 declined to fill out the consent form; and 16 did not return the questionnaire, despite completing the consent form. The reasons for not returning the questionnaire were not recorded, and it was hypothesized that those patients changed their initial opinion for participation in the study. Finally, a total of 103 individuals participated in the study. The current clinical status of the patients was not assessed in this study. However, psychotic decompensations or exacerbations of symptoms of mania may be disrupted for the environment of a residential facility and could lead to hospitalization. All the patients in the present sample were in a stable phase of their illness, in that they did not require hospitalization and could participate in the daily activities of the facility.
## 2.4. Procedures
The sample was recruited from psychosocial rehabilitation facilities falling under Corfu’s General Hospital administration. Permission for the study was granted by the Ethics Committee of Corfu’s General Hospital. The individuals were informed about the study procedures prior to their participation, and written informed consent was obtained from all the participants.
## 2.5. Questionnaire
The participants were asked to fill out a questionnaire consisting of statements on a five-point Likert scale created by Kourakos et al. [ 11], based on the semi-structured interview ‘Smoking in a forensic psychiatric service: a survey of inpatients’ views’ [17]. The questionnaire sought information about socio-demographic and clinical characteristics (number and length of hospitalizations, diagnosis according to ICD-10, medications, comorbidities) and the patient’s smoking habits, including the duration of smoking, age and reasons for starting smoking, number of cigarettes per day and their attitudes and opinions about smoking and health (10 items), attempts to and difficulty in quitting smoking (11 items) and smoking in residential facilities (12 items).
## 2.6. Statistical Analysis
The data analyses were performed using SPSS version 24.0. Frequency distributions (f%) were used to describe the respondents’ demographic characteristics. A factor analysis was conducted on the questionnaire items’ frequency scores to determine the number of factors. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was conducted to determine whether the data set was factorable. Values of at least 0.60 are required for good factor analysis [18]. The Cronbach’s alpha reliability coefficient was employed to assess the internal consistency to determine if the elements that form the questionnaire possess a sense of uniformity.
Once the factor structure was identified, the statistically significant differences in variables were evaluated employing a t-test, if they presented a normal distribution, and, if not, a Kruskal–Wallis test. A post hoc Bonferroni test, also called a multiple comparison test, was conducted to identify exactly which groups differ from each other. Spearman’s non-parametric correlation coefficient was employed to examine the relationship between the categorical variables or the variables that do not have a standard distribution. The Chi-square independence test was utilized to check for any dependence between the qualitative nominal variables. A p-value of <0.05 was considered statistically significant. p-value adjustments for multiple comparisons in order to reduce the probability of committing false statistical inferences were included in the present research.
## 3.1. Demographic Data
The total number of participants included in this study was 103 individuals. The socio-demographic and clinical characteristics of the patients are presented in Table 1. Almost two-thirds of the participants were male, and the majority ($60.2\%$) were 41–60 years old. The patients had been mostly raised in an urban ($38.8\%$) or a semi-urban ($26.2\%$) area, and more than half had only primary education. With regard to marital status, the participants were mostly unmarried ($76.7\%$) with no children ($79.4\%$).
## 3.2. Medical Diagnosis, Medication, Length of Stay
The most common diagnosis in the present sample was schizophrenia ($71.2\%$), followed by other psychotic disorders ($9.6\%$) and bipolar disorder ($8.7\%$). The patients’ primary medication was antipsychotics ($91.3\%$), antidepressants ($36.9\%$) and antiepileptics ($19.4\%$). Almost half of the patients had been living in the rehabilitation facility for 6–10 years, whereas about a quarter of the patients had been living in the facility for 5 years or less.
## 3.3. Questionnaire Items in the Sample: Number of Factors and Psychometric Properties
The 3-factor structure, which was found to represent the data most accurately, accounted for $54.12\%$ of the explained variance. Factor 1 accounts for $25.44\%$ of the overall variability and encompasses four of the five questions related to the “difficulty in quitting smoking” aspect. One question in the “difficulty in quitting smoking” factor was excluded because it created reliability issues. Factor 2 accounts for $16.33\%$ of the total variance and encompasses all questions regarding “avoiding smoking in residential facilities” and one question regarding “difficulty in quitting smoking.” Factor 3 accounts for $12.35\%$ of the total variability and includes all questions related to “confidence in a non-smoking reference person.” The factor analysis was deemed successful, as $92.30\%$ of the 13 questions in the questionnaire, or 12 out of 13, were properly allocated.
The internal consistency of the questionnaire was calculated using Cronbach’s alpha reliability coefficient. The value of Cronbach’s alpha is equal to 0.611 (α = 0.611), indicating a good level of consistency between the items and the reliability of the questionnaire. The 3 factors also show a good degree of internal consistency (Factor 1: α = 0.668, Factor 2: α = 0.754, Factor 3: α = 0.926).
## 3.4. Smoking Profile, Habits and Attitudes of the Patients
The majority of the patients ($$n = 90$$, $87.4\%$) reported having smoked in the past, and, from those, $78.8\%$ ($$n = 71$$, $68.9\%$ of the total sample) are current regular smokers, having started at the age of 21.6 (SD = 8.2) years, been smoking for 29.3 (SD = 12.2) years and consuming 18.5 (SD = 13.8) cigarettes per day, mostly ($85.9\%$) filtered cigarettes. The results of the Spearman correlations found statistically significant correlations between the age and current smoking (r = −0.239, $p \leq 0.05$) and also between the age and the years of smoking ($r = 0.717$, $p \leq 0.01$). Employing a Kruskal–Wallis Test, a statistically significant difference was found between the years of smoking and the educational level (H[3] = 10.156, $$p \leq 0.017$$, $p \leq 0.05$). A post hoc *Bonferroni analysis* showed that the primary education-only patients had smoked for more years compared to the patients with secondary education ($p \leq 0.05$, adj sig = 0.044). The years of smoking showed a statistically significant difference with regards to the antidepressant medication; an independent sample t-test showed that the patients who were not on antidepressant therapy reported more smoking years [t[65] = 2.255, $$p \leq 0.027$$ < 0.05]. Finally, a statistically significant relationship was found between antiepileptics and current smoking; all the patients who were on anti-epileptic medication were currently smoking (x2 = 6.021, $p \leq 0.01$).
Regarding the reasons for starting smoking, an equal number of participants ($33.8\%$) considered friends and stress or personal issues as factors that influenced the onset of their smoking habit, whereas $23.9\%$ of the smoking patients stated that they began smoking out of curiosity. Slightly less than half of the patients ($45.6\%$) believe that the smoking habit causes severe harm to overall health, compared to the $23.5\%$ who have no concerns regarding the effect of smoking on health. Half of the smoking patients reported that physicians had advised them to quit smoking.
## 3.5. Difficulty in Quitting Smoking
A percentage of $64.8\%$ of the 71 smoking patients reported having tried to quit smoking, whereas the remaining $35.2\%$ had never tried. Only slightly more than half ($50.7\%$) of the smoking patients reported having been advised by a physician to quit smoking. According to their beliefs on the 5-point Likert scale (1 = totally disagree to 5 = totally agree) of the questionnaire, the patients agreed that it is difficult to quit smoking ($M = 4.24$, SD = 1.21). Neutral views were expressed about the difficulty in quitting smoking when they see other patients smoke ($M = 3.37$, SD = 1.02) and when the atmosphere of the room is full of smoke ($M = 3.21$, SD = 1.01) and whether they had enough information about smoking cessation or not ($M = 3.07$, SD = 1.05).
For the majority ($70.4\%$), there were no other difficulties in the decision to quit smoking. For the rest, who answered ‘yes’ to the question ‘Is there anything else that prevents you from quitting smoking?’, a percentage of $23.8\%$ provided family issues, social circumstances and stress as obstacles to quitting smoking, whereas unemployment, addiction and the facility’s conditions were also reported. Most of the smoking patients ($57.7\%$) thought they would need help to quit smoking, whereas the rest believed that, with personal will and perseverance, they would achieve quitting smoking, should they decide to do so (Table 2). Difficulty in quitting smoking was statistically significantly related to the place of birth, according to a Kruskal–Wallis Test (H[2] = 6.271, $p \leq 0.05$). A post hoc *Bonferroni analysis* showed that the patients who were raised in semi-urban regions had much more difficulty in quitting compared to urban ($p \leq 0.05$, adj sig = 0.095) and rural ($p \leq 0.05$, adj sig = 0.062) areas.
## 3.6. Smoking Habits in Psychosocial Rehabilitation Facilities
Smoking avoidance reflects gender differences in smoking habits in the facility. Female patients tend to avoid smoking in the facility more than male patients [t[101] = −2.73, $$p \leq 0.007$$, $p \leq 0.05$]. In addition, the patients on mood stabilizers prefer not to smoke in the rehabilitation facilities as much as patients on other medications [t[102] = 3.128, $$p \leq 0.002$$, $p \leq 0.05$]. Regarding the personnel’s smoking habits in psychosocial rehabilitation facilities, the majority of patients ($70.9\%$) had seen the staff smoking at work. On several occasions, the patients were aware of staff members smoking in the relatives’ lounge within the facility (Table 3).
The participants’ attitudes toward smoking in the facility environment were also inquired about (Table 4). *They* generally agreed on the rules for smoking and thought that the staff should avoid smoking in order to be a nice role model for patients and also encourage smoking cessation and smoking restriction. The patients were not sure about whether the staff or the visitors should be allowed to smoke with the patients, but they partly agreed that staff should be allowed to smoke at work. With regard to their reference person among the staff members, the patients were neutral about whether they would trust more and collaborate better with a reference person who did smoke than with someone who did not.
A Spearman’s correlation of the length of stay in the psychosocial rehabilitation facilities with variables about the habits and attitudes of the patients found statistically significant results, as presented in Table 5. In specific, a longer stay period in the facilities correlates with current smoking ($r = 0.375$, $p \leq 0.01$), efforts to quit smoking ($r = 0.317$, $p \leq 0.01$) increased belief that smoking causes harm to overall health ($r = 0.257$, $p \leq 0.05$), advice from a physician to quit ($r = 0.328$, $p \leq 0.01$) and the belief that smoking should be avoided in psychosocial rehabilitation facilities ($r = 0.365$, $p \leq 0.01$).
## 4. Discussion
To the best of our knowledge, this is the first study that explores the attitudes toward smoking in patients with SMI exclusively in rehabilitation residential facilities in insular Greece. Almost $70\%$ of patients were current regular smokers. This rate differs from the recently reported rate of smoking ($54.5\%$) in rural patients with schizophrenia spectrum disorders who were attending a community mental health service [19]. It would be interesting to study and elucidate the factors that affect smoking habits in community-dwelling outpatients, compared to those residing in rehabilitation facilities. According to the results of the study, most of the patients currently smoked, and even more had smoked in the past. The participants had been smoking for almost three decades, starting in their early twenties, and their daily consumption was rather high. The majority had tried to quit smoking in the past, although only half of them had been advised by a physician to quit. These findings are in line with previous research among 356 psychiatric patients (inpatients and residents in rehabilitation facilities) in Athens, Greece [11] and highlight the extent of the problem of smoking in rehabilitation facilities.
The aforementioned findings should be commented on, taking into account the Greek cultural context. According to recent data from the European Commission, Greece has the second-highest smoking prevalence in the European Union [20]. More recent research suggested that the smoking prevalence was up to $33.5\%$ in a representative sample of adults and found current smoking to be correlated with chronic stress, depressive symptomatology, sleep problems and financial difficulties [21]. Most importantly, high rates of smoking in Greek adolescents have been reported and are strongly associated with the parents’ smoking status [22]. Moreover, as many as $72.9\%$ of Greek adults were exposed to passive smoke, which continues to be a significant public health concern, despite consecutive anti-smoking legislation [21].
The participants in the present study considered severe stress or personal issues and their friends’ smoking habits as factors that influenced the onset of their smoking habits. Previous research has suggested that the most common cause for starting smoking among psychiatric patients was the amelioration of their symptoms [23]. Other research has highlighted the barriers to smoking cessation that are related to managing mental health issues, such as preventing relapse, controlling side effects and negative symptoms, managing anxiety, anger, irritability and sadness, and improving cognitive, motivational and problem-solving factors [24]. The patients in the present study seemed to agree on the difficulty of quitting smoking. Some of the perceived reasons were family issues, social circumstances, stress, unemployment, addiction and everyday life in the facility. Recent research has shown that several psychiatric patients linked their cigarette intake to the institutional environment, claiming that in community-based residential facilities, the majority of people smoked and felt that they needed something to do [25]. Smoking seems to have a crucial role in psychiatric patients’ everyday lifestyle and identity by helping them maintain a routine and have an activity [24]. However, the relationship between smoking and SMI may be more complex. Recent research has shown that smoking may be associated with earlier onset of bipolar disorder, higher prevalence of suicide attempts, other substance use disorders, more frequent hospitalizations, more symptoms, poor functioning and a worse prognosis, along with the well-known effects on physical health [26]. Moreover, recent Mendelian randomization studies have suggested that there is a causal relationship between schizophrenia and smoking, probably mediated by a shared genetic predisposition. That is, not only was a genetic liability for schizophrenia causally associated with a higher risk of lifetime smoking but, also, a genetic predisposition to lifetime smoking was causally associated with a higher schizophrenia risk [27,28]. Several potential biological mechanisms for the bi-directional causal effects of smoking and schizophrenia have been proposed [29]. Notably, tobacco smoking has been linked to neuropsychiatric disease through oxidative stress and neuroinflammation [30]. More specifically, smoking has been associated with a pro-inflammatory status in the brain, which has been implicated in the pathophysiology of schizophrenia [31,32] and bipolar disorder [33], at least for some patients.
According to the results of the present study, if patients ever decided to quit smoking, they feel they would need help. Similarly, in Kourakos et al. ’s research [11], when patients were asked about quitting smoking, $90\%$ of them stated that they would need support. Another study in community mental health centers reported that, from a total of $44\%$ of the smokers who were interested in smoking cessation treatment and $25\%$ who would like to receive smoking cessation counseling treatment, only $13\%$ were currently using such medication and $5.4\%$ receiving consultation [34]. These findings indicate that more patients are interested in smoking cessation treatment than are actually receiving it [34]. Both in the present study and in Kourakos et al. ’s study [11], approximately only half of the patients were advised by a physician to quit. The lack of guidance for smoking cessation can be explained by the results of previous research in which $91\%$ of the participants’ psychiatrists reported “patients not interested” as a barrier that limited their smoking cessation treatment practice [34]. This finding is in line with the Himelhoch et al. study [35], in which $77\%$ of the clinicians participating in the study believed that patients were not interested in quitting smoking. With regard to mental healthcare practitioners’ practices, it has been shown that community mental health practitioners, as well as practitioners who smoke, were less likely to apply smoking cessation practices to patients [36]. In another study reviewing mental health professionals’ attitudes toward smoking and smoking cessation among people with mental illnesses, the most common recorded beliefs were that patients are not interested in quitting, quitting is too much for patients to handle, and smoking is perceived as “the norm” by many practitioners. Other barriers reported by practitioners were lack of time, confidence and training [37]. Accordingly, little information about the effects of smoking on mental and physical health may be provided to patients. For instance, in a sample of young people attending a mental health center, although $75\%$ acknowledged they should quit smoking in the future, most of them lacked information about the influence of smoking on mental and physical health [38].
Regarding attitudes towards smoking, although most patients see the staff smoking at work, they agree on the rules for smoking, and they believe that the staff should avoid smoking in order to be a good role model for patients and encourage smoking cessation and smoking restriction. This finding is in line with previous research, which found both staff and patients support a smoke-free policy rather than the continuation of smoking in psychiatric units, whereas $63\%$ of patients perceived the smoking ban policy as a feasible option [39].
The majority of smoking patients in the present study had been diagnosed with schizophrenia or other psychotic disorders and reported having started smoking in their early twenties. In a study on the profile of cigarette smokers and schizophrenia [40], the mean age for tobacco use onset in patients with schizophrenia was 17.2 years old. Similar to other studies, the early age of starting smoking was positively correlated with smoking in schizophrenia [41]. Starting smoking at such an early age may indicate a genetic vulnerability [42] or a specific association between tobacco use and schizophrenia [43,44]. de Leon et al. [ 45] found smoking in nine out of ten cases preceding the onset of schizophrenia and indicating a possible role of tobacco smoking on the onset or perhaps the maintenance of the symptoms. According to Diaz et al. [ 42], schizophrenia patients had a significantly higher risk of becoming daily smokers than controls of the same age, while the analyses did not present significant differences between patients with mood disorders and the controls.
In the present study, the patients receiving anti-epileptics reported more smoking years and had higher smoking rates compared to those who were on antidepressant medication. In a previous U.S. study [46] in patients with schizophrenia living in nursing homes, it was found that an increase in depression was associated with more smoking and that depression may be related to smoking behavior. Indeed, patients on antidepressants may currently have fewer depressive symptoms than those not receiving antidepressants. However, depressive symptomatology was not assessed in the present study.
In the present sample of patients, age was associated with smoking habits, that is, as patients grew older, they reported higher rates of non-smoking over the current period, while reporting more years of total smoking. This could be associated with the physical morbidity of older patients, although inquiring about co-morbidities was beyond the scope of this study. According to Mallet et al. [ 40], less tobacco use in schizophrenia is associated with negative symptoms, anticholinergic agents and clozapine or aripiprazole administration. The association between smoking and anticholinergics or specific antipsychotic medication needs further research and is not addressed in the present study. However, it should be noted that heavy smoking has been shown to reduce significantly the blood concentrations of certain drugs, such as olanzapine and clozapine, by inducing the activity of CYP1A2 [47].
Concerning education, the patients with primary education only reported smoking for more years compared to the higher education patients. Consistent with the literature, higher education in smoking patients with schizophrenia was associated with a lower frequency of tobacco smoking [40]. An earlier Australian study [48], which investigated the relationship between smoking and demographic characteristics, found that there was a correlation between educational level and smoking, perhaps due to the greater awareness of the harmful effects in higher education patients. In the present study, patients born in semi-urban areas had greater difficulty in quitting smoking. The literature does not provide further evidence for this finding. So, further research about smoking and the place of birth would provide interesting information.
Concerning the duration of stay in a rehabilitation residential facility, the patients with more years of residence reported current smoking, preferring filter cigarettes and being advised by physicians to quit smoking, which they have tried to do. In addition, they agreed, to a greater extent, that smoking is harmful to their health and that it should be avoided in psychosocial rehabilitation structures. The same group of patients claimed that staff members smoke at work and, especially, in the courtyard or smoking room. In Dimopoulos’ study [49], the overall support of the patients within a residential facility is associated with greater support for smoking cessation, as well as with medication protocols that allow health professionals to choose methods that satisfy both the medical needs as well as the personal preferences of people with mental illness. In another study [41], older patients or patients with less financial comfort show a greater desire to quit smoking, related to the time spent in the facility or the time receiving treatment.
## Limitations and Potential Implications for Rehabilitation
The present study has some limitations. The sample size did not allow the application of parametric controls, which are more reliable and have statistically greater power. Future multi-center research is warranted, with stratified sampling, using a larger sample size proportional to the size of the population. In this way, it could be checked if the results of the present study are generalizable. Apart from the patients with schizophrenia, the present study does not include sub-analyses for the remaining diagnoses, e.g., bipolar disorder, as the sample representing this diagnostic category was small ($$n = 9$$). Even though smoking patterns may differ across diagnoses, such differences could not be assessed. Another limitation of the study is the missing information from a proportion of patients ($$n = 16$$) who initially agreed to participate but did not return the questionnaires. Moreover, the data collection was made through self-reporting questionnaires and may be subject to reporting bias. Finally, the current clinical status of patients was not assessed in this study; rather, all the participants were thought to be in a stable phase of their illness.
On the other hand, the results of the present study are relevant and informative for the care of chronic patients with SMI in residential facilities. The results draw attention to the need for interventions for smoking cessation in those settings. The personnel in rehabilitation facilities should not adopt a priori the belief that patients with SMI are not interested in quitting smoking but rather inquire about their attitudes and encourage patients to make efforts for cessation or to seek appropriate consultation. Moreover, mental health professionals should avoid smoking in the facilities and encourage patients to conform to the facilities’ smoking regulations. Patients with physical morbidity, especially those suffering from cardiovascular disease, should be particularly encouraged to quit smoking or referred to specialists, and, in such cases, the personnel should assume a more active role to help patients, in order to prevent premature mortality. In a previous review of the literature, several barriers to smoking cessation in patients with schizophrenia were recorded, including craving and addiction, but also the perceived increased risk of negative effects associated with quitting smoking, stress management and the maintenance of social relationships. The most consistently mentioned facilitator to quitting smoking was physical health concerns [50].
## 5. Conclusions
The results of the present study point to the importance of more holistic care of psychiatric patients and highlight the notion that this population receives substantially lower levels of care for physical disease. Smoking and smoking-related health issues are common in people with severe mental disorders. This study drew attention to smoking in residential facilities, where it appears that most patients are chronic smokers, but a large proportion recognizes the effects of smoking on health and has made efforts to quit smoking. Indeed, there is some very recent evidence that, although patients with SMI frequently engage in modifiable health-risk behaviors, most perceive the maintenance of good health as important. Accordingly, those patients could be motivated to make the necessary behavioral changes to preserve or improve health [51]. Consequently, interventions designed to educate patients with SMI regarding the importance of health and health behaviors may motivate patients to reduce their engagement in health-risk behaviors [51]. This may mean that, with regular guidance, patients may conform to a healthier lifestyle, including smoking reduction or cessation. Patients need more support and encouragement in this respect, and mental health staff in rehabilitation facilities should assume a proactive role in helping patients to receive appropriate consultation, particularly those already suffering from cardiovascular disease. There is a need for further study concerning smoking in chronic patients residing in psychosocial facilities. Targeted smoking cessation programs are especially necessary for this population group. It may be useful to take into consideration the patients’ attitudes when implementing programs for smoking cessation and to individualize interventions according to their views and preferences. Interventions regarding the facility environment and changes in smoking policies and regulations should be also considered.
## References
1. **Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019**. *Lancet Psychiatry* (2022.0) **9** 137-150. DOI: 10.1016/S2215-0366(21)00395-3
2. Peritogiannis V., Gogou A., Samakouri M.. **Very long-term outcome of psychotic disorders**. *Int. J. Soc. Psychiatry* (2020.0) **66** 633-641. DOI: 10.1177/0020764020922276
3. Saha S., Chant D., McGrath J.. **A systematic review of mortality in schizophrenia: Is the differential mortality gap worsening over time?**. *Arch. Gen. Psychiatry* (2007.0) **64** 1123-1131. DOI: 10.1001/archpsyc.64.10.1123
4. Lee E.E., Liu J., Tu X., Palmer B.W., Eyler L.T., Jeste D.V.. **A widening longevity gap between people with schizophrenia and general population: A literature review and call for action**. *Schizophr. Res.* (2018.0) **196** 9-13. DOI: 10.1016/j.schres.2017.09.005
5. Barber S., Thornicroft G.. **Reducing the Mortality Gap in People With Severe Mental Disorders: The Role of Lifestyle Psychosocial Interventions**. *Front. Psychiatry* (2018.0) **9** 463. DOI: 10.3389/fpsyt.2018.00463
6. Correll C.U., Solmi M., Veronese N., Bortolato B., Rosson S., Santonastaso P., Thapa-Chhetri N., Fornaro M., Gallicchio D., Collantoni E.. **Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: A large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls**. *World Psychiatry* (2017.0) **16** 163-180. DOI: 10.1002/wps.20420
7. Peritogiannis V., Ninou A., Samakouri M.. **Mortality in Schizophrenia-Spectrum Disorders: Recent Advances in Understanding and Management**. *Healthcare* (2022.0) **10**. DOI: 10.3390/healthcare10122366
8. Gogos A., Skokou M., Ferentinou E., Gourzis P.. **Nicotine consumption during the prodromal phase of schizophrenia—A review of the literature**. *Neuropsychiatr. Dis. Treat.* (2019.0) **15** 2943-2958. DOI: 10.2147/NDT.S210199
9. Sagud M., Mihaljevic Peles A., Pivac N.. **Smoking in schizophrenia: Recent findings about an old problem**. *Curr. Opin. Psychiatry* (2019.0) **32** 402-408. DOI: 10.1097/YCO.0000000000000529
10. Kourakos M., Koukia E.. **A Study of Patients’ Smoking Habits in a Psychiatric Hospital**. *Int. J. Caring Sci.* (2014.0) **7** 592-601
11. Kourakos M., Kalokairinou A., Zyga S., Koukia E.. **Views and Attitudes of Patients in Mental Facilities Regarding Smoking**. *Glob. J. Health Sci.* (2016.0) **8** 54508. DOI: 10.5539/gjhs.v8n8p228
12. Kourakos M., Saridi M., Kafkia T., Rekleiti M., Souliotis K., Brokalaki H., Koukia E.. **Factors Affecting Mental Patients’ Behaviors and Attitudes Regarding Smoking**. *Asian Pac. J. Cancer Prev.* (2017.0) **18** 1991-1997. DOI: 10.22034/APJCP.2017.18.7.1991
13. Placentino A., Rillosi L., Papa E., Foresti G., Materzanini A., Rossi G., Tura G.B., Pérez J., Tura G.B.. **Clinical characteristics in long-term care psychiatric patients: A descriptive study**. *World J. Biol. Psychiatry* (2009.0) **10** 58-64. DOI: 10.1080/15622970701367922
14. Lee E.E., Martin A.S., Kaufmann C.N., Liu J., Kangas J., Daly R.E., Tu X.M., Depp C.A., Jeste D.V.. **Comparison of schizophrenia outpatients in residential care facilities with those living with someone: Study of mental and physical health, cognitive functioning, and biomarkers of aging**. *Psychiatry Res.* (2019.0) **275** 162-168. DOI: 10.1016/j.psychres.2019.02.067
15. Apostolopoulou A., Stylianidis S., Issari P., Chondros P., Alexiadou A., Belekou P., Giannou C., Karali E.K., Foi V., Tzaferou F.. **Experiences of Recovery in EPAPSY’s Community Residential Facilities and the Five CHIME Concepts: A Qualitative Inquiry**. *Front. Psychiatry* (2020.0) **11** 24. DOI: 10.3389/fpsyt.2020.00024
16. Grunebaum M.F., Weiden P.J., Olfson M.. **Medication supervision and adherence of persons with psychotic disorders in residential treatment settings: A pilot study**. *J. Clin. Psychiatry* (2001.0) **62** 394-399. DOI: 10.4088/JCP.v62n0515
17. Dickens G., Stubbs J., Popham R., Haw C.. **Smoking in a forensic psychiatric service, a survey of inpatients’ views**. *J. Psychiatr. Ment. Health Nurs.* (2005.0) **12** 672-678. DOI: 10.1111/j.1365-2850.2005.00892.x
18. Tabachnick B., Fidell L.. *Using Multivariate Statistics* (2007.0)
19. Bakola M., Manthopoulou T., Bonotis K., Peritogiannis V.. **Dietary Habits and Obesity in Patients with Psychotic Disorders in Rural Areas in Northwestern Greece**. *Psych* (2022.0) **4** 1-9. DOI: 10.3390/psych4010001
20. **Tobacco Consumption Statistics—Statistics Explained**
21. Michas G., Magriplis E., Micha R., Chourdakis M., Koutelidakis A., Dimitriadis G., Panagiotakos D., Zampelas A.. **Prevalence and factors associated with smoking in a nationally representative sample of Greek adults: The Hellenic National Nutrition and Health Survey (HNNHS)**. *Hell. J. Cardiol.* (2022.0) **67** 19-27. DOI: 10.1016/j.hjc.2022.05.005
22. Rachiotis G., Muula A.S., Rudatsikira E., Siziya S., Kyrlesi A., Gourgoulianis K., Hadjichristodoulou C.. **Factors associated with adolescent cigarette smoking in Greece: Results from a cross sectional study (GYTS Study)**. *BMC Public Health* (2008.0) **8**. DOI: 10.1186/1471-2458-8-313
23. Minichino A., Bersani F.S., Calò W.K., Spagnoli F., Francesconi M., Vicinanza R., Chiaie R.D., Biondi M.. **Smoking Behaviour and Mental Health Disorders—Mutual Influences and Implications for Therapy**. *Int. J. Environ. Res. Public Health* (2013.0) **10** 4790-4811. DOI: 10.3390/ijerph10104790
24. Trainor K., Leavey G.. **Barriers and Facilitators to Smoking Cessation Among People With Severe Mental Illness: A Critical Appraisal of Qualitative Studies**. *Nicotine Tob. Res.* (2016.0) **19** 14-23. DOI: 10.1093/ntr/ntw183
25. Mak Y.W., Chiang V.C.L., Loke A.Y.. **Experiences of Tobacco Use among Chinese Individuals with Schizophrenia in Community-Based Residential Settings: A Qualitative Study**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17010321
26. Grunze A., Mosolov S., Grunze H., Born C.. **The detrimental effects of smoking on the course and outcome in adults with bipolar disorder—A narrative review**. *Front. Psychiatry* (2023.0) **13** 1114432. DOI: 10.3389/fpsyt.2022.1114432
27. Wootton R.E., Richmond R.C., Stuijfzand B.G., Lawn R.B., Sallis H.M., Taylor G.M.J., Hemani G., Jones H.J., Zammit S., Smith G.D.. **Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: A Mendelian randomisation study**. *Psychol. Med.* (2020.0) **50** 2435-2443. DOI: 10.1017/S0033291719002678
28. Saccaro L.F., Gasparini S., Rutigliano G.. **Applications of Mendelian randomization in psychiatry: A comprehensive systematic review**. *Psychiatr. Genet.* (2022.0) **32** 199-213. DOI: 10.1097/YPG.0000000000000327
29. Quigley H., MacCabe J.H.. **The relationship between nicotine and psychosis**. *Ther. Adv. Psychopharmacol.* (2019.0) **9** 2045125319859969. DOI: 10.1177/2045125319859969
30. Hahad O., Daiber A., Michal M., Kuntic M., Lieb K., Beutel M., Münzel T.. **Smoking and Neuropsychiatric Disease—Associations and Underlying Mechanisms**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22147272
31. Khandaker G.M., Cousins L., Deakin J., Lennox B.R., Yolken R., Jones P.B.. **Inflammation and immunity in schizophrenia: Implications for pathophysiology and treatment**. *Lancet Psychiatry* (2015.0) **2** 258-270. DOI: 10.1016/S2215-0366(14)00122-9
32. Miller B.J., Goldsmith D.R.. **Evaluating the Hypothesis That Schizophrenia Is an Inflammatory Disorder**. *Focus* (2020.0) **18** 391-401. DOI: 10.1176/appi.focus.20200015
33. Saccaro L., Schilliger Z., Dayer A., Perroud N., Piguet C.. **Inflammation, anxiety, and stress in bipolar disorder and borderline personality disorder: A narrative review**. *Neurosci. Biobehav. Rev.* (2021.0) **127** 184-192. DOI: 10.1016/j.neubiorev.2021.04.017
34. Chen L.-S., Baker T., Brownson R.C., Carney R.M., Jorenby D., Hartz S., Smock N., Johnson M., Ziedonis D., Bierut L.J.. **Smoking Cessation and Electronic Cigarettes in Community Mental Health Centers: Patient and Provider Perspectives**. *Community Ment. Health J.* (2017.0) **53** 695-702. DOI: 10.1007/s10597-016-0065-8
35. Himelhoch S., Riddle J., Goldman H.H.. **Barriers to Implementing Evidence-Based Smoking Cessation Practices in Nine Community Mental Health Sites**. *Psychiatr. Serv.* (2014.0) **65** 75-80. DOI: 10.1176/appi.ps.201200247
36. Sharma R., Meurk C., Bell S., Ford P., Gartner C.. **Australian mental health care practitioners’ practices and attitudes for encouraging smoking cessation and tobacco harm reduction in smokers with severe mental illness**. *Int. J. Ment. Health Nurs.* (2018.0) **27** 247-257. DOI: 10.1111/inm.12314
37. Sheals K., Tombor I., McNeill A., Shahab L.. **A mixed-method systematic review and meta-analysis of mental health professionals’ attitudes toward smoking and smoking cessation among people with mental illnesses**. *Addiction* (2016.0) **111** 1536-1553. DOI: 10.1111/add.13387
38. Brown E., O’Donoghue B., White S.L., Chanen A., Bedi G., Adams S., Schely C., Do T.U., Sterjovska A., Moeller-Saxone K.. **Tobacco smoking in young people seeking treatment for mental ill-health: What are their attitudes, knowledge and behaviours towards quitting?**. *Ir. J. Psychol. Med.* (2021.0) **38** 30-39. DOI: 10.1017/ipm.2020.18
39. Beyraghi N., Mazaheri Meybodi A., Jafarian Bahri R.S.. **Smoking ban in psychiatric inpatient unit: An Iranian study on the views and attitudes of the mental health staff and psychiatric patients**. *Psychiatry J.* (2018.0) **2018** 2450939. DOI: 10.1155/2018/2450939
40. Mallet J., Le Strat Y., Schürhoff F., Mazer N., Portalier C., Andrianarisoa M., Aouizerate B., Berna F., Brunel L., Capdevielle D.. **Cigarette smoking and schizophrenia: A specific clinical and therapeutic profile? Results from the FACE-Schizophrenia cohort**. *Prog. Neuro-Psychopharmacol. Biol. Psychiatry* (2017.0) **79** 332-339. DOI: 10.1016/j.pnpbp.2017.06.026
41. Li X.H., An F.R., Ungvari G.S., Ng C.H., Chiu H.F., Wu P.P., Xiang Y.T.. **Prevalence of smoking in patients with bipolar disorder, major depressive disorder and schizophrenia and their relationships with quality of life**. *Sci. Rep.* (2017.0) **7** 8430. DOI: 10.1038/s41598-017-07928-9
42. Diaz F.J., Velásquez D.M., Susce M.T., de Leon J.. **The association between schizophrenia and smoking: Unexplained by either the illness or the prodromal period**. *Schizophr. Res.* (2008.0) **104** 214-219. DOI: 10.1016/j.schres.2008.06.004
43. Gurillo P., Jauhar S., Murray R.M., MacCabe J.H.. **Does tobacco use cause psychosis? Systematic review and meta-analysis**. *Lancet Psychiatry* (2015.0) **2** 718-725. DOI: 10.1016/S2215-0366(15)00152-2
44. Kendler K.S., Lönn S., Sundquist J., Sundquist K.. **Smoking and Schizophrenia in Population Cohorts of Swedish Women and Men: A Prospective Co-Relative Control Study**. *Am. J. Psychiatry* (2015.0) **172** 1092-1100. DOI: 10.1176/appi.ajp.2015.15010126
45. de Leon J., Diaz F.J., Rogers T., Browne D., Dinsmore L.. **Initiation of daily smoking and nicotine dependence in schizophrenia and mood disorders**. *Schizophr. Res.* (2002.0) **56** 47-54. DOI: 10.1016/S0920-9964(01)00217-1
46. Kotov R., Guey L.T., Bromet E.J., Schwartz J.E.. **Smoking in Schizophrenia: Diagnostic Specificity, Symptom Correlates, and Illness Severity**. *Schizophr. Bull.* (2008.0) **36** 173-181. DOI: 10.1093/schbul/sbn066
47. Tsuda Y., Saruwatari J., Yasui-Furukori N.. **Meta-analysis: The effects of smoking on the disposition of two commonly used antipsychotic agents, olanzapine and clozapine**. *BMJ Open* (2014.0) **4** e004216. DOI: 10.1136/bmjopen-2013-004216
48. Cooper J., Mancuso S., Borland R., Slade T., Galletly C., Castle D.. **Tobacco smoking among people living with a psychotic illness: The second Australian survey of psychosis**. *Aust. N. Z. J. Psychiatry* (2012.0) **46** 851-863. DOI: 10.1177/0004867412449876
49. Dimopoulos N.. **Investigation of attitudes and views on smoking of patients with mental illness at the Mental Health Center of Tripoli**. *Master’s Thesis* (2017.0)
50. Lum A., Skelton E., Wynne O., Bonevski B.. **A Systematic Review of Psychosocial Barriers and Facilitators to Smoking Cessation in People Living With Schizophrenia**. *Front Psychiatry* (2018.0) **9** 565. DOI: 10.3389/fpsyt.2018.00565
51. Peckham E., Lorimer B., Spanakis P., Heron P., Crosland S., Walker L., Gilbody S.. **Health-risk behaviours among people with severe mental ill health: Understanding modifiable risk in the Closing the Gap Health Study**. *Br. J. Psychiatry* (2023.0). DOI: 10.1192/bjp.2022.143
|
---
title: MicroRNA Profiles in Intestinal Epithelial Cells in a Mouse Model of Sepsis
authors:
- Siqingaowa Caidengbate
- Yuichi Akama
- Anik Banerjee
- Khwanchanok Mokmued
- Eiji Kawamoto
- Arong Gaowa
- Louise D. McCullough
- Motomu Shimaoka
- Juneyoung Lee
- Eun Jeong Park
journal: Cells
year: 2023
pmcid: PMC10001189
doi: 10.3390/cells12050726
license: CC BY 4.0
---
# MicroRNA Profiles in Intestinal Epithelial Cells in a Mouse Model of Sepsis
## Abstract
Sepsis is a systemic inflammatory disorder that leads to the dysfunction of multiple organs. In the intestine, the deregulation of the epithelial barrier contributes to the development of sepsis by triggering continuous exposure to harmful factors. However, sepsis-induced epigenetic changes in gene-regulation networks within intestinal epithelial cells (IECs) remain unexplored. In this study, we analyzed the expression profile of microRNAs (miRNAs) in IECs isolated from a mouse model of sepsis generated via cecal slurry injection. Among 239 miRNAs, 14 miRNAs were upregulated, and 9 miRNAs were downregulated in the IECs by sepsis. Upregulated miRNAs in IECs from septic mice, particularly miR-149-5p, miR-466q, miR-495, and miR-511-3p, were seen to exhibit complex and global effects on gene regulation networks. Interestingly, miR-511-3p has emerged as a diagnostic marker in this sepsis model due to its increase in blood in addition to IECs. As expected, mRNAs in the IECs were remarkably altered by sepsis; specifically, 2248 mRNAs were decreased, while 612 mRNAs were increased. This quantitative bias may be possibly derived, at least partly, from the direct effects of the sepsis-increased miRNAs on the comprehensive expression of mRNAs. Thus, current in silico data indicate that there are dynamic regulatory responses of miRNAs to sepsis in IECs. In addition, the miRNAs that were increased with sepsis had enriched downstream pathways including Wnt signaling, which is associated with wound healing, and FGF/FGFR signaling, which has been linked to chronic inflammation and fibrosis. These modifications in miRNA networks in IECs may lead to both pro- and anti-inflammatory effects in sepsis. The four miRNAs discovered above were shown to putatively target LOX, PTCH1, COL22A1, FOXO1, or HMGA2, via in silico analysis, which were associated with Wnt or inflammatory pathways and selected for further study. The expressions of these target genes were downregulated in sepsis IECs, possibly through posttranscriptional modifications of these miRNAs. Taken together, our study suggests that IECs display a distinctive miRNA profile which is capable of comprehensively and functionally reshaping the IEC-specific mRNA landscape in a sepsis model.
## 1. Introduction
Sepsis is a leading cause of global mortality. Epidemiological studies have shown that the mortality of the patients in intensive care units with sepsis is higher than $40\%$ [1]. Among the 49 million people who are affected annually worldwide, approximately 11 million individuals die [2]. Multiple organ dysfunction (MOD) is a pathologic condition which contributes to the increase in morbidity and mortality in sepsis [3,4,5]. The aberrant host response to polymicrobial infection and inflammation is the leading cause of MOD [6,7]. Sepsis has become increasingly recognized as a condition that promotes an overactive host immune response followed by MOD [6,8]. The pathophysiology of sepsis development is immunologically and spatiotemporally complex. Upregulation of both pro- and anti-inflammatory responses occurs during initial stages of infection, followed by morbid outcomes which are associated with hyperinflammation or immune paralysis [9,10].
The intestines are sensitive to sepsis-induced inflammation. Splanchnic ischemia and mucosal injury occur in the intestines upon onset of sepsis [11]. The injured mucosa upregulates and secretes pro-inflammatory mediators into the systemic vasculature, inducing systemic inflammation in multiple organs, including the brain [12]. Intestinal epithelial cells (IECs) constitute a single-layered lining that plays an important role in host defense by providing a physical barrier between the luminal surface containing microbe-derived factors and the host. These cells transfer signals bidirectionally between the host and microbes to mount appropriate immune responses [13,14,15,16,17]. Intrinsic and extrinsic inflammatory stimuli induced by sepsis disrupt the intestinal barrier and enhance epithelial permeability, resulting in the development of systemic inflammatory response and MOD [18,19]. IECs are one of the key players that regulate immune pathophysiology in sepsis [20,21]. Thus, understanding the IEC response to sepsis is highly significant. We investigated changes in the expression profiles of epigenetic regulators in septic IECs, such as small regulatory RNAs (e.g., microRNAs; miRNAs). Such approaches may be helpful to better understand how IECs reshape their post-sepsis gene expression and mediate changes in downstream signaling pathways.
Cecal slurry (CS) injection is a widely accepted model to induce chronic polymicrobial sepsis. In this model, cecal contents of other animals are administered into the peritoneal cavity of recipient mice, as described previously [22,23,24,25,26]. This model has been used to establish experimental sepsis in neonatal mice using freshly prepared samples [22,24]. The CS-injection model can induce differential mortality in a dose-dependent manner and is dependent more on bacterial infection than an endotoxin effect [27,28]. Starr et al. have also improved the method of CS preservation via maintaining bacterial viability in samples for at least several months [27].
In this study, we used the CS-injection model to induce chronic sepsis. We analyzed miRNA profiles specifically in IECs. IECs isolated from CS-injected groups dynamically responded to sepsis by altering their miRNA profiles. Subsequent in silico analysis showed that the miRNAs upregulated in IECs after sepsis regulate both pro- and anti-inflammatory downstream pathways, activating pathways related to protective and detrimental effects of epithelial inflammation.
## 2.1. Mice
C57BL/6J (13–15w-old male) mice were purchased from Japan SLC (Shizuoka, Japan). The mice were maintained in the Mie University Experimental Animal Facility at a specific pathogen-free condition under a 12-h light–dark cycle. The mice were given water and food ad libitum. All the experiments were conducted according to protocols approved by the Ethics Review Committee for Animal Experimentation of Mie University (approval number: #2019-41-1).
## 2.2. Polymicrobial Sepsis Induction
Polymicrobial sepsis was induced by intraperitoneally injecting CS, as previously described [25,27]. In brief, 0.25 mL of CS resuspended in $10\%$ glycerol/PBS was injected into each mouse. Mice in the sham cohort were given the same volume of $10\%$ glycerol intraperitoneally. Twelve hours after CS injection, mice in both CS and sham cohorts were intraperitoneally injected with antibiotics of 3 mg meropenem (Wako, Osaka, Japan) and 3 mg cilastatin (Wako) per mouse, seven times at twelve-hour intervals. All mice were subcutaneously injected with 0.7 mL $0.9\%$ saline. Previously, the survival rate observation and sampling were conducted from days 14 to 30 [25] and from days 15 to 17 after CS injection (an unpublished report by Akama et al.), respectively. Thus, we used day 17 after CS injection in the current model, as previously described, with slight modifications to provide the mice within same cohorts with similar sepsis conditions.
## 2.3. IEC Isolation and Enrichment
IECs were isolated as previously described [29,30,31] with slight modifications. In brief, small intestines were collected from mice after euthanasia and Peyer’s patches, mesentery, and fats were removed prior to further processing. The tissues were opened and washed with ice-cold RPMI-1640 (Nacalai, Kyoto, Japan). The rinsed tissues were cut into small pieces at 1-cm length and incubated in RPMI-1640 containing $10\%$ FBS (Equitech-Bio, Kerrville, TX, USA) and 2 mM ethylenediaminetetraacetic acid (EDTA) (Wako) for 30 min at 37 °C. The digested tissues were filtered using a 70-μm cell strainers (Corning, Glendale, AZ, USA). The filtered cell suspension was resuspended in $40\%$ Percoll (GE Healthcare Life Sciences, Chicago, IL, USA) and applied to gradients of 25, 40, and $75\%$ Percoll. After centrifugation in AX-511 (Tomy, Tokyo, Japan) at 780× g for 20 min at 22 °C, the interface between 25 and $40\%$ gradients was saved to collect IECs. IECs were further enriched using EpCAM microbeads (Miltenyi Biotec, Gaithersburg, MD, USA) and magnetic-activated cell sorting (MACS) cell separation columns (Miltenyi Biotec). The enriched IECs were tested for EpCAM expression using a monoclonal antibody to EpCAM (G8.8) (eBioscience, San Diego, CA, USA), the rat IgG2a isotype control antibody (BioLegend, San Diego, CA, USA), and a BD Accuri C6 Flow Cytometer and BD Accuri C6 Software (BD Biosciences, San Jose, CA, USA).
## 2.4. IEC MicroRNA (miRNA) and Messenger RNA (mRNA) Analysis Using Deep Sequencing
RNA was extracted from IECs using a miRNeasy Mini Kit (Qiagen, Germantown, MD, USA). Library construction and sequencing of small RNAs (including miRNAs) were achieved by using an Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific, Waltham, MA, USA) and the Ion Personal Genome machine (PGM) system (Thermo Fisher Scientific) according to the manufacturer’s instructions at the Mie University Center for Molecular Biology and Genetics (Tsu, Japan) as previously described [30]. Data collection was performed with Torrent Suite v4.0.1 software. The assessment of miRNA profiling was conducted as previously described [30]. In brief, detectable miRNAs (>0, RPKM) across all samples were chosen for differential expression and downstream pathway analysis [32]. Individual fold changes (RPKM in CS-injected sepsis mouse/RPKM in sham mouse) were calculated by taking the ratio of the candidate miRNA expression values with one sham control. Those miRNAs and mRNAs with a fold change of 2 or greater (FC > 2) were classified as upregulated miRNAs and mRNAs in the sepsis group compared to sham, while those with (FC < −2) were classified as downregulated miRNAs and mRNAs in the sepsis group compared to sham.
## 2.5. Cell Culture and LPS Treatment
Human IEC lines (C2Bbe1, HUTU80, and H747) were obtained from ATCC (Manassas, VA, USA). The cells were cultured in RPMI 1640 supplemented with $10\%$ fetal bovine serum (FBS) (Equitech-Bio, Kerrville, TX, USA) and penicillin (100 U/mL)/streptomycin (100 μg/mL) (Nacalai) in $5\%$ CO2 at 37 °C. The cells of 70 to $80\%$ confluency on a 6-well plate (Corning, Glendale, AZ, USA) were treated with lipopolysaccharide (LPS) (L3880, Sigma, St. Louis, MO, USA) at a concentration of 1 μg/mL and incubated for 24 h for further study, as described previously [33,34,35,36].
## 2.6. Real-Time Quantitative PCR (RT-qPCR)
RT-qPCR was performed as previously described [29,30,31] with slight modifications. Briefly, RNA was extracted from the cells and blood using a miRNeasy Kit (Qiagen, Hilden, Germany) and TRIzol reagent (Thermo Fisher Scientific) according to the manufacturers’ instructions. Approximately 1 μg RNA was subjected to a reaction of a reverse transcription using a Mir-X miRNA First-Strand Synthesis Kit (Takara Bio, Shiga, Japan) and a Prime Script RT Kit (Takara Bio), to detect the expressions of miRNAs and mRNAs, respectively. To examine relative gene expression, qPCR was conducted using a PowerUp SYBR Green Master Mix PCR kit (Applied Biosystems, Foster City, CA, USA) and the StepOne Real-Time PCR System (Applied Biosystems) according to manufacturer’s instructions. For endogenous controls, U6 and β-actin were used to normalize expressions of miRNAs and mRNAs, respectively. For miRNAs, the universal primer (Thermo Fisher Scientific) was utilized as the reverse primer for miRNA validation runs. All the PCR-primer sequences for RT-qPCR used in this study are listed in Supplementary Table S2. Relative expression was calculated using the comparative threshold (CT) method (2−dCT) normalized to endogenous control genes and expressed between two cohorts.
## 2.7. MiRNA-Target Network and Pathway Analyses
The miRNAs of the IECs were applied to miRNet 2.0 [37], which incorporates miRBase [38], and miRTarBase v8.0 [39], to construct the networks. The Reactome, Gene Ontology, and KEGG analyses were performed in miRNet 2.0 [37].
## 2.8. Statistical Analysis
Data are presented as the mean ± standard error of the mean (SEM). Results were analyzed using two-tailed Student’s t test for comparison of two groups. p-values < 0.05 were considered significant. Statistical analysis was completed using Prism 8 software (GraphPad, San Diego, CA, USA).
## 3.1. Sepsis Alters miRNAs and mRNAs in IECs in Mice
To examine the role of IEC-specific miRNAs and its downstream regulatory networks in sepsis, the CS injection model was used to induce sepsis in mice [27] and miRNA expression within IECs was investigated. The CS (25 mg of cecal contents resuspended in $10\%$ glycerol in PBS) was injected intraperitoneally into each mouse, followed by antibiotic treatment (3 mg meropenem plus 3 mg cilastatin per dose; 7 doses). Sham mice received the same volume of $10\%$ glycerol in PBS (0.25 mL per mouse) followed by treatment with antibiotics (Figure 1A). The survival rate was approximately $67\%$ ($\frac{12}{18}$ mice) at day 17 after CS injection, while $100\%$ survival ($\frac{10}{10}$ mice) was seen in shams.
At post-injection day 17, all mice were euthanized, the small intestines were removed, and IECs were isolated using Percoll density gradients [29,31] and further enriched using magnetic sorting with CD326 (epithelial cell adhesion molecule; EpCAM) microbeads (Figure 1B). After isolation of IECs, their exclusive expression of EpCAM was validated using flow cytometry (Supplementary Figure S1). The whole transcriptome of small RNAs including miRNAs of the isolated IECs was sequenced using the high-throughput Ion Xpress™ RNA-Seq platform. Among 1076 miRNAs initially detected using the sequencing platform, 239 miRNAs that had any expression (i.e., threshold detection hit of >0) for all analyzed samples (1 sham and 3 septic mice) were analyzed and listed in Supplementary Table S1.
The average miRNA expression level in IECs after sepsis was compared with a sham counterpart with a threshold cutoff of fold change (FC) of 2 or greater. As shown in Figure 2A, the mean expression of 35 miRNAs was upregulated in IECs after sepsis compared with sham IECs, while 15 miRNAs were downregulated following sepsis. Figure 2A depicts miRNA candidates plotted across both mean fold change and mean expression (RPKM values). We further compared miRNA reads of each sample with the respective sham control for a more robust identification of miRNAs differentially expressed after sepsis. Following this filtering criteria, our data indicated that 14 miRNAs (miR-669o, miR-3096, miR-466q, miR-511, miR-495, miR-467e, miR-434, miR-154, miR-669a-4, miR-127, miR-328, miR-669a-5, miR-378c, and miR-149; ordered by FC) were upregulated in IECs after sepsis. Nine miRNAs (miR-6238, miR-1258, miR-124-2hg, miR-17hg, miR-5125, miR-6240, miR-351, miR-717, and miR-1983; ordered by FC) were downregulated in IECs after sepsis (Figure 2B).
To investigate any downstream regulating effects of the identified miRNAs, we analyzed the whole transcriptome and acquired comprehensive expression signatures of mRNAs using the same IEC samples of sham and sepsis mice as used in the miRNA analysis. We found that, among a total of 14,316 mRNAs detected, 2248 mRNAs were downregulated with sepsis, while 612 mRNAs in the IECs were upregulated (Figure 2C). Both up- and down-regulated mRNAs, shown as dots, were determined by the same criteria used in examining miRNAs for the mean RPKM values across the sepsis IECs. The number of downregulated mRNAs was approximately 3.6-times higher than that of the mRNAs upregulated by sepsis. Thus, this suggests that the miRNAs upregulated in IECs after sepsis contribute, at least partly, to the downregulation of the comprehensive mRNA profile.
We next sought to further identify the upstream regulating factors that affect the changes in gene expression of RNAs (including both mRNAs and miRNAs). DNA methylation is an epigenetic marker that effectively silences transcription [40] and requires enzymatic activity of DNA methyltransferases (e.g., DNMT1 and DNMT3A) [41,42]. We thus examined the expression levels of DNMT1 and DNMT3A and found that both genes were significantly upregulated within the IECs of CS-injected sepsis mice compared to those of sham mice (Supplementary Figure S2). Thus, these results suggest that sepsis induces an alteration of the overall transcriptome that may be related to epigenetic modifications. This can be achieved by the enzymatic activity of DNMT1 and DNMT3A in DNA methylations and/or by the posttranscriptional regulation of the miRNAs (such as miR-149-5p, miR-466q, miR-495, and miR-511-3p) upregulated in the sepsis IECs.
## 3.2. Sepsis-Upregulated miRNAs Provide a Highly Complex miRNA–mRNA Regulatory Network in IECs
To further elucidate the functional role of these IEC miRNAs in the pathophysiology of sepsis, we constructed miRNA–mRNA target interaction networks using an analytic platform, miRNet 2.0 [37]. As shown in Figure 3A, upregulated miRNAs in IECs after sepsis showed complex networks with putative targets. A large continent network encompassing multiple miRNAs, including miR-149-5p (423 targets), miR-495-3p (207 targets), miR-511-3p (130 targets), and miR-466q (105 targets), was identified as pivotal nodes. In a separate island network, miR-127-5p potentially targeted only one gene transcript. Taken together, these results indicated that IECs dynamically respond to sepsis by altering miRNA profiles and downstream sepsis-induced regulatory gene networks.
In contrast to the networks containing upregulated miRNAs, only two networks (one continent and one island) were identified which were regulated by the pool of downregulated miRNAs in IECs after sepsis (Figure 3B). The continent network incorporated 3 miRNAs including miR-5125 (194 targets), miR-717 (101 targets), and miR-1983 (20 targets). In contrast, miR-351-5p created 1 island network with 20 targets. However, 5 other miRNAs (i.e., miR-6238, miR-1258, miR-124-2hg, miR-17hg, and miR-6240) did not show any network interactions in the miRNet analysis platform.
## 3.3. In Silico Analysis Reveals Enriched miRNA-Regulated Pathways in IECs following Sepsis
To examine underlying pathways regulated by the candidate miRNAs in sepsis, we used Reactome [43], a high-performance bioinformatics tool, within miRNet 2.0. Following Reactome analysis, we identified a total of 100 pathways potentially regulated by the 14 upregulated miRNAs in IECs after sepsis, filtered by the significance level (adjusted $p \leq 0.05$) and then ranked by the number of hits (the number of gene targets involved in the given pathway; Supplementary Table S3). The top 20 pathways were listed by a 3-way bubble plot depicted using the number of hits, the significance level, and the gene ratio (Figure 4A). Interestingly, 6 out of the 20 pathways shown to be altered with sepsis were related to the fibroblast growth factor receptor (FGFR) signaling. Although not identified within the top 20 pathways, apoptosis and programmed cell death pathways were significantly enriched, indicating that sepsis may trigger epithelial cell death in the small intestine. In addition, gene ontology (GO) analysis revealed that a total of 99 pathways (adjusted $p \leq 0.05$) were identified (Supplementary Table S4) and the top 20 pathways (ranked by the number of hits) were shown in Figure 4B, depicted by the same criteria used in Reactome analysis. To further identify candidate pathways, we performed systemic enrichment analysis on the identified upregulated pool of IEC miRNAs with sepsis, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) platform, a downstream pathway analysis tool within miRNet 2.0. The KEGG analysis showed a total of 57 pathways, filtered by the significance level (adjusted $p \leq 0.05$) and ranked by the number of hits (Supplementary Table S5). The top 20 pathways potentially regulated by miRNAs following sepsis were listed by a 3-way depiction, as described above (Figure 4C). Many of the notable pathways involved pathways in cancer. In addition, epithelial Wnt signaling in the small intestine is potentially altered by sepsis.
For the downregulated miRNAs seen in IECs after sepsis, we also performed Reactome, KEGG, and GO analyses. However, both the Reactome and GO analyses did not show any pathway. Only KEGG analysis revealed that Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling and ERBB signaling are putatively involved in the pathways regulated by the miRNAs downregulated in sepsis (e.g., miR-351-5p, miR-717, miR-1983, and miR-5125) (Supplementary Figure S3). In conclusion, upregulated miRNAs may potentially be actively involved in cell-specific responses in the IECs after sepsis.
## 3.4. Validation of the miRNAs Altered in Sepsis IECs and Blood and miR-511-3p Emerged as a Diagnostic Marker
We next performed validation of the identified miRNA candidates. Most of the miRNAs upregulated in sepsis, including miR-149-5p, miR-154-3p, miR-328-3p, miR-378c, miR-434-5p, miR-466q, miR-467e-5p, miR-495-5p, miR-511-3p&-5p, and miR-699o-3p&-5p, were validated for their increased expression via quantitative PCR using RNA and complementary DNA of sepsis and control (sham) IECs (Figure 5 and Supplementary Figure S4). These results suggest that sepsis produces a miRNA signature that is specific to the mucosal compartment and has the potential for the discovery of novel epithelium-specific biomarkers in sepsis.
To confirm that there were no off-target effects in sham mice that were injected with antibiotics (Figure 1), which were used as the control in this study, we included an additional cohort of mice which were not given antibiotics. To examine whether the levels of miRNAs increased in sepsis IECs are affected by the antibiotic injections alone, we assessed miRNA expression in IECs of the control sham mice in comparison with those of the mice with no injection (namely, normal mice). None of the 16 miRNAs that were upregulated in sepsis IECs showed any significant alterations in sham (control) mice injected with antibiotics when compared with those in uninjected normal mice (Supplementary Figure S5). Consequently, these data indicate that injections with antibiotics did not affect the expression level of any of the sepsis-increased 16 miRNAs tested.
To examine if human cells exhibit a similar expression pattern of the miRNAs (including miR-149-5p, miR-466q, miR-495, and miR-511-3p) increased in the IECs of sepsis mice, we validated the levels of these miRNAs in human IEC lines, such as C2Bbe1, HUTU80, and H747. To provide an in vitro sepsis environment, we treated LPS to the cells, as described previously [33,34,35,36], and examined their miRNA expression levels. Cells treated with LPS did not show a significant change in the expression of the miRNAs upregulated in the IECs of sepsis mice (Supplementary Figure S6). Thus, these results suggest that human IECs from cell lines may differ from sepsis-mouse IECs in miRNA expression in the currently used in vitro model of experimental sepsis. The profile of miRNAs in human IECs with sepsis requires further assessment using IECs isolated from the patients diagnosed with sepsis.
Next, we investigated the expression of the downregulated miRNAs (including miR-351-5p, miR-717, miR-1258-3p, miR-1983, and miR-5125) for validation and confirmed the reduction in miR-351-5p, miR-717, and miR-5125 expression levels in sepsis IECs by RT-qPCR. The expression of miR-1258-3p and miR-1983 did not show a significant downregulation in the IECs of the sepsis mice, compared to sham mice (Figure 6).
To ask if the expression patterns of up- and down-regulated miRNAs are restricted to sepsis IECs, we next investigated expression of the miRNAs in blood samples. The upregulated miRNAs selected for further study (miR-149-5p, miR-466q, miR-495-3p, miR-495-5p, and miR-511-3p) and the downregulated miRNAs (miR-351-5p, miR-717, miR-1258-3p, miR-1983, and miR-5125) were tested for their levels in blood of sham and sepsis mice. Among the 10 miRNAs (as shown in Figure 5 and Figure 6), only the miR-511-3p exhibited a significant augmentation, whereas the other miRNAs did not show significant changes (Figure 7). This suggests that this miRNA may be useful as a diagnostic marker in this sepsis model and should be examined in the blood of patients with sepsis in the future.
## 3.5. Reduced Expression of Putative Targets for the Sepsis-Increased miRNAs in IECs
We then explored gene expressions of specific mRNAs, such as LOX, PTCH1, COL22A1, FOXO1, and HMGA2, which were found to be bioinformatically targeted by one or more of the four identified miRNAs (miR-149-5p, miR-466q, miR-495, and miR-511-3p), as their gene products have been reported to associate with Wnt or inflammatory pathways. Specifically, the mRNA of the lysyl oxidase (LOX) is a putative target for miR-149-5p and miR-511-3p, and its downregulation activates the Wnt/β-catenin pathway [44,45]. The mRNA of the patched1 (PTCH1) is a putative target of miR-466q and miR-511-3p, and PTCH1 targeting by miR-511-3p can activate the hedgehog pathway to trigger hepatic sinusoidal obstruction syndrome [46], which is promoted by inflammatory and fibrinolytic pathways. The mRNA of collagen type XXII α1 (COL22A1) is a putative target of miR-149-5p and miR-466q, and targeting of COL22A1 by miR-149-5p regulates inflammation and fibrosis of cardiomyocytes [47]. The mRNA of Forkhead Box O1 (FOXO1) is a putative target for miR-466q, miR-495-5p, and miR-511-3p, and functional inhibition of FOXO1 is associated with the Wnt/β-catenin pathway [48]. The mRNA of high mobility group A2 (HMGA2) was shown to be a target for miR-495 [49,50], and the HMGA2 mediates the secretion of pro-inflammatory cytokines, while its downregulation induces hypermethylation [51]. Based on the literature and our current data that demonstrate a significant reduction in LOX, PTCH1, COL22A1, FOXO1, and HMGA2 in the IECs of sepsis mice, compared to sham mice (Figure 8), the sepsis-augmented miRNAs in the IECs may have the potential for regulating both anti- and pro-inflammatory responses, possibly through posttranscriptional modification of their functional target genes. The proposed model illustrating miRNA-mediated target-gene expression regulations for anti- and pro-inflammatory responses in the IECs of sepsis mice is shown in Figure 9.
To analyze expression of the target genes such as LOX, PTCH1, FOXO1, and HMGA2 in the human IEC lines subjected to LPS treatment, compared to control (no treatment), RT-qPCR was performed. Human cell lines (C2BBe1, HUTU80, and H747) treated with LPS showed no significant change in expression levels of the target genes (Supplementary Figure S7) that were decreased in the IECs of the sepsis mice (Figure 8). Human IEC lines were expected to be largely different from those in mouse cells, with regards to expression of the miRNAs and their regulatory pathways, at least under the currently used in vitro model of experimental sepsis. Future studies are needed to evaluate miRNAs from human IECs in vivo and in the blood from septic patients.
## 4. Discussion
Here we have shown that sepsis induces distinctively expressed miRNA profiles in IECs in a mouse model of sepsis. The upregulated miRNAs exhibited more complex and broader effects on comprehensive gene regulations in silico related to both Wnt signaling and inflammatory pathways. These upregulated miRNAs in sepsis IECs may contribute to quantitative downregulation of overall mRNAs. Intriguingly, several distinct miRNAs (miR-149-5p, miR-466q, miR-495, and miR-511-3p) may suppress expressions of LOX, PTCH1, COL22A1, FOXO1, or HMGA2. Current findings could provide us insight into the miRNA–mRNA crosstalk in IECs that may contribute to pro- and anti-inflammatory responses in sepsis.
IECs are pivotal for the surveillance of intestinal environment to protect the host from both local and systemic challenges [52]. Disruption of the IEC barrier’s integrity caused by intestinal infection and inflammation has been shown to significantly shift the transcriptomic patterns within IECs [53]. Alterations of cell-specific epigenetic factors have received much attention as a potential contributor to the regulation of host mucosal immunosurveillance and IEC barrier function [54,55]. In this study, we aimed to uncover the epigenetic alterations to impact the disruption of the intestinal epithelium during sepsis by investigating the miRNA signature profiles.
miRNAs have been proposed as potential biomarkers for diagnosing sepsis [56]. Our data showed that sepsis upregulated the expression levels of 14 miRNAs in IECs, compared with sham counterparts. Bioinformatics analysis further revealed that miR-149-5p, miR-495, miR-511-3p, and miR-466q might be key epithelial miRNAs in the small intestines, following sepsis. Hūbner et al. found that miR-149-5p plays an important role in TLR-mediated inflammation of bronchial epithelial cells by directly regulating chitinase-3-like 1 (CHI3L1), which has been known to regulate the bacterial infection [57]. CHI3L1 is highly expressed in IECs and can contribute to bacterial adhesion and invasion in intestinal inflammation [58]. Further, Heinsbroek et al. demonstrated that miR-511-3p expressed by immune cells regulates microbiota-associated intestinal inflammation [59]. Thus, further investigation on the roles of miRNAs and biomarker discovery in the context of sepsis-induced IEC disruption is warranted.
In silico analysis further revealed that the upregulated miRNAs can regulate several pathways, including FGFR signaling. Al Alam et al. found that FGF and FGFR are expressed in both human and mouse small intestines [60]. In addition, FGF is significantly involved in cell differentiation of goblet cells and Paneth cells that are pivotal for epithelial protection in the intestines. Huang et al. also showed that inhibition of FGFR by a selective inhibitor, AZD4547, protected septic mice from pulmonary inflammation [61]. Song et al. suggested a protective role of FGF in a mouse model of intestinal inflammation [62]. More specifically, they found that FGF2 expressed by regulatory T cells cooperates with the cytokine IL-17 derived from Th17 cells to promote epithelial repair in a mouse model of intestinal inflammation [62]. Therefore, the interaction of FGFs and FGFRs in sepsis-induced intestinal epithelial inflammation and how miRNAs play a role as a mitigator of sepsis-induced inflammation are warranted for further investigation.
In the intestines, Wnt signaling is fundamental for epithelial homeostasis [63]. Wnt signaling regulates several cellular functions of IECs, such as intestinal stem cells, related to their capacities for self-renewal and differentiation [64]. Interestingly, our KEGG analysis suggests that epithelial Wnt signaling might be significantly regulated by miRNAs after sepsis. In addition, recent studies have demonstrated that Wnt signaling can be a therapeutic target for the regeneration of intestinal epithelium. Xie et al. showed that Wnt mimetics (molecules mimicking endogenous Wnt) can regenerate the damaged epithelial tissues and reduce inflammation in a mouse model of colitis [65]. Xu et al. also demonstrated that miRNAs are significantly involved in the activation of the Wnt pathway [66]. Indeed, a target prediction tool, TargetScanMouse 7.1, revealed that most of the upregulated miRNAs in IECs after sepsis can bind to the 3′-UTR region of the mRNAs of multiple *Wnt* genes. Thus, the role of intestinal epithelial miRNAs regulating Wnt signaling in tissue regeneration after sepsis is warranted.
Among downregulated miRNAs following sepsis, four miRNAs (i.e., miR-5125, miR-351-5p, miR-717, and miR-1983) displayed potential miRNA–mRNA networks in our analysis. There is little information on the pathways affected by the downregulated miRNAs seen in IECs after sepsis; one possibility is that those miRNAs are still comparatively novel and less documented in the literature. Although there has been a limited number of studies that demonstrate the role of those miRNAs, our KEGG analysis revealed that the JAK-STAT signaling pathway and ERBB signaling pathway might putatively be altered in SIECs after sepsis. Notably, our data showed that ERBB signaling is a common pathway which can be regulated by both upregulated and downregulated miRNAs. It has been shown that ErbB receptors and their ligands are crucial in epithelial cell recovery in mucosal tissues [67,68]. Therefore, further studies will investigate the involvement of ErbB signaling in the regulation of intestinal epithelial injury.
Dysregulation of IEC remodeling may lead to a sustained mucosal inflammatory response in sepsis. Impaired wound-healing and remodeling capacities in the IECs disrupt their barrier integrity and further lead to bacterial translocation and subsequent inflammation in the intestine and other systemic compartments. IECs dynamically respond to sepsis by altering their miRNA profiles to regulate both epithelial injury and regeneration.
Collectively, our data suggest that sepsis-induced inflammation is centralized toward persistent inflammation and immunosuppression. These findings are reminiscent of the spectrum of host responses typically seen in sepsis [69,70]. Further extensive understanding of sepsis-induced epigenetic alterations in different cell types, such as human IECs or leukocytes, would provide insight into the identification of therapeutic targets for sepsis.
## References
1. Markwart R., Saito H., Harder T., Tomczyk S., Cassini A., Fleischmann-Struzek C., Reichert F., Eckmanns T., Allegranzi B.. **Epidemiology and burden of sepsis acquired in hospitals and intensive care units: A systematic review and meta-analysis**. *Intensive Care Med.* (2020) **46** 1536-1551. DOI: 10.1007/s00134-020-06106-2
2. Rudd K.E., Johnson S.C., Agesa K.M., Shackelford K.A., Tsoi D., Kievlan D.R., Colombara D.V., Ikuta K.S., Kissoon N., Finfer S.. **Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the Global Burden of Disease Study**. *Lancet* (2020) **395** 200-211. DOI: 10.1016/S0140-6736(19)32989-7
3. Bilevicius E., Dragosavac D., Dragosavac S., Araujo S., Falcao A.L., Terzi R.G.. **Multiple organ failure in septic patients**. *Braz. J. Infect. Dis.* (2001) **5** 103-110. DOI: 10.1590/S1413-86702001000300001
4. Blanco J., Muriel-Bombin A., Sagredo V., Taboada F., Gandia F., Tamayo L., Collado J., Garcia-Labattut A., Carriedo D., Valledor M.. **Incidence, organ dysfunction and mortality in severe sepsis: A Spanish multicentre study**. *Crit. Care* (2008) **12** R158. DOI: 10.1186/cc7157
5. Cecconi M., Evans L., Levy M., Rhodes A.. **Sepsis and septic shock**. *Lancet* (2018) **392** 75-87. DOI: 10.1016/S0140-6736(18)30696-2
6. Singer M., Deutschman C.S., Seymour C.W., Shankar-Hari M., Annane D., Bauer M., Bellomo R., Bernard G.R., Chiche J.D., Coopersmith C.M.. **The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)**. *JAMA* (2016) **315** 801-810. DOI: 10.1001/jama.2016.0287
7. Sauaia A., Moore F.A., Moore E.E.. **Postinjury Inflammation and Organ Dysfunction**. *Crit. Care Clin.* (2017) **33** 167-191. DOI: 10.1016/j.ccc.2016.08.006
8. Jarczak D., Kluge S., Nierhaus A.. **Sepsis-Pathophysiology and Therapeutic Concepts**. *Front. Med.* (2021) **8** 628302. DOI: 10.3389/fmed.2021.628302
9. Nedeva C., Menassa J., Puthalakath H.. **Sepsis: Inflammation Is a Necessary Evil**. *Front. Cell Dev. Biol.* (2019) **7** 108. DOI: 10.3389/fcell.2019.00108
10. Nakamori Y., Park E.J., Shimaoka M.. **Immune Deregulation in Sepsis and Septic Shock: Reversing Immune Paralysis by Targeting PD-1/PD-L1 Pathway**. *Front. Immunol.* (2021) **11** 624279. DOI: 10.3389/fimmu.2020.624279
11. Pastores S.M., Katz D.P., Kvetan V.. **Splanchnic ischemia and gut mucosal injury in sepsis and the multiple organ dysfunction syndrome**. *Am. J. Gastroenterol.* (1996) **91** 1697-1710. PMID: 8792684
12. Tamion F., Richard V., Sauger F., Menard J.F., Girault C., Richard J.C., Thuillez C., Leroy J., Bonmarchand G.. **Gastric mucosal acidosis and cytokine release in patients with septic shock**. *Crit. Care Med.* (2003) **31** 2137-2143. DOI: 10.1097/01.CCM.0000079600.49048.28
13. Goto Y., Ivanov I.I.. **Intestinal epithelial cells as mediators of the commensal-host immune crosstalk**. *Immunol. Cell Biol.* (2013) **91** 204-214. DOI: 10.1038/icb.2012.80
14. Lee J., Park E.J., Kiyono H.. **MicroRNA-orchestrated pathophysiologic control in gut homeostasis and inflammation**. *BMB Rep.* (2016) **49** 263-269. DOI: 10.5483/BMBRep.2016.49.5.041
15. Park E.J., Shimaoka M., Kiyono H.. **MicroRNA-mediated dynamic control of mucosal immunity**. *Int. Immunol.* (2017) **29** 157-163. DOI: 10.1093/intimm/dxx019
16. Allaire J.M., Crowley S.M., Law H.T., Chang S.Y., Ko H.J., Vallance B.A.. **The Intestinal Epithelium: Central Coordinator of Mucosal Immunity**. *Trends Immunol.* (2018) **39** 677-696. DOI: 10.1016/j.it.2018.04.002
17. Soderholm A.T., Pedicord V.A.. **Intestinal epithelial cells: At the interface of the microbiota and mucosal immunity**. *Immunology* (2019) **158** 267-280. DOI: 10.1111/imm.13117
18. Assimakopoulos S.F., Triantos C., Thomopoulos K., Fligou F., Maroulis I., Marangos M., Gogos C.A.. **Gut-origin sepsis in the critically ill patient: Pathophysiology and treatment**. *Infection* (2018) **46** 751-760. DOI: 10.1007/s15010-018-1178-5
19. Zhou Q., Verne G.N.. **Intestinal hyperpermeability: A gateway to multi-organ failure?**. *J. Clin. Investig.* (2018) **128** 4764-4766. DOI: 10.1172/JCI124366
20. Cabrera-Perez J., Badovinac V.P., Griffith T.S.. **Enteric immunity, the gut microbiome, and sepsis: Rethinking the germ theory of disease**. *Exp. Biol. Med.* (2017) **242** 127-139. DOI: 10.1177/1535370216669610
21. Park E.J., Shimaoka M., Kiyono H.. **Functional Flexibility of Exosomes and MicroRNAs of Intestinal Epithelial Cells in Affecting Inflammation**. *Front. Mol. Biosci.* (2022) **9** 854487. DOI: 10.3389/fmolb.2022.854487
22. Wynn J.L., Scumpia P.O., Delano M.J., O’Malley K.A., Ungaro R., Abouhamze A., Moldawer L.L.. **Increased mortality and altered immunity in neonatal sepsis produced by generalized peritonitis**. *Shock* (2007) **28** 675-683. DOI: 10.1097/shk.0b013e3180556d09
23. Gentile L.F., Nacionales D.C., Lopez M.C., Vanzant E., Cuenca A., Szpila B.E., Cuenca A.G., Joseph A., Moore F.A., Leeuwenburgh C.. **Host responses to sepsis vary in different low-lethality murine models**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0094404
24. Gentile L.F., Cuenca A.L., Cuenca A.G., Nacionales D.C., Ungaro R., Efron P.A., Moldawer L.L., Larson S.D.. **Improved emergency myelopoiesis and survival in neonatal sepsis by caspase-1/11 ablation**. *Immunology* (2015) **145** 300-311. DOI: 10.1111/imm.12450
25. Owen A.M., Patel S.P., Smith J.D., Balasuriya B.K., Mori S.F., Hawk G.S., Stromberg A.J., Kuriyama N., Kaneki M., Rabchevsky A.G.. **Chronic muscle weakness and mitochondrial dysfunction in the absence of sustained atrophy in a preclinical sepsis model**. *eLife* (2019) **8** e49920. DOI: 10.7554/eLife.49920
26. Laitano O., Robinson G.P., Garcia C.K., Mattingly A.J., Sheikh L.H., Murray K.O., Iwaniec J.D., Alzahrani J., Morse D., Hidalgo J.. **Skeletal Muscle Interleukin-6 Contributes to the Innate Immune Response in Septic Mice**. *Shock* (2021) **55** 676-685. DOI: 10.1097/SHK.0000000000001641
27. Starr M.E., Steele A.M., Saito M., Hacker B.J., Evers B.M., Saito H.. **A new cecal slurry preparation protocol with improved long-term reproducibility for animal models of sepsis**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0115705
28. Lee M.J., Kim K., Jo Y.H., Lee J.H., Hwang J.E.. **Dose-dependent mortality and organ injury in a cecal slurry peritonitis model**. *J. Surg. Res.* (2016) **206** 427-434. DOI: 10.1016/j.jss.2016.08.054
29. Lee J., Park E.J., Yuki Y., Ahmad S., Mizuguchi K., Ishii K.J., Shimaoka M., Kiyono H.. **Profiles of microRNA networks in intestinal epithelial cells in a mouse model of colitis**. *Sci. Rep.* (2015) **5** 18174. DOI: 10.1038/srep18174
30. Appiah M.G., Park E.J., Darkwah S., Kawamoto E., Akama Y., Gaowa A., Kalsan M., Ahmad S., Shimaoka M.. **Intestinal Epithelium-Derived Luminally Released Extracellular Vesicles in Sepsis Exhibit the Ability to Suppress TNF-a and IL-17A Expression in Mucosal Inflammation**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21228445
31. Lee J., Mohsen A., Banerjee A., McCullough L.D., Mizuguchi K., Shimaoka M., Kiyono H., Park E.J.. **Distinct Age-Specific miRegulome Profiling of Isolated Small and Large Intestinal Epithelial Cells in Mice**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22073544
32. Mai Z., Xiao C., Jin J., Zhang G.. **Low-cost, Low-bias and Low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing**. *Sci. Rep.* (2017) **7** 1053. DOI: 10.1038/s41598-017-01165-w
33. Lee M., Shim S.Y., Sung S.H.. **Triterpenoids Isolated from Alnus japonica Inhibited LPS-Induced Inflammatory Mediators in HT-29 Cells and RAW264.7 Cells**. *Biol. Pharm. Bull.* (2017) **40** 1544-1550. DOI: 10.1248/bpb.b16-00895
34. Qi X., Qin L., Du R., Chen Y., Lei M., Deng M., Wang J.. **Lipopolysaccharide Upregulated Intestinal Epithelial Cell Expression of Fn14 and Activation of Fn14 Signaling Amplify Intestinal TLR4-Mediated Inflammation**. *Front. Cell. Infect. Microbiol.* (2017) **7** 315. DOI: 10.3389/fcimb.2017.00315
35. Takasawa S., Tsuchida C., Sakuramoto-Tsuchida S., Uchiyama T., Makino M., Yamauchi A., Itaya-Hironaka A.. **Upregulation of REG IV gene in human intestinal epithelial cells by lipopolysaccharide via downregulation of microRNA-24**. *J. Cell. Mol. Med.* (2022) **26** 4710-4720. DOI: 10.1111/jcmm.17498
36. Yuan T., Zhang L., Yao S., Deng S.Y., Liu J.Q.. **miR-195 promotes LPS-mediated intestinal epithelial cell apoptosis via targeting SIRT1/eIF2a**. *Int. J. Mol. Med.* (2020) **45** 510-518. DOI: 10.3892/ijmm.2019.4431
37. Chang L., Zhou G., Soufan O., Xia J.. **miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology**. *Nucleic Acids Res.* (2020) **48** W244-W251. DOI: 10.1093/nar/gkaa467
38. Griffiths-Jones S., Grocock R.J., van Dongen S., Bateman A., Enright A.J.. **miRBase: microRNA sequences, targets and gene nomenclature**. *Nucleic Acids Res.* (2006) **34** D140-D144. DOI: 10.1093/nar/gkj112
39. Hsu S.D., Lin F.M., Wu W.Y., Liang C., Huang W.C., Chan W.L., Tsai W.T., Chen G.Z., Lee C.J., Chiu C.M.. **miRTarBase: A database curates experimentally validated microRNA-target interactions**. *Nucleic Acids Res.* (2011) **39** D163-D169. DOI: 10.1093/nar/gkq1107
40. Suzuki M.M., Bird A.. **DNA methylation landscapes: Provocative insights from epigenomics**. *Nat. Rev. Genet.* (2008) **9** 465-476. DOI: 10.1038/nrg2341
41. Bestor T.H.. **The DNA methyltransferases of mammals**. *Hum. Mol. Genet.* (2000) **9** 2395-2402. DOI: 10.1093/hmg/9.16.2395
42. Feng J., Zhou Y., Campbell S.L., Le T., Li E., Sweatt J.D., Silva A.J., Fan G.. **Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons**. *Nat. Neurosci.* (2010) **13** 423-430. DOI: 10.1038/nn.2514
43. Fabregat A., Sidiropoulos K., Viteri G., Forner O., Marin-Garcia P., Arnau V., D’Eustachio P., Stein L., Hermjakob H.. **Reactome pathway analysis: A high-performance in-memory approach**. *BMC Bioinform.* (2017) **18**. DOI: 10.1186/s12859-017-1559-2
44. Giampuzzi M., Oleggini R., Di Donato A.. **Altered adhesion features and signal transduction in NRK-49F cells transformed by down-regulation of lysyl oxidase**. *Biochim. Biophys. Acta* (2003) **1647** 239-244. DOI: 10.1016/S1570-9639(03)00058-X
45. Jiang W.Y., Xing C., Wang H.W., Wang W., Chen S.Z., Ning L.F., Xu X., Tang Q.Q., Huang H.Y.. **A Lox/CHOP-10 crosstalk governs osteogenic and adipogenic cell fate by MSCs**. *J. Cell. Mol. Med.* (2018) **22** 5097-5108. DOI: 10.1111/jcmm.13798
46. Yang L., Xu X., Chen Z., Zhang Y., Chen H., Wang X.. **miR-511-3p promotes hepatic sinusoidal obstruction syndrome by activating hedgehog pathway via targeting Ptch1**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2021) **321** G344-G354. DOI: 10.1152/ajpgi.00081.2021
47. Li X., Teng Y., Tian M., Qiu H., Zhao J., Gao Q., Zhang Y., Zhuang J., Chen J.. **Enhancement of LncRNA-HFRL expression induces cardiomyocyte inflammation, proliferation, and fibrosis via the sequestering of miR-149-5p-mediated collagen 22A inhibition**. *Ann. Transl. Med.* (2022) **10** 523. DOI: 10.21037/atm-22-1756
48. Luo W., Jiang Y., Yi Z., Wu Y., Gong P., Xiong Y.. **1a,25-Dihydroxyvitamin D(3) promotes osteogenesis by down-regulating FGF23 in diabetic mice**. *J. Cell. Mol. Med.* (2021) **25** 4148-4156. DOI: 10.1111/jcmm.16384
49. Sun J., Qiao Y., Song T., Wang H.. **MiR-495 suppresses cell proliferation by directly targeting HMGA2 in lung cancer**. *Mol. Med. Rep.* (2019) **19** 1463-1470. DOI: 10.3892/mmr.2018.9773
50. Tian Z., Zhou H., Xu Y., Bai J.. **MicroRNA-495 Inhibits New Bone Regeneration via Targeting High Mobility Group AT-Hook 2 (HMGA2)**. *Med. Sci. Monit.* (2017) **23** 4689-4698. DOI: 10.12659/MSM.904404
51. Huang H., Li H., Chen X., Yang Y., Li X., Li W., Huang C., Meng X., Zhang L., Li J.. **HMGA2, a driver of inflammation, is associated with hypermethylation in acute liver injury**. *Toxicol. Appl. Pharmacol.* (2017) **328** 34-45. DOI: 10.1016/j.taap.2017.05.005
52. Okumura R., Takeda K.. **Roles of intestinal epithelial cells in the maintenance of gut homeostasis**. *Exp. Mol. Med.* (2017) **49** e338. DOI: 10.1038/emm.2017.20
53. Howell K.J., Kraiczy J., Nayak K.M., Gasparetto M., Ross A., Lee C., Mak T.N., Koo B.K., Kumar N., Lawley T.. **DNA Methylation and Transcription Patterns in Intestinal Epithelial Cells from Pediatric Patients with Inflammatory Bowel Diseases Differentiate Disease Subtypes and Associate with Outcome**. *Gastroenterology* (2018) **154** 585-598. DOI: 10.1053/j.gastro.2017.10.007
54. Kraiczy J., Nayak K., Ross A., Raine T., Mak T.N., Gasparetto M., Cario E., Rakyan V., Heuschkel R., Zilbauer M.. **Assessing DNA methylation in the developing human intestinal epithelium: Potential link to inflammatory bowel disease**. *Mucosal Immunol.* (2016) **9** 647-658. DOI: 10.1038/mi.2015.88
55. Hagihara Y., Yoshimatsu Y., Mikami Y., Takada Y., Mizuno S., Kanai T.. **Epigenetic regulation of T helper cells and intestinal pathogenicity**. *Semin. Immunopathol.* (2019) **41** 379-399. DOI: 10.1007/s00281-019-00732-9
56. Rocchi A., Chiti E., Maiese A., Turillazzi E., Spinetti I.. **MicroRNAs: An Update of Applications in Forensic Science**. *Diagnostics* (2020) **11**. DOI: 10.3390/diagnostics11010032
57. Hubner K., Karwelat D., Pietsch E., Beinborn I., Winterberg S., Bedenbender K., Benedikter B.J., Schmeck B., Vollmeister E.. **NF-kappaB-mediated inhibition of microRNA-149-5p regulates Chitinase-3-like 1 expression in human airway epithelial cells**. *Cell Signal.* (2020) **67** 109498. DOI: 10.1016/j.cellsig.2019.109498
58. Mizoguchi E.. **Chitinase 3-like-1 exacerbates intestinal inflammation by enhancing bacterial adhesion and invasion in colonic epithelial cells**. *Gastroenterology* (2006) **130** 398-411. DOI: 10.1053/j.gastro.2005.12.007
59. Heinsbroek S.E., Squadrito M.L., Schilderink R., Hilbers F.W., Verseijden C., Hofmann M., Helmke A., Boon L., Wildenberg M.E., Roelofs J.J.. **miR-511-3p, embedded in the macrophage mannose receptor gene, contributes to intestinal inflammation**. *Mucosal Immunol.* (2016) **9** 960-973. DOI: 10.1038/mi.2015.113
60. Al Alam D., Danopoulos S., Schall K., Sala F.G., Almohazey D., Fernandez G.E., Georgia S., Frey M.R., Ford H.R., Grikscheit T.. **Fibroblast growth factor 10 alters the balance between goblet and Paneth cells in the adult mouse small intestine**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2015) **308** G678-G690. DOI: 10.1152/ajpgi.00158.2014
61. Huang Y., Wang F., Li H., Xu S., Xu W., Pan X., Hu Y., Mao L., Qian S., Pan J.. **Inhibition of Fibroblast Growth Factor Receptor by AZD4547 Protects against Inflammation in Septic Mice**. *Inflammation* (2019) **42** 1957-1967. DOI: 10.1007/s10753-019-01056-4
62. Song X., Dai D., He X., Zhu S., Yao Y., Gao H., Wang J., Qu F., Qiu J., Wang H.. **Growth Factor FGF2 Cooperates with Interleukin-17 to Repair Intestinal Epithelial Damage**. *Immunity* (2015) **43** 488-501. DOI: 10.1016/j.immuni.2015.06.024
63. Mah A.T., Yan K.S., Kuo C.J.. **Wnt pathway regulation of intestinal stem cells**. *J. Physiol.* (2016) **594** 4837-4847. DOI: 10.1113/JP271754
64. Weissman I.L.. **Stem cells: Units of development, units of regeneration, and units in evolution**. *Cell* (2000) **100** 157-168. DOI: 10.1016/S0092-8674(00)81692-X
65. Xie L., Fletcher R.B., Bhatia D., Shah D., Phipps J., Deshmukh S., Zhang H., Ye J., Lee S., Le L.. **Robust colonic epithelial regeneration and amelioration of colitis via FZD-specific activation of Wnt signaling**. *Cell. Mol. Gastroenterol. Hepatol.* (2022) **14** 435-464. DOI: 10.1016/j.jcmgh.2022.05.003
66. Xu Y., Yang J., Chen X., Deng J., Gong H., Li F., Ouyang M.. **MicroRNA-182-5p aggravates ulcerative colitis by inactivating the Wnt/beta-catenin signaling pathway through DNMT3A-mediated SMARCA5 methylation**. *Genomics* (2022) **114** 110360. DOI: 10.1016/j.ygeno.2022.110360
67. Vermeer P.D., Einwalter L.A., Moninger T.O., Rokhlina T., Kern J.A., Zabner J., Welsh M.J.. **Segregation of receptor and ligand regulates activation of epithelial growth factor receptor**. *Nature* (2003) **422** 322-326. DOI: 10.1038/nature01440
68. Schramm F., Schaefer L., Wygrecka M.. **EGFR Signaling in Lung Fibrosis**. *Cells* (2022) **11**. DOI: 10.3390/cells11060986
69. Gentile L.F., Cuenca A.G., Efron P.A., Ang D., Bihorac A., McKinley B.A., Moldawer L.L., Moore F.A.. **Persistent inflammation and immunosuppression: A common syndrome and new horizon for surgical intensive care**. *J. Trauma Acute Care Surg.* (2012) **72** 1491-1501. DOI: 10.1097/TA.0b013e318256e000
70. Mira J.C., Gentile L.F., Mathias B.J., Efron P.A., Brakenridge S.C., Mohr A.M., Moore F.A., Moldawer L.L.. **Sepsis Pathophysiology, Chronic Critical Illness, and Persistent Inflammation-Immunosuppression and Catabolism Syndrome**. *Crit. Care Med.* (2017) **45** 253-262. DOI: 10.1097/CCM.0000000000002074
|
---
title: 'Prevalence of Symptomatic Knee Osteoarthritis in Saudi Arabia and Associated
Modifiable and Non-Modifiable Risk Factors: A Population-Based Cross-Sectional Study'
authors:
- Omar W. Althomali
- Junaid Amin
- Tolgahan Acar
- Syed Shahanawaz
- Alanazi Talal Abdulrahman
- Dalia Kamal Alnagar
- Meshari Almeshari
- Yasser Alzamil
- Kamal Althomali
- Noorah Alshoweir
- Othman Althomali
- Monira I. Aldhahi
- Bodor H. Bin Sheeha
journal: Healthcare
year: 2023
pmcid: PMC10001239
doi: 10.3390/healthcare11050728
license: CC BY 4.0
---
# Prevalence of Symptomatic Knee Osteoarthritis in Saudi Arabia and Associated Modifiable and Non-Modifiable Risk Factors: A Population-Based Cross-Sectional Study
## Abstract
Objective: This study aimed to determine the prevalence of knee osteoarthritis (OA) in Saudi Arabia and the association between knee OA and modifiable and non-modifiable risk factors. Methods: A self-reported, population-based, cross-sectional survey between January 2021 and October 2021 was conducted. A large, population-representative sample ($$n = 2254$$) of adult subjects aged 18 years and over from all regions of Saudi Arabia was collected electronically using convenience sampling. The American College of Rheumatology (ACR) clinical criteria were used to diagnose OA of the knee. The knee injury and osteoarthritis outcome score (KOOS) was used to investigate the severity of knee OA. This study focused on modifiable risk factors (body mass index, education, employment status, marital status, smoking status, type of work, previous history of knee injury, and physical activity level) and non-modifiable risk factors (age, gender, family history of OA, and presence of flatfoot). Results: The overall prevalence of knee OA was $18.9\%$ ($$n = 425$$), and women suffered more compared to their male counterparts ($20.3\%$ vs. $13.1\%$, $$p \leq 0.001$$). The logistic regression analysis model showed age (OR: 1.06 [$95\%$ CI: 1.05–1.07]; $p \leq 0.01$), sex (OR: 2.14 [$95\%$ CI: 1.48–3.11]; $p \leq 0.01$), previous injury (OR: 3.95 [$95\%$ CI: 2.81–5.56]; $p \leq 0.01$), and obesity (OR: 1.07 [$95\%$ CI: 1.04–1.09]; $p \leq 0.01$) to be associated with knee OA. Conclusions: A high prevalence of knee OA underlines the need for health promotion and prevention programmes that focus on modifiable risk factors to decrease the burden of the problem and the cost of treatment in Saudi Arabia.
## 1. Introduction
Osteoarthritis (OA) is the most common type of arthritis. It is a complex disorder that can affect the articular cartilage, bones, ligaments, and synovium. It contributes to degenerative and reparative processes and inflammation of the joint [1]. OA may affect different body joints, both proximal and distal (large, medium, and small joints), and most commonly occurs in the knee joint [2]. Several risk factors can increase the likelihood of having knee OA, and these can be divided into non-modifiable and modifiable factors [3]. There are six main well-known categories of modifiable risk factors: obesity and overweight, comorbidities (diabetic, depression, and cardiovascular disease), occupational factors, physical activity, biomechanical factors, and dietary exposure. Treatment should target the modifiable risk factors, as it is possible to reduce pain disability [3].
According to the International Classification of Functioning, Disability, and Health (ICF) framework, knee OA leads to activity limitation and participation restriction as well as impairment [4]. It is considered the primary cause of physical disability in the general population [5,6]. Physical disability resulting from pain and lack of functional capability decreases quality of life and raises the risk for more morbidity. Global statistics show that around 250 million people worldwide are affected by knee OA. In Saudi Arabia, it is one of the most common and growing health situations [7]. It is necessary to consider the prevalence of OA to understand the impact of the disease on society. Recent studies in Saudi Arabia have shown that knee OA increases with age, reaching up to $60.6\%$ in people aged 66–75 years compared with $30.8\%$ in those aged 46–55 years [8]. Other studies have shown that $39.75\%$ of the population, including $53.3\%$ of males and $60.9\%$ of females, suffers from knee OA [1,9].
Prior studies related to the prevalence of OA were conducted in Saudi Arabia with some limitations. Those studies introduced threats to internal validity, and the data collected were from local regions or cities with a small sample size that could not represent the general population of the kingdom [1,8,10]. Moreover, gender-based differences were not addressed, and diagnostic criteria for OA were mainly based on radiological findings. Interestingly, a previous study showed $50\%$ of subjects to be clinically asymptomatic with a radiographic finding and vice versa [11]. In Saudi Arabia, there is still insufficient data taken from a large sample size on knee OA and its risk factors. The clinical guidelines also do not recommend routine X-rays to diagnose OA [12]. Therefore, the current study uses clinical criteria to diagnose knee OA and includes participants in all regions of the kingdom so that there is an optimal number of participants to ensure a population-representative sample. Furthermore, the prevalence of OA in general has variations based on race and ethnicity [13]. Therefore, it is imperative to obtain an updated prevalence of knee OA and identify the modifiable risk factors for timely prevention and early intervention. The findings of the current study will also help health agencies and stakeholders to plan educational and preventive programmes to address the modifiable risk factors to ease the social-economic burden of OA.
The present study aims to determine the prevalence of symptomatic knee OA in Saudi Arabia and examine the association of knee OA with modifiable and non-modifiable risk factors. The secondary objective is to compare affected with non-affected individuals with knee OA using knee injury and osteoarthritis outcome score (KOOS).
## 2.1. Study Design and Participants
This population-based cross-sectional study was conducted between January and October 2021 among the *Saudi* general population. The study was approved by the research ethics committee of the University of Hail (Ethical approval no: H-2021-009). A convenience sampling method was used to collect data from all 13 regions of Saudi Arabia. Written informed consent was obtained from each participant before participation. Adult individuals aged 18 years and above were included. Individuals who had severe mental disorders and physical disabilities or deformities in the lower limbs were excluded. Individuals with severe mental disorders were excluded due to their inability to give informed consent and the desired information. Individuals with physical disabilities or deformities were also excluded due to the potential for their existing disability or deformity to affect pain, stiffness, and loss of function. The required sample size was calculated based on the previously published equation N = Z2P(1 − P)/d2, with a confidence interval of $95\%$ [14]. Z (confidence level) value of $95\%$ was selected since this is the most commonly used [14]. P (prevalence) was considered to be 0.22 ($22\%$) based on two previous studies, one of which showed $16\%$ of knee OA prevalence on a global scale [15], and the second investigated the prevalence of OA among Gulf Cooperation Council countries (average of studies for knee OA equal to $27\%$) [16]. This led us to take the average of the two studies ($21.5\%$), which was rounded to $22\%$. The d (precision) was considered to be 0.018, being more conservative [14], and this led to 2035 participants, and around $11\%$ (219 participants) were added to avoid any missing data.
## 2.2. American College of Rheumatology (ACR) Knee OA Assessment Criteria
OA is a pathological condition affecting the structures of the entire joint, such as cartilage degeneration, bone remodelling, osteophyte production and synovial inflammation that cause pain, stiffness, oedema and loss of function [17]. To diagnose knee OA, ACR clinical criteria were used. The criteria defined knee OA as pain felt in the greatest number of days over the previous 30 days accompanied by three of the following: [1] age of 51 years and above, [2] bony enlargement, [3] 30 min of joint stiffness, [4] bony tenderness and [5] crepitus [18].
## 2.3. Implementation of the Assessment Criteria
To address the current study’s aim and apply the ACR clinical assessment criteria, a self-reported survey was conducted, and a closed-ended questionnaire was designed. The questionnaire consisted of three sections. The first section contained demographic characteristics, lifestyle, and health-related issues. Collected information included gender (male or female), age (years), weight (kg), height (m), education (illiterate, primary, intermediate, high school, diploma, bachelor’s degree, or higher degrees), work type (office work, fieldwork, both office and fieldwork, retired, housewife, or unemployed), marital status (single or married), smoking habits (yes or no), previous knee injuries (yes or no), presence of flat feet (yes, no, or I do not know), family history of OA (yes, no, or I do not know), and physical activity level (inactive, low intensity, moderate intensity, or high intensity).
In the second section, questions related to ACR clinical criteria were asked [18]. The questions were as follows: [1] “Have you felt pain in one or both knees in most of the previous 30 days?” [ 2] “In which knee do you have pain (right, left, both, or no pain)?” [ 3] “How long have you had the pain?” [ 4] “Do you feel pain when pressing or compressing your knee/knees?” [ 5] “Do you think your knee/knee bones is/are larger than normal (enlarged)?” [ 6] “Does/do your knee/knees produce the sound of clicking or crepitus?” [ 7] “Do you think your knee/knees feel stiff for the first 30 min in the morning?” The third section included the KOOS scale. All questions were addressed in Arabic since KOOS has been shown to be valid and reliable in the Arabic language [19].
## 2.4. KOOS Scale
KOOS was used to collect and investigate the severity of the knee OA and to compare, according to the American College of Rheumatology (ACR) clinical criteria, individuals with knee OA with non-OA individuals and individuals with knee pain without knee OA diagnosis. The psychometric properties of the KOOS scale have been assessed, and it has been found to be a reliable and valid instrument for assessing knee and associated problems [20].
The subscales of KOOS are pain (5 items), symptoms (4 items), ADL (9 items), sport/recreation (3 items), and QOL (2 items). The total KOOS is based on the individual score calculated for each subscale. Each item is scored ranging from 0 to 4 (0 = none, 1 = mild, 2 = moderate, 3 = severe, and 4 = extreme). The maximum score is 100, indicating no problem, while 0 indicates extreme problems. An Excel spreadsheet downloaded from the official website (http://www.koos.nu/index.html, accessed on 1 December 2020) was used to calculate KOOS.
## 2.5. Scoring to Diagnose Individuals with Knee OA
The first step was to identify individuals who had suffered knee pain in the majority of the previous 30 days and answered “yes” to the question “Have you felt pain in one or both knees in most of the previous 30 days?”, for which they were given a score of 1. In the second step, a score of 1 was given for the presence of any of the following symptoms: crepitus (“Do you feel pain when pressing or compressing your knee/knees?”), bony enlargement (“Do you think your knee/knee bones is/are larger than normal?”), bony tenderness (“Do you feel pain when pressing or compressing your knee/knees?”), the presence of 30 min of morning joint stiffness (“Do you think your knee/knees feel stiff for the first 30 min in the morning”), and age above 50 years (“How old are you?”). If the total score reached 3 or above, the ACR was fulfilled, and the participant was diagnosed as having clinical knee OA. If the question in the first step concerning the presence of knee pain in the majority of the previous 30 days had been answered as no or yes, and the total score was less than 3, the participant was diagnosed as healthy (Figure 1).
## 2.6. Data Collection
The electronic data collection was executed via an online Google form. The link to the form was shared with potential participants through their WhatsApp, Twitter, and email accounts. The link was republished more than once to increase the response rate. The link was shared with individuals in all regions of Saudi Arabia (Makkah Region, Riyadh Region, Eastern Region, Asir region, Jazan Region, Medina Region, Al-Qassim Region, Tabuk Region, Ha’il Region, Najran Region, Al-Jawf Region, Al-Bahah Region, and Northern Borders Region).
## 2.7. Statistical Analysis
The collected data were extracted from the Google form into a Microsoft Excel (version 16.33) spreadsheet and then exported to SPSS version 25 (SPSS Inc. Chicago, IL, USA). Body mass index (BMI) was divided into four categories based on the WHO classification (underweight < 18.5, normal = 18.5–24.9, overweight = 25–29.9, and obese > 29.9) [21]. Individuals were grouped by age into three categories (18–30, 31–49, and ≥50) to enable comparison. Descriptive analysis was performed for categorical data and presented as frequencies and percentages. The prevalence of knee OA was compared between the different demographics, lifestyles, and health-related characteristics using the chi-square test. KOOS subscales and total KOOS were compared for individuals with no incidence of knee OA to individuals with knee OA, and a comparison was conducted between individuals with knee pain and no OA and individuals with knee OA using an independent sample t-test after checking the for normality. Cohen’s d was calculated to show the effect size and interpreted as large (≥0.8), medium (0.5–0.79), and small (0.2–0.49). Forward binominal logistic regression was used to investigate the risk factors related to knee OA that were significant when the chi-square test was applied. Age and BMI were entered into the model as continuous variables. A p-value less than 0.05 was set as a statistically significant level.
## 3. Results
A total of 2254 individuals from the 13 regions of Saudi Arabia responded to the questionnaire. The age of respondents ranged from 18 to 80 years (mean 35 ± 13.11). Most of the respondents ($80.88\%$) were females, and $44.23\%$ were aged 18–30 years; $60.74\%$ were married, and $35.58\%$ were of a normal body weight. Most of the participants ($60.03\%$) had completed a bachelor’s level of education, $29.41\%$ were office workers, and $27.06\%$ were unemployed (Table 1). The majority of respondents ($89.66\%$) reported no previous injury to the knee (ACL, meniscus). Family history of knee OA was reported in $63.75\%$ of the participants, and $6.83\%$ reported having flat feet (Table 2).
A total of 1262 ($55.99\%$) participants reported having knee pain. Approximately $21.21\%$ had had knee pain for 1–5 years, while $3.06\%$ reported having it for longer than 15 years; $2.62\%$ had been absent from work for more than 15 days in the previous 12 months due to knee pain. The prevalence of knee OA based on ACR clinical criteria was $18.86\%$ ($$n = 425$$) (Table 2).
The prevalence of knee OA significantly differed between gender, age group, marital status, BMI category, previous knee injury, family history of OA, presence of flat feet, educational level, smoking habits, and physical activity level (Table 3). The prevalence of knee OA increased with age; $6.82\%$ of participants aged 18–30 years were affected by knee OA, whereas $45.77\%$ of the participants 50 years and above were affected. The prevalence of knee OA was significantly higher among females than among their male counterparts ($20.25\%$ vs. $12.99\%$, $p \leq 0.01$). The prevalence of knee OA was higher in married ($25.57\%$, $p \leq 0.01$) and obese ($34.30\%$) individuals. Conversely, the prevalence of knee OA was not common in smokers compared with non-smokers ($19.42\%$ vs. $11.11\%$, $$p \leq 0.012$$). Moreover, the prevalence was highest in illiterate individuals ($84.62\%$), and the least was observed in individuals with master’s/Ph.D. degrees ($14.74\%$, $p \leq 0.01$). The prevalence of knee OA was $49.57\%$ ($p \leq 0.01$) among those with previous injuries to the knee and $31.82\%$ ($p \leq 0.01$) and $22.30\%$ ($p \leq 0.01$) in respondents who reported having flat feet and family history of OA, respectively.
The logistic regression model was statistically significant ($p \leq 0.01$). The model showed age, gender, previous injury, level of physical activity, education level, smoking, family history of OA, and BMI to be associated with increased or decreased prevalence of knee OA. Females had more than twofold the risk for developing knee OA than males (OR: 2.14 [$95\%$ CI: 1.48–3.11]; $p \leq 0.01$). Ageing was also associated with an increase in the risk for knee OA (OR: 1.06 [$95\%$ CI:1.05–1.07]; $p \leq 0.01$). Previous injury to the knee (ACL, meniscus) was also found to be a risk factor for knee OA (OR: 3.95 [$95\%$ CI: 2.81–5.56]; $p \leq 0.01$). Smoking was found to decrease the risk for knee OA (OR: 0.51 [$95\%$ CI: 0.27–0.96]; $p \leq 0.01$). Individuals with higher degrees were found to have less risk for knee OA in comparison to those who were illiterate. Higher BMI was found to be associated with an increase in the risk for knee OA (OR: 1.07 [$95\%$ CI: 1.04–1.09]; $p \leq 0.01$). Interestingly, individuals with moderate levels of physical activity were found have decreased risk for knee OA compared with inactive individuals, while those with a high level of physical activity showed an increased level of risk compared with inactive individuals. Individuals with a family history of OA showed a higher risk for developing knee OA than individuals with no family history of OA (Table 4).
Interestingly, when comparing non-OA participants with those with knee OA using KOOS, all subscales showed significant differences with p-values less than 0.01 and a large effect size. On comparing non-OA individuals with knee pain and individuals with OA, there was a significant difference in all KOOS subscales and total KOOS scores with a large effect size (Table 5).
## 4. Discussion
Knee OA is a common, progressive, and degenerative disease that affects the daily lives of many people across the globe. Pain, stiffness, and limited mobility associated with the condition have negative influences on people’s quality of life [22]. Therefore, the overarching aim of this study is to determine the prevalence of symptomatic knee OA and the associated risk factors in Saudi Arabia.
Our study reported a high prevalence of knee OA ($18.86\%$, $$n = 425$$). A recent systematic review and meta-analysis summarizing 88 previous studies showed similar findings [16], highlighting that the pooled global prevalence of knee OA for those aged 15 and above was $16\%$, ranging from $14.3\%$ to $17.8\%$ [15], highlighting that the pooled global prevalence of knee OA for those aged 15 and above was $16\%$, ranging from $14.3\%$ to $17.8\%$. Our study also showed that the prevalence increases with shifting the minimum age. A previous study in Al-Qaseem city reported the prevalence of knee OA using the same diagnostic criteria as were used in our study and showed a prevalence of $13\%$ [8], which was $4.9\%$ lower than the findings in this study.
Importantly, the current study showed a statistically significant association between OA and age, gender, BMI, previous knee injury, level of education, level of physical activity, family history of OA, and smoking. Previous studies have also shown age [23], female sex [24], obesity [25], genetic factors [26], and previous injury [27] to be linked with the increased risk for knee OA. Interestingly, the current study found smoking to be protective and that it can reduce the risk for OA. A previous systematic review was also aligned with the current study’s findings [28]. Moreover, higher education levels were protective and reduced the risk for developing knee OA, which is also in agreement with our study [29]. On comparing the risk factors in the current study, the results showed that previous history of knee injury ranked as the highest risk by a 3.95 odds ratio.
Our evidence suggests that age increases in the risk for osteoarthritis. The age of the subjects in our study ranged from 18 to 80 years, and the occurrence of knee OA increased up to $45.77\%$ among persons aged over 50 years compared with $6.82\%$ in those aged between 18 and 30 years. This can potentially be attributed to ageing cellular and physiological change that is associated with decreased muscle strength and mass, poor proprioception, and cartilage thinning [30]. It has been reported by the WHO Scientific Group on Rheumatic Diseases that an estimated $10\%$ of the world’s population aged 60 years and older have significant clinical problems that can be attributed to OA [31].
Our study found a high prevalence of knee OA in females and obese individuals. Previous studies have also shown an increasing trend for knee OA in females, which is likely to be due to hormonal factors and anatomical and kinematic differences in females [32]. Obesity is responsible for additional overloading of the weight-bearing joints, which further contributes to triggering the wear-and-tear process inside joints among individuals with high BMI [33]. A recent study in Saudi Arabia showed similar results regarding the increased risk for developing knee OA in individuals with higher BMI [34]. A high prevalence of obesity has been reported in Saudi females ($33.5\%$) compared with males ($24.1\%$) [35]. Based on these statistics, Saudi females with obesity may be more likely to have additional risk for knee OA development. Moreover, advancing age in Saudi females with obesity may further pose a risk for knee OA. Hence, modifiable risk factors should be targeted to reduce the risk for developing knee OA in the future and lower the burden of OA in the Saudi community. A previous study showed that weight loss resulted in a reduction in the risk for developing OA and symptoms in individuals affected by OA [36]. A weight-loss programme can be used as a proactive strategy to prevent and manage many non-communicable diseases, including OA, in Saudi Arabia.
Several factors can explain the reduced risk for individuals with a higher education level and smokers developing OA. Smoking may promote the proliferation of chondrocytes, improve the expression of cartilage-specific type 2 collagen, and have anti-inflammatory effects [28]. A previous study could not conclude a reason for education level reducing the risk for developing OA [29]. However, higher education could mean more knowledge about disease prevention, while those with lower education most likely work in office jobs, which may require standing for long periods and bending. These claims are just speculatory in nature and need to be proved. Notably, the prevalence of knee OA in individuals with flatfoot was significantly higher ($31.82\%$) than in those with normal feet ($17.03\%$), supporting reported evidence that bilateral flat feet are significantly associated with worse OA-related knee pain and disability [37]. This may be explained by changing the load distribution on the knee when the foot is flat. A previous study showing knee pain and knee OA cartilage damage to be associated with flatfoot supports our earlier claim [38].
Despite the current study using clinical criteria to diagnose individuals with knee OA, the KOOS subscales showed that there was a significant difference between those affected with knee OA and those who were healthy and those who were healthy with knee pain, which strengthens the study. The current study showed that individuals with knee OA had 58.24 ± 18.32, 55.29 ± 16.28, 58.74 ± 20.15, 38.61 ± 25.46, and 46.75 ± 22.05 scores for pain, symptoms, ADL, sport, and QOL, respectively. A previous study that investigated the reliability of KOOS in the Saudi population with knee OA showed similar results for pain (45.6 ± 18.6), symptoms (52.9 ± 21.3), ADL (47.4 ± 20.1), sport (17.7 ± 18.9), and QOL (31.3 ± 16.8) subscales [39]. These findings are in line with a study that showed poor KOOS outcomes with knee OA after a joint injury compared to uninjured controls [40]. Interestingly, the association between the prevalence of knee OA and level of physical activity showed that moderate levels reduced the risk for knee OA compared to sedentary lifestyle, whereas high levels led to increased risk for knee OA. According to the Physical Activity Guidelines for Americans, moderate levels of physical activity (150 min/week of moderate-intensity exercise in bouts lasting ≥10 min) and lower levels of physical activity (at least 45 total minutes/week of moderate-intensity exercise) were associated with improved function and gait speed in OA patients. The reverse impact of high levels of physical activity on knee OA may be explained by the nature of the physical activity, where high-impact activity and a large number of weight-bearing exercises may lead to joint destruction [41].
## Strengths and Limitations
This study has certain strengths, one of which is that it is the first population-representative study from Saudi Arabia with a large sample size. This study also informs the fundamental knowledge and highlights the prevalence of knee OA in the Saudi population. Another strength is its use of valid clinical criteria for detecting knee OA. Nevertheless, measuring the association between both modifiable and non-modifiable risk factors would have further strengthened this study. Using an online form to collect the data has the advantages of reducing cost and being able to reach remote areas, although it raises concerns about the accuracy and reliability of the data. The modifiable risk factors associated with knee OA will further guide the design of effective interventions to reduce the burden of disease in the community.
However, some limitations should be acknowledged, such as the current study’s use of a cross-sectional design, which has a major limitation in reporting causal explanations. The current study also used a convenience sampling technique, which may reduce the representativeness of the sample in the general population. Furthermore, due to a lack of logistic support, only a self-reported method was used, which may have generated reporting bias in the study, especially since an exaggerated difference in prevalence between sexes has been reported in the literature, as females may be more likely to report OA [42]. Future research with clinical-based diagnoses by specialized health providers is warranted to attain robust findings. Likewise, future studies should explore other risk variables that may increase the risk for developing knee OA, such as the type of shoes worn or knee adduction moment during activity. A previous study showed that higher knee adduction movement led to progression in the knee OA by increasing the load on the medial side of the knee [43] by increasing the load on the medial side of the knee. Using footwear such as lateral wedge insoles can reduce knee adduction movement [44].
## 5. Conclusions
The study reveals a high prevalence of knee OA among the Saudi population. It contributes to a better understanding of the modifiable and non-modifiable risk factors associated with symptomatic knee OA. The study identifies associated non-modifiable risk factors (age, gender, and family history of OA) and modifiable risk factors (BMI, previous knee injury, smoking, physical activity level, and level of education) with knee OA. The information from this study is helpful for identifying people at risk for developing knee OA and targeting them by designing prevention plans such as weight-loss strategies and improving their physical activity levels. The findings of the current study can also help clinicians, policymakers, and stakeholders to target the associated modifiable risk factors explored in this study to decrease the burden and treatment cost of knee OA. The design of the current study (cross-sectional and using an online survey) may limit its generalisability, and therefore, longitudinal studies are needed.
## References
1. Al-Arfaj A.S., Al-Boukai A.A.. **Prevalence of radiographic knee osteoarthritis in Saudi Arabia**. *Clin. Rheumatol.* (2002) **21** 142-145. DOI: 10.1007/s10067-002-8273-8
2. O’Neill T.W., Felson D.T.. **Mechanisms of Osteoarthritis (OA) Pain**. *Curr. Osteoporos. Rep.* (2018) **16** 611-616. DOI: 10.1007/s11914-018-0477-1
3. Georgiev T., Angelov A.K.. **Modifiable risk factors in knee osteoarthritis: Treatment implications**. *Rheumatol. Int.* (2019) **39** 1145-1157. DOI: 10.1007/s00296-019-04290-z
4. Vongsirinavarat M., Nilmart P., Somprasong S., Apinonkul B.. **Identification of knee osteoarthritis disability phenotypes regarding activity limitation: A cluster analysis**. *BMC Musculoskelet. Disord.* (2020) **21**. DOI: 10.1186/s12891-020-03260-y
5. Dominick K.L., Ahern F.M., Gold C.H., Heller D.A.. **Health-related quality of life and health service use among older adults with osteoarthritis**. *Arthritis Care Res.* (2004) **51** 326-331. DOI: 10.1002/art.20390
6. Bonnin M.C.. *Osteoarthritis of the Knee* (2008)
7. Bindawas S.M., Vennu V., Alfhadel S., Al-Otaibi A.D., Binnasser A.S.. **Knee pain and health-related quality of life among older patients with different knee osteoarthritis severity in Saudi Arabia**. *PLoS ONE* (2018) **13**. DOI: 10.1371/journal.pone.0196150
8. Al-Arfaj A.S., Alballa S.R., Al-Saleh S.S., Al-Dalaan A.M., Bahabry S.A., Mousa M.A., Al-Sekeit M.A.. **Knee osteoarthritis in Al-Qaseem, Saudi Arabia**. *Saudi Med. J.* (2003) **24** 291-293. PMID: 12704507
9. Alrowaili M.G.. **Magnetic resonance evaluation of knee osteoarthritis among the Saudi Population**. *Pak. J. Med Sci.* (2019) **35** 1575-1581. DOI: 10.12669/pjms.35.6.874
10. Ahlberg A., Linder B., Binhemd T.A.. **Osteoarthritis of the hip and knee in Saudi Arabia**. *Int. Orthop.* (1990) **14** 29-30. DOI: 10.1007/BF00183360
11. Javaid M.K., Kiran A., Guermazi A., Kwoh C.K., Zaim S., Carbone L., Harris T., McCulloch C.E., Arden N.K., Lane N.E.. **Individual magnetic resonance imaging and radiographic features of knee osteoarthritis in subjects with unilateral knee pain: The health, aging, and body composition study**. *Arthritis Rheum.* (2012) **64** 3246-3255. DOI: 10.1002/art.34594
12. **Osteoarthritis: Care and management clinical guideline [CG177]**. *Musculoskeletal Conditions* (2014)
13. Zhang Y., Jordan J.M.. **Epidemiology of osteoarthritis**. *Clin. Geriatr. Med.* (2010) **26** 355-369. DOI: 10.1016/j.cger.2010.03.001
14. Pourhoseingholi M.A., Vahedi M., Rahimzadeh M.. **Sample size calculation in medical studies**. *Gastroenterol. Hepatol. Bed Bench* (2013) **6** 14-17. PMID: 24834239
15. Cui A., Li H., Wang D., Zhong J., Chen Y., Lu H.. **Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies**. *EClinicalMedicine* (2020) **29** 100587. DOI: 10.1016/j.eclinm.2020.100587
16. Alenazi A.M., Alhowimel A.S., Alotaibi M.A., Alqahtani B.A., Alshehri M.M., Alanazi A.D., Alanazi A.A., Alanazi S.F., Bindawas S.M.. **Prevalence and incidence of osteoarthritis among people living in the Gulf Cooperation Council countries: A systematic review and meta-analysis**. *Clin. Rheumatol.* (2021) **40** 3523-3531. DOI: 10.1007/s10067-021-05662-2
17. Kraus V.B., Blanco F.J., Englund M., Karsdal M.A., Lohmander L.S.. **Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use**. *Osteoarthr. Cartil.* (2015) **23** 1233-1241. DOI: 10.1016/j.joca.2015.03.036
18. Heidari B.. **Knee osteoarthritis diagnosis, treatment and associated factors of progression: Part II**. *Casp. J. Intern. Med.* (2011) **2** 249-255
19. Almangoush A., Herrington L., Attia I., Jones R., Aldawoudy A., Aziz A.A., Waley A.. **Cross-cultural adaptation, reliability, internal consistency and validation of the Arabic version of the Knee injury and Osteoarthritis Outcome Score (KOOS) for Egyptian people with knee injuries**. *Osteoarthr. Cartil.* (2013) **21** 1855-1864. DOI: 10.1016/j.joca.2013.09.010
20. Collins N.J., Misra D., Felson D.T., Crossley K.M., Roos E.M.. **Measures of knee function: International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form, Knee Injury and Osteoarthritis Outcome Score (KOOS), Knee Injury and Osteoarthritis Outcome Score Physical Function Short Form (KOOS-PS)**. *Arthritis Care Res.* (2011) **63** S208-S228. DOI: 10.1002/acr.20632
21. Anuurad E., Shiwaku K., Nogi A., Kitajima K., Enkhmaa B., Shimono K., Yamane Y.. **The new BMI criteria for asians by the regional office for the western pacific region of WHO are suitable for screening of overweight to prevent metabolic syndrome in elder Japanese workers**. *J. Occup. Health* (2003) **45** 335-343. DOI: 10.1539/joh.45.335
22. Gomes-Neto M., Araujo A.D., Junqueira I.D.A., Oliveira D., Brasileiro A., Arcanjo F.L.. **Comparative study of functional capacity and quality of life among obese and non-obese elderly people with knee osteoarthritis**. *Rev. Bras. Reumatol.* (2016) **56** 126-130. DOI: 10.1016/j.rbre.2015.08.014
23. Hong J.W., Noh J.H., Kim D.J.. **The prevalence of and demographic factors associated with radiographic knee osteoarthritis in Korean adults aged ≥ 50 years: The 2010-2013 Korea National Health and Nutrition Examination Survey**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0230613
24. Heidari B.. **Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I**. *Casp. J. Intern. Med.* (2011) **2** 205-212
25. Silverwood V., Blagojevic-Bucknall M., Jinks C., Jordan J.L., Protheroe J., Jordan K.P.. **Current evidence on risk factors for knee osteoarthritis in older adults: A systematic review and meta-analysis**. *Osteoarthr. Cartil* (2015) **23** 507-515. DOI: 10.1016/j.joca.2014.11.019
26. Warner S.C., Valdes A.M.. **The Genetics of Osteoarthritis: A Review**. *J. Funct. Morphol. Kinesiol.* (2016) **1** 140-153. DOI: 10.3390/jfmk1010140
27. Snoeker B., Turkiewicz A., Magnusson K., Frobell R., Yu D., Peat G., Englund M.. **Risk of knee osteoarthritis after different types of knee injuries in young adults: A population-based cohort study**. *Br. J. Sports Med.* (2020) **54** 725-730. DOI: 10.1136/bjsports-2019-100959
28. Kong L., Wang L., Meng F., Cao J., Shen Y.. **Association between smoking and risk of knee osteoarthritis: A systematic review and meta-analysis**. *Osteoarthr. Cartil.* (2017) **25** 809-816. DOI: 10.1016/j.joca.2016.12.020
29. Lee J.Y., Han K., Park Y.G., Park S.H.. **Effects of education, income, and occupation on prevalence and symptoms of knee osteoarthritis**. *Sci. Rep.* (2021) **11** 13983. DOI: 10.1038/s41598-021-93394-3
30. Anderson A.S., Loeser R.F.. **Why is osteoarthritis an age-related disease?**. *Best Pract. Res. Clin. Rheumatol.* (2010) **24** 15-26. DOI: 10.1016/j.berh.2009.08.006
31. Woolf A.D., Pfleger B.. **Burden of major musculoskeletal conditions**. *Bull. World Health Organ.* (2003) **81** 646-656. PMID: 14710506
32. Hame S.L., Alexander R.A.. **Knee osteoarthritis in women**. *Curr. Rev. Musculoskelet. Med.* (2013) **6** 182-187. DOI: 10.1007/s12178-013-9164-0
33. Jiang L., Tian W., Wang Y., Rong J., Bao C., Liu Y., Zhao Y., Wang C.. **Body mass index and susceptibility to knee osteoarthritis: A systematic review and meta-analysis**. *Jt. Bone Spine* (2012) **79** 291-297. DOI: 10.1016/j.jbspin.2011.05.015
34. Thigah A., Khan A.. **Prevalence of Knee Osteoarthritis among Adult Patients Attending Al-iskan Primary Health Care Center**. *Ann. Clin. Anal. Med.* (2020) **9** 272-278
35. AlQarni S.S.M.. **A Review of Prevalence of Obesity in Saudi Arabia**. *J. Obes. Eat Disord.* (2016) **2** 1-6. DOI: 10.21767/2471-8203.100025
36. Bliddal H., Leeds A.R., Christensen R.. **Osteoarthritis, obesity and weight loss: Evidence, hypotheses and horizons—A scoping review**. *Obes. Rev.* (2014) **15** 578-586. DOI: 10.1111/obr.12173
37. Iijima H., Ohi H., Isho T., Aoyama T., Fukutani N., Kaneda E., Ohi K., Abe K., Kuroki H., Matsuda S.. **Association of bilateral flat feet with knee pain and disability in patients with knee osteoarthritis: A cross-sectional study**. *J. Orthop. Res.* (2017) **35** 2490-2498. DOI: 10.1002/jor.23565
38. Gross K.D., Felson D.T., Niu J., Hunter D.J., Guermazi A., Roemer F.W., Dufour A.B., Gensure R.H., Hannan M.T.. **Flat Feet Are Associated with Knee Pain and Cartilage Damage in Older Adults NIH Public Access**. *Arthritis Care Res* (2011) **63** 937-944. DOI: 10.1002/acr.20431
39. Alfadhel S.A., Vennu V., Alnahdi A.H., Omar M.T., Alasmari S.H., AlJafri Z., Bindawas S.M.. **Cross-cultural adaptation and validation of the Saudi Arabic version of the Knee Injury and Osteoarthritis Outcome Score (KOOS)**. *Rheumatol. Int.* (2018) **38** 1547-1555. DOI: 10.1007/s00296-018-4072-7
40. Whittaker J.L., Woodhouse L.J., Nettel-Aguirre A., Emery C.A.. **Outcomes associated with early post-traumatic osteoarthritis and other negative health consequences 3–10 years following knee joint injury in youth sport**. *Osteoarthr. Cartil.* (2015) **23** 1122-1129. DOI: 10.1016/j.joca.2015.02.021
41. Kraus V.B., Sprow K., Powell K.E., Buchner D., Bloodgood B., Piercy K., George S.M., Kraus W.E.. **Effects of Physical Activity in Knee and Hip Osteoarthritis: A Systematic Umbrella Review**. *Med. Sci. Sports Exerc.* (2019) **51** 1324-1339. DOI: 10.1249/MSS.0000000000001944
42. Srikanth V.K., Fryer J.L., Zhai G., Winzenberg T.M., Hosmer D., Jones G.. **A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis**. *Osteoarthr. Cartil.* (2005) **13** 769-781. DOI: 10.1016/j.joca.2005.04.014
43. Chang A., Moisio K., Chmiel J., Eckstein F., Guermazi A., Prasad P., Zhang Y., Almagor O., Belisle L., Hayes K.. **External knee adduction and flexion moments during gait and medial tibiofemoral disease progression in knee osteoarthritis**. *Osteoarthr Cartil.* (2015) **23** 1-8. DOI: 10.1016/j.joca.2015.02.005
44. Radzimski A.O., Mündermann A., Sole G.. **Effect of footwear on the external knee adduction moment—A systematic review**. *Knee* (2012) **19** 163-175. DOI: 10.1016/j.knee.2011.05.013
|
---
title: 'Making “Joy Pie” to Stay Joyful: Self-Care Interventions Alleviate College
Students’ Mental Health Challenges'
authors:
- Bu Zhong
- Lola Xie
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001250
doi: 10.3390/ijerph20053823
license: CC BY 4.0
---
# Making “Joy Pie” to Stay Joyful: Self-Care Interventions Alleviate College Students’ Mental Health Challenges
## Abstract
As more college students are facing mental health challenges, it is imperative to explore innovative ways of improving their mental health, including developing self-care interventions that help mitigate their stressors. Based on the Response Styles Theory and self-care conceptions, this study creates the “Joy Pie” project that consists of five self-care strategies, aiming to regulate negative emotions and increase self-care efficacy. Using an experimental design and two-wave data collected from a representative sample of Beijing college students (n1 = 316, n2 = 127), this study assesses the effects of the five proposed interventions on the students’ self-care efficacy and mental health management. The results show that self-care efficacy helped improve mental health through emotion regulation, which is mediated by age, gender, and family income. The promising results support the effectiveness of the “Joy Pie” interventions in strengthening self-care efficacy and improving mental health. This study offers insights into building back better mental health security among college students at this critical time when the world is recovering from the COVID-19 pandemic.
## 1. Introduction
College students’ anxiety, depression, and other mental health disorders were growing concerns in many countries even before the COVID-19 pandemic hit the world [1,2]. The three-year-long COVID-19 pandemic has been a very stressful period for college students worldwide, as their mental health has been severely exacerbated by the pandemic conditions [3,4]. A great deal of work has been devoted to identifying mental health challenges that college students experienced during the pandemic [5], including college students in China [6], in India [7], and in the United States [8]. Given that college students are less likely to seek professional help under stress, Internet-based interventions provide great resources for students to perform self-care themselves. The existing literature on web- or mobile-based psycho-interventions tends to focus on providing self-care and therapy resources online for students who are experiencing chronic mental health conditions, with the goal to make psychological services more accessible for those who do not have access to offline facilities. Less is known about how to increase students’ self-care efficacy, making them more likely to utilize self-help resources online. Moreover, as past research has been primarily conducted among students with existing mental health conditions, there is limited knowledge about how to motivate college students with no history of mental health problems to practice self-care and utilize available resources for better mental health management.
Moving beyond identifying mental health issues and providing self-care resources online for college students, this study proposes a mobile communication-based intervention with five strategies for improving college students’ self-care efficacy and mental health that is based on the Response Styles Theory [9]. Drawing on previous studies on coping strategies, the intervention was designed as the five-step “Joy Pie” project that consists of five strategies: [1] delay worries; [2] talk with someone; [3] be less critical; [4] design a to-go strategy for yourself, and [5] weekend reflection. As students practice all five strategies in a row, they make a “Joy Pie” as a way to mitigate school-related stressors, helping them stay joyful. The goal is to empower college students by building up their self-care reliance for coping with campus stressors. Using an experimental design, this research assesses the effectiveness of the “Joy Pie” self-care strategies; the findings shine a light on mental health self-care among college students, particularly by regulating negative emotions such as stress, anxiety, and depression.
## 2. Literature Review
Among China’s 28 million college students [10], one in every four ($24.6\%$) has experienced depression and other mental health problems [11]. The COVID-19 pandemic further added to the complexity of students’ mental health issues, which has become a social and public health concern in China [6]. A large-scale longitudinal study associated the pandemic with a rise in depressive and anxiety symptoms in Chinese college students, including acute stress, anxiety, and depression [12]. It also found that about $10\%$ of nearly 70,000 Chinese college students under study showed persistent or developed new mental health problems during the COVID-19 pandemic. Youth mental health issues are well documented to be directly associated with impairments in academic success and personal relationships, leading to social withdrawal, self-harm, and even suicidality [13]. The importance of prevention and intervention strategies has been recognized as a means of addressing youth mental health needs, especially in reducing stress, anxiety, and depression [14]. The enormous size of China’s youth population with mental health problems highlights the need for creating self-care interventions to improve mental health management.
## 2.1. The Response Styles Theory
Assessing the plausibility of non-medical interventions for improving college students’ mental health requires us to apply novel theoretical approaches to understand their vulnerability to mental health challenges. This study employs the Response Styles Theory of Depression [9,15] to study how youths try to regulate emotions, treating it as a theoretical framework to guide our experimental design for measuring the effectiveness of our proposed intervention strategies. The Response Styles Theory proposes two major responses to depression: distraction and rumination [15]. Rumination refers to the process of individuals focusing on their own experiences, thoughts, and feelings, especially “thinking perseveratively about one’s feelings and problems” ([16], p. 400). Nolen-Hoeksema argues that there are three major mechanisms involving rumination. First, rumination intensifies the depressed mood upon thinking, making it more likely that people will use the negative thoughts and memories activated by their depressed mood to understand their current circumstances. Second, rumination interferes with effective problem solving, in part by making one’s thinking more pessimistic. Third, rumination interferes with instrumental behavior, leading to increases in stressful circumstances [16].
After analyzing over 220 studies that examined maladaptive forms of self-focused attention in people prone to depression, anxiety, or other forms of mental issues, Mor and Winquist [17] found that rumination was the form most strongly and consistently related to depressive symptoms and depression disorders. After controlling for depression levels, research has repeatedly found that rumination is closely associated with certain negative cognitive styles such as hopelessness, pessimism, perfectionism, self-criticism, and neuroticism [16,18]. Rumination, as a special kind of self-focus, could mediate, partially or fully, the relationship between major depressive episodes and risk factors such as maladaptive attitudes and negative cognitive styles [19]. Those who use rumination as a response to dysphoria could experience more intense episodes of depression [9].
The Response Styles Theory states that an alternative for regulating depressive emotions is to use pleasant or neutral distractions to regulate negative feelings such as stress, anxiety, or a depressed mood. Such distracting responses, as a part of emotion regulation, are found to be able to divert people’s attention away from depressive ideas and consequences, allowing them to refocus on pleasant or positive ideas and activities [16]. In many cases, persistent and intense episodes of depression could easily lead to worse consequences, including suicidal thoughts. Suicidal thoughts, as a negative emotion, inhibit the ability to readjust health behavior to cope with stressors or mental health challenges. Moreover, Nolen-Hoeksema and her colleagues [16] emphasize that effective distractions do not include inherently dangerous or self-destructive activities, such as heavy drinking, drug abuse, or aggressive behavior. Such high-risk distractions may temporarily take attention away from current stressors in the short-term, but could be harmful in the long run [16]. Thus, this theory and the conceptions of self-care efficacy are an appropriate theoretical framework for guiding the present study.
## 2.2. Self-Care Efficacy and Self-Care Intervention
A promising alternative to improve mental health is to utilize interventions that allow youths to administer self-care. Self-care interventions are highly cost-effective and can help maximize youths’ autonomy by decreasing their reliance on mental health professionals [20,21]. Existing research associates self-care interventions with reduced depression, anxiety, and stress among college students [22]. However, relatively few self-care intervention programs have been implemented in China, and their effectiveness remains poorly understood among Chinese students. Considering the effectiveness of online interventions used for U.S. college students [23], it is imperative to see how online interventions may help Chinese students cope with mental issues. An individual with high self-care efficacy is expected to exhibit increased adherence to activities that promote health and decreased psychological symptoms. Other studies support a positive relationship between self-care efficacy and quality of life, and a negative correlation with negative moods [24].
Self-efficacy has been identified as a prerequisite for behavioral change in patients with chronic illness [24]. An individual’s sense of self-efficacy can provide the foundation for motivation, well-being, and personal accomplishment. Self-care self-efficacy, which will be referred to as “self-care efficacy” hereafter for brevity, is defined as an individual’s confidence in being able to perform relevant self-care behaviors in a given situation [25]. An individual with high self-care efficacy is found to exhibit increased adherence to self-care activities [26]. Studies support a positive relationship between self-care efficacy and life satisfaction [27], and between self-care efficacy and positive health outcomes, whereas self-care efficacy has a negative correlation with negative moods [24].
For instance, among cancer patients, a high degree of self-care efficacy was found to be significantly associated with decreased physical and psychological symptoms [28]. In diabetes mellitus patients, self-care efficacy has been found to be the most important predictor of self-care behaviors, as well as acting as a clinical pathway through which diabetes care could be improved [29].
Therefore, this study first explores how self-care efficacy may influence Chinese college students’ experience of negative emotions and mental health related to major campus stressors involving personal relationships and schoolwork pressures. Drawing on previous studies, we propose: In recent years, self-care interventions have been found to be a good first step in managing college students’ mental health issues [30]. While formal psychological treatments for college students’ mental disorders are effective, many students tended to delay or avoid seeking professional help because of system-related barriers (e.g., long waiting list) and attitudinal barriers (e.g., embarrassment or stigma) [31]. Given these barriers, delivering evidence-based interventions to provide help with stress management in a self-care format might be a more practical and psychologically acceptable approach [32]. Because of the features of scalability at a low cost and time flexibility, self-care interventions have the potential to overcome system-related and attitudinal barriers [32].
This study develops a self-care intervention, the “Joy Pie” intervention (Figure 1), to help college students improve their mental health condition through self-care. Different from regular interventions in which participants receive the treatment with the help of a professional guide or therapist [33], self-care interventions involve personal coping strategies and are informed by conceptions of self-care that emphasize personal autonomy, resilience, self-efficacy, self-control, self-actualization, and self-stewardship [34]. As a supplement to other interventions, self-care interventions can be easily self-administered by individual users at their own pace and in an environment where they feel relaxed, which is critical to improve one’s mental health [35].
## 2.3. Coping Strategies and Mental Health
Extensive research has been carried out on how individuals cope with stress, highlighting the correlation between the management of stressful life events and mental or physical health [36]. Results from previous studies indicate that the existence of stress originating from stressful life events may be less important to mental health than how an individual copes with stress [37]. Different strategies have been identified depending on the conceptualization of coping, and yet, only some of them have been repeatedly examined and tested before. Based on prior studies, we proposed the “Joy Pie” project consisting of five self-care interventions.
Among the optional strategies, distraction and avoidance behaviors have been found to be the most common forms of coping mechanisms [38], although there are still unresolved issues concerning the effects. While some studies related the escape-avoidance strategy with an increase in psychological distress and depression [39], some others associated it with positive outcomes, especially in uncontrollable situations [36,40]. In a study on the coping mechanisms of healthcare workers during the COVID-19 pandemic in Japan, the authors identified that over $70\%$ of study participants adopted the escape-avoidance strategy for coping with stress [36]. While this strategy was positively associated with depression at low levels of stress, it has been associated with more adaptive functioning of these outcomes at higher levels of stress [40], which means that in a given situation with a high level of restrictions and strong external pressure, an evasive strategy may have been the best way to maintain mental health. Drawing on the literature, we proposed an evasive strategy of “delay worries”, as we expected our participants to be facing high levels of pressure as they had to manage relationship and schoolwork stresses while their schools went into lockdown from time to time under the zero-COVID policy in China.
Another coping strategy that has been frequently adopted is communication, which also involves relying on people for social support in times of stress [41,42]. The ability to communicate both verbally and non-verbally about how one feels has been associated with positive health outcomes [42]. In a study on high-achieving students in high school [41], family communication was negatively correlated with perceived stress and positively associated with global life satisfaction, suggesting that family communication may be a more adaptive coping strategy for high-achieving students. Family communication also emerged as the strongest predictor of school functioning. By talking to others about the stresses and negative emotions, one can also mobilize both information and emotional support that can ease anxiety and relieve emotional distress [36]. Therefore, we encouraged our participants to talk to others about their stresses and anxiety by proposing the strategy of “initiate a chat”.
Meta-analytic findings have consistently related self-criticism and blame to depressive symptoms and poor health [43,44]. Self-criticism is a reflexive psychological behavior elicited when individuals are unsatisfied with the acquired outcome of important decisions or experience crucial failures [45,46]. Multiple studies have associated increased levels of self-criticism with increased levels of depression [45]. In response, self-forgiveness has been proposed as an emotion-focused coping approach to dealing with stresses that result from personal failure, guilt/shame, or general incongruence between personal values and actual behavior [47]. Self-forgiveness involves reducing negative, and increasing positive, thoughts and emotions regarding oneself [48]. The use of self-forgiveness as a coping strategy is expected to encourage adaptive coping including self-care, lessen stressors, and alleviate mental health problems [43]. Thus, we proposed the strategy of “be less critical” to encourage our participants to engage in self-forgiveness.
Meanwhile, meditation practices can form a key component of coping strategies for mitigating anxiety and stress [49]. As a non-pharmacological self-management solution, meditation practices aim at training one’s self-awareness of the present experience, including one’s thoughts, feelings, and sensations without any judgment, filter, or expectations [50]. It has been used widely for the reduction of stress and the promotion of health, and the results broadly support the beneficial effects of such practices on physical and mental health, as well as on cognitive performance [51]. Based on the existing literature, we proposed “weekend reflection” to enhance the participants’ interoceptive awareness of their emotional and mental phenomena [52]. In addition, previous studies also suggest that the same strategy can be effective or ineffective in different situations, as each problem or situation requires the use of a specific coping strategy [53]. Additionally, it is almost impossible to include the entire universe of potential coping strategies in only one study. The effectiveness of self-care strategies could vary between people in different situations, and even vary for the same person at different times. Therefore, we also encouraged our participants to create their own self-care strategies to capture the potentially wide variety of strategies that could be incorporated by the participants and be sensitive to situational differences.
## 2.4. The “Joy Pie” Interventions
The “Joy Pie” interventions include five strategies: [1] delay worries; [2] initiate a chat with others; [3] be less critical; [4] create one’s own self-care strategies; and [5] weekend reflection (see Figure 1). They were developed by drawing on the literature of self-care and coping strategies [25,34,54,55]. Informed by conceptions of self-care that emphasize personal autonomy, resilience, self-efficacy, self-control, self-actualization, and self-stewardship [34], the “Joy Pie” project encourages self-care reliance in managing one’s mental health conditions. Here, self-care is defined as the actions that individuals take to enhance, restore, or maintain health; prevent or limit illness; and cope with illness with or without the support of healthcare professionals [54,56]. A systematic review found that a very small number of studies concerning the improvement of individual self-care capabilities actually measured self-care capability changes [57], which highlights the importance of our study. Based on the literature reviewed, the following research questions were formulated to evaluate the effects of the proposed intervention strategies:RQ1. How do the “Joy Pie” interventions affect college students’ self-care efficacy?RQ2. How do the “Joy Pie” interventions affect college students’ experience of negative emotions and mental health when facing relationship and schoolwork pressures?
## 3.1. Participants and Procedures
College students in Beijing universities were recruited in two waves in June and July 2021 on Jishuyun (https://www.databnu.com/#/index, accessed on 20 December 2022), a Beijing-based online panel platform. In the panel, there were over 100,000 students from 92 universities and colleges in Beijing, which can be taken as representative of all college students in Beijing. For this study, Jishuyun randomly sent survey invitations to participants within the panel, who were registered and confirmed as students from Beijing colleges. Participants who completed the initial survey were then randomly assigned to either an experimental condition—requiring them to practice the five “Joy Pie” interventions for four weeks—or a control group—reporting only their mental health conditions twice without practicing the interventions. A total of 316 students completed the initial survey (T1) in June 2021 when they were at school, and 127 of them completed the second-wave survey (T2) in July 2021. The second wave recruited fewer students due to the start of their summer vacation. Electronic informed consent was then obtained online from the participants, who were informed that they could withdraw from the study at any time.
## 3.2. The Procedure
This study consisted of two parts: [1] through an online survey, it explored how major predictors, including self-care efficacy and negative emotions such as depression, anxiety, and stress, may affect Chinese college students’ mental health conditions; and [2] using an experimental design, this study assessed the effectiveness of the proposed intervention strategies in improving mental health conditions. As shown in Figure 1, the intervention program consists of five self-care strategies. Detailed practice instructions were provided about each of the strategies. During the weekdays, the students were asked to: On the weekends, they were asked to designate 30–60 min for self-reflecting on stressors experienced and how to overcome them. If no major stressors occurred in a particular week, they were asked to meditate by taking deep breaths for 30–60 min. The self-care interventions were presented as a color image so that they could be easily disseminated and saved to a smartphone.
## 3.3. Survey Design and the Pre-Test
The participants completed a survey at T1 and responded to validated measures including self-care efficacy, the 21-item Depression Anxiety Stress Scale (DASS-21), and mental health. The survey was designed in English, and the stimulus scenarios and measures were later translated into Chinese by two of the authors whose native language is Chinese. The authors discussed the appropriateness of the translation and pilot tested the survey with a small sample ($$n = 24$$) prior to data collection for the primary study. In the pre-test, the top stress sources for Beijing college students were identified as being school-related, e.g., school workload and academic performance, and relationship-related, e.g., their relationships with schoolmates, professors, and family members.
After removing the data that failed the quality check (i.e., minimum time spent on the survey and straightlining responses), the final dataset included responses from 316 college students at T1. The average age of the students was 20.65 years old (SD = 1.77). A majority of them were females ($61.7\%$). Participants reported their self-perceived family income: $69.3\%$ were middle income, $29.7\%$ low income, and $0.9\%$ high income. Among the participants, $39.6\%$ were from big cities, $47.8\%$ from medium-sized cities, and $12.7\%$ from small towns or rural areas. Participants also reported their parents’ education levels: $38\%$ of their parents received an education lower than high school, $25.0\%$ a high school diploma, $35.1\%$ a college degree, and $1.9\%$ a master’s or doctorate degree.
## 3.4. Experiment Design
The students who participated in the T1 survey were randomly assigned into either an experimental or a control group. For the control group, participants were invited to take a follow-up survey four weeks later (T2). The survey questions at T2 were the same as those at T1, which measured any changes in their self-care efficacy, experience of negative emotions, and mental health conditions. For the experimental group, participants were reminded over the following four week to practice the “Joy Pie” self-care interventions through a weekly message, which asked them to try to manage any stressors they experienced in life. Four weeks later, they were invited to take a follow-up survey and report their self-care efficacy, experience of negative emotions, and mental health conditions at T2. They were also asked to report their practice frequency and evaluate their perceived usefulness of the “Joy Pie” intervention strategies. After removing data that failed the quality check (i.e., minimum time spent on the survey and straightlining responses), the final dataset containing 127 responses from Beijing college students at T1 and T2 were analyzed.
## 3.5.1. Self-Care Efficacy
Self-care efficacy was operationalized as the extent to which respondents felt confident in their ability to carry out specific self-care behaviors in the face of their stressors. The 7-item measure adapted from previous research [25] was used to measure respondents’ self-care efficacy. Students were asked to rate their agreement level to seven statements on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree) (see Appendix A). The items have a good reliability with higher scores, indicating higher efficacy (α = 0.74).
## 3.5.2. Negative Emotions
The widely adopted Depression Anxiety Stress Scale-21 (DASS-21) [58] was used to measure respondents’ experience of negative emotions. Considering the cultural sensitivity of the topic, we took out one item from the original scale (i.e., “I felt I wasn’t worth much as a person”). Seven items were used to measure respondents’ stress level (e.g., “I found it hard to wind down”), seven items were used to measure their anxiety level (e.g., “I was aware of dryness of my mouth”), and six items were used to measure their depression level (e.g., “I couldn’t seem to experience any positive feeling at all”). Students were asked to rate to what extent the statements applied to them from 1 (did not apply to me at all) to 4 (applied to me very much) (see Appendix A). Together, the 20 items formed a reliable index, α = 0.89, with higher scores indicating higher levels of symptoms.
## 3.5.3. Mental Health Indicator
Findings from the pilot study indicated that most respondents’ stressors mainly originated from two aspects: personal relationships with others, such as friends, and schoolwork pressures. Therefore, two scenarios were presented to the participants to measure their mental health when facing relationship and schoolwork stressors. Eight items from a previous study [59] were used to measure their mental health when facing relationship stressors (Scenario 1) and schoolwork pressures (Scenario 2). Respondents were asked to indicate how they would agree to the 8 statements in the face of the stressors as described in the scenario on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly disagree) (See Appendix A). Both measures of relationship-related mental health (α = 0.83) and schoolwork-related mental health (α = 0.88) were reliable, with higher scores indicating better mental health under stress.
## 3.6. Practice Frequency and Intervention Effectiveness
Respondents in the experimental group were asked to report their frequency of practicing the intervention strategies since the T1 survey, which served as a manipulation check of the experiment as students were instructed to practice the strategies on their own. Practice frequency was measured with the item: “In the past weeks, how often did you practice the Joy Pie strategies?” The item was measured on a 5-point Likert-type scale (1 = none or not much to 5 = very often/everyday). In addition, respondents in the experimental group were also asked to report the perceived helpfulness of each of the intervention strategies. They were also asked to report their perceived improvements in different aspects of their life after practicing the “Joy Pie” interventions, including overall improvements, health condition improvements, and improvements in schoolwork and personal relationships. The items were measured on a 5-point Likert scale, with higher scores indicating higher levels of improvement. The items used are presented in Appendix A.
## 3.7. Statistical Analysis
We first ran descriptive statistics on all of our variables for both waves of the surveys, as well as on the data on students’ self-report practice frequency and perceived helpfulness of the intervention program. To test our first two hypotheses, we ran a correlation test among all studied variables, including self-care efficacy, negative emotions, and mental health scores. Then, we performed hierarchy regressions on both negative emotions and mental health scores to examine whether self-care efficacy plays a role in alleviating negative emotions and improving mental health. Next, we further examined the effectiveness of the “Joy Pie” intervention using a hierarchy regression to analyze self-care efficacy against practice frequency, answering RQ1. Lastly, we used paired samples t-tests to evaluate how students’ self-reporting of negative emotions and mental health changed after the intervention.
## 4. Results
Descriptive statistics for self-care efficacy, negative emotions, relationship-related mental health, and schoolwork-related mental health in both waves of the survey are presented in Table 1.
Meanwhile, we also ran a descriptive analysis of students’ practice frequency and perceived helpfulness of the “Joy Pie” interventions. The analysis shows that for every strategy, the average helpfulness score was between 3.68 and 3.06 out of 5, indicating that the respondents considered the interventions to have moderate to high levels of effectiveness. Among the five strategies, Strategy 2, “initiate a chat with others”, was considered the most effective ($M = 3.76$, SD = 0.92), followed by Strategy 4, i.e., “create one’s own self-care strategies” ($M = 3.68$, SD = 1.05), and Strategy 5, “weekend reflection” ($M = 3.69$, SD = 1.13). The average score for Strategy 3, “be less critical”, was 3.53 (SD = 0.94). Strategy 1, “delay worries”, was considered the least helpful ($M = 3.06$, SD = 1.14).
They were also asked to report their perceived improvements in different aspects of their life after practicing the “Joy Pie” interventions, including overall improvements ($M = 3.80$, SD = 0.67), health condition improvements ($M = 3.83$, SD = 0.71), and improvements in schoolwork ($M = 3.60$, SD = 0.93) and personal relationships ($M = 3.34$, SD = 1.06). The average scores were above 3.5 out of 5 except for personal relationships, indicating moderate levels of effectiveness of the intervention in the aspects mentioned above. Then, we tested each hypothesis and answered the research questions using inferential statistics, as shown in the next subsection.
## 4.1. Associations between Self-Care Efficacy, Negative Emotions (H1), and Mental Health (H2)
H1 proposed that self-care efficacy would be negatively associated with college students’ experience of negative emotions. H2 proposed that self-care efficacy would be positively associated with their mental health while facing stressors induced by relationships and schoolwork pressures. The associations were first examined by calculating Pearson’s correlation coefficient. The results indicated that self-care efficacy was negatively associated with negative emotions, and positively associated with students’ mental health when facing both relationship-related and schoolwork-related stresses. See Table 2 for the study variables’ Pearson’s correlation coefficients.
The hierarchical regression analyses were then conducted. Control variables including age, gender, family household income, geolocation, and parents’ education were entered as the first step of each analysis to control their potential effects on mental health wellness. In the second step, self-care efficacy was entered to examine its main effects.
For H1, variables in step one of the regression model accounted for a non-significant $3\%$ of the variance in students’ negative emotions (F[5, 315] = 1.72, $$p \leq 0.13$$). Results show that none of the demographic variables affected respondents’ experience of negative emotions. In step two, the main effects of self-care efficacy explained an additional $29.7\%$ increment in the variance in the experience of negative emotions (F[6, 315] = 24.68, $p \leq 0.001$). The regression weights from the full six-variable model demonstrated that self-care efficacy was negatively associated with negative emotions (B = −0.56, t = −11.65, $p \leq 0.001$). This suggests that participants with higher self-care efficacy reported fewer experiences of negative emotions. Therefore, H1 is supported. The detailed results are reported in Table 3.
For H2, the variables in step one of the regression model measuring relationship-related mental health accounted for a significant $5.5\%$ of the variance in mental health (F[5, 315] = 3.62, $p \leq 0.01$). Results show that none of the demographic variables, except age, affected respondents’ relationship-related mental health ($B = 0.16$, $t = 2.80$, $$p \leq 0.005$$). The older students had significantly better mental health in face of relationship-related stressors than their younger counterparts. None of the other demographic variables significantly influenced students’ mental health when facing relationship-related stressors.
In step two, the main effects of self-care efficacy explained an additional $25.4\%$ increment in the variance in relationship-related mental health (F[6, 315] = 23.05, $p \leq 0.001$). The regression weights from the full six-variable model demonstrated that self-care efficacy was positively associated with relationship-related mental health ($B = 0.52$, $t = 10.66$, $p \leq 0.001$). This suggests that participants with higher self-care efficacy reported better mental health in the face of relationship stressors. Meanwhile, students’ age remained a significant positive predictor of their mental health when dealing with relationship stresses ($B = 0.10$, $t = 2.02$, $p \leq 0.05$). Students’ parents’ educational level also emerged as a significant positive predictor of their mental health when dealing with relationship-related stressors ($B = 0.10$, $t = 2.07$, $p \leq 0.05$). This means that students with parents who had higher educational levels exhibited better mental health when facing relationship stressors (see the detailed results in Table 4).
As shown in Table 5, in step 1 of the regression of schoolwork-related mental health, the control variables accounted for a significant $3.9\%$ of the variance in mental health (F[5, 315] = 2.50, $$p \leq 0.03$$). Results show that among the demographic variables, age affected respondents’ schoolwork-related mental health ($B = 0.11$, $t = 2.04$, $$p \leq 0.04$$). This means that older respondents had better mental health in the face of schoolwork-related stressors compared to their younger counterparts. In Step 2, the main effects of self-care efficacy explained an additional $20.6\%$ increment in the variance in schoolwork-related mental health (F[6, 315] = 16.72, $p \leq 0.001$). The regression weights from the full six-variable model demonstrated that self-care efficacy was positively associated with schoolwork-related mental health ($B = 0.46$, $t = 9.19$, $p \leq 0.001$). This means that participants with higher self-care efficacy reported better mental health in the face of schoolwork stressors. Thus, H2 is supported. The detailed results are reported in Table 5.
## 4.2. Effects of the “Joy Pie” Intervention on Self-Care Efficacy (RQ1)
RQ1 asked how the “Joy Pie” interventions affect college students’ self-care efficacy. A hierarchical regression analysis was conducted to address RQ1. The result indicates that, after controlling for the effects of demographic variables and self-care efficacy at T1, the main effect of practice frequency explained an additional $3.6\%$ increment in the variance in schoolwork-related mental health (F[7, 119] = 7.77, $p \leq 0.001$). The regression weights from the full seven-variable model demonstrated that practice frequency was positively associated with self-care efficacy ($B = 0.19$, $t = 2.50$, $$p \leq 0.01$$). This means that practicing the “Joy Pie” interventions helps enhance college students’ self-care efficacy. The detailed results are reported in Table 6.
## 4.3. Effects of “Joy Pie” Interventions on Negative Emotions and Mental Health (RQ2)
RQ2 asked how the “Joy Pie” interventions affect college students’ experience of negative emotions and mental health. To answer the research question, paired samples t-tests were performed to compare the means at T1 and T2 for both the control and experimental groups. Results show that for the experiment group, there was a significant decrease in negative emotions from T1 ($M = 2.88$, SD = 0.59) to T2 ($M = 2.61$, SD = 0.73, $$p \leq 0.009$$) after four weeks of practicing the intervention strategies. There was a significant improvement in both relationship-related and schoolwork-related mental health. Students’ relationship-related mental health improved significantly from T1 ($M = 3.53$, SD = 0.80) to T2 ($M = 3.78$, SD = 0.68, $$p \leq 0.04$$). There was also a significant improvement in schoolwork-related mental health among the respondents from T1 ($M = 3.46$, SD = 0.77) to T2 (3.76, SD = 0.70, $$p \leq 0.008$$). The detailed results are reported in Table 7.
In comparison, the results from the control group show no significant difference in any of the study variables at T2 compared to T1 (Table 8). Overall, the results suggest the effectiveness of our intervention strategies in strengthening the respondents’ self-care efficacy, and as a result, in improving their mental health. Table 7 and Table 8 report the paired differences of the study variables for each group.
## 5. Discussion
This study, guided by the Response Styles Theory, conceptions of self-care, and coping strategies published in the literature, proposes the “Joy Pie” intervention consisting of five strategies for college students to manage life stresses and improve their mental health management. Results indicated the effectiveness of the self-care strategies among a group of college students in Beijing after practicing them for four weeks. The findings concerning the benefits of self-care strategies add fresh evidence to the importance of embedding the methods and concepts from humanities and social sciences into caring for youth mental health [60].
Consistent with the findings from the West [17,61], this study shows that self-care efficacy is negatively related to Chinese students’ experience of negative emotions, including depression, anxiety, and stress, and is positively related to mental well-being in the face of relationship-related and schoolwork-related stressors. It also shows the role of age, gender, and parent education in managing such campus stressors. Older college students maintained better mental health than their younger peers when facing relationship-related stressors. Young men had significantly better mental health in the face of schoolwork-related stressors than their female counterparts, which is consistent with the findings reported in previous studies [62]. The students with parents who had received a higher education reported better mental health that their peers when facing the same relationship-related stressors, suggesting that the latter might have limited resources to deal with stressors [63]. However, no significant association was detected between mental health and the students’ hometown location, regardless if they were from big cities, medium cities, or small towns/rural areas.
The data show that college students generally reported the “Joy Pie” interventions to have a moderate to high effect on their mental health management. Among the five strategies, Strategy 4, or “Create your own self-care strategies,” was considered most effective, followed by Strategy 5—“weekend reflection,” Strategy 2—“*Initiate a* chat with others”, and Strategy 3—“Be less critical”, with the last being Strategy 1—”Delay worries”. Specifically, the college students reported that the more they practiced the “Joy Pie” strategies, the better they felt in their daily activities and the better personal relationships they had with others around them. The use of the “Joy Pie” interventions also contributed to stronger self-care efficacy after controlling for the level of such efficacy at T1, showing the effectiveness of our strategies in improving participants’ confidence in being able to perform relevant self-care behaviors in a given situation [25]. Since self-care efficacy has been identified as a prerequisite for behavioral change in patients with chronic illness [24], our strategies are expected to enhance participants’ motivation to adhere to self-care activities [26].
Comparing the data collected at T1 and T2, the analyses discovered that the college students’ mental health related to campus stressors such as relationships and schoolwork improved significantly after the four-week practice of the “Joy Pie” project. However, it might be possible that if they could practice the strategies for longer than four weeks, some differences might emerge in future studies. Overall, the results suggested the effectiveness of our intervention strategies in strengthening college students’ self-care efficacy, leading to improved mental health.
## 5.1. Theoretical Implications
Theoretically, the current research has expanded the previously proven significance of self-care efficacy by adding new evidence that relates to youth mental health management. The finding of a negative relationship between self-care self-efficacy and negative emotions supports the theoretical proposition that self-efficacy is a cognitive factor associated with the development of depressive symptoms [25]. It also adds to the empirical evidence in support of the positive relationship between self-care efficacy and mental health [24]. This finding is important because there is a strong consensus that knowing what one should do for his/her health does not necessarily mean that self-care behaviors will follow [64]. An individual makes judgments about his/her capacity to engage in self-care behaviors to produce desired outcomes, and it is reasonable to hypothesize that students would be more likely to adhere to self-care behaviors if they are more confident in their ability to carry out these behaviors. The conception of self-care efficacy provides a bridge between knowledge and actual self-care behaviors [64].
Our findings also provide empirical support for the effectiveness of self-care approaches in preventing or alleviating the high stress and associated mental problems among college students. Previous studies found that mental health effects can be different depending on the selected stress coping strategy [36]. Among the five “joy pie” strategies, strategy 2, “initiate a chat with others”, was considered the most effective. Students coping by communicating with and relying on important people around them are found to have fewer mental health problems [41]. Communication appears to be an effective way to contend with life stresses in health situations. Strategy 4, “create one’s own self-care strategies”, was considered the second most effective coping method. Encouraging participants to create their own self-care strategies may induce a feeling of control [65]. People who are confident in their abilities and engage in activities that promote health are also expected to adhere to such activities. Engaging in activities that they personally feel comfortable with may give them greater confidence and enhance their self-care efficacy, thus reducing their risk of developing negative emotions.
In addition, strategy 5, “weekend reflection”, and strategy 3, “be less critical”, were considered moderately effective, whereas strategy 1, “delay worries”, was considered the least effective, despite it still being above 3 points out of 5. Scholars hold different opinions about the effectiveness of the escape-avoidance strategy to improve mental health [36,39,40]. Our finding supports there being a moderate effect and highlights the significance of this strategy in a situation with high levels of restrictions and strong external pressures, such as that during the period of the COVID-19 pandemic [66,67]. As the students faced restrictions in their daily experiences to prevent the spread of COVID-19, an evasive strategy may have been be the only way for them to manage negative emotions and maintain mental health. Our finding also highlights the significance of context when analyzing the effectiveness of coping strategies. The use of a coping strategy should be considered in the specific situation, as the same strategy can be effective or not depending on the individual’s perception of the situation as threatening or not [53]. Future studies should also consider the levels of restrictions and external pressures in specific situations while examining the effectiveness of similar strategies.
## 5.2. Practical Implications
Going beyond the prior research in identifying mental health challenges among college students [12,23], this study focused on developing self-care interventions that assist youths in their self-management of mental health. The key findings are that college students could be sensitive to self-care interventions, which could be useful adjunctive treatment options. The finding of a positive relationship between self-care self-efficacy and mental health supports and extends the work of previous investigators who established that self-care efficacy is an important factor for quality of life [25,68]. Clinically, these findings suggest the need for paying close attention to the self-care self-efficacy in college students experiencing stressors and negative emotions.
For college students facing campus stressors, in addition to seeking medications, psychiatric consultations, and therapies from mental health professionals, it is also beneficial to adopt non-medical interventions to manage mental health issues. Lessons and training could also be provided to enhance their self-care efficacy in managing mental health issues during their college years.
## 5.3. Limitations
The findings of the study need to be considered in light of several limitations. First, the study relied on self-report data rather than observational data. Future researchers should consider collecting objective data. Another limitation is that the T2 sample was smaller than the T1 sample due to the difficulty of reaching out to the students when the pandemic became worse in China during those months, which was unexpected for T2, although the dropout rates were comparable to those in previous studies such as 1–$50\%$ [69], 2–$50\%$ [70], or 0–$82\%$ [71]. These considerations should be factored into the interpretation of the results.
On the other hand, self-help may increase willingness to seek professional help among college students [33]. Therefore, innovative methods are worth investigating, such as different ways of increasing adherence and transferring common factors associated with improvements in face-to-face/guided programs into self-guided interventions. As a result, the most accurate strategies can be developed to reach college students who are in need of mental health help, but do not access the relevant resources due to barriers.
## 6. Conclusions
Young adults generally believe that they are entitled to happiness, which is assumed to drop on them automatically without much effort. This study indicates that for college students, it is easier to be stressed or depressed than happy, as they find that the latter requires hard work. For college students, making “Joy Pie,” or practicing self-care strategies, can be a path to happiness. The findings show that increased self-efficacy is associated with decreased negative emotions and better mental health conditions. The concept of self-care efficacy highlights that college students have the capacity to learn about their mental health needs and manage their own mental health. An intervention program that provides self-care efficacy training is warranted, which could lead to increased adherence to treatment, behaviors perceived as promoting health, and decreased physical and psychological symptoms. In addition, the “Joy Pie” strategies were found to be effective at enhancing participants’ self-care efficacy, lessening negative emotions, and improving mental health. Hence, this study provided college students, parents, and health professionals with information concerning self-care efficacy, negative emotion regulation, and mental health management. Overall, this research offers timely health insights into building back better mental health security among college students at this critical time when the world is recovering from the COVID-19 pandemic.
## References
1. Halladay J.E., Dawdy J.L., McNamara I.F., Chen A.J., Vitoroulis I., McInnes N., Munn C.. **Mindfulness for the mental health and well-being of post-secondary students: A systematic review and meta-analysis**. *Mindfulness* (2019.0) **10** 397-414. DOI: 10.1007/s12671-018-0979-z
2. Auerbach R.P., Mortier P., Bruffaerts R., Alonso J., Benjet C., Cuijpers P., Demyttenaere K., Ebert D.D., Green J.G., Hasking P.. **WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders**. *J. Abnorm. Psychol.* (2018.0) **127** 623-638. DOI: 10.1037/abn0000362
3. Pfefferbaum B., North C.S.. **Mental Health and the Covid-19 Pandemic**. *New Engl. J. Med.* (2020.0) **383** 510-512. DOI: 10.1056/NEJMp2008017
4. Ward C., McLafferty M., McLaughlin J., McHugh R., McBride L., Brady J., Bjourson A.J., Walsh C.P., O’Neill S.M., Murray E.K.. **Suicidal behaviours and mental health disorders among students commencing college**. *Psychiatry Res.* (2022.0) **307** 1-7. DOI: 10.1016/j.psychres.2021.114314
5. Vindegaard N., Benros M.E.. **COVID-19 pandemic and mental health consequences: Systematic review of the current evidence**. *Brain Behav. Immun. Corrected Proof* (2020.0) **89** 531-542. DOI: 10.1016/j.bbi.2020.05.048
6. Chen R., Liang S., Peng Y., Li X., Chen J., Tang S., Zhao J.. **Mental health status and change in living rhythms among college students in China during the COVID-19 pandemic: A large-scale survey**. *J. Psychosom. Res.* (2020.0) **137** 110219. DOI: 10.1016/j.jpsychores.2020.110219
7. Wasil A.R., Malhotra T., Nandakumar N., Tuteja N., DeRubeis R.J., Stewart R.E., Bhatia A.. **Improving mental health on college campuses: Perspectives of Indian college students**. *Behav. Ther.* (2021.0) **53** 348-364. DOI: 10.1016/j.beth.2021.09.004
8. Gopalan M., Linden-Carmichael A., Lanza S.. **College students’ sense of belonging and mental health amidst the COVID-19 pandemic**. *J. Adolesc. Health* (2021.0) **70** 228-233. DOI: 10.1016/j.jadohealth.2021.10.010
9. Nolen-Hoeksema S.. **Responses to depression and their effects on the duration of depressive episodes**. *J. Abnorm. Psychol.* (1991.0) **100** 569-582. DOI: 10.1037/0021-843X.100.4.569
10. **China Statistical Yearbook 2019**
11. Fu X., Zhang K., Chen X., Chen Z.. *Blue Book of Mental Health: Report on National Mental Health Development in China (2017–2018)* (2019.0)
12. Li Y., Zhao J., Ma Z., McReynolds L.S., Lin D., Chen Z., Wang T., Wang D., Zhang Y., Zhang J.. **Mental health among college students during the COVID-19 epidemic in China: A 2-wave longitudinal survey**. *J. Affect. Disord.* (2021.0) **281** 1-8. DOI: 10.1016/j.jad.2020.11.109
13. Auerbach R.P., Stewart J.G., Stanton C.H., Mueller E.M., Pizzagalli D.A.. **Emotion processing biases and resting EEG activity in depressed adolescents**. *Depress. Anxiety* (2015.0) **32** 693-701. DOI: 10.1002/da.22381
14. Feiss R., Dolinger S.B., Merritt M., Reiche E., Martin K., Yanes J.A., Thomas C.M., Pangelinan M.. **A systematic review and meta-analysis of school-based stress, anxiety, and depression prevention programs for adolescents**. *J. Youth Adolesc.* (2019.0) **48** 1668-1685. DOI: 10.1007/s10964-019-01085-0
15. Nolen-Hoeksema S., Papageorgiou C., Wells A.. **The response styles theory**. *Depressive Rumination Nature, Theory, and Treatment* (2004.0) 105-123. DOI: 10.1002/9780470713853.ch6
16. Nolen-Hoeksema S., Wisco B.E., Lyubomirsky S.. **Rethinking rumination**. *Perspect. Psychol. Sci.* (2008.0) **3** 400-424. DOI: 10.1111/j.1745-6924.2008.00088.x
17. Mor N., Winquist J.. **Self-focused attention and negative affect: A meta-analysis**. *Psychol. Bull.* (2002.0) **128** 638-662. DOI: 10.1037/0033-2909.128.4.638
18. Robinson M., Alloy L.B.. **Negative cognitive styles and stress-reactive rumination interact to predict depression: A prospective**. *Cogn. Ther. Res.* (2003.0) **27** 275-291. DOI: 10.1023/A:1023914416469
19. Spasojević J., Alloy L.B.. **Rumination as a common mechanism relating depressive risk to d**. *Emotion* (2001.0) **1** 25-37. DOI: 10.1037/1528-3542.1.1.25
20. Wolitzky-Taylor K.B., Telch M.J.. **Efficacy of self-administered treatments for pathological academic worry: A randomized controlled trial**. *Behav. Res. Ther.* (2010.0) **48** 840-850. DOI: 10.1016/j.brat.2010.03.019
21. Seah S.J., Zheng H., Boon R., Lim T.. **Efficacy of community-based self-care interventions to improve biophysical, psychosocial or behavioural outcomes among community-dwelling older adults with type 2 diabetes: A systematic review and meta-analysis**. *Diabetes Res. Clin. Pract.* (2020.0) **169** 1-17. DOI: 10.1016/j.diabres.2020.108411
22. Choukas-Bradley S., Prinstein M.J., Lewis M., Rudolph K.D.. **Peer relationships and the development of psychopathology**. *Handbook of Developmental Psychopathology* (2014.0) 185-204. DOI: 10.1007/978-1-4614-9608-3_10
23. Frazier P., Meredith L., Greer C., Paulsen J.A., Howard K., Dietz L.R., Qin K.. **Randomized controlled trial evaluating the effectiveness of a web-based stress management program among community college students**. *Anxiety Stress Coping* (2015.0) **28** 576-586. DOI: 10.1080/10615806.2014.987666
24. Eller L.S., Lev E.L., Yuan C., Watkins A.V.. **Describing self-care self-efficacy: Definition, measurement, outcomes, and implications**. *Int. J. Nurs. Knowl.* (2018.0) **29** 38-48. DOI: 10.1111/2047-3095.12143
25. Lev E.L., Owen S.V.. **A measure of self-care self-efficacy**. *Res. Nurs. Health* (1996.0) **19** 421-429. DOI: 10.1002/(SICI)1098-240X(199610)19:5<421::AID-NUR6>3.0.CO;2-S
26. Karadag E.. **Increase in COVID-19 cases and case-fatality and case-recovery rates in Europe: A cross-temporal meta-analysis**. *J. Med. Virol.* (2020.0) **92** 1511-1517. DOI: 10.1002/jmv.26035
27. Proctor C.L., Linley P.A., Maltby J.. **Youth life satisfaction: A review of the literature**. *J. Happiness Stud.* (2009.0) **10** 583-630. DOI: 10.1007/s10902-008-9110-9
28. Boland L., Bennett K., Connolly D.. **Self-management interventions for cancer survivors: A systematic review**. *Support. Care Cancer* (2018.0) **26** 1585-1595. DOI: 10.1007/s00520-017-3999-7
29. Kav S., Yilmaz A.A., Bulut Y., Dogan N.. **Self-efficacy, depression and self-care activities of people with type 2 diabetes in Turkey**. *Collegian* (2017.0) **24** 27-35. DOI: 10.1016/j.colegn.2015.09.005
30. Ribeiro I.J.S., Freire I.V., de Araújo T.M.. **Are stress management interventions effective in reducing stress, anxiety, and depression in College Students?**. *Clin. Psychol. Sci. Pract.* (2021.0) 1-3. DOI: 10.1111/cpsp.12385
31. Cuijpers P., Miguel C., Ciharova M., Aalten P., Batelaan N.S., Elske Spinhoven P., Struijs S.d.W., Leonore Gentili C., Ebert D., Harrer M.. **Prevention and treatment of mental health and psychosocial problems in college students: An umbrella review of meta-analyses**. *Clin. Psychol. Sci. Pract.* (2021.0) **28** 229-244. DOI: 10.1037/cps0000030
32. Amanvermez Y., Zhao R., Cuijpers P., de Wit L.M., Ebert D.D., Kessler R.C., Bruffaerts R., Karyotaki E.. **Effects of self-guided stress management interventions in college students: A systematic review and meta-analysis**. *Internet Interv.* (2022.0) **28** 100503. DOI: 10.1016/j.invent.2022.100503
33. Cuijpers P., Schuurmans J.. **Self-help interventions for anxiety disorders: An overview**. *Curr. Psychiatry Rep.* (2007.0) **9** 284-290. DOI: 10.1007/s11920-007-0034-6
34. Lewis S., Willis K., Bismark M., Smallwood N.. **A time for self-care? Frontline health workers’ strategies for managing mental health during the COVID-19 pandemic**. *SSM—Ment. Health* (2022.0) **2** 100053. DOI: 10.1016/j.ssmmh.2021.100053
35. **Depression in adults: Treatment and management**. *NICE Guideline, No. 222* (2022.0)
36. Tahara M., Mashizume Y., Takahashi K.. **Coping mechanisms: Exploring strategies utilized by Japanese healthcare workers to reduce stress and improve mental health during the COVID-19 pandemic**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph18010131
37. Aldwin C.M., Revenson T.A.. **Does coping help? A reexamination of the relation between coping and mental health**. *J. Personal. Soc. Psychol.* (1987.0) **53** 337-348. DOI: 10.1037/0022-3514.53.2.337
38. Shen P., Slater P.. **The effect of occupational stress and coping strategies on mental health and emotional well-being among university academic staff during the COVID-19 outbreak**. *Int. Educ. Stud.* (2021.0) **14** 82-95. DOI: 10.5539/ies.v14n3p82
39. Penley J.A., Tomaka J., Wiebe J.S.. **The association of coping to physical and psychological health outcomes: A meta-analytic review**. *J. Behav. Med.* (2002.0) **25** 551-603. DOI: 10.1023/A:1020641400589
40. Gonzales N.A., Tein J.-Y., Sandler I.N., Friedman R.J.. **On the limits of coping: Interaction between stress and coping for inner-city adolescents**. *J. Adolesc. Res.* (2001.0) **16** 372-395. DOI: 10.1177/0743558401164005
41. Suldo S.M., Shaunessy E., Hardesty R.. **Relationships among stress, coping, and mental health in high-achieving high school students**. *Psychol. Sch.* (2008.0) **45** 273-290. DOI: 10.1002/pits.20300
42. Wanzer M., Booth-Butterfield M., Booth-Butterfield S.. **“If we didn’t use humor, we’d cry”: Humorous coping communication in health care settings**. *J. Health Commun.* (2005.0) **10** 105-125. DOI: 10.1080/10810730590915092
43. Davis D.E., Ho M.Y., Griffin B.J., Bell C., Hook J.N., Van Tongeren D.R., DeBlaere C., Worthington E.L., Westbrook C.J.. **Forgiving the self and physical and mental health correlates: A meta-analytic review**. *J. Couns. Psychol.* (2015.0) **62** 329-335. DOI: 10.1037/cou0000063
44. Löw C.A., Schauenburg H., Dinger U.. **Self-criticism and psychotherapy outcome: A systematic review and meta-analysis**. *Clin. Psychol. Rev.* (2020.0) **75** 101808. DOI: 10.1016/j.cpr.2019.101808
45. Belen H.. **Self-blame regret, fear of COVID-19 and mental health during post-peak pandemic**. *Int. J. Psychol. Educ. Stud.* (2021.0) **8** 186-194. DOI: 10.52380/ijpes.2021.8.4.447
46. Whelton W.J., Greenberg L.S.. **Emotion in self-criticism**. *Personal. Individ. Differ.* (2005.0) **38** 1583-1595. DOI: 10.1016/j.paid.2004.09.024
47. Toussaint L.L., Webb J.R., Hirsch J.K., Woodyatt L., Worthington J.E.L., Wenzel M., Griffin B.J.. **Self-forgiveness and health: A stress-and-coping model**. *Handbook of the Psychology of Self-Forgiveness* (2017.0) 87-99. DOI: 10.1007/978-3-319-60573-9
48. Worthington J., Everett L., Langberg D.. **Religious considerations and self-forgiveness in treating trauma in present and former soldiers**. *J. Psychol. Theol.* (2012.0) **40** 274-288. DOI: 10.1177/009164711204000403
49. Tang Y.-Y., Hölzel B.K., Posner M.I.. **The neuroscience of mindfulness meditation**. *Nat. Rev. Neurosci.* (2015.0) **16** 213-225. DOI: 10.1038/nrn3916
50. Devillers-Réolon L., Mascret N., Sleimen-Malkoun R.. **Online mindfulness intervention, mental health and attentional abilities: A randomized controlled trial in university students during COVID-19 lockdown**. *Front. Psychol.* (2022.0) **13** 1-13. DOI: 10.3389/fpsyg.2022.889807
51. Gál É., Ștefan S., Cristea I.A.. **The efficacy of mindfulness meditation apps in enhancing users’ well-being and mental health related outcomes: A meta-analysis of randomized controlled trials**. *J. Affect. Disord.* (2021.0) **279** 131-142. DOI: 10.1016/j.jad.2020.09.134
52. Matiz A., Fabbro F., Paschetto A., Cantone D., Paolone A.R., Crescentini C.. **Positive impact of mindfulness meditation on mental health of female teachers during the COVID-19 outbreak in Italy**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17186450
53. Morales-Rodríguez F.M., Pérez-Mármol J.M.. **The role of anxiety, coping strategies, and emotional intelligence on general perceived self-efficacy in university students**. *Front. Psychol.* (2019.0) **10** 1-9. DOI: 10.3389/fpsyg.2019.01689
54. Godfrey C., Harrison M.B., Lysaght R., Lamb M., Graham I.D., Oakley P.. **Care of self-care by other—Care of other: The meaning of self-care from research, practice, policy and industry perspectives**. *Int. J. Evid.-Based Healthc.* (2011.0) **9** 3-24. DOI: 10.1111/j.1744-1609.2010.00196.x
55. Turton A., Langsford M., Lorenzo D.D., Zahra D., Henshelwood J., Griffiths T.. **An audit of emotional logic for mental health self-care improving social connection**. *Eur. J. Integr. Med.* (2020.0) **37** 101167. DOI: 10.1016/j.eujim.2020.101167
56. **Self Care for Health: A Handbook for Community Health Workers and Volunteers**. (2014.0)
57. Wang C., Bakhet M., Roberts D., Gnani S., El-Osta A.. **The efficacy of microlearning in improving self-care capability: A systematic review of the literature**. *Public Health* (2020.0) **186** 286-296. DOI: 10.1016/j.puhe.2020.07.007
58. Lovibond S.H., Lovibond P.F.. **The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety**. *Behav. Res. Ther.* (1995.0) **33** 335-343. DOI: 10.1016/0005-7967(94)00075-U
59. Diener E., Wirtz D., Biswas-Diener R., Tov W., Kim-Prieto C., Choi D.-W., Oishi S., Diener E.. **New measures of well-being**. *Assessing Well-Being* (2009.0) 247-266. DOI: 10.1007/978-90-481-2354-4
60. Zhong B., Chen J.. **Health information helps mitigate adolescent depression: A multivariate analysis of the links between health information use and depression management**. *Child Care Health Dev.* (2020.0) **47** 201-207. DOI: 10.1111/cch.12831
61. Hughes J.G., Sharma R., Brough N., Majumdar A., Fisher P.. **‘The care that you give to yourself’: A qualitative study exploring patients’ perceptions of self-care**. *Eur. J. Integr. Med.* (2020.0) **40** 101246. DOI: 10.1016/j.eujim.2020.101246
62. Marakaki C., Pervanidou P., Papassotiriou I., Mastorakos G., Hochberg Z.E., Chrousos G., Papadimitriou A.. **Increased symptoms of anxiety and depression in prepubertal girls, but not boys, with premature adrenarche: Associations with serum DHEAS and daily salivary cortisol concentrations**. *Int. J. Biol. Stress* (2018.0) **21** 564-568. DOI: 10.1080/10253890.2018.1484446
63. Höltge J., Theron L., Ungar M.. **A multisystemic perspective on the temporal interplay between adolescent depression and resilience-supporting individual and social resources**. *J. Affect. Disord.* (2022.0) **297** 225-232. DOI: 10.1016/j.jad.2021.10.030
64. Tsay S.-L., Healstead M.. **Self-care self efficacy, depression, and quality of life among patients receiving hemodialysis in Taiwan**. *Int. J. Nurs. Stud.* (2002.0) **39** 245-251. DOI: 10.1016/S0020-7489(01)00030-X
65. Remes O., Wainwright N., Surtees P., Lafortune L., Khaw K.-T., Brayne C.. **Generalised anxiety disorder and hospital admissions: Findings from a large, population cohort study**. *BMJ Open* (2018.0) **8** e018539. DOI: 10.1136/bmjopen-2017-018539
66. Zhong B., Huang Y., Liu Q.. **Mental health toll from the coronavirus: Social media usage reveals Wuhan residents’ depression and secondary trauma in the COVID-19 outbreak**. *Comput. Hum. Behav.* (2021.0) **114** 106524. DOI: 10.1016/j.chb.2020.106524
67. Xie L., Pinto J., Zhong B.. **Building community resilience on social media to help recover from the COVID-19 pandemic**. *Comput. Hum. Behav.* (2022.0) **133** 107294. DOI: 10.1016/j.chb.2022.107294
68. Martín-Núñez J., Heredia-Ciuró A., Valenza-Peña G., Granados-Santiago M., Hernández-Hernández S., Ortiz-Rubio A., Valenza M.C.. **Systematic review of self-management programs for prostate cancer patients, a quality of life and self-efficacy meta-analysis**. *Patient Educ. Couns.* (2023.0) **107** 107583. DOI: 10.1016/j.pec.2022.107583
69. Christensen H., Pallister E., Smale S., Hickie I.B., Calear A.L.. **Community-based prevention programs for anxiety and depression in youth: A systematic review**. *J. Prim. Care Community Health* (2010.0) **31** 139-170. DOI: 10.1007/s10935-010-0214-8
70. Harrer M., Adam S.H., Baumeister H., Cuijpers P., Karyotaki E., Auerbach R.P., Kessler R.C., Bruffaerts R., Berking M., Ebert D.D.. **Internet interventions for mental health in university students: A systematic review and meta-analysis**. *Int. J. Methods Psychiatr. Res.* (2019.0) **28** e1759. DOI: 10.1002/mpr.1759
71. Melville K.M., Casey L.M., Kavanagh D.J.. **Dropout from Internet-based treatment for psychological disorders**. *Br. J. Clin. Psychol.* (2010.0) **49** 455-471. DOI: 10.1348/014466509X472138
|
---
title: Metabolic Profile of Einkorn, Spelt, Emmer Ancient Wheat Species Sourdough
Fermented with Strain of Lactiplantibacillus plantarum ATCC 8014
authors:
- Larisa Rebeca Șerban
- Adriana Păucean
- Maria Simona Chiș
- Carmen Rodica Pop
- Simona Maria Man
- Andreea Pușcaș
- Floricuța Ranga
- Sonia Ancuța Socaci
- Ersilia Alexa
- Adina Berbecea
- Cristina Anamaria Semeniuc
- Vlad Mureșan
journal: Foods
year: 2023
pmcid: PMC10001257
doi: 10.3390/foods12051096
license: CC BY 4.0
---
# Metabolic Profile of Einkorn, Spelt, Emmer Ancient Wheat Species Sourdough Fermented with Strain of Lactiplantibacillus plantarum ATCC 8014
## Abstract
The continuous development of bakery products as well as the increased demands from consumers transform ancient grains into alternatives with high nutritional potential for modern wheat species. The present study, therefore, follows the changes that occur in the sourdough obtained from these vegetable matrices fermented by *Lactiplantibacillus plantarum* ATCC 8014 during a 24 h. period. The samples were analyzed in terms of cell growth dynamics, carbohydrate content, crude cellulose, minerals, organic acids, volatile compounds, and rheological properties. The results revealed significant microbial growth in all samples, with an average value of 9 log cfu/g but also a high accumulation of organic acids with the increase in the fermentation period. Lactic acid content ranged from 2.89 to 6.65 mg/g, while acetic acid recorded values between 0.51 and 1.1 mg/g. Regarding the content of simple sugars, maltose was converted into glucose, and fructose was used as an electron acceptor or carbon source. Cellulose content decreased as a result of the solubilization of soluble fibers into insoluble fibers under enzymatic action, with percentages of 3.8 to $9.5\%$. All sourdough samples had a high content of minerals; the highest of which—Ca (246 mg/kg), Zn (36 mg/kg), Mn (46 mg/kg), and Fe (19 mg/kg)—were recorded in the einkorn sourdough.
## 1. Introduction
One of the earliest types of natural starters is sourdough, which is typically used as an alternative to baker’s yeast for preparing leavened baked items. As endogenous lactic acid bacteria (LAB) and/or yeasts are naturally present in the raw materials, this was actually the original method of manufacturing bread through natural leavening before commercial yeast was utilized for leavening [1,2,3]. At present, starter cultures of lactic acid bacteria (LAB) are mostly used for the fermentation of bakery products on a large scale because they control the fermentation process and the quality of the final product [4,5]. Other advantages of starter cultures are decreasing fermentation times, reducing energy costs, improving the sensory qualities of the products, and minimizing their spoilage risks [6].
The sourdough-making process involves a mixture of flour, water, and lactic acid bacteria fermented at a constant temperature for a predefined period [7,8,9]. Nowadays, sourdough technology has gained popularity among bakers due to its ability to enhance bread quality [10,11,12,13,14]. Thus, organic acids produced by LAB metabolism increase the shelf life of the bread, prevent its fungal and bacterial alteration, restrict the activity of endogenous amylase, and have a great effect on the dough’s capacity to bind water and hold gas [15,16,17]. At the same time, sourdough plays a significant role in a variety of other aspects, such as improving the rheological properties of bread, increasing the bioavailability of minerals, protein digestibility and soluble fibers, contributing to the formation of flavor compounds, reducing the glycemic index, lowering the levels of phytate and trypsin inhibitors, and others [18,19,20,21].
Lactic acid bacteria (LAB) are some of the most commonly used microorganisms in the food industry. Due to their diversity and variability, LAB offers an endless source of perspectives for technological processes [22]. Lactiplantibacillus plantarum (Lb. plantarum) is a facultative heterofermentative bacteria extensively used in the bakery, which is recognized and appreciated for the structure and acidification it provides to the cereal grains used for fermentation [23,24]. Lb. plantarum is a versatile bacterial strain that can adapt to different substrates [25]. It was successfully used in sourdough together with a wide variety of cereals, such as Kamut® wheat (Lb. plantarum M4), wheat flour (Lb. plantarum ATCC 14917, Lb. plantarum ATCC 8014), emmer wheat (Lb. plantarum 6E, Lb. plantarum 10E), spelt wheat (Lb. plantarum ATCC 31S), rye, oats, and barley (Lb. plantarum LUHS135) [26,27,28,29,30]. Nevertheless, due to the interaction between the raw matrix (flour) and microbial activity, a deep understanding of the metabolic profile of different types of dough is still needed.
Due to their unique chemical composition, ancient grains like einkorn, emmer, and spelt have retracted the interest of consumers; also, farmers are interested and enticed by their low cultivation and maintenance requirements [20,31,32,33,34,35,36,37,38]. Between the compounds of interest, β-glucans, essential amino acids, phenols, proteins, and minerals are considered highly important and studied. Moreover, compared to common wheat and durum wheat, einkorn stands out for its higher content of total phenolic compounds (2.06–8.11 µmol GAE/g), lipids 2.4–$3.2\%$ with a high content of mono-unsaturated fatty acids ($26.85\%$), poly-unsaturated fatty acids ($56.55\%$), ferulic acid (148.67–764.04 µg/g), p-coumaric acid (5.06–54.09 µg/g), and certain minerals, such as zinc (5.4 mg/100 g) [39,40]. Being richer in lutein ($90\%$ of total carotenoids) than modern wheat, the emmer variety’s remarkable nutritional value is provided by its high level of antioxidant compounds, and dietary fibers, (11.5–$15.5\%$) such as cellulose, arabinoxylans, and β-glucans, which are the major components of the grain’s cell wall [31,41]. Regarding spelt, it is recognized for its high percentage of proteins ($15.17\%$ emmer wheat vs. $11.58\%$ common wheat), and vitamins such as niacin (5.5 mg/100 g), which is found in a higher quantity in this cereal than in einkorn or wheat (2.5 mg/100 g) [42,43].
All these qualities offer ancient wheat species flours a uniqueness that can lead to the development of innovative, healthy, and functional products. Several studies have revealed the health-beneficial nature of these grains, such as their involvement in the prevention and alleviation of some diseases such as diabetes type 2, cancer, obesity, coronary heart disease, ischemic stroke, osteoporosis, and others [44,45,46]. Since the technological properties of these ancient species are inferior to those of modern wheat species and standard processing technologies cannot be applied, researchers have begun to adopt and develop new methods, strategies, and protocols to obtain high-quality bakery products [47,48].
Metabolomics is the approach that can provide a more specific perspective through observing the evolution of the sourdough’s profile during the fermentation process. As a general definition, metabolic represents a complex research field aiming to study the biochemical processes that involve small metabolites. These analyses are composed of several stages, including sample preparation, data acquisition, data processing, analysis, and interpretation of the results [49]. The most importance are separation and detection, while a few of the most frequently used techniques of separation are high-performance liquid chromatography (HPLC), gas chromatography (GC), mass spectrometry (MS), frequently ultraviolet (UV), nuclear magnetic resonance (NMR), and near -infrared (NIR) spectrometry [50]. Additionally, another important part of metabolomics is related to statistical models on metabolite profiles, which are designed to anticipate variables that are difficult to determine in other ways [49]. Depending on the purpose of the metabolic approach, it can have three roles, including informative, descriptive, and predictive [49]. In the bakery sector, metabolomics is used mainly to determine the effect that the type of flour used and fermentation have on the formation of volatile compounds, but also to quantify carbohydrates, amino acids, organic acids, and other specific compounds [51,52].
The literature review on the topic of ancient wheat species revealed few studies that reported on the metabolic profile of sourdough obtained by fermenting these flours with *Lactiplantibacillus plantarum* strains. Moreover, the existing studies report mainly on Durum wheat, spelt, and KAMUT® khorasan wheat [53,54,55,56,57] and a wider image of other ancient wheat species like emmer and einkorn that was not reported. In addition, the volatile derivative content of sourdough needs to be deeply investigated since these compounds play a highly important role in the bread’s sensory characteristics. In this view, the present research study aimed to use a metabolomic approach to assess the adaptability of *Lactiplantibacillus plantarum* ATCC 8014 in the sourdough obtained from ancient wheat flours (einkorn, spelt, and emmer) in order to be used in breadmaking. This approach could give a more complete image to the performance of these wheat species for sourdough production by providing a comparison between their metabolic profiles and common wheat, which is mainly used in breadmaking. This will provide the possibility to monitor the traceability of the bioactive compounds from the raw flour to bread via sourdough technology and to obtain bakery products fortified with these biocompounds with good sensorial features due to the specific aroma compounds.
## 2.1. Materials
Whole meal flours (einkorn, spelt, emmer, and common wheat) were purchased from specialized stores in Romania. Lactiplantibacillus plantarum ATTC 8014 was acquired from Microbiologics (Minnesota, USA), and all reagents and chemicals used for analysis came from Sigma Aldrich (Taufkirchen, Germany) and Chempur (Piekary Śląskie, Poland), and were of analytical grade. The equipment used included the following: laboratory glassware, analytical balance, technical balance, pH meter (GroLine H1285-7, Woonsocket, Rhode Island, USA), furnace (Nabertherm B150, Lilienthal, Germany), centrifuge (Eppendorf AG 5804, Hamburg, Germany), vortex (Heidolph Reax Top vortex), Shimadzu UV-1900 (Shimadzu Scientific Instruments, Kyoto, Japan), optical microscope (Zeiss 40X, Primo Star, Germany), colony counter (Colony Star 8500, Funke Gerber, Berlin, Germany), Agilent 1200 HPLC System (Agilent Technologies, Santa Clara, CA, USA), FOSS 2010 (Fibertec 2010, Hillerød, Denmark), Varian 220 FAA equipment (Germany), Gas Chromatograph Mass Spectrometer QP 2010 (Shimadzu Scientific Instruments, Kyoto, Japan), Anton Paar MCR 302 rheometer (Anton Paar, Graz, Austria).
## 2.2. Sourdough Formulation, Lactiplantibacillusplantarum ATCC 8014 Activation, and Cell Count Determination
Shortly, the inoculum was obtained from freeze-dried cells suspended in10 mL Man Rogosa Sharpe (MRS) broth, incubated under aerobic conditions at a temperature of 37 °C for 48 h, and then sub-cultured into 95 mL MRS and incubated in the same conditions. Afterward, the biomass was centrifuged at 2300× g (Eppendorf R 5804 centrifuge, Hamburg, Germany) for 10 min, at a temperature of 4 °C, washed three times with sterile water, and inoculated in the prepared matrix in order to achieve an initial cell count of 108 cfu/mL. The microbial optical density of the inoculum was determined using the spectrophotometer Shimadzu UV-1900 (Shimadzu Scientific Instruments, Kyoto, Japan), and absorbance was read at a wavelength of 600 nm [58].
Sourdough samples were obtained by mixing flour with distilled water at a dough yield of (DY = 1:0.8), while Lb. plantarum strain was added at a level of 108 cfu/mL to the mixture. Samples were taken at 0, 12, and 24 h of fermentation at 35 °C and analyzed for microbial cell growth dynamics. To determine the increase in cfu/g, decimal dilutions were performed, and 1 mL of each sourdough was mixed with 9 mL of saline solution. In a Petri dish with MRS agar, 1 mL of the sample was added and incubated for 48 h at 37 °C [58]. The microbiological analysis was performed in three replicates ($$n = 3$$).
At sampling times, 5 mL of each sample was taken out and combined with 45 mL of sterile sodium chloride ($0.85\%$ w/v). One milliliter of this solution was used for serial dilutions and plating on MRS agar under the following incubation: 37 °C, 48 h. The final stage involved the analysis of Petri plates with colonies under an optical microscope (Zeiss 40X, Primo Star, Germany) in order to identify microorganisms, while for counting, a colony counter (Colony Star 8500, Funke Gerber, Berlin, Germany) was used [24].
The formulations for the four types of sourdough (einkorn, spelt, emmer, and common wheat as a control sample) and their codifications are shown in Table 1.
## 2.3. Sourdough Acidification—Total Titratable Acidity (TTA) and pH Determination
The pH was determined using a pH meter (GroLine HI1285-7, Woonsocket, RI, USA) after it was initially calibrated with a standard solution.
The total titratable acidity (TTA) was determined by blending 10 g of sourdough with 90 mL of water, followed by the neutralization of the mixture obtained with NaOH 0.1 N until the pH dropped to 8.3. Finally, the total acidity is expressed as the volume (mL) of NaOH used in the titration [59,60].
## 2.4. Determination of Carbohydrates, Organic Acids, and Ethanol Content by HPLC-RID
The identification of carbohydrates (maltose, glucose, and fructose), organic acids (lactic, acetic, and citric), and ethanol was carried out with the help of the Agilent 1200 series HPLC system, which was equipped with quaternary pumps, a solvent degasser, and a manual injector coupled with a refractive index detector (RID). Agilent Technologies, CA, USA, provided the Polaris Hi-Plex H column, 300 × 7.7 mm, which was utilized to separate the compounds. The mobile phase H2SO4 5 mM was used at a flow rate of 0.6 mL/min, column temperature $T = 80$ °C, and RID temperature $T = 35$ °C; compounds were eluded for 25 min. For result interpretation, the OpenLab—ChemStation (Agilent Technologies, Santa Clara, CA, USA) system was used. In the end, obtained retention times were compared with standard times for glucose, fructose, maltose, citric acid, lactic, acetic acid, and ethanol (Sigma-Aldrich, Germany) to identify the compounds.
Briefly, 2 g of the sample and 4 mL of ultrapure water (UPW) is vortexed (Heidolph Reax Top vortex) for 1 min, sonicated for 30 min (Elmasonic E15H sonication bath), and centrifuged (Eppendorf AG 5804 centrifuge) at 7155× g for another 10 min. The resulting supernatant is filtered using a 0.45 µm nylon filter (CHROMAFIL Xtra PA-$\frac{45}{13}$), and 20 µL of this is injected into the Agilent 1200 HPLC system [58].
## 2.5. Determination of Crude Fiber Content (Crude Cellulose)
Crude cellulose was determined according to the standardized method (ISO 5498:1981) that was performed using the fiber analyzer FOSS 2010 (Fibertec 2010, Hillerød, Denmark). Briefly, 1 g of the sample was defatted by washing it three times with acetone. The defatted sample was boiled with sulfuric acid 12,$5\%$ for 30 min, washed with distilled water, and boiled with KOH 12,$5\%$ for 30 min. The resulting sample was calcined in an oven (BINDER GmbH, Tuttlingen, Germany) at 525 °C for 3 h, cooled and weighed. Crude cellulose was calculated as the ratio between the sample weight after calcination and the initial weight of the sample [61].
## 2.6. Determination of Micro and Macroelements by Atomic Absorption Spectrophotometry (AAS)
Macro and microelements contained in sourdough were identified using an atomic absorption spectrophotometry (ASS) (Varian 220 FAA Atomic Absorption Spectrometer, Varian Inc., Germany.
The samples (3 g) were analyzed with the Varian 220 FAA equipment after preliminary processing, which consisted of their calcination for 10 h at 500 ± 100 °C in a furnace (Nabertherm B150, Lilienthal, Germany). The resulting residue was then treated with 5 mL of HCl 6 mol/L and subsequently dissolved in an exact volume, 20 mL of HNO3 0.1 mol/L. The values obtained at the end of the analysis are expressed as parts per million (ppm), each being the average of three independent determinations [62].
## 2.7. Determination of Volatile Compounds by ITEX/GC-MS Technique
The ITEX/GC-MS technique was used for the analysis of aroma compounds and assumed the use of the CombiPAL AOC-5000 autosampler. in which 1 g of each sample was inserted, sealed, and incubated for 20 min at 60 °C, under continuous stirring. At the end of the incubation, the volatile compounds accumulated in the headspace phase were adsorbed into a Tenax carbon fiber (ITEX-2TRAPTXTA, Tenax TA $\frac{80}{100}$ mesh) and subsequently thermally desorbed in the gas chromatograph injector [63,64].
GCMS QP-2010 (Shimadzu Scientific Instruments, Kyoto, Japan) mass spectrometer performed the separation of aroma compounds on a ZB-5ms capillary column of 30 m × 0.25 mm i.d. × 0.25 µm (film thickness). The chromatographic column used the following temperature program: in the first phase, 35 °C was held for 5 min, followed by an increase to 110 °C with 4 °/min in the second phase, and an increase to 250 °C with 20 °/min for another 5 min in the third phase. Helium was used as a carrier gas, at a constant flow rate of 1 mL/min.; also, the temperature for the injector, ion source, and interface was chosen to be 250 °C. The mass spectrometry detector was operated in electron impact ionization mode over a scan range of 40–400 m/z [64,65].
The identification of volatile compounds was achieved by comparing the mass spectra of each chromatographic peak with the NIST27 and NIST147 libraries, considering only compounds that registered a degree of similarity of at least $85\%$.
## 2.8. Determination of Rheological Properties
The rheological measurements of the sourdough were realized with an Anton Paar MCR 302 rheometer (Anton Paar, Graz, Austria), using a parallel plate geometry (PP50) with a diameter of 50 mm. The method assumes placing 3 g of each sample on the lower plate of the device and lowering the upper plate to a plate distance set at a gap of 1 mm. The next steps consist of cleaning the sourdough surplus resulting from the pressing and adding silicone oil in order to avoid reducing the moisture of the sample through testing. The working temperature of the rheometer was set at 25 °C, and the storage modulus (G’) and the loss modulus (G″) were tested at an angular frequency of 0.628–628 rad/s−1, and the shear deformation was set at a value of $0.1\%$ [66,67].
## 2.9. Statistical Analyses
The Duncan multiple comparison test (SPSS version 19 software version 19; IBM Corp., Armonk, NY, USA) was used to compare the obtained data. The analyses were performed in three independent assays, and small letters indicated the significant differences (p ˂ 0.05) between the 4 types of sourdough at the same moment.
Principal component analysis (PCA) was performed using the Unscrambler software (version 10.5.1; CAMO Software AS, Oslo, Norway), while the Hierarchical Cluster Analysis (HCA) and Heatmap Visualization were performed with MetaboAnalyst software (version 5.0; Xia Lab at McGill University, Quebec, QC, Canada).
## 3.1. Cell Viability in Sourdough Samples
In Figure 1, significant bacterial cell growth of *Lactiplantibacillus plantarum* ATCC 8014 can be observed in all four types of wheat flour sourdoughs (common and ancient wheat species), demonstrating the adaptability that Lb. plantarum ATCC 8014 has in these flours. The microbial growth dynamic in the control sample (M0 wheat flour sourdough) started at a value of 6.0 log cfu/g to when it registered after 24 h cell growth of 9.4 log cfu/g. The highest final concentrations, after 24 h of fermentation were recorded, for sample M1 (einkorn flour sourdough) at 9.6 log cfu/g, followed by M2 sample (spelt flour sourdough) reaching 9.4 log cfu/g after 24 h. The lowest growth, but still appreciable, was determined in the sample with the emmer flour (M3) which began at 6.8 log cfu/g and reached 9.0 log cfu/g at the end of the fermentation period. After 24 h of fermentation, significant differences ($p \leq 0.05$) between the microbial dynamics of the four types of flours were recorded.
Similar results were reported by Çakır et al. [ 68], who recorded that in einkorn sourdough fermented with different strains of Lb. plantarum (AAS3, FM02, 1838, GM1043), values between 9.26 log cfu/mL and 9.47 log cfu/mL (after 24 h fermentation). In another study, the reported values went above 9.0 log cfu/g (after 24 h fermentation) in the sourdough obtained from common wheat and Lb. plantarum M4 [26].
The most important aspect in terms of LAB growth and viability is the nutrients availability. It was stated that einkorn, spelt, and emmer contained high amounts of proteins, amino acids, vitamin E, vitamins of B-group, and minerals like calcium, magnesium, iron and zinc, which are strongly necessary for Lactobacillus ssp. growth [69]. The microorganisms consume firstly free amino acids, vitamins, and simple sugars, all compounds that are easily metabolized. After that, the growth of Lb.plantarum depended on its ability to breakdown the protein chain into the peptides and amino acid necessary to meet its nitrogen requirements [24]. As it was reported in our previous work, Șerban et al. [ 20], einkorn and spelt have the highest protein content compared to emmer and common wheat and this aspect could explain their different microbial growth during 24 h. Coda et al. [ 70] stated in their research that the proteolysis of spelt flour leads to essential amino acids (isoleucine, leucine, valine, and methionine), which on the one hand sustain bacterial metabolism and on the other hand contribute to the health benefits by supporting the production of bioactive peptides.
Regarding carbohydrates, Lactobacillus ssp. uses them as a carbon source to sustain the development of the microbial cells. The studied flours are rich in simple sugars, which are primarily used for microbial growth at the beginning of fermentation. Compared to common wheat flour (0.41 g/100 g), spelt (2.94 g/100 g) and einkorn (2.67 mg/100 g) contain a significantly higher amount of simple sugars [20,40]. These simple sugars initiated cell multiplication, giving a good start for the ancient wheat species, as it is sustained by the values obtained after 12 h of fermentation.
Vitamins from B -group such as thiamine (einkorn—1.118 µg/g, spelt—3.46 µg/g, emmer—0.952 µg/g, wheat—0.964 µg/g), riboflavin (einkorn—1.118 µg/g, spelt—1.64 µg/g, emmer—0.952 µg/g, wheat—0.964 µg/g), and niacin (einkorn—55 µg/g, spelt—66 µg/g, emmer—85.11 µg/g, wheat—47.66 µg/g) supports the bacterial growth in these flours [20,71,72].
Thus, the chemical composition of einkorn, spelt, and emmer could sustain the cell dynamics of Lb. plantarum during 24 h of fermentation and give an advantage in cell development compared to common wheat. However, the differences recorded on the final cell count might be due to the variations in flour quality based on provenance, environment, production practices, and storage conditions [73].
According to Clément et al. [ 74], flour ash content also has an important role in microbial growth, in wheat sourdough with a high mineral content recording a cell growth from 9.9 × 107 to 6 × 108 cfu/g in 48 h of fermentation. It is important to mention that this content is influenced by two main factors, namely the flour extraction rate and the milling process [74]. In the present study, all flour samples were whole meal flours with a high content of minerals, which also sustained the bacterial growth.
Other factors that can influence microbial growth are those related to water activity; the required values ranging between 0.90 and 0.96 for Lactobacillus species [75]. Thus, of great relevance is the availability of water in a sourdough starter. This factor refers to dough yield (DY = [flour weight + water weight] × 100/flour weight) and hydration (the percent of water to flour) [9]. The importance of hydration is demonstrated by the fact that water diffuses proteolytic enzymes and nutrients, and influences the composition and activity of the bacteria from the starter [9]. According to Di Cagno et al. [ 76] and Minervini et al. [ 77], who studied sourdoughs obtained from durum wheat (Triticum durum), Lb. plantarum prefers and dominates in firm ones, which present a dough yield (DY) between 150 and 200. In the case of the present study, DY = 180 was used to sustain the microbial cell dynamics.
In this regard, we can assume that ancient wheat species, namely einkorn, spelt, emmer, are a good matrix for the growth of Lb. plantarum ATCC 8014.
## 3.2. pH and TTA Values
The pH and TTA are two important indicators in monitoring the fermentation progress. The pH of the four types of sourdough with ancient and common flours started from a value slightly above a 6 at the moment of inoculation and reached, in the next 24 h, values below a 4. Respectively, the lowest pH was recorded in sample M2 (spelt flour sourdough) at 3.84, which was closely followed by sample M1 (einkorn flour sourdough) at 3.85; the pH differences between the samples were not found statistically significant ($p \leq 0.05$) (Figure 2). In the case of common wheat sourdough (M0), the pH value after 24 h of fermentation was 3.89.
Similar values were recorded by Casado et al. [ 78] for sourdough with wheat flour (3.9) fermented for 24 h at 35 °C. Regarding ancient wheat flours, a study carried out on einkorn flour sourdough fermented with different strains of LAB showed a pH decrease from 6.18 to 3.81 after 4 days of fermentation [68]. Additionally, the pH of the sourdough with spelt flour recorded in the first 24 h values between 4 and 5, as it was reported by [79]. Emmer wheat bran was part of a study that revealed that after 24 h of fermentation by Lb. plantarum T6 B10 and *Weissella confusa* BAN8, the pH of the obtained dough reached the 3.9 value [80].
According to Arora et al. [ 2], depending on the type of flour and the protocol used, the pH of sourdough is most often between 3.4 and 4.9. Values below 3 were normally recorded only in cases where the fermentation took longer than 48 h or if other special ingredients were used in the composition of the sourdough, such as brewer’s spent grains.
The pH value is influenced by the amount of acids formed during fermentation [68].
The total titratable acidity (TTA) helps to measure the total acids produced by Lb. plantarum ATTC 8014. On the other hand, TTA is considered an important indicator regarding acid flavor characteristics of sourdough because the production of lactic acid (the main metabolite of fermentation) has great relevance in terms of the aroma and shelf life of the final product [81].
In this case, it increased proportionally with the increase of fermentation time, reaching values of 15.6 mL of 0.1 N NaOH/10 g for the emmer sample (M3), and 19.8 mL of NaOH/10 g for the spelt sample (M2) after 24 h of fermentation at 35 °C. Einkorn (M1) and wheat flour (M0) led to an acidification rate of 23.2 and 23.4 mL of NaOH/10 g, the differences between these two samples not being statistic significative ($p \leq 0.05$) at the end of fermentation.
Values of total titratable acidity equal to 22.3 mL NaOH 1 N/100 g for wheat flour sourdough (after 24 h) [82] and around 25 mL 0.1 N NaOH/10 g for sourdough with rye and spelt flour (after 3 days) had been reported [83].
According to Arora et al. [ 2], the most common interval for TTA is established between 4.0 and 25.0 mL of 0.1 M NaOH/10 g of dough. The highest values being specific for sourdough fermented by heterofermentative bacteria [84].
The acidity value can be influenced by the metabolic activities of the bacteria, and affected by proteolysis, lipolysis, and amylolysis that occur during fermentation [85]. It is not without interest to mention that for this study, whole meal flours were used, which contribute to these acidification rates. The less refined a flour is, the higher its ash content (flour mineral content) [86], and according to Clément et al. [ 74], ash content shows a positive effect in terms of carbon dioxide production and acidity in the sourdough. Higher fermentation activity that occurs in the bread with high ash content sourdough leads to obtaining products with an increased volume and implicitly a lower density [74]. The whole meal flours used in the study had the following ash contents, according to the producers: $1.88\%$ wheat, $2.48\%$ einkorn, $1.65\%$ spelt flour, and $1.50\%$ emmer flour, which correlated with the determined acidity and supported this conclusion.
## 3.3. Carbohydrates and Organic Acids Content
Lb. plantarum ATCC 8014 induced a heterofermentative metabolism in common and ancient wheat sourdoughs, as can be seen in Table 2. The glucose content had an upward evolution after 24 h in the case of the 3 sourdough samples with ancient wheat flour (M1/einkorn—4.99 mg/g, M2/spelt—5.36 mg/g, M3/emmer—2.71 mg/g), and a downward evolution in the case of the control sourdough (common wheat flour—1.47 mg/g). The fructose content decreased after 24 h of fermentation in all samples, and a valid explanation could be given by its conversion to mannitol by mannitol dehydrogenase, as previously specified, but also due to its use as an alternative external electron acceptor by the lactic acid bacteria [87]. Due to the conversion of maltose to glucose and consumption during the fermentation process [29], the concentration of maltose decreased significantly in the first 3 samples after 24 h of fermentation (M0—1.39 mg/g, M1—1.11 mg/g, M2—1.875 mg/g). According to De Vuyst et al. [ 87], the use of maltose as the main source of energy through a dedicated catabolic pathway is characteristic of lactic acid bacteria that can be used in sourdough fermentation.
In Table 2 it can be observed that the sourdough with the einkorn flour (M1) presented higher values of glucose (4.99 mg/g) and fructose (2.07 mg/g) after 24 h of fermentation compared to the sourdough with common wheat (M0). A similar situation was described by other researchers, who reported that sourdough bread with einkorn organic flour presents a higher amount of carbohydrates (53.03 mg/100 g) than sourdough bread with wheat commercial flour (51.70 g/100 g), and sourdough bread with wheat organic flour (51.76 g/100 g) [88].
Our results are supported by a study conducted by Zörb et al. [ 89], who showed that spelt whole meal wheat flour compared to wheat whole meal flour is richer in free sugars, such as maltose (2.35 mg/g vs. 1.37 mg/g), fructose (0.36 mg/g vs. 0.17 mg/g), glucose (0.36 mg/g vs. 0.15 mg/g), sucrose (7.47 mg/g vs. 5.91 mg/g), or 1-kestose (3.08 mg/g vs. 2.00 mg/g).
Pozzo et al. [ 90] reported in the case of spelt flour fermented with a sourdough starter (Lievitamente SNC, Viareggio, Lucca, Italy) at the time of 0 for fermentation, the following values: glucose 1.74 mg/g, maltose 7.64 mg/g, fructose 3.17 mg/g, and sucrose 4.04 mg/g; and after 24 h of fermentation—glucose 15.37 mg/g, maltose 12.66 mg/g, 22.16 fructose mg/g, and sucrose 0.65 mg/g. Additionally, after another 24 h, except for maltose (7.77 mg/g), all other carbohydrates recorded values below 1 mg/g sample.
The differences between the maltose, glucose, and fructose consumption during fermentation of einkorn, emmer, and spelt comparing to common wheat are due to their higher content in starch and free simple sugars.
It was also reported that emmer sourdough revealed higher amounts of glucose and fructose than spelt or common wheat sourdough [57].
Regarding organic acids, these are products of lactic fermentation, with lactic acid being the most prevalent and significant of them, even if citric acid was present in unfermented flours in relative high amount. A variety of factors, including metabolic activity, technological performance, and sourdough’s acidification properties, influence the quantity of the acids produced [91]. Organic acids also play an important role in terms of the rheological properties of the dough. Particularly, lactic acid is recognized for the elastic structure it gives to the dough, while the acetic acid, on the contrary, leads to the formation of a harder gluten. Other benefits that are attributed to organic acids are their ability to protect products, from the point of view of microbiological safety [91].
In Table 3, a progressive increase in lactic acid concentration can be observed along with the increase in fermentation time. The highest values of lactic acid were recorded for samples M0—control sample, (6.65 mg/g), and M1—einkorn flour (6.36 mg/g), with the opposite pole being the sample with M3—emmer flour (2.89 mg/g).
Acetic acid is a minor product of heterofermentative metabolism and recorded maximum values of 1.09 mg/g in common wheat sourdough (M0), the most satisfactory concentrations being determined in samples with wheat and einkorn; its accumulation in sourdough being conditioned by the starter, flour type, and fermentation conditions [91].
Two major types of metabolic pathways act in the biosynthesis of aromatic substances in bread: the citric acid cycle and the amino acid metabolism. The first of them assumes that lactic acid bacteria are able to produce acetoin, diacetyl, butanediol, and other compounds in the process of metabolizing citrate [92]. In Table 2 samples M1 with 2.95 mg/g and M2 with 2.66 g/mg are significantly different ($p \leq 0.05$) from the wheat sample (M0) in terms of citric acid content, which can lead us to form the hypothesis that einkorn and spelt flours represent plant matrices that support the synthesis of citric acid by Lb. plantarum ATCC 8014. From a metabolomic point of view, this could be an important finding since specific aromatic compounds are formed as a result of the interaction between the raw flour and the LAB strain, and so the sensorial characteristics could be influenced.
In contrast with homofermentative lactic acid bacteria that only produce lactic acid, heterofermentative lactic acid bacteria also produce, among other compounds, ethanol [93]. In this study, ethanol showed low levels in all varieties of sourdough, and in some it was even imperceptible. However, compared to the start of fermentation, a slight accumulation can be observed in the samples after 24 h, as a result of glycolysis and the decomposition of pyruvate. The highest value was recorded in the sample with wheat flour (M0)—0.28 mg/g and emmer flour (M3)—0.12 mg/g. Two of the most important advantages that the accumulation of ethanol in sourdough brings are that it helps to strengthen the gluten network and that, according to Pérez-Alvarado et al. [ 94], ethanol and lactic acid isomers (at pH 4) can cause an increase in the metabolic activity of LAB.
Shewry et al. [ 57] reported higher values for lactic and organic acids in emmer and spelt sourdoughs fermented with a commercial starter culture compared to bread wheat, but the differences were not found to be significant. Novotni et al. [ 95] determined that in whole meal wheat sourdough fermented with Lb. plantarum DSM 2601 until a pH value of 4 was reached, lower concentrations of lactic acid (0.96 g/100 g) and acetic acid (0.01 g/100 g). This accumulation of acids was probably influenced by a lower time of fermentation. However, more close concentrations were reported by Ventimiglia et al. [ 96] when fifteen durum wheat sourdough samples were fermented with 28 strains of Lb. plantarum, ranging between 1.97and 9.41 mg/g lactic acid and 0.36 and 1.46 mg/g acetic acid at pH varying from 3.81 to 4.60.
Because the flavor of bakery products is greatly influenced by the organic acids that are formed during fermentation; the quotient of fermentation (QF) which represents the molar ratio between lactic and acetic acid, is a common and widely used parameter to correlate acidity and aroma. Most often, it is recommended to keep it at a value below 5 [2], or below 4 according to Coda et al. [ 28], when emmer and spelt sourdoughs were discussed. In this study, however, most of the samples registered a quotient of fermentation beyond these limits; the highest values were obtained after 24 h in the samples with einkorn flour (14.81) and the one with spelt flour (13.24). An explanation for these values is provided by Casado et al. [ 78] who note that a high fermentation temperature, such as 35 °C, facilitates microbial activity and implicitly increases the quotient of fermentation. Higher values of this parameter were also reported in other studies made on wheat sourdough: 15.64 in Galli et al. [ 97] research and 9.3 in Lattanzi et al. [ 98]. The molar ratio between lactic and acetic acids (fermentation quotient—QF) is greatly influenced by the ratio of dough yield and fermentation temperature. Thus, for the accumulation of acetic acid in larger quantities, temperatures between 25 and 30 °C are suitable, while lactic acid prefers temperatures of 35–37 °C [3], as they were set in the present case.
## 3.4. Crude Cellulose Content
Fiber solubilization is one of the most important processes during sourdough fermentation. Thus, fibers change their physical and chemical properties depending on the degree of fermentation. The ratio between soluble and insoluble fibers can be modified as a result of enzymatic reactions; in sourdough, there are two types of enzymatic hydrolysis that fibers can suffer. The first case supposes that when the flour is hydrated, certain hydrolytic enzymes intrinsic to the grains are activated, an example being hemicellulases. In the second case, LAB releases enzymes with glycolytic activity that can also act on the fibers in the dough [99].
Based on this information, it can also be observed (Figure 3) in the present case, there is a gradual decrease in the concentration of cellulose with the increase in the fermentation time of the sourdough. Thus, the sample with common flour (M0) had at time 0 h of the fermentation a cellulose value of $2.31\%$, which after 24 h decreased to $2.09\%$; sourdough with spelt flour (M2) left at $2.06\%$ and arrived at $1.91\%$ cellulose in the final, while the smallest amount was found in the emmer flour sample (M3), from $1.09\%$ to $1.05\%$.
Until now, the amount of research occurring on ancient cereals has been quite limited, making it difficult to make comparisons and adopt unanimously accepted opinions regarding their chemical composition, and especially their crude cellulose content. However, according to Kulathunga et.al. [ 100] emmer flour has an insoluble fiber content between 7.8 and $13.8\%$, spelt flour has 10.6–$11.4\%$, and einkorn flour has 6.9–$7.53\%$. The same authors recorded in the breads produced from these flours the following concentrations in terms of total insoluble fibers: 8.1–$8.4\%$ einkorn flour bread, 7.6–$8.1\%$ spelt flour bread, and 7.2–$7.3\%$ emmer flour bread [100]. A slight increase in the percentage of soluble fibers was reported, which, was attributed to the solubilization of insoluble fibers occurring during the fermentation or baking processes [100]. On the other hand, KAMUT® Khorasan (another ancient wheat species) flour bread with sourdough fermented at low temperature recorded 13.26 g/100 g insoluble fibers, while the same type of bread obtained with sourdough fermented at high temperature had a value of 18.11 g/100 g. These results demonstrate once again that the enzymatic processes during fermentation and baking are an important factor in terms of the functional properties of the final product [101].
The addition of cellulose to bakery products to increase their total fiber content has been the subject of several studies [102,103,104,105,106]. In principle, the consumption of dietary fiber is associated with the prevention or treatment of various diseases [107,108,109]. The insoluble fibers, including cellulose, which are mainly found in cereals, have revealed certain health benefits such as reduced blood sugar, prevention of cardiovascular risks and coronary artery disease, growth of intestinal peristalsis, decreased contact time between toxic compounds and intestinal mucosa, speeding up intestinal transit, helping in the detoxification process, and weight loss [110,111,112].
## 3.5. Minerals Content
The importance of minerals in human health is well known and has been widely demonstrated through a series of studies [113,114,115]. The sources from which they can be procured are various, with the largest quantities being found in milk, dairy products, green leafy vegetables (spinach, cabbage, kale), broccoli, citrus fruits, kiwis, and bananas [116]. In addition, cereals and cereal products also contain important quantities of iron, zinc, manganese, phosphorus, and sodium [117].
Additionally, the increase in the bioavailability of minerals through sourdough fermentation has been supported by several researchers [99,118,119]. The mechanism underlying this process is related to the acidification of the sourdough; which in an indirect way activates the endogenous phytases of the cereal, as well as microbial enzyme activities [2]. Phytic acid/phytate is a substance that is naturally found in the aleurone layer of grains and that exhibits a strong chelating capacity, it also affects the absorption of minerals in the body by forming insoluble complexes with dietary cations [120]. In the case of increasing bioavailability of macro and micronutrients, the optimal pH for acidification must be between 4.3 and 4.6 and phytic acid must drop above $70\%$ [2].
Regarding the present study, the mineral content (Table 4) of the four varieties of sourdough increased with the increase of the duration of fermentation. Einkorn flour sourdough (M1) stood out for its high content in calcium—246 mg/kg (Ca), zinc—36 mg/kg (Zn), manganese—46 mg/kg (Mn), and iron—19 mg/kg (Fe) reached after 24 h of fermentation [121]. The sample with spelt flour (M2) was highlighted by the significant magnesium (Mg) content of 155 mg/kg, which is similar to that of the wheat flour sourdough (M0). The lowest values for most minerals were determined in the sample with emmer flour (M3), but this can be explained by the fact that this cereal has a lower content of minerals such as zinc (22.8 mg/kg), iron (34.1 mg/kg), calcium (360 mg/kg), and manganese (24 mg/kg) [31,122] compared to einkorn flour—M1 (Zn—54.8 mg/kg, Fe—47 mg/kg, Ca—420 mg/kg, Mn 49.3 mg/kg) [40], spelt (Zn—22.9 mg/kg, Fe—45.9 mg/g, Ca—390 mg/kg, Mn—27 mg/kg) [31,122], and wheat (Zn—34.6 mg/kg, Fe—37.5 mg/g, Ca—430 mg/kg, Mn—26 mg/kg) [122]. According to Zahra et al. [ 123], the wheat dough fermented for 6 h with Lb. plantarum E90 registered an increase from 3.08 ($0\%$ culture dose) to 8.95 mg/kg ($2\%$ culture dose) in terms of iron content, and from 3.45 ($0\%$ culture dose) to 11.04 mg/kg ($2\%$ culture dose) in the case of zinc content.
The mineral content of the sourdough is closely related to the initial content of the flour used, which is in turn influenced by several factors related to the growth and development of the plant. According to Spisni et al. [ 124], both in the case of ancient and modern varieties of wheat, the mineral content is influenced by several variables such as climate, soil type, and geographical area.
The consumption of minerals is essential for a healthy body because they perform different metabolic functions; for example, calcium has a role in blood coagulation, sodium helps to decrease blood pressure, magnesium is involved in muscle relaxation, and zinc acts in protein synthesis [125]. Therefore, the production of functional foods with a high nutritional value is a necessity, and the utilization of materials rich in minerals and bioactive compounds, such as ancient flours, represent the first steps in this direction [46].
## 3.6. Volatile Compounds Content
The sensorial quality of bakery products is significantly influenced by their aromatic profile. According to Pétel et al. [ 126] over the years, more than 500 volatile compounds were identified in bread. On the other hand, in sourdough and sourdough bread, only around 200 compounds have been identified, with the studies on these being in a much smaller number [127]. In sourdough products, the lactic bacteria (LAB) are the ones that form the basis of the generation of volatiles, while factors that condition their activity like water content and temperature are responsible for the amount formed [128]. Normally, lactobacilli carry out the acidification of the product and also, releases flavor precursors such as free amino acids that increase during sourdough fermentation [128].
Cereals contain a wide variety of specific volatile compounds, which, depending on the type and concentration in which they are found, form the olfactory perception [129]. Their formation is conditioned by certain factors such as pH, amino acid profile, sugar profile, heating temperature, and time [129]. Chai et al. [ 130] identified over 90 volatiles in wheat flour. Most of them are from the class of aldehydes, and contain volatiles such as hexanal, nonanal, 3-methyl-butanal, heptanal, octanal, and (E)-2-nonenal which are also the most involved in the development of the bread profile aroma. Among the ketones, the following stood out: 2,3-butanedione (the most relevant compound present in bread), 2,3-Pentanedione, and 6-methyl-5-hepten-2-one; and the class of furans includes acetophenone, benzaldehyde, and furfural.
Sourdough—made with common wheat or with ancient wheat—also, contains a number of typical volatile compounds such as pental, hexanal, 5-methyl-3-hexanone, 1-pentanol 2-octenal, acetoin, furan 2,6-dimethyl-4-heptanone, octyl acetat, diacetyl, 4-methyl-3-penten-2-one, 6-methyl-5-hepten-2-one, as reported in other research studies [26,126,131].
In the present study, a total of 43 aromatic compounds were identified and classified into alcohols, aldehydes, ketones, acids, and other compounds (Table 5). From the class of alcohols, the most representative was 1-Hexanol with values (after 24 h) between $11.74\%$ (M0—wheat flour sourdough) and $69.5\%$ (M3—emmer flour sourdough) of the total surface of the peaks; this was perceived as having a delicate fatty-fruity, fermented, and woody profile. It has also been reported in other studies as one of the most abundant alcohols [126,132], and together with its aldehyde (hexanal), they are most abundant in the bread loaf (without sourdough) [7]. From the group of aldehydes (except hexanal), benzaldehyde was the compound identified in all samples, at the end of the fermentation process registering the highest percentage in the sample with einkorn flour ($2.80\%$) and wheat flour ($1.17\%$). This compound had a spicy, almond flavor, and it is formed in the dough in two ways, the degradation of amino acids or autoxidation of 2,4-decadienal [131]. Regarding ketones, mainly acetophenone (0.18–$7.53\%$) and 2-heptanone (0.3–$10.4\%$) were identified. These degrade with increasing fermentation time, but are considered to be important indicators regarding the freshness of the sourdough and the final product [131]. Acids were found in a low proportion, among them caproic acid/hexanoic acid, which were recognized for their ability to inhibit the growth of fungi [133]. Of the rest of the reported compounds, 2-pentylfuran, limonene, d-limonene, and butanoic acid, ethyl ester deserves to be highlighted. They were also identified in other studies and are recognized for the sweet, fruity aroma they give to the dough [126,134,135]. The presence of compounds with a less pleasant aroma, such as dimethyl disulfide and dimethyl trisulfide was also observed, but in small amounts and mainly in the wheat flour samples—M0.
In support of these results comes a study conducted by Starr et al. [ 138] who identified 88 compounds in wheat varieties, such as hexanal, hexanol, 2-pentylfuran, benzaldehyde, 2-methylbutanal, 3-methyl-1-butanol, 6-methyl-5-heptene- 2- one, 2-methyl-1-butanol or 2-nonenal. As can be seen in Table 4, there is a decrease in some volatile compounds with the increase of the fermentation time; this trend was also confirmed in another study, where during the fermentation of sourdough made with whole wheat there was registered a decrease in the concentration of ketones, aldehydes, and heterocycles [131].
However, the results obtained show a wide range of volatiles that form a complex aromatic profile. Their importance to the final product is major in the sense that they help to establish the degree of acceptability of the product by consumers.
## 3.7. Rheological Values
The rheological properties of the sourdough with wheat, einkorn, spelt, and emmer are presented in Figure 4; the storage modulus (G’) and the loss modulus (G’’) were measured for the three fermentation times (0, 12, 24 h) at an angular frequency between 0.628 and 628 rad/s. These two moduli are the most often used to characterize the dynamic properties of the dough; the first, respectively, the storage modulus indicates the materials’ capacity to store elastic deformation energy, while the second, the loss modulus, indicates the viscous portion of the materials [66].
Mainly, the two moduli grew with the increase of the angular frequency, a fact that can be explained by the increase in the structure of the sourdough [139]; also, G’ was higher than G’’ which means that the dough has an elastic behavior. After 24 h of fermentation at an angular frequency of 628 rad/s, the highest value for storage modulus (G’) was recorded in the case of sample M3 with 3988.9 Pa, and for loss modulus (G’’) the major value was 915.8 Pa in sample M0. Additionally, the two moduli registered decreases during the 24 h for all flour variants, a possible reason for this reduction being the pH, namely, sourdough acidification affects chemical compounds, thus improving the interactions between water molecules and structural components like starch and proteins [139].
Hadnađev et al. [ 140] studied the rheological properties of some of the types of flour obtained from ancient cereals and came to the conclusion that they are influenced to a large extent by the quantity and quality of wheat gluten. Thus, compared to spelt, emmer flour presented a high wet gluten value and a low gluten index thus producing a dough with an elastic structure that was able to generate and retain the largest amount of gas during fermentation, but also quickly collapsed and lost its structure, releasing the most quantity of carbon dioxide into the atmosphere.
Fermentation has a significant impact on the rheological properties of the dough, being in turn influenced by the species of microorganisms, their metabolic activity, and the pH value that develops over time [141]. There are a number of papers that have studied the influence that the water content has on the rheological properties of the dough [142,143,144]. In the present study, proteolysis, which occurs during the fermentation process and lowers the starch level, can be the cause of the decrease in sourdough viscosity and elasticity [141].
## 3.8. Effect of Sourdough Type on Metabolic Profile
Based on the 66 parameters determined in this experiment (see Figure 5 legend), after a weighted standard deviation pre-treatment applied for providing a relative significance to each value, a Principal Component Analysis (PCA) was performed. Principal component Analysis (PCA) and cluster analysis using a heatmap (Figure 6) were used for a deeper investigation of the effect of sourdough type (ancient wheat flour type) and fermentation moment on their metabolic profiles. As the PCA plot shows (Figure 5), the two principal components (PC-1, PC-2) and their scores explain $24\%$ and $17\%$ of the range in data variation. The plot indicates a clear separation of the ancient wheat flour (0 h of fermentation) from the sourdoughs after 12 and 24 h of fermentation. It also shows a clear distinction of the sourdoughs obtained with wheat (M0), einkorn (M1), and spelt (M2) after 24 h of fermentation from the other samples. In the case of emmer (M3) flour, the separation between the samples at 0, 12 and 24 h of fermentation exists, but is smaller, indicating that emmer flour had a different behavior during sourdough fermentation. This aspect could be observed from the heatmap too (Figure 6), indicating that maltose, glucose, and fructose were metabolized differently in emmer flour than in the other flours. This behaviour could be explained by a higher content of starch in emmer compared to einkorn and spelt, but also due to its high content of resistant starch [145,146]. Moreover, the lactic acid concentration was smaller during the emmer sourdough fermentation in comparison to the rest of the flours. It is also possible that the smaller amounts of certain minerals in emmer, which are important for the microbial cell development, negatively influenced the Lb. plantarum ATCC 8014 metabolism. The volatile profile of emmer sourdough showed differences as a result of the microbial activity. The volatiles formed in the highest concentration (dark red) were: 1 butanol-3 methyl, 1-heptanol, 1 hexanol, 2 butanone-3 hydroxy, hexanoic acid, acetic acid, hexyl ester and acetic acid, pentyl ester. Inside the cluster indicates a grouping of these volatile compounds, also.
As indicated on the heatmap, three aldehydes, decanal, octanal, and nonanal were in the highest concentrations in the einkorn sourdough at the inoculation moment. Their level decreased during fermentation. The specific microbial volatile metabolites that formed in the highest amounts as a result of the interaction between einkorn flour and Lb. plantarum metabolism were: 2-nonanone, 3-buten-1-ol, 3-methyl, 1-butanol, 3-methyl, 2-heptanone, 1-Butanol, 2-methyl. HCA analysis indicated a clear the clusterisation of these volatile derivatives. Common wheat flour was characterized by a group of volatiles (dark red) composed of: acetophenone, 1-pentanol-4-methyl, heptanol, and benzaldehyde. The volatiles profile during fermentation showed increasing levels of butanoic acid, butanoic acid, ethyl ester, butanoic acid, propyl ester, disulfide, dimethyl, dimethyl trisulfide, 1-butanol, 3-methyl and 1-Penten-3-ol, 4-methyl, and within the cluster their grouping is evident. Spelt flour sourdough reveals increased levels of 1-butanol-2 methyl, furan-2 pentyl, citric acid, benzene-acetaldehyde, benzoic acid, 2 octen-1-ol, 2-hexanol-5 methyl. These volatile compounds could be found grouped within the cluster. The levels of K, Mg, Fe also showed increasing levels during the fermentation period in all flours except emmer sourdough. Mn and Zn were grouped within the cluster and showed the highest amounts in einkorn sourdough; this could explain the great support of einkorn flour for the Lb. plantarum cell development since these minerals are co-factors for enzymes.
The rheological parameters G’ and G’’ also showed a clear separation between the sourdough samples. The storage modulus (G’) and the loss modulus (G’’) revealed a distinct rheological profile for common wheat sourdough than in the case of the ancient wheat sourdoughs. This behavior is due to its higher glutenin (GLUT) contents compared to einkorn, emmer, and spelt, resulting in increasing ratios of gliadin to glutenin (GLIA/GLUT), as reported by [147]. High glutenin content and a low ratio of GLIA/GLUT are the two parameters influencing the dough quality. However, the decreasing values of the two moduli during the fermentation of einkorn, spelt, and emmer (used in this study) indicate good rheological behavior suitable for good baking performance.
## 4. Conclusions
According to the obtained results, it can be stated that *Lactiplantibacillus plantarum* ATCC 8014 shows good adaptability, with a high cell count and a good acidification rate, in the sourdough obtained with wheat flour, einkorn flour, spelt flour, and emmer flour. The metabolic profiles of the ancient wheat and common wheat sourdoughs indicated clear differences between them. It was possible to mark out specific metabolites as a result of the interaction between ancient wheat flours and Lb. plantarum ATCC 8014. From the ancient wheat, emmer showed distinctive behaviour during fermentation in terms of cell dynamics, sugar metabolization, lactic acid formation. This could be explained by its higher content in resistant starch, but also due to the smaller amounts in Zn and Mn, important factors for the microbial cell propagation.
The cluster analysis showed specific volatile compounds for each type of sourdough. Moreover, by this approach, it is possible to identify volatile derivatives with pleasant or unpleasing odours resulting from the interaction between the raw flour and the bacterial strain. It could be facilitating the setation of some desired sensorial characteristics in terms of flavour to obtain whole-meal breads with a higher degree of consumer acceptability.
Future studies will be conducted on using emmer, einkorn, and spelt sourdough as biocarrier of nutritional valuable compounds (fibers, minerals, amino acids, etc.) to obtain bread assortments with low glycemic index and improved health benefits.
## References
1. De Vuyst L., Comasio A., Kerrebroeck S.. **Van Sourdough production: Fermentation strategies, microbial ecology, and use of non-flour ingredients**. *Crit. Rev. Food Sci. Nutr.* (2021.0) 1-33. DOI: 10.1080/10408398.2021.1976100
2. Arora K., Ameur H., Polo A., Di Cagno R., Rizzello C.G., Gobbetti M.. **Thirty years of knowledge on sourdough fermentation: A systematic review**. *Trends Food Sci. Technol.* (2021.0) **108** 71-83. DOI: 10.1016/j.tifs.2020.12.008
3. Cappelle S., Guylaine L., Gänzle M., Gobbetti M., Gobbetti M., Gänzle M.. **History and social aspects of sourdough**. *Handbook on Sourdough Biotechnology* (2013.0) 1-10
4. Blaiotta G., Romano R., Trifuoggi M., Aponte M., Miro A.. **Development of a Wet-Granulated Sourdough Multiple Starter for Direct Use**. *Foods* (2022.0) **11**. DOI: 10.3390/foods11091278
5. Capozzi V., Fragasso M., Romaniello R., Berbegal C., Russo P., Spano G.. **Spontaneous food fermentations and potential risks for human health**. *Fermentation* (2017.0) **3**. DOI: 10.3390/fermentation3040049
6. Corsetti A., Perpetuini G., Schirone M., Tofalo R., Suzzi G.. **Application of starter cultures to table olive fermentation: An overview on the experimental studies**. *Front. Microbiol.* (2012.0) **3** 248. DOI: 10.3389/fmicb.2012.00248
7. Siepmann F.B., Sousa de Almeida B., Waszczynskyj N., Spier M.R.. **Influence of temperature and of starter culture on biochemical characteristics and the aromatic compounds evolution on type II sourdough and wheat bread**. *LWT-Food Sci. Technol.* (2019.0) **108** 199-206. DOI: 10.1016/j.lwt.2019.03.065
8. Capurso A., Capurso C.. **The Mediterranean way: Why elderly people should eat wholewheat sourdough bread—A little known component of the Mediterranean diet and healthy food for elderly adults**. *Aging Clin. Exp. Res.* (2020.0) **32** 1-5. DOI: 10.1007/s40520-019-01392-3
9. Calvert M.D., Madden A.A., Nichols L.M., Haddad N.M., Lahne J., Dunn R.R., McKenney E.A.. **A review of sourdough starters: Ecology, practices, and sensory quality with applications for baking and recommendations for future research**. *PeerJ* (2021.0) **9** e11389. DOI: 10.7717/peerj.11389
10. Codină G.G., Sarion C., Dabija A.. **Effects of dry sourdough on bread-making quality and acrylamide content**. *Agronomy* (2021.0) **11**. DOI: 10.3390/agronomy11101977
11. Gil-Cardoso K., Saldaña G., Luengo E., Pastor J., Virto R., Alcaide-Hidalgo J.M., Del Bas J.M., Arola L., Caimari A.. **Consumption of Sourdough Breads Improves Postprandial Glucose Response and Produces Sourdough-Specific Effects on Biochemical and Inflammatory Parameters and Mineral Absorption**. *J. Agric. Food Chem.* (2021.0) **69** 3044-3059. DOI: 10.1021/acs.jafc.0c07200
12. Rašević V., Vranac A., Žuljević S.O.. **Impact of sourdough addition on the bread quality**. *Proceedings of the Radovi Poljoprivrednog Fakulteta Univerziteta u Sarajevu\Works of the Faculty of Agriculture University of Sarajevo* **Volume 62** 401-410
13. Menezes L.A.A., Minervini F., Filannino P., Sardaro M.L.S., Gatti M., Lindner J.D.D.. **Effects of sourdough on FODMAPs in bread and potential outcomes on irritable bowel syndrome patients and healthy subjects**. *Front. Microbiol.* (2018.0) **9** 1972. DOI: 10.3389/fmicb.2018.01972
14. Gobbetti M., Rizzello C.G., Di Cagno R., De Angelis M.. **How the sourdough may affect the functional features of leavened baked goods**. *Food Microbiol.* (2014.0) **37** 30-40. DOI: 10.1016/j.fm.2013.04.012
15. Park D.M., Bae J.H., Kim M.S., Kim H., Kang S.D., Shim S., Lee D., Seo J.H., Kang H., Han N.S.. **Suitability of**. *J. Microbiol. Biotechnol.* (2019.0) **29** 1729-1738. DOI: 10.4014/jmb.1907.07039
16. Zaidiyah Y.M., Lubis C.A.R.G., Putri S.. **Physicochemical properties of sourdough bread made from local variety sweet potato and pineapple juice**. *IOP Conf. Ser. Earth Environ. Sci.* (2020.0) **425** 012079. DOI: 10.1088/1755-1315/425/1/012079
17. Quattrini M., Liang N., Fortina M.G., Xiang S., Curtis J.M., Gänzle M.. **Exploiting synergies of sourdough and antifungal organic acids to delay fungal spoilage of bread**. *Int. J. Food Microbiol.* (2019.0) **302** 8-14. DOI: 10.1016/j.ijfoodmicro.2018.09.007
18. Arendt E.K., Ryan L.A.M., Dal Bello F.. **Impact of sourdough on the texture of bread**. *Food Microbiol.* (2007.0) **24** 165-174. DOI: 10.1016/j.fm.2006.07.011
19. Rizzello C.G., Portincasa P., Montemurro M., di Palo D.M., Lorusso M.P., de Angelis M., Bonfrate L., Genot B., Gobbetti M.. **Sourdough fermented breads are more digestible than those started with baker’s yeast alone: An in vivo challenge dissecting distinct gastrointestinal responses**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11122954
20. Șerban L.R., Păucean A., Man S.M., Chiş M.S., Mureşan V.. **Ancient wheat species: Biochemical profile and impact on sourdough bread characteristics—A review**. *Processes* (2021.0) **9**. DOI: 10.3390/pr9112008
21. Woo S.H., Shin Y.J., Jeong H.M., Kim J.S., Ko D.S., Hong J.S., Choi H.D., Shim J.H.. **Effects of maltogenic amylase from**. *J. Cereal Sci.* (2020.0) **93** 102976. DOI: 10.1016/j.jcs.2020.102976
22. Lavermicocca P., Reguant C., Bautista-Gallego J.. **Editorial: Lactic Acid Bacteria Within the Food Industry: What Is New on Their Technological and Functional Role**. *Front. Microbiol.* (2021.0) **12** 711013. DOI: 10.3389/fmicb.2021.711013
23. Zhang L., Taal M., Boom R.M., Chen X.D., Schutyser M.A.I.. **Viability of**. *Proceedings of the The 20th International Drying Symposium*
24. Chiș S.M., Păucean A., Stan L., Mureșan V.. *Rom. Biotechnol. Lett.* (2018.0) **23** 13581-13591
25. da Silva Sabo S., Vitolo M., González J.M.D., Oliveira R.P.d.S.. **Overview of**. *Food Res. Int.* (2014.0) **64** 527-536. DOI: 10.1016/j.foodres.2014.07.041
26. Di Renzo T., Reale A., Boscaino F., Messia M.C.. **Flavoring production in Kamut**. *Front. Microbiol.* (2018.0) **9** 429. DOI: 10.3389/fmicb.2018.00429
27. Mohsen S.M., Aly M.H., Attia A.A., Osman D.B.. **Effect of Sourdough on Shelf Life, Freshness and Sensory Characteristics of Egyptian Balady Bread**. *J. Appl. Environ. Microbiol.* (2016.0) **4** 39-45. DOI: 10.12691/jaem-4-2-3
28. Coda R., Nionelli L., Rizzello C.G., De Angelis M., Tossut P., Gobbetti M.. **Spelt and emmer flours: Characterization of the lactic acid bacteria microbiota and selection of mixed starters for bread making**. *J. Appl. Microbiol.* (2010.0) **108** 925-935. DOI: 10.1111/j.1365-2672.2009.04497.x
29. Păucean A., Vodnar D.C., Socaci S.A., Socaciu C.. **Carbohydrate metabolic conversions to lactic acid and volatile derivatives, as influenced by**. *Eur. Food Res. Technol.* (2013.0) **237** 679-689. DOI: 10.1007/s00217-013-2042-6
30. Bartkiene E., Bartkevics V., Pugajeva I., Krungleviciute V., Mayrhofer S., Domig K.. **Parameters of rye, wheat, barley, and oat sourdoughs fermented with**. *Int. J. Food Sci. Technol.* (2017.0) **52** 1473-1482. DOI: 10.1111/ijfs.13412
31. Čurná V., Lacko-Bartošová M.. **Chemical composition and nutritional value of emmer wheat (**. *J. Cent. Eur. Agric.* (2017.0) **18** 117-134. DOI: 10.5513/JCEA01/18.1.1871
32. Bencze S., Makádi M., Aranyos T.J., Földi M., Hertelendy P., Mikó P., Bosi S., Negri L., Drexler D.. **Re-introduction of ancient wheat cultivars into organic agriculture-Emmer and Einkorn cultivation experiences under marginal conditions**. *Sustainability* (2020.0) **12**. DOI: 10.3390/su12041584
33. Van Boxstael F., Aerts H., Linssen S., Latré J., Christiaens A., Haesaert G., Dierickx I., Brusselle J., De Keyzer W.. **A comparison of the nutritional value of Einkorn, Emmer, Khorasan and modern wheat: Whole grains, processed in bread, and population-level intake implications**. *J. Sci. Food Agric.* (2020.0) **100** 4108-4118. DOI: 10.1002/jsfa.10402
34. Hidalgo A., Brandolini A., Pompei C., Piscozzi R.. **Carotenoids and tocols of einkorn wheat (**. *J. Cereal Sci.* (2006.0) **44** 182-193. DOI: 10.1016/j.jcs.2006.06.002
35. Costanzo A., Amos D.C., Dinelli G., Sferrazza R.E., Accorsi G., Negri L., Bosi S.. **Performance and nutritional properties of Einkorn, Emmer and Rivet Wheat in response to different rotational position and soil tillage**. *Sustainability* (2019.0) **11**. DOI: 10.3390/su11226304
36. Geisslitz S., Scherf K.A.. **Rediscovering Ancient Wheats**. *Cereal Foods World* (2020.0) **65** 13. DOI: 10.1094/CFW-65-2-0013
37. Longin C.F.H., Ziegler J., Schweiggert R., Koehler P., Carle R., Würschum T.. **Comparative study of hulled (einkorn, emmer, and spelt) and naked wheats (durum and bread wheat): Agronomic performance and quality traits**. *Crop Sci.* (2016.0) **56** 302-311. DOI: 10.2135/cropsci2015.04.0242
38. Escarnot E., Jacquemin J.M., Agneessens R., Paquot M.. **Comparative study of the content and profiles of macronutrients in spelt and wheat, a review**. *Biotechnol. Agron. Société Environ.* (2012.0) **16** 243-256
39. Şahin Y., Yıldırım A., Yücesan B., Zencirci N., Erbayram Ş., Gürel E.. **Phytochemical content and antioxidant activity of einkorn (**. *Nutrition* (2017.0) **19** 450-459. DOI: 10.23751/pn.v19i4.5847
40. Hidalgo A., Brandolini A.. **Nutritional properties of einkorn wheat (**. *J. Sci. Food Agric.* (2014.0) **94** 601-612. DOI: 10.1002/jsfa.6382
41. Zencirci N., Karakas F.P., Ordu B.. **Macro-Micro Element Variation in Traditionally Grown Einkorn (**. *Int. J. Second. Metab.* (2021.0) **8** 227-245. DOI: 10.21448/ijsm.778596
42. Frakolaki G., Giannou V., Topakas E., Tzia C.. **Chemical characterization and breadmaking potential of spelt versus wheat flour**. *J. Cereal Sci.* (2018.0) **79** 50-56. DOI: 10.1016/j.jcs.2017.08.023
43. Kohajdová Z., Karovičová J.. **Nutritional Value and Baking Applications of Spelt Wheat ***. *ACTA Acta Sci. Pol. Technol. Aliment* (2008.0) **7** 5-14
44. Dinu M., Whittaker A., Pagliai G., Benedettelli S., Sofi F.. **Ancient wheat species and human health: Biochemical and clinical implications**. *J. Nutr. Biochem.* (2018.0) **52** 1-9. DOI: 10.1016/j.jnutbio.2017.09.001
45. Thorup A.C., Gregersen S., Jeppesen P.B.. **Ancient Wheat Diet Delays Diabetes Development in a Type 2 Diabetes Animal Model**. *Rev. Diabet. Stud.* (2014.0) **11** 245-257. DOI: 10.1900/RDS.2014.11.245
46. Shewry P.R., Hey S.. **Do “ancient” wheat species differ from modern bread wheat in their contents of bioactive components?**. *J. Cereal Sci.* (2015.0) **65** 236-243. DOI: 10.1016/j.jcs.2015.07.014
47. Guerrini L., Parenti O., Angeloni G., Zanoni B.. **The bread making process of ancient wheat: A semi-structured interview to bakers**. *J. Cereal Sci.* (2019.0) **87** 9-17. DOI: 10.1016/j.jcs.2019.02.006
48. Sereti V., Lazaridou A., Biliaderis C.G., Valamoti S.M.. **Reinvigorating modern breadmaking based on ancient practices and plant ingredients, with implementation of a physicochemical approach**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10040789
49. Păucean A., Mureșan V., Maria-Man S., Chiș M.S., Mureșan A.E., Șerban L.R., Pop A., Muste S.. **Metabolomics as a tool to elucidate the sensory, nutritional and safety quality of wheat bread—A review**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22168945
50. Cevallos-Cevallos J.M., Reyes-De-Corcuera J.I., Etxeberria E., Danyluk M.D., Rodrick G.E.. **Metabolomic analysis in food science: A review**. *Trends Food Sci. Technol.* (2009.0) **20** 557-566. DOI: 10.1016/j.tifs.2009.07.002
51. Weckx S., Van Kerrebroeck S., De Vuyst L.. **Omics approaches to understand sourdough fermentation processes**. *Int. J. Food Microbiol.* (2019.0) **302** 90-102. DOI: 10.1016/j.ijfoodmicro.2018.05.029
52. Saa D.L.T., Nissen L., Gianotti A.. **Metabolomic approach to study the impact of flour type and fermentation process on volatile profile of bakery products**. *Food Res. Int.* (2019.0) **119** 510-516. DOI: 10.1016/j.foodres.2019.01.024
53. Colosimo R., Gabriele M., Cifelli M., Longo V., Domenici V., Pucci L.. **The effect of sourdough fermentation on**. *Food Chem.* (2020.0) **305** 125510. DOI: 10.1016/j.foodchem.2019.125510
54. Righetti L., Rubert J., Galaverna G., Folloni S., Ranieri R., Stranska-Zachariasova M., Hajslov J., Dall’Asta C.. **Characterization and discrimination of ancient grains: A metabolomics approach**. *Int. J. Mol. Sci.* (2016.0) **17**. DOI: 10.3390/ijms17081217
55. Balestra F., Laghi L., Taneyo Saa D., Gianotti A., Rocculi P., Pinnavaia G.G.. **Physico-chemical and metabolomic characterization of KAMUT**. *Food Chem.* (2015.0) **187** 451-459. DOI: 10.1016/j.foodchem.2015.04.041
56. Ferri M., Serrazanetti D.I., Tassoni A., Baldissarri M., Gianotti A.. **Improving the functional and sensorial profile of cereal-based fermented foods by selecting**. *Food Res. Int.* (2016.0) **89** 1095-1105. DOI: 10.1016/j.foodres.2016.08.044
57. Shewry P.R., America A.H.P., Lovegrove A., Wood A.J., Plummer A., Evans J., van den Broeck H.C., Gilissen L., Mumm R., Ward J.L.. **Comparative compositions of metabolites and dietary fibre components in doughs and breads produced from bread wheat, emmer and spelt and using yeast and sourdough processes**. *Food Chem.* (2022.0) **374** 131710. DOI: 10.1016/j.foodchem.2021.131710
58. Chiş M.S., Păucean A., Man S.M., Vodnar D.C., Teleky B.E., Pop C.R., Stan L., Borsai O., Kadar C.B., Urcan A.C.. **Quinoa sourdough fermented with**. *Appl. Sci.* (2020.0) **10**. DOI: 10.3390/app10207140
59. Coda R., Di Cagno R., Rizzello C.G., Nionelli L., Edema M.O., Gobbetti M.. **Utilization of African Grains for Sourdough Bread Making**. *J. Food Sci.* (2011.0) **76** 329-335. DOI: 10.1111/j.1750-3841.2011.02240.x
60. Pontonio E., Arora K., Dingeo C., Carafa I., Celano G., Scarpino V., Genot B., Gobbetti M., Di Cagno R.. **Commercial Organic Versus Conventional Whole Rye and Wheat Flours for Making Sourdough Bread: Safety, Nutritional, and Sensory Implications**. *Front. Microbiol.* (2021.0) **12** 674413. DOI: 10.3389/fmicb.2021.674413
61. Hotea I., Colibar O., Sîrbu C., Berbecea A., Radulov I.. **Considerations on the fiber and protein content interinfluence in oats used as feedstuff in animal diets**. *Lucr. Științifice-Univ. Științe Agric. a Banat. Timișoara, Med. Vet.* (2020.0) **53** 42-49
62. Păucean A., Moldovan O.P., Mureșan V., Socaci S.A., Dulf F.V., Alexa E., Man S.M., Mureșan A.E., Muste S.. **Folic acid, minerals, amino-acids, fatty acids and volatile compounds of green and red lentils. Folic acid content optimization in wheat-lentils composite flours**. *Chem. Cent. J.* (2018.0) **12** 88. DOI: 10.1186/s13065-018-0456-8
63. Salanţă L.-C., Tofană M., Socaci S.A., Lazar C., Michiu D., Fărcaş A.. **Determination of the Volatile Compounds from Hop and Hop Products using ITEX/GC-MS Technique**. *J. Agroaliment. Process. Technol.* (2012.0) **18** 110-115
64. Socaci S.A., Socaciu C., Tofanǎ M., Raţi I.V., Pintea A.. **In-tube extraction and GC-MS analysis of volatile components from wild and cultivated sea buckthorn (**. *Phytochem. Anal.* (2013.0) **24** 319-328. DOI: 10.1002/pca.2413
65. Chiș M.S., Păucean A., Stan L., Suharoschi R., Socaci S.A., Man S.M., Pop C.R., Muste S.. **Impact of protein metabolic conversion and volatile derivatives on gluten-free muffins made with quinoa sourdough**. *CYTA-J. Food* (2019.0) **17** 744-753. DOI: 10.1080/19476337.2019.1646320
66. Teleky B.E., Martău A.G., Ranga F., Chețan F., Vodnar D.C.. **Exploitation of lactic acid bacteria and Baker’s yeast as single or multiple starter cultures of wheat flour dough enriched with soy flour**. *Biomolecules* (2020.0) **10**. DOI: 10.3390/biom10050778
67. Tanislav A.E., Pușcaș A., Păucean A., Mureșan A.E., Semeniuc C.A., Mureșan V., Mudura E.. **Evaluation of Structural Behavior in the Process Dynamics of Oleogel-Based Tender Dough Products**. *Gels* (2022.0) **8**. DOI: 10.3390/gels8050317
68. Çakır E., Arıcı M., Durak M.Z., Karasu S.. **The molecular and technological characterization of lactic acid bacteria in einkorn sourdough: Effect on bread quality**. *J. Food Meas. Charact.* (2020.0) **14** 1646-1655. DOI: 10.1007/s11694-020-00412-5
69. Śliżewska K., Chlebicz-Wójcik A.. **Growth kinetics of probiotic**. *Biology* (2020.0) **9**. DOI: 10.3390/biology9120423
70. Coda R., Di Cagno R., Gobbetti M., Rizzello C.G.. **Sourdough lactic acid bacteria: Exploration of non-wheat cereal-based fermentation**. *Food Microbiol.* (2014.0) **37** 51-58. DOI: 10.1016/j.fm.2013.06.018
71. Pehlivan Karakas F., Keskin C.N., Agil F., Zencirci N.. **Profiles of vitamin B and E in wheat grass and grain of einkorn (**. *J. Cereal Sci.* (2021.0) **98** 103177. DOI: 10.1016/j.jcs.2021.103177
72. Duliński R., Starzyńska-Janiszewska A.. **Content and in vitro bioavailability of selected b vitamins and myo-inositol in spelt wheat (**. *J. Food Nutr. Res.* (2020.0) **59** 1-6
73. Korcari D., Ricci G., Quattrini M., Fortina M.G.. **Microbial consortia involved in fermented spelt sourdoughs: Dynamics and characterization of yeasts and lactic acid bacteria**. *Lett. Appl. Microbiol.* (2020.0) **70** 48-54. DOI: 10.1111/lam.13241
74. Clément H., Prost C., Chiron H., Ducasse M.B., Della Valle G., Courcoux P., Onno B.. **The effect of organic wheat flour by-products on sourdough performances assessed by a multi-criteria approach**. *Food Res. Int.* (2018.0) **106** 974-981. DOI: 10.1016/j.foodres.2018.01.053
75. Hamad S.H., Bhat R., Alias A.K., Paliyath G.. **Factors Affecting the Growth of Microorganisms in Food**. *Progress in Food Preservation* (2012.0) 405-427
76. Di Cagno R., Pontonio E., Buchin S., De Angelis M., Lattanzi A., Valerio F., Gobbetti M., Calasso M.. **Diversity of the lactic acid bacterium and yeast microbiota in the switch from firm- to liquid-sourdough fermentation**. *Appl. Environ. Microbiol.* (2014.0) **80** 3161-3172. DOI: 10.1128/AEM.00309-14
77. Minervini F., Lattanzi A., De Angelis M., Di Cagno R., Gobbetti M.. **Influence of artisan bakery- or laboratory-propagated sourdoughs on the diversity of lactic acid bacterium and yeast microbiotas**. *Appl. Environ. Microbiol.* (2012.0) **78** 5328-5340. DOI: 10.1128/AEM.00572-12
78. Casado A., Álvarez A., González L., Fernández D., Marcos J.L., Tornadijo M.E.. **Effect of fermentation on microbiological, physicochemical and physical characteristics of sourdough and impact of its use on bread quality**. *Czech J. Food Sci.* (2017.0) **35** 496-506. DOI: 10.17221/68/2017-CJFS
79. Van Der Meulen R., Scheirlinck I., Van Schoor A., Huys G., Vancanneyt M., Vandamme P., De Vuyst L.. **Population dynamics and metabolite target analysis of lactic acid bacteria during laboratory fermentations of wheat and spelt sourdoughs**. *Appl. Environ. Microbiol.* (2007.0) **73** 4741-4750. DOI: 10.1128/AEM.00315-07
80. Pontonio E., Dingeo C., Di Cagno R., Blandino M., Gobbetti M., Rizzello C.G.. **Brans from hull-less barley, emmer and pigmented wheat varieties: From by-products to bread nutritional improvers using selected lactic acid bacteria and xylanase**. *Int. J. Food Microbiol.* (2020.0) **313** 108384. DOI: 10.1016/j.ijfoodmicro.2019.108384
81. Jekle M., Houben A., Mitzscherling M., Becker T.. **Effects of selected lactic acid bacteria on the characteristics of amaranth sourdough**. *J. Sci. Food Agric.* (2010.0) **90** 2326-2332. DOI: 10.1002/jsfa.4091
82. Ognean C.F.. **The effects of water content and weight of inoculum on the production of sourdough**. *J. Agroaliment. Process. Technol.* (2015.0) **21** 351-357
83. Boreczek J., Litwinek D., Żylińska-Urban J., Izak D., Buksa K., Gawor J., Gromadka R., Bardowski J.K., Kowalczyk M.. **Bacterial community dynamics in spontaneous sourdoughs made from wheat, spelt, and rye wholemeal flour**. *Microbiologyopen* (2020.0) **9** e1009. DOI: 10.1002/mbo3.1009
84. Sevgili A., Erkmen O., Koçaslan S.. **Identification of lactic acid bacteria and yeasts from traditional sourdoughs and sourdough production by enrichment**. *Czech J. Food Sci.* (2021.0) **39** 312-318. DOI: 10.17221/56/2021-CJFS
85. Silva J.D.R., Rosa G.C., Neves N.d.A., Leoro M.G.V., Schmiele M.. **Production of sourdough and gluten-free bread with brown rice and carioca and cowpea beans flours: Biochemical, nutritional and structural characteristics**. *Res. Soc. Dev.* (2021.0) **10** e303101623992. DOI: 10.33448/rsd-v10i16.23992
86. Czaja T., Sobota A., Szotak R.. **Quantification of Ash and Moisture in Wheat Flour by**. *Foods* (2020.0) **9**. DOI: 10.3390/foods9030280
87. De Vuyst L., Vrancken G., Ravyts F., Rimaux T., Weckx S.. **Biodiversity, ecological determinants, and metabolic exploitation of sourdough microbiota**. *Food Microbiol.* (2009.0) **26** 666-675. DOI: 10.1016/j.fm.2009.07.012
88. Bo S., Seletto M., Choc A., Ponzo V., Lezo A., Demagistris A., Evangelista A., Ciccone G., Bertolino M., Cassader M.. **The acute impact of the intake of four types of bread on satiety and blood concentrations of glucose, insulin, free fatty acids, triglyceride and acylated ghrelin. A randomized controlled cross-over trial**. *Food Res. Int.* (2017.0) **92** 40-47. DOI: 10.1016/j.foodres.2016.12.019
89. Zörb C., Betsche T., Langenkämper G., Zapp J., Seifert M.. **Free sugars in spelt wholemeal and flour**. *J. Appl. Bot. Food Qual.* (2007.0) **81** 172-174
90. Pozzo L., Alcántara C., Selma-Royo M., Garcia-Mantrana I., Bramanti E., Longo V., Collado M.C., Pucci L.. **The impact of sourdough fermentation of spelt (**. *J. Funct. Foods* (2022.0) **91** 105007. DOI: 10.1016/j.jff.2022.105007
91. Saeed M., Randhawa M.A., Pasha I., Anjum Murtaza M., Afzal J.. **Amino acids and organic acids production by single strain starter culture in sourdough fermentation**. *Br. J. Agric. Sci.* (2014.0) **9** 121-127
92. Wang Y., Wu J., Lv M., Shao Z., Hungwe M., Wang J., Bai X., Xie J., Wang Y., Geng W.. **Metabolism Characteristics of Lactic Acid Bacteria and the Expanding Applications in Food Industry**. *Front. Bioeng. Biotechnol.* (2021.0) **9** 612285. DOI: 10.3389/fbioe.2021.612285
93. De Vuyst L., Van Kerrebroeck S., Leroy F.. **Microbial Ecology and Process Technology of Sourdough Fermentation**. *Advances in Applied Microbiology* (2017.0) **Volume 100** 49-160
94. Pérez-Alvarado O., Zepeda-Hernández A., Garcia-Amezquita L.E., Requena T., Vinderola G., García-Cayuela T.. **Role of lactic acid bacteria and yeasts in sourdough fermentation during breadmaking: Evaluation of postbiotic-like components and health benefits**. *Front. Microbiol.* (2022.0) **13** 969460. DOI: 10.3389/fmicb.2022.969460
95. Novotni D., Ćurić D., Bituh M., Barić I.C., Škevin D., Čukelj N.. **Glycemic index and phenolics of partially-baked frozen bread with sourdough**. *Int. J. Food Sci. Nutr.* (2011.0) **62** 26-33. DOI: 10.3109/09637486.2010.506432
96. Ventimiglia G., Alfonzo A., Galluzzo P., Corona O., Francesca N., Caracappa S., Moschetti G., Settanni L.. **Codominance of**. *Food Microbiol.* (2015.0) **51** 57-68. DOI: 10.1016/j.fm.2015.04.011
97. Galli V., Venturi M., Pini N., Guerrini S., Granchi L., Vincenzini M.. **Liquid and firm sourdough fermentation: Microbial robustness and interactions during consecutive backsloppings**. *LWT-Food Sci. Technol.* (2019.0) **105** 9-15. DOI: 10.1016/j.lwt.2019.02.004
98. Lattanzi A., Minervini F., Di Cagno R., Diviccaro A., Antonielli L., Cardinali G., Cappelle S., De Angelis M., Gobbetti M.. **The lactic acid bacteria and yeast microbiota of eighteen sourdoughs used for the manufacture of traditional Italian sweet leavened baked goods**. *Int. J. Food Microbiol.* (2013.0) **163** 71-79. DOI: 10.1016/j.ijfoodmicro.2013.02.010
99. Fernández-Peláez J., Paesani C., Gómez M.. **Sourdough technology as a tool for the development of healthier grain-based products: An update**. *Agronomy* (2020.0) **10**. DOI: 10.3390/agronomy10121962
100. Kulathunga J., Simsek S.. **Dietary fiber variation in ancient and modern wheat species: Einkorn, emmer, spelt and hard red spring wheat**. *J. Cereal Sci.* (2022.0) **104** 103420. DOI: 10.1016/j.jcs.2022.103420
101. Saa D.T., Di Silvestro R., Dinelli G., Gianotti A.. **Effect of sourdough fermentation and baking process severity on dietary fibre and phenolic compounds of immature wheat flour bread**. *LWT-Food Sci. Technol.* (2017.0) **83** 26-32. DOI: 10.1016/j.lwt.2017.04.071
102. Fuckerer K., Hensel O., Schmitt J.J.. **Rye Bread Fortified With Cellulose and Its Acceptance by Elderlies in Nursing Homes and Young Adults**. *J. Food Stud.* (2016.0) **5** 1. DOI: 10.5296/jfs.v5i1.8847
103. Lauková M., Kohajdová Z., Karovičová J., Kuchtová V., Minarovičová L., Tomášiková L.. **Effects of cellulose fiber with different fiber length on rheological properties of wheat dough and quality of baked rolls**. *Food Sci. Technol. Int.* (2017.0) **23** 490-499. DOI: 10.1177/1082013217704122
104. Ren Y., Linter B.R., Foster T.J.. **Starch replacement in gluten free bread by cellulose and fibrillated cellulose**. *Food Hydrocoll.* (2020.0) **107** 105957. DOI: 10.1016/j.foodhyd.2020.105957
105. Atzler J.J., Sahin A.W., Gallagher E., Zannini E., Arendt E.K.. **Investigation of different dietary-fibre-ingredients for the design of a fibre enriched bread formulation low in FODMAPs based on wheat starch and vital gluten**. *Eur. Food Res. Technol.* (2021.0) **247** 1939-1957. DOI: 10.1007/s00217-021-03762-6
106. Correa M.J., Ferrero C.. **A comparative study of commercial modified celluloses as bread making additives**. *Int. J. Food Prop.* (2015.0) **18** 849-861. DOI: 10.1080/10942912.2013.869598
107. Canja C.M., Măzărel A., Lupu M.I., Mărgean A., Pădureanu V.. **Dietary fiber role and place in baking products**. *Bull. Transilv. Univ. Brasov* (2016.0) **9** 91-96
108. McRae M.P.. **Dietary Fiber Intake and Type 2 Diabetes Mellitus: An Umbrella Review of Meta-analyses**. *J. Chiropr. Med.* (2018.0) **17** 44-53. DOI: 10.1016/j.jcm.2017.11.002
109. Nirmala Prasadi V.P., Joye I.J.. **Dietary fibre from whole grains and their benefits on metabolic health**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12103045
110. Ioniță Mîndrican C.B., Ziani K., Mititelu M., Oprea E., Neacșu S.M., Moros E., Dumitrescu D.-E., Ros A.C., Drăgănescu D., Negrei C.. **Therapeutic Benefits and Dietary Restrictions of Fiber Intake: A State of the Art Review**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14132641
111. Lattimer J.M., Haub M.D.. **Effects of dietary fiber and its components on metabolic health**. *Nutrients* (2010.0) **2** 1266-1289. DOI: 10.3390/nu2121266
112. McRorie J.W., McKeown N.M.. **Understanding the Physics of Functional Fibers in the Gastrointestinal Tract: An Evidence-Based Approach to Resolving Enduring Misconceptions about Insoluble and Soluble Fiber**. *J. Acad. Nutr. Diet.* (2017.0) **117** 251-264. DOI: 10.1016/j.jand.2016.09.021
113. Gharibzahedi S.M.T., Jafari S.M.. **The importance of minerals in human nutrition: Bioavailability, food fortification, processing effects and nanoencapsulation**. *Trends Food Sci. Technol.* (2017.0) **62** 119-132. DOI: 10.1016/j.tifs.2017.02.017
114. Gupta U.C., Gupta S.C.. **Sources and Deficiency Diseases of Mineral Nutrients in Human Health and Nutrition: A Review**. *Pedosphere* (2014.0) **24** 13-38. DOI: 10.1016/S1002-0160(13)60077-6
115. Tardy A.L., Pouteau E., Marquez D., Yilmaz C., Scholey A.. **Vitamins and minerals for energy, fatigue and cognition: A narrative review of the biochemical and clinical evidence**. *Nutrients* (2020.0) **12**. DOI: 10.3390/nu12010228
116. Melse-Boonstra A.. **Bioavailability of Micronutrients From Nutrient-Dense Whole Foods: Zooming in on Dairy, Vegetables, and Fruits**. *Front. Nutr.* (2020.0) **7** 101. DOI: 10.3389/fnut.2020.00101
117. Aslam M.F., Ellis P.R., Berry S.E., Latunde-Dada G.O., Sharp P.A.. **Enhancing mineral bioavailability from cereals: Current strategies and future perspectives**. *Nutr. Bull.* (2018.0) **43** 184-188. DOI: 10.1111/nbu.12324
118. Samtiya M., Aluko R.E., Puniya A.K., Dhewa T.. **Enhancing micronutrients bioavailability through fermentation of plant-based foods: A concise review**. *Fermentation* (2021.0) **7**. DOI: 10.3390/fermentation7020063
119. Fulea Fekensa W.. **The Effect of Traditional Sourdough Starter Culture and Involved Microorganisms on Sensory and Nutritional Quality of Whole Wheat Bread**. *J. Food Nutr. Sci.* (2021.0) **9** 178. DOI: 10.11648/j.jfns.20210906.15
120. Bloot A.P.M., Kalschne D.L., Amaral J.A.S., Baraldi I.J., Canan C.. **A Review of Phytic Acid Sources, Obtention, and Applications**. *Food Rev. Int.* (2021.0) 1-20. DOI: 10.1080/87559129.2021.1906697
121. Kumar A., Sharma E., Marley A., Samaan M.A., Brookes M.J.. **Iron deficiency anaemia: Pathophysiology, assessment, practical management**. *BMJ Open Gastroenterol.* (2022.0) **9** e000759. DOI: 10.1136/bmjgast-2021-000759
122. Suchowilska E., Wiwart M., Kandler W., Krska R.. **A comparison of macro- and microelement concentrations in the whole grain of four**. *Plant Soil Environ.* (2012.0) **58** 141-147. DOI: 10.17221/688/2011-PSE
123. Zahra A., Farooq U., Saeed M.T., Quddoos M.Y., Hameed A., Iftikhar M., Noreen A., Mahvish S., Bukhari S.R., Naqvi S.N.. **Enhancement of sensory attributes and mineral content of Sourdough bread by means of microbial culture and yeast (**. *Food Chem. Adv.* (2022.0) **1** 100094. DOI: 10.1016/j.focha.2022.100094
124. Spisni E., Imbesi V., Giovanardi E., Petrocelli G., Alvisi P., Valerii M.C.. **Differential physiological responses elicited by ancient and heritage wheat cultivars compared to modern ones**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11122879
125. Quintaes K.D., Diez-Garcia R.W., de la Guardia M., Garrigue S.. **The importance of minerals in the human diet**. *Handbook of Mineral Elements in Food* (2015.0) 1-21
126. Pétel C., Onno B., Prost C.. **Sourdough volatile compounds and their contribution to bread: A review**. *Trends Food Sci. Technol.* (2017.0) **59** 105-123. DOI: 10.1016/j.tifs.2016.10.015
127. Martin-Garcia A., Comas-bast O., Riu-Aumatell M., Latorre-Moratalla M., López-Tamames E.. **Changes in the Volatile Profile of Wheat Sourdough Produced with the Addition of Cava Lees**. *Molecules* (2022.0) **27**. DOI: 10.3390/molecules27113588
128. De Luca L., Aiello A., Pizzolongo F., Blaiotta G., Aponte M., Romano R.. **Volatile organic compounds in breads prepared with different sourdoughs**. *Appl. Sci.* (2021.0) **11**. DOI: 10.3390/app11031330
129. Xu J., Zhang W., Adhikari K., Shi Y.C.. **Determination of volatile compounds in heat-treated straight-grade flours from normal and waxy wheats**. *J. Cereal Sci.* (2017.0) **75** 77-83. DOI: 10.1016/j.jcs.2017.03.018
130. Chai D., Li C., Zhang X., Yang J., Liu L., Xu X., Du M., Wang Y., Chen Y., Dong L.. **Analysis of volatile compounds from wheat flour in the heating process**. *Int. J. Food Eng.* (2019.0) **15** 20190252. DOI: 10.1515/ijfe-2019-0252
131. Pizarro F., Franco F.. **Volatile organic compounds at early stages of sourdough preparation via static headspace and GC/MS analysis**. *Curr. Res. Nutr. Food Sci.* (2017.0) **5** 89-99. DOI: 10.12944/CRNFSJ.5.2.05
132. Zhang G.-H., Wu T., Sadiq F.A., Yang H.-Y., Liu T.J., Ruan H., He G.. **qing A study revealing the key aroma compounds of steamed bread made by Chinese traditional sourdough**. *J. Zhejiang Univ. Sci. B* (2016.0) **17** 787-797. DOI: 10.1631/jzus.B1600130
133. Arena M.P., Russo P., Spano G., Capozzi V.. **From Microbial Ecology to Innovative Applications in Food Quality Improvements: The Case of Sourdough as a Model Matrix**. *J—Multidiscip. Sci. J.* (2020.0) **3** 9-19. DOI: 10.3390/j3010003
134. Yan B., Sadiq F.A., Cai Y., Fan D., Zhang H., Zhao J., Chen W.. **Identification of key aroma compounds in type i sourdough-based chinese steamed bread: Application of untargeted metabolomics analysisp**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20040818
135. Boyaci Gunduz C.P., Agirman B., Gaglio R., Franciosi E., Francesca N., Settanni L., Erten H.. **Evaluation of the variations in chemical and microbiological properties of the sourdoughs produced with selected lactic acid bacteria strains during fermentation**. *Food Chem. X* (2022.0) **14** 100357. DOI: 10.1016/j.fochx.2022.100357
136. **The Good Scents Company Information System**
137. **Flavornet**
138. Starr G., Petersen M.A., Jespersen B.M., Hansen A.S.. **Variation of volatile compounds among wheat varieties and landraces**. *Food Chem.* (2015.0) **174** 527-537. DOI: 10.1016/j.foodchem.2014.11.077
139. Longoria S., Contreras J., Belmares R., Cruz M., Flores M.. **Effect of short fermentation times with**. *Appl. Sci.* (2020.0) **10**. DOI: 10.3390/app10041383
140. Hadnađev M., Tomić J., Škrobot D., Dapčević-Hadnađev T.. **Rheological behavior of emmer, spelt and khorasan flours**. *J. Food Process. Preserv.* (2021.0) **46** e15873. DOI: 10.1111/jfpp.15873
141. Olojede A.O., Sanni A.I., Banwo K.. **Rheological, textural and nutritional properties of gluten-free sourdough made with functionally important lactic acid bacteria and yeast from Nigerian sorghum**. *LWT-Food Sci. Technol.* (2020.0) **120** 108875. DOI: 10.1016/j.lwt.2019.108875
142. Hardt N.A., Boom R.M., van der Goot A.J.. **Wheat dough rheology at low water contents and the influence of xylanases**. *Food Res. Int.* (2014.0) **66** 478-484. DOI: 10.1016/j.foodres.2014.10.011
143. Mohammed I., Ahmed A.R., Senge B.. **Dynamic rheological properties of chickpea and wheat flour dough’s**. *J. Appl. Sci.* (2011.0) **11** 3405-3412. DOI: 10.3923/jas.2011.3405.3412
144. Meerts M., Cardinaels R., Oosterlinck F., Courtin C.M., Moldenaers P.. **The Impact of Water Content and Mixing Time on the Linear and Non-Linear Rheology of Wheat Flour Dough**. *Food Biophys.* (2017.0) **12** 151-163. DOI: 10.1007/s11483-017-9472-9
145. Kulathunga J., Reuhs B.L., Zwinger S., Simsek S.. **Comparative study on kernel quality and chemical composition of ancient and modern wheat species: Einkorn, emmer, spelt and hard red spring wheat**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10040761
146. Dhanavath S., Prasada Rao U.J.S.. **Nutritional and Nutraceutical Properties of**. *J. Food Sci.* (2017.0) **82** 2243-2250. DOI: 10.1111/1750-3841.13844
147. Geisslitz S., Longin C.F.H., Scherf K.A., Koehler P.. **Comparative Study on Gluten Protein Composition of Ancient (Einkorn, Emmer and Spelt) and Modern Wheat Species (Durum and Common Wheat)**. *Foods* (2019.0) **8**. DOI: 10.3390/foods8090409
|
---
title: Multi-Parametric Cardiac Magnetic Resonance for Prediction of Heart Failure
Death in Thalassemia Major
authors:
- Antonella Meloni
- Laura Pistoia
- Maria Rita Gamberini
- Liana Cuccia
- Roberto Lisi
- Valerio Cecinati
- Paolo Ricchi
- Calogera Gerardi
- Gennaro Restaino
- Riccardo Righi
- Vincenzo Positano
- Filippo Cademartiri
journal: Diagnostics
year: 2023
pmcid: PMC10001258
doi: 10.3390/diagnostics13050890
license: CC BY 4.0
---
# Multi-Parametric Cardiac Magnetic Resonance for Prediction of Heart Failure Death in Thalassemia Major
## Abstract
We assessed the prognostic value of multiparametric cardiovascular magnetic resonance (CMR) in predicting death from heart failure (HF) in thalassemia major (TM). We considered 1398 white TM patients (30.8 ± 8.9 years, 725 women) without a history of HF at baseline CMR, which was performed within the Myocardial Iron Overload in Thalassemia (MIOT) network. Iron overload was quantified by using the T2* technique, and biventricular function was determined with cine images. Late gadolinium enhancement (LGE) images were acquired to detect replacement myocardial fibrosis. During a mean follow-up of 4.83 ± 2.05 years, $49.1\%$ of the patients changed the chelation regimen at least once; these patients were more likely to have significant myocardial iron overload (MIO) than patients who maintained the same regimen. Twelve ($1.0\%$) patients died from HF. Significant MIO, ventricular dysfunction, ventricular dilation, and replacement myocardial fibrosis were identified as significant univariate prognosticators. Based on the presence of the four CMR predictors of HF death, patients were divided into three subgroups. Patients having all four markers had a significantly higher risk of dying for HF than patients without markers (hazard ratio (HR) = 89.93; $95\%$CI = 5.62–1439.46; $$p \leq 0.001$$) or with one to three CMR markers (HR = 12.69; $95\%$CI = 1.60–100.36; $$p \leq 0.016$$). Our findings promote the exploitation of the multiparametric potential of CMR, including LGE, for better risk stratification for TM patients.
## 1. Introduction
Beta thalassemia major (β-TM) is a genetic blood disease with a high incidence in the Mediterranean basin, the Middle East, the Indian subcontinent, Central Asia, and the Far East [1]. However, due to the increased migration flux, thalassemia has become a global health problem. β-TM is characterized by a reduced or absent synthesis of the β chains of hemoglobin with a consequent excess of α chains which aggregate and precipitate in the red cells, leading to chronic hemolysis and the destruction of red cells and their precursors in the bone marrow or peripheral blood [2]. These abnormalities result in severe anemia, which needs lifelong regular blood transfusions. Due to the absence of a physiologic excretory pathway for excess iron, the major drawback of this treatment is iron overload, which, being highly cytotoxic, can cause organ dysfunction and damage [3]. Iron-induced heart failure (HF) remains the main cause of morbidity and mortality in TM patients, although the introduction of T2* Cardiovascular Magnetic Resonance (CMR) for the non-invasive assessment of myocardial iron overload (MIO) led to a significant increase in the survival rate [4,5]. Indeed, this technique offers the possibility to design tailor-made iron chelation therapies customized for each patient and to evaluate their efficacy [6,7,8,9]. In addition to direct myocardial injury, iron overload may affect the heart indirectly because hepatic dysfunction and endocrinopathies (diabetes mellitus, hypothyroidism, and hypoparathyroidism) arising from iron accumulation increase the risk for heart failure independently of cardiac iron status [10,11,12]. Nevertheless, the pathophysiology of heart failure in TM can be multifactorial with significant contributions from physiologic, immunoinflammatory, and genetic factors [13,14,15].
Thanks to its multiparametric potential, CMR represents a unique tool for the characterization and quantification of myocardial involvement and damage. CMR is the gold standard for the quantification of biventricular size and function by cine images. Because it does not incorporate ionizing radiation, does not exhibit window or geometric limitations, and provides precise ventricular endocardial definition, it allows for highly reproducible and accurate measurements of ventricular volumes. This is of particular value in TM, where the “normal” heart pumps at larger volumes and against lower peripheral resistances than the normal heart in non-thalassemic individuals, and where the heart’s biventricular size can be influenced by the iron previously accumulated. Moreover, late gadolinium enhancement (LGE) CMR is the only non-invasive imaging method that can detect replacement myocardial fibrosis, a common finding among TM patients [14,16,17].
A study of 481 Italian TM patients showed that heart iron, ventricular dysfunction, and replacement myocardial fibrosis could predict the future development of heart failure. Moreover, all three of these CMR markers remained independent prognosticators in a multivariate model that included a previous history of heart failure [11]. To the best of our knowledge, the association between CMR findings and HF death in TM patients has not yet been demonstrated.
The aim of this multicenter study was to evaluate the prognostic value of multiparametric CMR (cardiac iron, function, and replacement fibrosis) in predicting death from heart failure in a large cohort of well-treated TM patients.
## 2.1. Study Population
We considered 1485 TM patients (31.04 ± 8.88 years; 771 women) consecutively enrolled in the Myocardial Iron Overload in Thalassemia (MIOT) network, comprising 70 thalassemia centers and 10 magnetic resonance imaging (MRI) centers, where MRI exams were performed using homogeneous, standardized, and validated procedures [5,18]. The inclusion criteria of the MIOT project were: [1] male and female patients, of all ages, with thalassemia syndromes or structural hemoglobin variants, requiring MRI to quantify the cardiac and liver iron burden; [2] written informed consent; [3] written authorization for use and disclosure of protected health information; [4] no absolute contraindications to MRI.
All patients were from an Italian background and were uniformly treated. They had been regularly transfused since early childhood and started undergoing chelation therapy from the mid-to-late 1970s, whereas patients born after the 1970s received chelation therapy from early childhood.
All patients performed their first MRI scan between April 2006 and November 2015. All scans were performed in the week immediately prior to the scheduled blood transfusion. The clinical-anamnestic history of the patients, from birth to the date of the first MRI scan, was recorded in the MIOT web-based database. At every MRI follow-up, which was performed by protocol every 18 ± 3 months, the clinical, instrumental, and laboratory data were updated.
All patients gave informed consent in compliance with the Declaration of Helsinki, and the study was approved by the institutional ethics committees of all MRI sites.
## 2.2. Magnetic Resonance Imaging
All patients underwent MRI using the clinical 1.5 T scanners of three main vendors (GE Healthcare, Milwaukee, WI; Philips, Best, Netherlands; Siemens Healthineers, Erlangen, Germany) equipped with phased-array coils. Breath-holding in end-expiration and ECG-gating were used.
For iron overload assessment, a validated T2* gradient–echo multiecho sequence was used. The intersite, interstudy, intraobserver, and interobserver variability of the proposed methodology had been previously assessed [19,20]. For the measurement of MIO, a multislice approach was adopted [21]. Three parallel short-axis views (basal, medium, and apical) of the left ventricle (LV) were acquired at 10 echo times (TE) (first TE = 2.0 ms, echo spacing = 2.26 ms) in a single end-expiratory breath-hold. Acquisition sequence details are provided in [22]. A medium hepatic slice was obtained at 10 TEs (echo spacing = 2.26 ms) in a single end-expiratory breath-hold [23]. T2* image analysis was performed by trained MRI operators (>10 years of experience) using a custom-written, previously validated software (HIPPO MIOT®) [24]. The software provided the T2* value for all the 16 segments of the LV, according to the standard American Heart Association (AHA)/American College of Cardiology (ACC) model [20]. The image analysis procedure included the manual delineation of the endocardial and epicardial borders of the LV wall, the identification of the upper intersection of the left and the right wall, and the automatic fitting of the signal decay over the TEs with an appropriate decay model. Susceptibility and geometric artifacts were corrected using an appropriate correction map [24]. The global heart T2* value was obtained by averaging all segmental values. Hepatic T2* values were calculated in a circular region of interest, defined in a homogeneous area of parenchyma without blood vessels [23], and were converted into liver iron concentration (LIC) with an appropriate calibration curve [25].
Steady-state free precession (SSFP) cines were acquired in sequential 8-mm short axis slices (gap 0 mm) from the atrio-ventricular ring to the apex to assess biventricular function parameters quantitatively in a standard way [26]. Thirty cardiac phases were acquired per heartbeat, and 10–14 slices were required to cover the heart over its entire extension. The most apical slice included was the first slice which showed no blood pool at end-diastole. The most basal slice included was the one that showed a remaining part of the thick myocardium and was below the aortic valve. The analysis was based on the manual recognition of the endocardial and epicardial borders of the wall, at least in the end-diastolic and end-systolic phases in each slice. Moreover, the papillary muscles were delineated and were considered myocardial mass rather than part of the blood pool. Biventricular volumes were indexed to the body surface area. The inter-center variability for the quantification of cardiac function was previously reported [27]. The left and right atrial areas were measured from the 4-chamber view projection in the ventricular end-systolic phase.
Late gadolinium enhancement short-axis images were acquired 10–18 min after Gadobutrol (Gadovist®; Bayer Schering Pharma; Berlin, Germany) intravenous administration at the standard dose of 0.2 mmol/kg using a fast gradient-echo inversion recovery sequence. In addition, vertical, horizontal, and oblique long-axis views were acquired. Inversion times were adjusted to null the normal myocardium (from 210 ms to 300 ms). LGE was evaluated visually by two independent observers using a two-point scale (enhancement absent or present) and was considered present when visualized in two different views [14]. LGE images were not acquired in patients with a glomerular filtration rate < 30 mL/min/1.73 m2 and in patients who refused the contrast medium administration.
## 2.3. Diagnostic Criteria and Follow-Up
A T2* measurement of 20 ms was taken as a “conservative” normal value for the segmental and global T2* values [28]. A LIC < 3 mg/g dry weight (dw) indicated no significant hepatic iron overload [29].
The mean serum ferritin level in the year preceding the MRI was taken into account, and a value ≥ 1000 ng/mL was considered indicative of significant body iron burden [30].
Previously derived reference ranges for biventricular volumes and function, specific to TM patients, were used [26]. Ventricular dilation was diagnosed in the presence of an LV and/or right ventricular (RV) end-diastolic volume index (EDVI) >2 standard deviations (SD) from the mean values normalized to age and gender. Ventricular dysfunction was diagnosed in the presence of an LV and/or RV ejection fraction (EF) <1 SD from the mean values normalized to age and gender.
Atrial dilatation was diagnosed if the left and/or right atrial area indexed by body surface area was ≥15 cm2/m2 [31].
The endpoint used in this study was HF-mortality. HF was identified based on symptoms (breathlessness, ankle swelling, and fatigue), signs, biomarkers, and instrumental parameters, according to the current guidelines [32].
The follow-up date coincided with the date of the last available MRI. For patients who did not perform a follow-up MRI, a case report form detailing patient outcomes between the baseline MRI and September 2018 was completed by the caring hematologist.
## 2.4. Statistical Analysis
All data were analyzed using SPSS version 27.0 (IBM Corp, Armonk, NY, USA) statistical package.
Continuous variables were described as mean ± SD. Categorical variables were expressed as frequencies and percentages.
The normality of the distribution of the continuous variables was assessed by using the Kolmogorov–Smirnov test.
For continuous values with normal distributions, comparisons between groups were made by performing the independent-samples t-test (2 groups) or a one-way analysis of variance (ANOVA) (>2 groups). Wilcoxon’s signed rank test or the Kruskal–Wallis test were applied for continuous values with non-normal distribution. χ2 testing was performed for non-continuous variables. The Bonferroni post hoc test was used for multiple comparisons between pairs of groups.
Correlation analysis was performed using Pearson’s test or Spearman’s test where appropriate.
The Cox proportional hazard model was used to test the association between the considered prognostic variables and the outcome (HF death). The results are presented as hazard ratios (HR) with $95\%$ confidence intervals (CI). Kaplan–Meier curves were generated by relating the development of an outcome over time to each significant prognosticator. The log rank test was used to compare different strata in Kaplan–Meier analyses.
In all tests, a 2-tailed $p \leq 0.05$ was considered statistically significant.
## 3.1. Selection of the Patients
At the baseline MRI, eighty-seven ($5.9\%$) patients had a history of heart failure and were excluded from this study.
Compared to HF-free patients, patients with a history of HF were characterized at the baseline MRI by a significantly higher age (34.33 ± 7.69 years vs. 30.84 ± 8.91 years; $$p \leq 0.001$$), significantly lower global heart T2* values (23.82 ± 13.38 ms vs. 29.46 ± 12.03 ms; $p \leq 0.0001$), a significantly higher number of segments with a T2* < 20 ms (7.33 ± 7.04 vs. 4.47 ± 6.09; $p \leq 0.0001$), significantly higher LV EDVI (94.37 ± 23.97 mL/m2 vs. 86.60 ± 18.71 mL/m2; $$p \leq 0.005$$) and RV EDVI (93.15 ± 37.33 mL/m2 vs. 82.59 ± 18.73 mL/m2; $$p \leq 0.011$$), and significantly lower LV EF (57.72 ± $10.41\%$ vs. 61.58 ± $7.09\%$; $p \leq 0.0001$) and RV EF (56.04 ± $10.17\%$ vs. 61.37 ± $8.14\%$; $p \leq 0.0001$).
## 3.2. Patients’ Characteristics
Table 1 shows the demographic, clinical, and MRI features of the considered 1398 TM patients at the baseline MRI. The mean age was 30.8 ± 8.9 years, and 725 ($51.9\%$) patients were women.
Bi-atrial areas were present for 1138 patients due to technical reasons.
The contrast medium was not administrated in 286 ($20.5\%$) patients. Among the 187 ($16.8\%$) patients with replacement myocardial fibrosis, none had an ischemic pattern, and two or more foci were detected in $59.9\%$ of cases. The septum was involved in $80.6\%$ of the cases. Patients with replacement myocardial fibrosis were significantly older than patients without replacement myocardial fibrosis (33.25 ± 7.79 years vs. 30.89 ± 8.48 years; $p \leq 0.0001$), but they showed comparable global heart T2* values (27.56 ± 12.62 ms vs. 29.06 ± 11.94 ms; $$p \leq 0.124$$).
At baseline, serum ferritin levels showed a significant positive correlation with MRI LIC values ($R = 0.713$; $p \leq 0.0001$) and a significant inverse correlation with global heart T2* values (R = −0.326; $p \leq 0.0001$). A significant inverse correlation was detected between global heart T2* and MRI LIC values (R = −0.303; $p \leq 0.0001$). Global heart T2* values were not correlated with biventricular volume indexes or LV cardiac indexes but showed a weak positive association with both LV EF ($R = 0.182$; $p \leq 0.0001$) and RV EF ($R = 0.102$; $$p \leq 0.005$$).
The mean follow-up time was 4.83 ± 2.05 years (median: 5.01 years).
## 3.3. Chelation Therapy
At the baseline MRI, patients received the following chelation regimens: deferoxamine ($33.3\%$), deferiprone ($19.0\%$), deferasirox ($25.0\%$), combined deferoxamine + deferiprone ($17.2\%$), sequential deferoxamine/deferiprone ($5.0\%$), and others ($0.5\%$).
During the follow-up, $49.1\%$ of the patients changed their chelation regimen at least once, i.e., they switched to a different type of chelator or underwent modification of dose and/or frequency. Compared to patients who maintained the same regimen, those who changed the chelation regimen were more likely to have a baseline global heart T2* value < 20 ms ($33.2\%$ vs. $19.7\%$; $p \leq 0.0001$) and to have a baseline LIC ≥ 3 mg/g/dw ($69.3\%$ vs. $57.4\%$; $p \leq 0.0001$).
The percentage of patients with good compliance (correspondence between the time history of drug administration and the prescribed regimen > $60\%$) was significantly higher at the end of the study than at the baseline MRI ($94.6\%$ vs. $92.5\%$; $p \leq 0.0001$).
## 3.4. Patient Outcomes
Twelve ($1.0\%$) patients died from heart failure. Ten patients had HF with reduced EF at echocardiography. The majority of them presented to the healthcare provider with a reduction in their effort tolerance due to dyspnea and/or fatigue. One patient presented not only with fatigue but also with chest pain and tachycardia and had elevated troponin levels. Two patients presented with palpitations. Two patients had chronic heart failures diagnosed >1 year after the CMR scan, that rapidly worsened. One patient had an HF with mildly reduced EF. One patient had HF with preserved EF and had evidence of structural heart disease. The mean age at death was 35.06 ± 8.68 years (range: 17–47 years).
Mean time from the baseline MRI to the HF-related death was 1.68 ± 1.78 years and, six ($50.0\%$) deaths occurred within the first year of follow-up.
When compared to the other patients, patients who died by HF showed at the baseline MRI significantly higher serum ferritin levels and MRI LIC values, significantly lower global heart T2* values, a significantly higher numbers of segments with T2* < 20 ms, significantly lower biventricular EFs, and a significantly higher incidence of replacement myocardial fibrosis (Table 1).
One patient who died from HF had a previous history of myocarditis.
## 3.5. Prediction of Heart Failure Mortality
Table 2 shows the results of the univariate Cox regression analysis. No association was detected between age or gender and HF mortality. Significant MIO (global heart T2* < 20 ms), ventricular dysfunction, ventricular dilation, and replacement myocardial fibrosis were identified as significant univariate prognosticators. Figure 1 shows the Kaplan–Meier survival curves. The log-rank test revealed a significant difference in the curves for each prognosticator (significant MIO: $$p \leq 0.010$$, ventricular dysfunction: $$p \leq 0.030$$, ventricular dilation: $p \leq 0.0001$, and replacement myocardial fibrosis: $$p \leq 0.010$$).
Due to the low number of deaths for HF, it was not possible to perform a multivariate model. However, based on the presence of the four CMR prognosticators of HF death, patients were divided into three subgroups:[1]patients with none of the four CMR markers (group 0; $$n = 488$$);[2]patients with one to three CMR markers (group 1; $$n = 617$$);[3]patients with four CMR markers (group 2; $$n = 7$$).
Table 3 shows the comparison of the baseline data among the three groups. No difference in terms of age, age at the start of regular transfusions or chelation was detected. All patients with four CMR markers were male, whereas distribution by sex was homogeneous in the other two groups. Serum ferritin levels and MRI LIC values were significantly higher in group 1 than in group 0 ($p \leq 0.0001$ in both comparisons). Global heart T2* values were significantly lower in group 2 than in groups 0 and 1 ($p \leq 0.0001$ and $$p \leq 0.006$$, respectively) and in group 1 than in group 0 ($p \leq 0.0001$), whereas the number of segments with a T2* < 20 ms was significantly higher in group 2 than in groups 0 and 1 ($p \leq 0.0001$ and $$p \leq 0.018$$, respectively) and in group 1 than in group 0 ($p \leq 0.0001$). Significantly lower LV EF and RV EF values were found in group 2 compared to both group 1 ($p \leq 0.0001$ for both ventricles) and group 0 ($p \leq 0.0001$ for both ventricles) and in group 1 compared to group 0 ($p \leq 0.0001$ for both ventricles). LV EDVI and RVEDVI were significantly increased in group 2 compared to group 1 ($p \leq 0.0001$ for both ventricles) and to group 0 ($p \leq 0.0001$ for both ventricles) and in group 1 compared to group 0 ($p \leq 0.0001$ and $$p \leq 0.003$$, respectively). The frequency of replacement myocardial fibrosis was significantly higher in group 2 than in groups 1 and 0 and in group 1 than in group 0 ($p \leq 0.0001$ for all comparisons).
The frequency of HF death was significantly higher in group 2 than in both group 0 ($14.3\%$ vs. $0.2\%$; $p \leq 0.0001$) and group 1 ($14.3\%$ vs. $1.5\%$; $$p \leq 0.021$$) (Figure 2).
Patients having all four markers had a significantly higher risk of dying by HF than patients without markers (HR = 89.93; $95\%$ CI = 5.62–1439.46; $$p \leq 0.001$$) or with one to three CMR markers (HR = 12.69; $95\%$ CI = 1.60–100.36; $$p \leq 0.016$$). Figure 3 shows the Kaplan–Meier survival curve. The log-rank test revealed a significant difference in the curves ($p \leq 0.0001$).
## 4. Discussion
In our homogeneous white Italian/Mediterranean population, which had been well-treated since early childhood and followed for a mean of 4.8 years after the baseline MRI, we detected a low incidence of deaths from heart failure because the T2* report guided the patient-specific adjustment of the chelation regimen. Indeed, the patients who changed their chelation regimen (drug or frequency/dosage) during the follow-up were more likely to have significant MIO at baseline. Moreover, all MRI scans were performed after 2006, the year when a new era of chelation treatment had started thanks to the availability in the clinical arena of three different iron chelators and the evidence that they could be used in association to intensify chelation or make it more tolerable [33].
No prospective association was detected between hepatic iron or serum ferritin levels and HF mortality. These parameters cannot be used to infer cardiac iron status, as demonstrated by weak cross-sectional correlation with the cardiac T2* found in the present study and in other published studies [28,34,35,36]. The relationship between cardiac and hepatic iron is complex due to the differences in iron uptake and elimination between the two organs as well as the strong influence of both the type and pattern of chelation [6,9,37]. Cardiac T2* can identify preclinical cardiac iron deposition in patients with excellent control of total body iron stores [35,38,39,40].
As expected, MIO was a significant prognosticator of HF death. Excess iron can be detrimental to human cells through the production of hydroxyl radicals via Haber–Weiss–Fenton reactions, which cause oxidative damage to cellular components like lipids, proteins, and DNA [41,42]. Free iron can directly interact and interfere with a variety of ion channels of cardiomyocytes, including the L-type calcium channel, the ryanodine-sensitive calcium channel, voltage-gated sodium channel, and delayed rectifier potassium channel, making cardiomyocytes particularly vulnerable to iron overload. Excessive production of reactive oxygen species can also directly induce ferroptosis (a non-apoptotic mode of cell death) in cardiomyocytes by catalyzing the oxidation of phospholipids in the cell membrane [43]. Importantly, other CMR parameters, namely ventricular dilatation, ventricular dysfunction, and replacement myocardial fibrosis, also emerged as unfavorable prognosis determinants. Our findings are in line with the study by Pepe et al., where, in a multivariate model, replacement myocardial fibrosis, MIO, and ventricular dysfunction independently predicted non-fatal HF [11]. Initially, MIO may cause a reduction of ventricular dimensions through vascular and ventricular stiffening [44] but the ventricular systolic function can remain well preserved so that at the onset of the disease patients are generally asymptomatic. In end-stage disease, MIO may increase ventricular dimensions and decrease systolic function [45]. In the Italian TM population, replacement myocardial fibrosis was demonstrated to be a relatively common finding (~$20\%$) [14,46,47], correlated with aging, negative cardiac remodeling, hepatitis C virus (HCV) infection, and diabetes mellitus in adult TM patients [10,14], and with lower cardiac T2* values in pediatric patients free of complications [40]. Moreover, a recent study showed an association between replacement fibrosis and decreased native T1 values measured by CMR [26], suggesting a potential pathophysiological role of MIO in the development of myocardial fibrosis. Indeed, native T1 mapping seems to have a higher sensitivity for low amounts of iron in comparison to the T2* technique. Although iron could be removed via chelation treatment, the induced heart damage may be not totally reversible. The findings of the present study further highlight the prognostic implications of replacement myocardial fibrosis. In fact, in different pathologies, such as dilatative cardiomyopathy, hypertrophic cardiomyopathy, aortic stenosis, and infiltrative diseases, replacement myocardial fibrosis represents a final common pathway of myocardial disease and is independently associated with cardiac and all-cause mortality [48].
Importantly, when the four CMR indices (cardiac iron, dilatation, dysfunction, and replacement fibrosis) were evaluated in combination, they fine-tuned the prognostic stratification of TM patients. Thus, the results of our study strengthen the usefulness of a multiparametric CMR approach which integrates biventricular ejection fractions and volumes and LGE with cardiac T2* to further ameliorate the prognosis of TM patients via the early identification of high-risk patients. Conversely, relying only on cardiac T2* as a unique marker of cardiac death may lead to suboptimal prognostic stratification.
It deserves mention that in our study, both ventricular dysfunction and dilation were diagnosed using previously derived “normal for TM” reference ranges in order to avoid a misdiagnosis of cardiomyopathy (underdiagnosis of dysfunction and overdiagnosis of dilatation) [26]. Indeed, despite transfusion therapy, TM represents a chronically anemic condition characterized by an elevation of blood volume (increased preload) and a decrease in systemic vascular resistance (decreased afterload) [49]. Both conditions enhance ventricular pump performance, and the anatomical–functional expression of this hemodynamic state is the enlargement of cardiac cavities [50].
## Limitations
This study suffers from several limitations.
The small number of HF deaths that occurred during the follow-up did not allow us to perform a multivariate analysis that included all variables identified in the univariate analysis. For this reason, we performed a model with the four CMR univariate prognosticators.
The prognostic value of the CMR mapping techniques (T1, T2, and extracellular volume) was not evaluated because they were not available at the time of patient enrolment.
We did not measure myocardial deformation (strain), which could be a more sensitive marker of myocardial dysfunction than EF [51]. Although feature-tracking (FT) CMR allows quantification of myocardial deformation on routine SSFP cine images, the dedicated post-processing FT software packages were not available in the MIOT centers.
More studies are needed to evaluate the transferability of our results to other TM populations with a lower prevalence of HCV infection, in which a lower frequency of myocardial fibrosis may be expected.
## 5. Conclusions
In TM patients, significant MIO, ventricular dysfunction, ventricular dilation, and replacement myocardial fibrosis were associated with a significantly higher risk of heart failure death, and the combined use of all four CMR indexes provided incremental prognostic information. Hence, the present study’s findings promote exploiting the multiparametric potential of CMR, including LGE, for better risk stratification for TM patients. Further studies are needed to verify if, in addition to the adjustment of iron chelation therapy, the adoption of treatment directed to myocardial performance may further open the prognosis of TM patients.
## References
1. Weatherall D.J.. **Thalassemia as a global health problem: Recent progress toward its control in the developing countries**. *Ann. N. Y. Acad. Sci.* (2010) **1202** 17-23. DOI: 10.1111/j.1749-6632.2010.05546.x
2. Weatherall D.J., Clegg J.B.. **Thalassemia—A global public health problem**. *Nat. Med.* (1996) **2** 847-849. DOI: 10.1038/nm0896-847
3. Rund D., Rachmilewitz E.. **Beta-thalassemia**. *N. Engl. J. Med.* (2005) **353** 1135-1146. DOI: 10.1056/NEJMra050436
4. Modell B., Khan M., Darlison M., Westwood M.A., Ingram D., Pennell D.J.. **Improved survival of thalassaemia major in the UK and relation to T2* cardiovascular magnetic resonance**. *J. Cardiovasc. Magn. Reson.* (2008) **10** 42. DOI: 10.1186/1532-429X-10-42
5. Pepe A., Pistoia L., Gamberini M.R., Cuccia L., Lisi R., Cecinati V., Maggio A., Sorrentino F., Filosa A., Rosso R.. **National networking in rare diseases and reduction of cardiac burden in thalassemia major**. *Eur. Heart J.* (2022) **43** 2482-2492. DOI: 10.1093/eurheartj/ehab851
6. Berdoukas V., Chouliaras G., Moraitis P., Zannikos K., Berdoussi E., Ladis V.. **The efficacy of iron chelator regimes in reducing cardiac and hepatic iron in patients with thalassaemia major: A clinical observational study**. *J. Cardiovasc. Magn. Reson.* (2009) **11** 20. DOI: 10.1186/1532-429X-11-20
7. Pepe A., Meloni A., Rossi G., Cuccia L., D’Ascola G.D., Santodirocco M., Cianciulli P., Caruso V., Romeo M.A., Filosa A.. **Cardiac and hepatic iron and ejection fraction in thalassemia major: Multicentre prospective comparison of combined deferiprone and deferoxamine therapy against deferiprone or deferoxamine monotherapy**. *J. Cardiovasc. Magn. Reson.* (2013) **15** 1. DOI: 10.1186/1532-429X-15-1
8. Pennell D.J., Porter J.B., Cappellini M.D., Chan L.L., El-Beshlawy A., Aydinok Y., Ibrahim H., Li C.K., Viprakasit V., Elalfy M.S.. **Deferasirox for up to 3 years leads to continued improvement of myocardial T2* in patients with beta-thalassemia major**. *Haematologica* (2012) **97** 842-848. DOI: 10.3324/haematol.2011.049957
9. Pepe A., Meloni A., Pistoia L., Cuccia L., Gamberini M.R., Lisi R., D’Ascola D.G., Rosso R., Allo M., Spasiano A.. **MRI multicentre prospective survey in thalassaemia major patients treated with deferasirox versus deferiprone and desferrioxamine**. *Br. J. Haematol.* (2018) **183** 783-795. DOI: 10.1111/bjh.15595
10. Pepe A., Meloni A., Rossi G., Caruso V., Cuccia L., Spasiano A., Gerardi C., Zuccarelli A., D’Ascola D.G., Grimaldi S.. **Cardiac complications and diabetes in thalassaemia major: A large historical multicentre study**. *Br. J. Haematol.* (2013) **163** 520-527. DOI: 10.1111/bjh.12557
11. Pepe A., Meloni A., Rossi G., Midiri M., Missere M., Valeri G., Sorrentino F., D’Ascola D.G., Spasiano A., Filosa A.. **Prediction of cardiac complications for thalassemia major in the widespread cardiac magnetic resonance era: A prospective multicentre study by a multi-parametric approach**. *Eur. Heart J. Cardiovasc. Imaging* (2018) **19** 299-309. DOI: 10.1093/ehjci/jex012
12. Albakri A.. **Iron overload cardiomyopathy: A review of literature on clinical status and meta-analysis of diagnostic and clinical management using iron chelators**. *Int. Med. Care* (2018) 2. DOI: 10.15761/IMC.1000117
13. Kremastinos D.T., Tiniakos G., Theodorakis G.N., Katritsis D.G., Toutouzas P.K.. **Myocarditis in beta-thalassemia major. A cause of heart failure**. *Circulation* (1995) **91** 66-71. DOI: 10.1161/01.CIR.91.1.66
14. Pepe A., Positano V., Capra M., Maggio A., Lo Pinto C., Spasiano A., Forni G., Derchi G., Favilli B., Rossi G.. **Myocardial scarring by delayed enhancement cardiovascular magnetic resonance in thalassaemia major**. *Heart* (2009) **95** 1688-1693. DOI: 10.1136/hrt.2008.156497
15. Wood J.C.. **Cardiac complications in thalassemia major**. *Hemoglobin* (2009) **33** S81-S86. DOI: 10.3109/03630260903347526
16. Pepe A., Meloni A., Borsellino Z., Cuccia L., Borgna-Pignatti C., Maggio A., Restaino G., Gagliardotto F., Caruso V., Spasiano A.. **Myocardial fibrosis by late gadolinium enhancement cardiac magnetic resonance and hepatitis C virus infection in thalassemia major patients**. *J. Cardiovasc. Med.* (2015) **16** 689-695. DOI: 10.2459/JCM.0000000000000278
17. Meloni A., Martini N., Positano V., De Luca A., Pistoia L., Sbragi S., Spasiano A., Casini T., Bitti P.P., Allò M.. **Myocardial iron overload by cardiovascular magnetic resonance native segmental T1 mapping: A sensitive approach that correlates with cardiac complications**. *J. Cardiovasc. Magn. Reson.* (2021) **23** 70. DOI: 10.1186/s12968-021-00765-w
18. Meloni A., Ramazzotti A., Positano V., Salvatori C., Mangione M., Marcheschi P., Favilli B., De Marchi D., Prato S., Pepe A.. **Evaluation of a web-based network for reproducible T2* MRI assessment of iron overload in thalassemia**. *Int. J. Med. Inform.* (2009) **78** 503-512. DOI: 10.1016/j.ijmedinf.2009.02.011
19. Pepe A., Lombardi M., Positano V., Cracolici E., Capra M., Malizia R., Prossomariti L., de Marchi D., Midiri M., Maggio A.. **Evaluation of the efficacy of oral deferiprone in beta-thalassemia major by multislice multiecho T2***. *Eur. J. Haematol.* (2006) **76** 183-192. DOI: 10.1111/j.1600-0609.2005.00587.x
20. Ramazzotti A., Pepe A., Positano V., Rossi G., De Marchi D., Brizi M.G., Luciani A., Midiri M., Sallustio G., Valeri G.. **Multicenter validation of the magnetic resonance t2* technique for segmental and global quantification of myocardial iron**. *J. Magn. Reson. Imaging* (2009) **30** 62-68. DOI: 10.1002/jmri.21781
21. Pepe A., Positano V., Santarelli F., Sorrentino F., Cracolici E., De Marchi D., Maggio A., Midiri M., Landini L., Lombardi M.. **Multislice multiecho T2* cardiovascular magnetic resonance for detection of the heterogeneous distribution of myocardial iron overload**. *J. Magn. Reson. Imaging* (2006) **23** 662-668. DOI: 10.1002/jmri.20566
22. Meloni A., Positano V., Pepe A., Rossi G., Dell’Amico M., Salvatori C., Keilberg P., Filosa A., Sallustio G., Midiri M.. **Preferential patterns of myocardial iron overload by multislice multiecho T*2 CMR in thalassemia major patients**. *Magn. Reson. Med.* (2010) **64** 211-219. DOI: 10.1002/mrm.22410
23. Meloni A., Luciani A., Positano V., De Marchi D., Valeri G., Restaino G., Cracolici E., Caruso V., Dell’amico M.C., Favilli B.. **Single region of interest versus multislice T2* MRI approach for the quantification of hepatic iron overload**. *J. Magn. Reson. Imaging* (2011) **33** 348-355. DOI: 10.1002/jmri.22417
24. Positano V., Pepe A., Santarelli M.F., Scattini B., De Marchi D., Ramazzotti A., Forni G., Borgna-Pignatti C., Lai M.E., Midiri M.. **Standardized T2* map of normal human heart in vivo to correct T2* segmental artefacts**. *NMR Biomed.* (2007) **20** 578-590. DOI: 10.1002/nbm.1121
25. Meloni A., Rienhoff H.Y., Jones A., Pepe A., Lombardi M., Wood J.C.. **The use of appropriate calibration curves corrects for systematic differences in liver R2* values measured using different software packages**. *Br. J. Haematol.* (2013) **161** 888-891. DOI: 10.1111/bjh.12296
26. Meloni A., Righi R., Missere M., Renne S., Schicchi N., Gamberini M.R., Cuccia L., Lisi R., Spasiano A., Roberti M.G.. **Biventricular Reference Values by Body Surface Area, Age, and Gender in a Large Cohort of Well-Treated Thalassemia Major Patients Without Heart Damage Using a Multiparametric CMR Approach**. *J. Magn. Reson. Imaging* (2021) **53** 61-70. DOI: 10.1002/jmri.27169
27. Marsella M., Borgna-Pignatti C., Meloni A., Caldarelli V., Dell’Amico M.C., Spasiano A., Pitrolo L., Cracolici E., Valeri G., Positano V.. **Cardiac iron and cardiac disease in males and females with transfusion-dependent thalassemia major: A T2* magnetic resonance imaging study**. *Haematologica* (2011) **96** 515-520. DOI: 10.3324/haematol.2010.025510
28. Anderson L.J., Holden S., Davis B., Prescott E., Charrier C.C., Bunce N.H., Firmin D.N., Wonke B., Porter J., Walker J.M.. **Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload**. *Eur. Heart J.* (2001) **22** 2171-2179. DOI: 10.1053/euhj.2001.2822
29. Angelucci E., Brittenham G.M., McLaren C.E., Ripalti M., Baronciani D., Giardini C., Galimberti M., Polchi P., Lucarelli G.. **Hepatic iron concentration and total body iron stores in thalassemia major**. *N. Engl. J. Med.* (2000) **343** 327-331. DOI: 10.1056/NEJM200008033430503
30. Porter J.B.. **Practical management of iron overload**. *Br. J. Haematol.* (2001) **115** 239-252. DOI: 10.1046/j.1365-2141.2001.03195.x
31. Maceira A.M., Cosin-Sales J., Roughton M., Prasad S.K., Pennell D.J.. **Reference left atrial dimensions and volumes by steady state free precession cardiovascular magnetic resonance**. *J. Cardiovasc. Magn. Reson.* (2010) **12** 65. DOI: 10.1186/1532-429X-12-65
32. McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M., Burri H., Butler J., Čelutkienė J., Chioncel O.. **2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC**. *Eur. Heart J.* (2021) **42** 3599-3726. DOI: 10.1093/eurheartj/ehab368
33. Di Maggio R., Maggio A.. **The new era of chelation treatments: Effectiveness and safety of 10 different regimens for controlling iron overloading in thalassaemia major**. *Br. J. Haematol.* (2017) **178** 676-688. DOI: 10.1111/bjh.14712
34. Aessopos A., Fragodimitri C., Karabatsos F., Hatziliami A., Yousef J., Giakoumis A., Dokou A., Gotsis E.D., Berdoukas V., Karagiorga M.. **Cardiac magnetic resonance imaging R2* assessments and analysis of historical parameters in patients with transfusion-dependent thalassemia**. *Haematologica* (2007) **92** 131-132. DOI: 10.3324/haematol.10455
35. Meloni A., Restaino G., Borsellino Z., Caruso V., Spasiano A., Zuccarelli A., Valeri G., Toia P., Salvatori C., Positano V.. **Different patterns of myocardial iron distribution by whole-heart T2* magnetic resonance as risk markers for heart complications in thalassemia major**. *Int. J. Cardiol.* (2014) **177** 1012-1019. DOI: 10.1016/j.ijcard.2014.09.139
36. Wahidiyat P.A., Liauw F., Sekarsari D., Putriasih S.A., Berdoukas V., Pennell D.J.. **Evaluation of cardiac and hepatic iron overload in thalassemia major patients with T2* magnetic resonance imaging**. *Hematology* (2017) **22** 501-507. DOI: 10.1080/10245332.2017.1292614
37. Noetzli L.J., Carson S.M., Nord A.S., Coates T.D., Wood J.C.. **Longitudinal analysis of heart and liver iron in thalassemia major**. *Blood* (2008) **112** 2973-2978. DOI: 10.1182/blood-2008-04-148767
38. Borgna-Pignatti C., Meloni A., Guerrini G., Gulino L., Filosa A., Ruffo G.B., Casini T., Chiodi E., Lombardi M., Pepe A.. **Myocardial iron overload in thalassaemia major. How early to check?**. *Br. J. Haematol.* (2014) **164** 579-585. DOI: 10.1111/bjh.12643
39. Meloni A., Positano V., Ruffo G.B., Spasiano A., D’Ascola D.G., Peluso A., Keilberg P., Restaino G., Valeri G., Renne S.. **Improvement of heart iron with preserved patterns of iron store by CMR-guided chelation therapy**. *Eur. Heart J. Cardiovasc. Imaging* (2015) **16** 325-334. DOI: 10.1093/ehjci/jeu191
40. Casale M., Meloni A., Filosa A., Cuccia L., Caruso V., Palazzi G., Rita Gamberini M., Pitrolo L., Caterina Putti M., Giuseppe D’Ascola D.. **Multiparametric Cardiac Magnetic Resonance Survey in Children With Thalassemia Major: A Multicenter Study**. *Circ. Cardiovasc. Imaging* (2015) **8** e003230. DOI: 10.1161/CIRCIMAGING.115.003230
41. Li S., Zhang X.. **Iron in Cardiovascular Disease: Challenges and Potentials**. *Front. Cardiovasc. Med.* (2021) **8** 707138. DOI: 10.3389/fcvm.2021.707138
42. Fang X., Ardehali H., Min J., Wang F.. **The molecular and metabolic landscape of iron and ferroptosis in cardiovascular disease**. *Nat. Rev. Cardiol.* (2023) **20** 7-23. DOI: 10.1038/s41569-022-00735-4
43. Dixon S.J., Lemberg K.M., Lamprecht M.R., Skouta R., Zaitsev E.M., Gleason C.E., Patel D.N., Bauer A.J., Cantley A.M., Yang W.S.. **Ferroptosis: An Iron-Dependent Form of Nonapoptotic Cell Death**. *Cell* (2012) **149** 1060-1072. DOI: 10.1016/j.cell.2012.03.042
44. Cheung Y.F., Chan G.C., Ha S.Y.. **Effect of deferasirox (ICL670) on arterial function in patients with beta-thalassaemia major**. *Br. J. Haematol.* (2008) **141** 728-733. DOI: 10.1111/j.1365-2141.2008.07092.x
45. Pennell D.J., Udelson J.E., Arai A.E., Bozkurt B., Cohen A.R., Galanello R., Hoffman T.M., Kiernan M.S., Lerakis S., Piga A.. **Cardiovascular function and treatment in beta-thalassemia major: A consensus statement from the American Heart Association**. *Circulation* (2013) **128** 281-308. DOI: 10.1161/CIR.0b013e31829b2be6
46. Meloni A., Pepe A., Positano V., Favilli B., Maggio A., Capra M., Lo Pinto C., Gerardi C., Santarelli M.F., Midiri M.. **Influence of myocardial fibrosis and blood oxygenation on heart T2* values in thalassemia patients**. *J. Magn. Reson. Imaging* (2009) **29** 832-837. DOI: 10.1002/jmri.21704
47. Meloni A., Favilli B., Positano V., Cianciulli P., Filosa A., Quarta A., D’Ascola D., Restaino G., Lombardi M., Pepe A.. **Safety of cardiovascular magnetic resonance gadolinium chelates contrast agents in patients with hemoglobinopaties**. *Haematologica* (2009) **94** 1625-1627. DOI: 10.3324/haematol.2009.010181
48. Bing R., Dweck M.R.. **Myocardial fibrosis: Why image, how to image and clinical implications**. *Heart* (2019) **105** 1832-1840. DOI: 10.1136/heartjnl-2019-315560
49. Varat M.A., Adolph R.J., Fowler N.O.. **Cardiovascular effects of anemia**. *Am. Heart J.* (1972) **83** 415-426. DOI: 10.1016/0002-8703(72)90445-0
50. Dahiya A., Vollbon W., Jellis C., Prior D., Wahi S., Marwick T.. **Echocardiographic assessment of raised pulmonary vascular resistance: Application to diagnosis and follow-up of pulmonary hypertension**. *Heart* (2010) **96** 2005-2009. DOI: 10.1136/hrt.2010.204834
51. Kraigher-Krainer E., Shah A.M., Gupta D.K., Santos A., Claggett B., Pieske B., Zile M.R., Voors A.A., Lefkowitz M.P., Packer M.. **Impaired systolic function by strain imaging in heart failure with preserved ejection fraction**. *J. Am. Coll. Cardiol.* (2014) **63** 447-456. DOI: 10.1016/j.jacc.2013.09.052
|
---
title: Functionalizing Collagen Membranes with MSC-Conditioned Media Promotes Guided
Bone Regeneration in Rat Calvarial Defects
authors:
- Siddharth Shanbhag
- Carina Kampleitner
- Niyaz Al-Sharabi
- Samih Mohamed-Ahmed
- Karol Ali Apaza Alccayhuaman
- Patrick Heimel
- Stefan Tangl
- Andreas Beinlich
- Neha Rana
- Mariano Sanz
- Einar K. Kristoffersen
- Kamal Mustafa
- Reinhard Gruber
journal: Cells
year: 2023
pmcid: PMC10001262
doi: 10.3390/cells12050767
license: CC BY 4.0
---
# Functionalizing Collagen Membranes with MSC-Conditioned Media Promotes Guided Bone Regeneration in Rat Calvarial Defects
## Abstract
Functionalizing biomaterials with conditioned media (CM) from mesenchymal stromal cells (MSC) is a promising strategy for enhancing the outcomes of guided bone regeneration (GBR). This study aimed to evaluate the bone regenerative potential of collagen membranes (MEM) functionalized with CM from human bone marrow MSC (MEM-CM) in critical size rat calvarial defects. MEM-CM prepared via soaking (CM-SOAK) or soaking followed by lyophilization (CM-LYO) were applied to critical size rat calvarial defects. Control treatments included native MEM, MEM with rat MSC (CEL) and no treatment. New bone formation was analyzed via micro-CT (2 and 4 weeks) and histology (4 weeks). Greater radiographic new bone formation occurred at 2 weeks in the CM-LYO group vs. all other groups. After 4 weeks, only the CM-LYO group was superior to the untreated control group, whereas the CM-SOAK, CEL and native MEM groups were similar. Histologically, the regenerated tissues showed a combination of regular new bone and hybrid new bone, which formed within the membrane compartment and was characterized by the incorporation of mineralized MEM fibers. Areas of new bone formation and MEM mineralization were greatest in the CM-LYO group. Proteomic analysis of lyophilized CM revealed the enrichment of several proteins and biological processes related to bone formation. In summary, lyophilized MEM-CM enhanced new bone formation in rat calvarial defects, thus representing a novel ‘off-the-shelf’ strategy for GBR.
## 1. Introduction
The reconstruction of complex bone defects, where tissue deficiency occurs tri-dimensionally, is a clinical challenge [1]. In the alveolar bone, the recommended treatment approaches for such defects have been either guided bone regeneration (GBR) using autologous bone grafts in combination with a bone substitute material and a barrier membrane or autogenous or allogeneic bone blocks [2,3]. Although GBR has demonstrated a high degree of clinical success and predictability [4,5], in the presence of large defects, the need for extensive autologous bone harvesting may result in additional patient morbidity and risks of clinical complications [6].
Bone tissue engineering is increasingly being used to overcome these limitations [7] by combining autologous transplantation of ex vivo expanded adult mesenchymal stromal cells (MSC), usually from the bone marrow (BMSC), with biomaterial scaffolds [8,9]. However, this approach has important logistic and regulatory complications that limit its efficiency and predictability. In fact, in a recent meta-analysis, our group reported that the clinical evidence for effectiveness of cell therapy was limited, i.e., there were relatively small effect sizes vs. traditional GBR/grafting procedures, and these were mainly limited to studies of maxillary sinus augmentation [7]. Moreover, the large-scale translation of this strategy is limited by the need for expensive Good Manufacturing Practice (GMP)-grade laboratories for ex vivo cell expansion and stringent regulation of MSC as Advanced Therapeutic Medicinal Products (ATMP) by health authorities. Furthermore, the traditional hypothesis that MSC act via engraftment, differentiation and replacement at the injury site has, in recent years, been challenged by evidence of a predominantly paracrine mechanism of action [10,11].
It is widely accepted that MSC may exert their beneficial effects by secreting a wide range of bioactive factors, including soluble proteins (growth factors, cytokines and chemokines), lipids, nucleic acids and extracellular vesicles at or near the site of injury [11,12,13]. These factors, in turn, stimulate tissue-resident progenitor (osteogenesis), endothelial (angiogenesis) and immune cells (immune modulation) to drive the subsequent regeneration processes [14]. These findings have provided the biological basis for developing ‘cell-free’ strategies, which use the secretome contained in MSC-conditioned media (CM) to stimulate tissue regeneration. An additional advantage of this strategy is the possibility of storing and using MSC-CM as ‘off-the-shelf’ products [15]. Although the preclinical efficacy of MSC-CM for bone regeneration has previously been reported [16,17], data for the optimal dose(s) and mode(s) of CM delivery are lacking.
GBR techniques are based on the use of barrier membranes that act as occlusive barriers to the rapidly proliferating cells of epithelial and connective tissues, while promoting repopulation with slower-growing osteoprogenitor cells [18,19]. Bioabsorbable collagen membranes (MEM) are the most frequently used membranes in GBR, and are either applied alone or combined with bone substitute materials [20]. In addition to functioning as occlusive barriers, MEM have also shown an inherent biological activity via their ability to adsorb and release signaling molecules, e.g., growth factors [21,22]. This property has been exploited in several preclinical studies where MEM have been functionalized with bioactive molecules, e.g., bone-derived proteins [23,24] and recombinant growth factors (see review [4]). We have previously demonstrated that MEM can adsorb the growth factor-activity from human biological products ex vivo [25]. Thus, it is reasonable to hypothesize that MEM can also adsorb bioactive factors from CM and serve as carriers or ‘scaffolds’ in GBR settings [14,25]. To test this hypothesis, we propose using the calvarial critical size defect model in rodents, since this model is extensively used for testing other GBR strategies [26]. Thus, the objective of the present study was to investigate the efficacy of CM-functionalized MEM (MEM-CM) for promoting GBR in vivo in rat calvarial defects. A secondary objective was to compare two different methods, i.e., soaking vs. lyophilization of CM, for MEM functionalization.
## 2.1. Cell Culture
The use of human cells and tissues was approved by the Regional Committees for Medical Research Ethics in Norway (2013-1248/REK-sør-øst and 2016-1266/REK-nord). Bone marrow specimens were obtained following parental consent from five independent donors (2 females and 3 males; 8–10 years) undergoing reconstructive surgery at the Department of Plastic Surgery, Haukeland University Hospital. BMSC were isolated and expanded following previous protocols [27]. Briefly, the cells were cultured in T75 or T175 flasks (Thermo Fisher Scientific, Carlsbad, CA, USA) using sterile filtered growth media (GM) comprising of Dulbecco’s Modified Eagle’s medium (DMEM, Invitrogen, Carlsbad, CA, USA) supplemented with $5\%$ (v/v) pooled human platelet lysate (HPL; Bergenlys, Bergen, Norway), $1\%$ (v/v) penicillin/streptomycin (GE Healthcare, South Logan, UT, USA) and 1 IU/mL heparin (Leo Pharma AS, Lysaker, Norway) [27]. The cells were sub-cultured and expanded under standard incubation conditions, i.e., 37 °C and $5\%$ CO2, following a validated protocol with a seeding density of 4000 cells/cm2 [28]. Passage 1 (p1) and 2 (p2) BMSC were characterized based on immunophenotype and multi-lineage differentiation potential, as previously reported [27]. For all the cell cultures, growth and morphology were monitored regularly under an inverted light microscope (Nikon Eclipse TS100, Tokyo, Japan).
## 2.2. CM Preparation
Pooled CM were prepared from BMSC ($$n = 3$$ donors) as previously described [29]. Briefly, p1 and p2 BMSC were expanded in T175 flasks in GM until 70–$80\%$ confluency under standard incubation. At this point, the cells were washed three times with phosphate-buffered saline (PBS; Invitrogen), and then cultured in plain DMEM (without HPL or antibiotics) for another 48 h to produce CM. After 48 h, the CM from p1 and p2 BMSC from each of the three donors were collected, pooled, centrifuged at 4000× g for 10 min to remove any debris, aliquoted and stored at −80 °C. The CM were further concentrated using 3 kDa Amicon Ultra-15 centrifugal filter devices (Merck Millipore, Billerica, MA, USA) using the manufacturers protocol. Briefly, following the equilibration of filter devices with PBS, the CM were centrifuged at 4000× g for 30 min at 4 °C, followed by PBS buffer exchange and another centrifugation cycle (4000× g for 30 min), resulting in concentrated CM (~30-fold). Based on previous reports [15,30], mannitol (Sigma Aldrich, St. Louis, MO, USA) was added as a cryo-preservative ($0.5\%$ v/v), and the concentrated CM were then used for MEM functionalization.
## 2.3. MEM Functionalization and Bioassay
Bi-layered, non-cross-linked MEM (25 mm × 25 mm; Bio-Gide®, Geistlich Pharma, Wolhusen, Switzerland) were used in this study. The MEM were cut using sterile scissors into smaller pieces (7 mm × 6 mm) and incubated with 100 μL of serum-free DMEM (control) or concentrated CM at 37 °C for 1 h based on previous experiments where the incubation conditions for optimal adsorption of proteins were determined [22]. For equal comparison, mannitol was also added to serum-free DMEM at a final concentration of $0.5\%$ (v/v). After 1 h, the supernatants were aspirated, and MEM soaked with serum-free DMEM (native MEM, control group) and half of the MEM soaked with CM (CM-SOAK) were stored at 4 °C. The remaining MEM soaked with CM were stored in a −80 °C freezer for subsequent lyophilization. Lyophilization of the MEM-CM was performed in a FreeZone™ freeze dryer (Labconco, Kansas, MO, USA) at 0.014 mBar of pressure and at −51 °C. The lyophilized MEM-CM (CM-LYO) were stored at 4 °C until their use in the experiments (up to 24 h). As a bioassay, the effects of CM alone and MEM-CM (CM-SOAK and CM-LYO) on BMSC were tested via a quantitative real-time polymerase chain reaction (qPCR) using TaqMan® real-time PCR assays (Thermo Scientific) as previously described [31]. Primary BMSC (different from those used for CM preparation) were exposed to GM and CM in a monolayer culture, and to CM-SOAK, CM-LYO and native MEM in a three-dimensional (3D) culture for 48 h. Expressions of osteogenesis-related genes (Supplementary Table S1) were assessed as previously described [31].
## 2.4. Cell Viability on MEM
To test the cytocompatibility of the functionalized MEM-CM (CM-SOAK and CM-LYO), i.e., whether the resident cells could populate the MEM following in vivo implantation, the in vitro viability of rat MSC (rMSC) seeded on native and functionalized MEM was determined after 1 and 3 days using the LIVE/DEAD cell viability assay (Invitrogen) as previously described [32]. Briefly, previously isolated rMSC [33] were cultured in DMEM supplemented with $10\%$ (v/v) fetal bovine serum (FBS) and $1\%$ (v/v) penicillin/streptomycin; p3-5 cells were used in experiments. Pooled rMSC (~105 cells) were seeded on MEM and allowed to attach for 1 h before supplementing them with the corresponding growth media for 1–3 days. At each time point, MEM were stained and observed under a confocal microscope (Andor Dragonfly 5050, Oxford Instruments, Abingdon, UK) coupled with Imaris software ver. 9.5.1 (Oxford Instruments), and green (live) and red (dead) cells were visualized qualitatively in each condition. For the animal experiment, pooled rMSC (1.5 × 106 cells) were seeded on the MEM as described above and cultured in growth media for 24 h before implantation. Cell viability was confirmed just prior to implantation using the aforementioned viability assay.
## 2.5. Rat Calvaria Defect Model
The calvarial defect model in rats was performed following ethical approval (Norwegian Animal Research Authority, FOTS-17443) and in accordance with the ARRIVE guidelines, as previously described [32]. Briefly, 20 male Lewis rats (LEW/OrlRj, Janvier Labs, Le Genest-Saint-Isle, France) that were 8 weeks old and weighing 200–350 g were used. Following acclimatization, the animals were anesthetized (Sevoflurane, Abbott Laboratories, Berkshire, UK), and two full-thickness defects were surgically created, one in each parietal bone, using a trephine bur with an outer diameter of 5 mm (Meisinger GmbH, Neuss, Germany) under saline irrigation. Three animals died during the surgery due to anesthesia-related complications; therefore 17 animals were available for the experiment. The following treatments were then randomly applied to the defects: CM-LYO ($$n = 8$$), CM-SOAK ($$n = 8$$), MEM seeded with allogeneic pooled rMSC (CEL, 1.5 × 106 cells; $$n = 7$$), native MEM soaked with serum-free DMEM (MEM; $$n = 6$$) and no treatment (‘empty’ defects; $$n = 5$$). Membranes were fixed to the calvaria using 3–5 μL of tissue adhesive (Histoacryl®, B. Braun, Tuttingen, Germany) at the defect edges [34,35]; the fixation of MEM is advised to prevent micromovements and promote healing [36]. Randomization of defects/groups was performed using the Research Randomizer online software [37], and the animals were coded via ear clips. For all subsequent handling/analyses, the animals/specimens were identified by numbers to facilitate blinding of the observers to the treatment groups. After 2 weeks, the animals were subjected to in vivo micro-computed tomography (μCT), and after 4 weeks, they were euthanized with an overdose of CO2. The primary outcome was new bone formation after 2 weeks via in vivo μCT and after 4 weeks via ex vivo μCT, histology and histomorphometry. The secondary outcomes included the characterization of new bone tissues via scanning electron microscopy (SEM), microhardness testing, relative bone density and Raman spectroscopy.
## 2.6. μ CT
To track early in vivo bone regeneration, the live animals were scanned at 2 weeks post-surgery under anesthesia using a small-animal CT scanner and Mediso workstation (both from nanoScan Mediso, Budapest, Hungary) with a voxel size of 40 μm (resolution), 70 kV energy, an exposure time of 300 ms, 720 projections and 1:1 binning. After a period of observation, the animals were returned to their original cages and housing locations until euthanasia. After 4 weeks, the calvaria were harvested and fixed in $10\%$ buffered formalin. The specimens were scanned using a SCANCO 50 μCT scanner (SCANCO Medical AG, Bruttisellen, Switzerland) at 90 kV and 200 μA with an isotropic resolution of 20 μm. Reconstruction and analysis were performed as previously described [32]. Briefly, scans were reconstructed using Amira software (Thermo Scientific) by orienting the drill direction along the Z-axis, with the defect in the approximate center of the image. Using ImageJ software (NIH, Bethesda, MD, USA), a standardized volume of interest (VOI) including the entire thickness of the calvaria and excluding 0.5 mm of marginal bone was defined for each defect. Specific density thresholds were defined for in vivo and ex vivo µCT scans (based on scanning resolutions) and percentages of new bone volume relative to total defect volume (BV/TV%) and bone coverage (%) were calculated in ImageJ (NIH) using custom defined rulesets.
## 2.7. Histology and Histomorphometry
After μCT scanning, the calvaria specimens were processed for undecalcified histology as previously described [32]. Briefly, the specimens were dehydrated in ascending grades of alcohol and embedded in light-curing resin (Technovit 7200 + $1\%$ benzoyl peroxide, Kulzer & Co., Wehrheim, Germany). The blocks were further processed using EXAKT cutting and grinding equipment (EXAKT Apparatebau, Norderstedt, Germany). Standardized thin-ground sections (~100 μm) were prepared in the centre of each defect, parallel to the sagittal suture and perpendicular to the parietal bone, and stained with Levai-Laczko dye (Morphisto GmbH, Frankfurt, Germany). In this staining process, mature bone appears light pink, woven bone is dark pink and soft tissues (including collagen) are dark blue. The sections were scanned using an Olympus BX61VS digital virtual microscopy system (DotSlide 2.4, Olympus, Tokyo, Japan) with a 20× objective, resulting in a resolution of 0.32 µm per pixel.
Histomorphometric analysis was performed to analyze the tissue components filling the defects as previously described [38]. Briefly, the scanned images were manually segmented using Photoshop CS 6 (Adobe Systems Inc., San Jose, CA, USA) and quantified using a custom script in ImageJ (NIH). Two regions of interest (ROI) were defined for each sample based on the position of the membrane in relation to the defect: the central defect region, delimited superiorly by the MEM, inferiorly by the dura and laterally by the defect edges, and the defect edge or ‘side’ region, which was the area adjacent to the central defect on both sides (Supplementary Figure S1). In both ROIs, the respective areas of new bone without embedded MEM fibers (hereafter termed ‘new bone’), new bone with embedded MEM fibers (hereafter termed ‘hybrid bone’), total new bone (sum of new and hybrid bone), mineralized MEM fibers, residual MEM (non-mineralized MEM fibers) and soft tissue were measured, and corresponding percentages were calculated as a ratio of the ROI area.
## 2.8. Characterization of New Bone Tissues
The structural, mechanical and compositional properties of new bone tissues were analyzed based on SEM, microhardness, relative bone density and Raman spectroscopy. The objective herein was to compare the different tissue types, i.e., regular new bone and hybrid new bone, and not the different treatment conditions. Representative sections from each experimental group (MEM, CM-LYO, CM-SOAK and CEL) were used, and native calvarial bone was analyzed as a control.
SEM: The ultrastructure of the new bone tissues, i.e., regular new bone and hybrid bone, was further analyzed using SEM. Briefly, back-scattered electron imaging of carbon coated resin-embedded calvaria sections was performed using a Zeiss Supra 55VP microscope (Carl Zeiss, Oberkochen, Germany), with an acceleration voltage of 15 kV and an 8 mm working distance.
Microhardness: Vickers microhardness testing was performed as previously described [39]. Briefly, micro-indentations were created on the tissue surfaces using an MHT-10 microhardness tester equipped with a Vickers diamond indenter tip and a video measuring system (Anton Paar, Graz, Austria) attached to a light microscope with a 50× objective (Leica DMR, Wetzlar, Germany). A load of 50 g was applied for 10 s to produce each indentation; at least 10 separated indentations were made per tissue type, per section. The length of the diagonals of each indentation was measured using the inbuilt software (Anton Paar), and a Vickers hardness value (HV) was automatically calculated.
Relative bone density: The selected sections were scanned using a SkyScan 1172 μCT scanner (Bruker, Kontich, Belgium) with an X-ray source of 60 kV/200 µA and 0.5 mm aluminum filter for a resolution of 13.3 µm; beam hardening was adjusted to compensate for the difference in density between the plastic microscope slides. A maximum intensity projection of each slide was created, and using a custom ImageJ script, the average intensity in each tissue type was measured. Details of the measurement protocol are presented in the Supplementary Materials.
Raman spectroscopy: This technique was used to study bone composition via an estimation of the crystallinity and mineral-to-matrix ratio. Raman spectra of each tissue type were collected using a confocal Raman microscope (LabRam, Horiba Jobin Yvon, Edison, NJ, USA) equipped with a 488 nm excitation laser and 50× objective coupled with the LabSpec ver. 5 software (Horiba Jobin Yvon) with the following settings: a spatial resolution of 0.5 cm−1, a spectral range of 400–1800 cm−1 and 10 accumulations with 1 s exposure time per measurement. The spectral measurements were calibrated using a silicon standard, and at least five measurements were taken per tissue type per section. The spectra were processed using a custom script in Matlab (Mathworks, Natick, MA, USA) for the selected peaks of interest, i.e., v1 phosphate (v1 PO43) at ~960 cm−1, representing the mineral/inorganic phase of bone, and CH2 wag at ~1448 cm−1, representing the organic/matrix phase, i.e., collagens, lipids and non-collagenous proteins [40]. Background fluorescence correction and smoothing using the Savitzky–Golay polynomial function in the 2nd order were applied to the spectra in the appropriate wave number range (±80 cm−1) using a custom MatLab script. The following parameters were determined for the selected peaks of interest: peak height, peak area and the full peak width at half maximum intensity (FWHM). To ensure the correct identification of the different tissue types, stained histological sections were used, which resulted in an additional background peak from the dye (pararosaniline); the dye-peak at 913 cm−1 [41] could be clearly differentiated from the bone peaks and did not interfere with the analysis. The following compositional parameters were then calculated: crystallinity, represented by the inverse of the full peak width at half maximum intensity (FWHM−1) for v1 PO43, and mineral/matrix ratio, represented by the peak height ratio of v1 PO43 to CH2 wag.
## 2.9. Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) of Lyophilized CM
The proteomic composition of the pooled CM were analyzed using LC-MS/MS as previously described [42]. Briefly, the total protein concentration was measured using a bicinchoninic acid assay (Pierce BCA Kit, Thermo Fisher), and 10 μg of lyophilized protein was processed to obtain tryptic peptides. About 0.5 µg protein as tryptic peptides dissolved in $2\%$ acetonitrile and $0.5\%$ formic acid was injected into an Ultimate 3000 RSLC system connected online to a Exploris 480 mass spectrometer equipped with EASY-spray nano-electrospray ion source (all from Thermo Scientific, Sunnyvale, CA, USA). Additional details of LC-MS/MS are reported in the Supplementary Materials.
## 2.10. Bioinformatic Analysis
The LC-MS/MS raw files were searched using Proteome Discoverer software (version 2.5.0.400; Thermo Scientific). Perseus software (version 2.3.0.1; Max Planck Institute for Biochemistry, Martinsread, Germany) was used to process and filter the results. An over-representation analysis of the exclusive proteins in each CM group was performed using the WebGestalt tool (wGSEA) [43,44]. Gene ontology (GO) slim subsets were retrieved based on the human genome (Homo sapiens) as a reference. Relevant GO terms (Homo sapiens) for bone-related biological processes were retrieved from the QuickGO database (https://www.ebi.ac.uk/QuickGO/, accessed on 14 November 2022), and the corresponding gene names were compared to the proteins identified in CM [42]. A list of identified bone-related proteins is presented in Supplementary Table S2.
## 2.11. Multiplex Immunoassay
The Quantibody Human Bone Metabolism Array Q1 (RayBiotech Inc., Norcross, GA, USA) was used to analyze 31 bone related cytokines (Supplementary Table S3) according to the manufacturer’s protocol. This array is based on the sandwich enzyme-linked immunosorbent assay (ELISA) technology, and each antibody is spotted in quadruplicate. Array hybridization was performed using concentrated CM (0.1–0.2 mg/mL of total protein) and standard cytokines. Array scanning was performed using a laser scanner (GenePix 4000B, Axon Instruments, San Jose, CA, USA) at different photomultiplier tube gains. Data extraction was performed using the GenePix Pro software ver. 5.0 (Axon Instruments). Concentrations of candidate proteins were calculated based on linear standard curves and normalized to the corresponding total protein concentration.
## 2.12. Statistical Analysis
Statistical analysis was performed using the Prism 9 software (GraphPad, San Diego, CA, USA). Data are presented as means (± SD and/or range), unless they are specified. Analyses of the gene expression data are based on delta-CT values, and the results are presented as relative (log/non-linear) fold changes using scatter plots. All other linear data are presented as scatter or bar graphs. Normality testing was performed via the Shapiro–Wilk test. The one-way analysis of variance (ANOVA), followed by a post hoc Tukey’s test, was applied, and statistical significance was set at $p \leq 0.05.$
## 3.1. Functionalized MEM Supported Cell Growth and Function
The live/dead assay revealed the high viability of rMSC on the MEM-CM, i.e., CM-LYO and CM-SOAK (Figure 1A), both of which were similar to that on the native MEM. The stacking of z-sections revealed the 3D migration of cells between the fibrillar network (pores) of the MEM (Figure 1B). The viability of rMSC on native MEM was also confirmed just prior to in vivo implantation (Supplementary Figure S2). With regard to cell function, the qPCR bioassay showed a significant upregulation of osteogenic gene markers in the BMSC exposed to CM vs. those in GM in monolayer cultures (Supplementary Figure S3). In the 3D cultures, i.e., BMSC seeded on native or functionalized MEM, a remarkable upregulation of all osteogenic genes was observed compared to cells in monolayer cultures, regardless of MEM functionalization. Thus, in 3D cultures, the effect of the MEM itself on the BMSC confounded the effects of MEM-CM. Although it was not statistically significant, a trend for enhanced gene expression was observed in the CM-LYO vs. CM-SOAK MEM-CM (Supplementary Figure S3).
## 3.2. CM-LYO Enhanced In Vivo New Bone Formation More than Other Treatments Did
All animals included in the experiment recovered from the surgery, and no adverse events were observed. After 2 weeks, µCT revealed a significantly greater coverage of bone defects in the CM-LYO (85.5 ± $15.49\%$) vs. that in the CM-SOAK group (21.67 ± $21.13\%$; $p \leq 0.001$), suggesting a clear benefit of lyophilization (Figure 2A,B). Moreover, the bone coverage was also greater in CM-LYO vs. that in the CEL (18.81 ± $20.03\%$; $p \leq 0.001$), native MEM (18.57 ± $20.83\%$; $p \leq 0.001$) and empty groups (11.67 ± $12.02\%$; $p \leq 0.001$). A similar trend was observed with regard to BV/TV in CM-LYO (4.48 ± $1.4\%$) vs. CM-SOAK (0.72 ± 0.74; $p \leq 0.001$), CEL (0.98 ± 1.17; $p \leq 0.001$), native MEM (0.69 ± 0.77; $p \leq 0.001$) and empty groups (0.56 ± 0.83; $p \leq 0.001$) (Figure 2B). Early mineralization within the membrane compartment occurred more frequently in CM-LYO ($\frac{8}{8}$) vs. in the CM-SOAK ($\frac{0}{8}$), CEL ($\frac{1}{7}$) and MEM ($\frac{2}{6}$) groups (Figure 3).
After 4 weeks, only CM-LYO (78.9 ± $13.08\%$) showed significantly greater bone coverage than the untreated control group did (28.99 ± $18.64\%$; $$p \leq 0.027$$); those of the CM-SOAK (46.97 ± $37.12\%$), CEL (56.07 ± $29.22\%$) and native MEM groups (57.89 ± $30.9\%$) were similar. No differences in BV/TV were observed in CM-LYO (8.41 ± $1.87\%$) vs. that in the CM-SOAK (6.64 ± $7.01\%$; $$p \leq 0.947$$), CEL (7.72 ± $5.78\%$; $$p \leq 0.99$$), native MEM (5.7 ± $4.21\%$; $$p \leq 0.83$$) and empty groups (2.68 ± $2.24\%$; $$p \leq 0.25$$). Notably, the smallest intra-group variation was observed in the CM-LYO group (Figure 2A,B and Figure 3).
## 3.3. CM-LYO Promoted Histological New Bone Formation Better than Other Treatments Did
After 4 weeks, all the groups revealed a heterogeneous histological pattern combining the following tissue components: regular new bone (without incorporated MEM fibers), hybrid new bone (with incorporated MEM fibers), mineralized MEM fibers, residual MEM and soft tissues (Figure 4 and Figure 5). New bone was typically seen at the base of the defect towards the dura, i.e., outside the MEM compartment, characterized by well-structured woven bone (dark pink) and enclosed by layers of parallel-fibered bone (light pink) and osteoid matrix (grey). Adjacent to this newly formed bone, areas with a hybrid pattern characterized by the presence of immature woven bone and incorporated collagen fibers from the MEM (pink) were evident, indicating that some new bone formation occurred within the MEM compartment. In some cases, the MEM fibers appeared to be mineralized and formed bridges to the woven bone, while in other instances we observed mineralized ‘free-standing’ fibers (without surrounding woven bone) or remnant collagen fibers (unmineralized). Hybrid bone also appeared to vary based on the degree of mineralization of the embedded MEM fibers; the orientation of fibers followed the structure of the MEM. These different tissue types were observed in all the experimental groups, albeit in different proportions, as revealed in the histomorphometric analysis (Figure 5 and Figure 6).
The quantification of tissues in the central defect area revealed significantly greater amount of new bone in CM-LYO (34.35 ± $17.27\%$) vs. that in the CM-SOAK (9.66 ± $11.36\%$; $$p \leq 0.005$$), native MEM (8.63 ± $9.09\%$; $$p \leq 0.007$$) and CEL groups (13.46 ± $12.76\%$; $$p \leq 0.025$$) (Figure 6). Conversely, CM-LYO revealed the least amount of hybrid bone (5.42 ± $3.77\%$) vs. that in the CM-SOAK (11.67 ± $11.89\%$), MEM (18.5 ± $20.74\%$) and CEL groups (13.7 ± $19.23\%$), although this was not statistically significant ($$p \leq 0.43$$). The quantification of total new bone (new bone + hybrid bone) revealed a non-significant trend ($$p \leq 0.45$$) in CM-LYO (39.77 ± $19.85\%$) vs. that in the CM-SOAK (21.33 ± $22.25\%$), MEM (27.14 ± $28.02\%$) and CEL groups (27.16 ± $23.24\%$). CM-LYO also revealed the greatest area of mineralized MEM fibers (12.88 ± $16.01\%$) and the least area of residual MEM (5.5 ± $10.79\%$) vs. that of the other groups; the latter comparison was statistically significant ($p \leq 0.001$) (Figure 6). The intra-group variations, particularly for new bone, total new bone and residual MEM areas were relatively large. The quantification of tissue fractions in the defect edge areas revealed similar trends for the total new bone, mineralized MEM fibers and residual MEM between the groups (Supplementary Figure S4).
## 3.4. Structural, Mechanical and Compositional Differences Were Observed between Regular New Bone and Hybrid New Bone
The SEM analysis of the ultrastructure of new bone tissues confirmed that new bone (without incorporated MEM fibers) was most similar to the native calvaria bone, while hybrid bone (with incorporated MEM fibers) was more heterogenous. Detailed SEM analysis of hybrid bone revealed clear differences based on the degree of mineralization of the incorporated MEM fibers. Accordingly, the hybrid bone was further categorized as stage 1 (early stage, less mature and moderately mineralized) or stage 2 hybrid bone (later stage, more mature and highly mineralized) (Figure 7A). In the corresponding histology and SEM, stage 2 hybrid bone revealed evidence of resorption (cement lines) and remodeling, with the associated new bone deposition enveloping the hybrid bone (Figure 7A).
The quantitative analysis of the regular and hybrid new bone tissues was also performed based on their microhardness (Vickers test) and composition (µCT and Raman spectroscopy), in comparison to the native calvaria bone. Mechanical loading by Vickers indentation revealed significantly greater hardness in stage 2 vs. that in stage 1 hybrid bone and new bone ($$p \leq 0.002$$; Figure 7B). The bone density analysis demonstrated similar densities in the stage 2 hybrid bone and new bone, but a significantly lower density in stage 1 hybrid bone ($p \leq 0.001$; Figure 7B). No significant differences were observed in the mineral/matrix ratio (v1 PO43/CH2 wag) (Figure 7B) or crystallinity (v1 PO43 FWHM−1) between the new bone and stage 1 and 2 hybrid bones. A trend for lower mineral/matrix ratio was observed in stage 1 vs. stage 2 hybrid bone and new bone ($$p \leq 0.07$$). However, mineral/matrix ratios of all three tissues were significantly lower than that of native bone (Figure 7B). Representative *Raman spectra* are presented in Supplementary Figure S5.
## 3.5. Qualitative Proteomic Analysis of Lyophilized CM Revealed Enrichment of Biological Processes Related to Bone Formation
The proteomic analysis revealed 2684 proteins in the lyophilized pooled CM, of which 255 proteins were involved in selected biological processes related to bone formation (Table 1, Supplementary Table S2). Among the classical growth factors, transforming growth factor beta-1 (TGFβ1), TGFβ2, BMP1, platelet derived growth factor subunit-A (PDGFA), vascular endothelial growth factor-C (VEGFC), insulin-like growth factor-2 (IGF2), c-type lectin domain containing 11A or stem cell growth factor (CLEC11A/SCGF) and colony stimulating factor-1 (CSF1) were identified. Several proteins related to angiogenesis (VEGF-C, von Willebrand factor (VWF), vascular cell adhesion molecule-1 (VCAM1) and platelet endothelial cell adhesion molecule-1 (PECAM1), etc.) and ECM (collagens, laminins, fibronectin, etc.) were also identified in the CM.
The concentrations of selected bone-related cytokines in the lyophilized CM were further determined using a multiplex immunoassay; of the 31 array cytokines, 7 were present at detectable concentrations (Figure 8). Consistent with the proteomic analysis, matrix metalloproteinases-2 (MMP2) and -13 (MMP13), interleukins-6 (IL6) and -11 (IL11) and VCAM1 were detected in the pooled CM.
## 4. Discussion
Cell-free strategies using MSC-CM are emerging as cost-effective, ‘off-the-shelf’ alternatives to MSC transplantation for the regeneration of bone defects [17]. In the present study, we tested the efficacy of CM-functionalized MEM (CM-LYO, CM-SOAK) vs. that of native MEM or MEM seeded with allogeneic rMSC (CEL) for GBR in critical size rat calvaria defects. The main finding was that a trend for enhanced bone regeneration was observed in the CM-LYO group compared to the CM-SOAK, native MEM and CEL groups based on µCT and histological analysis.
The secretome/CM of MSC contains a plethora of different proteins, including growth factors, cytokines, chemokines and cell adhesion molecules, as well as lipids, nucleic acids and extracellular vesicles, which promote tissue healing and regeneration [45,46,47,48]. Consistent with previous reports [16], the data from the present study show that the exposure of BMSC to CM resulted in a significant upregulation of osteogenic gene markers. Additionally, we have recently reported that CM contain several antiapoptotic and antioxidative factors, which may inhibit apoptosis and/or promote cell survival [49]. From a clinical perspective, CM delivery presents clear advantages compared to implementing autologous cell therapies, since it is easier, cheaper and enables large-scale production [50,51]. Moreover, its application may be under less stringent regulation compared to that of cell therapies, which may facilitate faster clinical translation.
In the present study, MSC cultured in ‘xeno-free’ HPL-supplemented media were used for CM preparation. Most studies thus far have investigated CM from MSC cultured in ‘xenogeneic’, i.e., FBS-supplemented media, both in vitro [16] and in vivo (Supplementary Table S4). However, the exclusion of animal-derived supplements in MSC cultures is important for clinical translation and is in fact recommended by regulatory authorities [52]. Pooled HPL has been identified as the optimal xeno-free supplement for clinical grade MSC cultures [53], with particular benefits for osteogenic differentiation [27,54]. Indeed, the type of supplement can influence the composition and efficacy of the CM [55,56]. A few studies have investigated the composition of CM from HPL- vs. FBS-cultured MSC [47,57,58]. Recent evidence suggests that CM from HPL-cultured MSC are more ‘enriched’ in certain growth factors related to wound healing, angiogenesis and extra-cellular matrix (ECM) production [58], which may further promote their in vivo regenerative potential.
The in vivo applications of MSC-CM for bone regeneration have recently been reviewed [17] and are summarized in Supplementary Table S4. All the studies reported superior outcomes in bone defects treated with CM vs. those of control treatments in experimental in vivo investigations (mainly in rodent or rabbit models), although one study in a canine model also reported superior periodontal regeneration when comparing CM vs. PBS [59]. While a majority of the studies applied CM to bone defects using biomaterials, interestingly, CM also promoted regeneration when injected locally [60,61] and systemically [62] in challenging rodent models. Overall, while the current literature supports the use of CM for bone regeneration, certain aspects of this strategy remain unclear, such as, the optimal method/biomaterial for CM delivery and the optimal method of biomaterial functionalization for the best in vivo efficacy.
CM have been delivered using different biomaterials including collagen sponges, hydrogels, bone substitutes, barrier membranes and other scaffolds (Supplementary Table S4). Specifically, barrier membranes such as poly(lactic-co-glycolic acid) [63] and collagen [64,65] have been used, given their ability to absorb and release biomolecules at regeneration sites [19,21]. These biomolecules have included bone-derived proteins [23,24], BMP2 [66,67,68], fibroblast growth factor-2 [69] and dexamethasone [70], as recently summarized [4]. With regard to the collagen membranes used in the present study (MEM), we have previously demonstrated their ability to adsorb growth factor, specifically TGFβ, activity from human biological material [25]. Furthermore, Qiu et al. [ 19] recently reported the application of MEM soaked with CM from human periodontal- or gingiva-derived MSC in rat periodontal defects; significantly greater bone formation was observed in the defects treated with MEM-CM vs. those treated with native MEM [65]. Together, these data suggest that MEM are efficient carriers of bioactive factors, including CM, for GBR applications.
To identify the optimal method of functionalization herein, MEM were treated with concentrated CM (~30-fold) by either soaking only (CM-SOAK) or soaking followed by lyophilization (CM-LYO). The application of concentrated CM is reported to enhance tissue regeneration as compared that of unconcentrated CM [71]. Moreover, lyophilization or ‘freeze drying’ is reported to preserve the biological activity of proteins, e.g., growth factors, and other biological components for long-term storage [72]. Following MEM functionalization, both CM-LYO and CM-SOAK showed high cell viability, suggesting that they could be rapidly populated by resident cells following in vivo implantation. In the context of growth factors, the lyophilization of BMP2 on scaffolds, as compared to soaking, has been shown to enhance in vivo release and bone regeneration [73]. Indeed, CM-LYO showed superior bone regeneration compared to CM-SOAK in the present study. The lyophilization process may have resulted in superior concentration/immobilization and the subsequent in vivo release of proteins on/from the MEM, thus enhancing bone formation [74,75]. To exclude any effect of lyophilization on the MEM properties, we later studied the effects of lyophilized MEM (with serum-free DMEM) in the same calvaria defect model; a similar histological pattern was seen for the lyophilized MEM group as in the present study, suggesting no significant effect of the lyophilization process (unpublished data). Since lyophilization is a well-established and GMP-compliant process, this strategy could offer new possibilities for ‘off-the-shelf’ CM-based therapies for GBR.
Allogeneic MSC transplantation has been proposed as an easier, more cost-effective, and equally safe and efficacious alternative to autologous cell therapy [76]. This is based on the unique ability of MSC to modulate immune responses and avoid detection/rejection in dissimilar hosts [77]. Comparable or superior outcomes have been reported in in vivo models of bone regeneration when the researchers were using allogeneic vs. autologous cells [78,79,80,81,82]. In the present study, MEM seeded with pooled non-autologous rMSC (CEL) from syngeneic donor rats were also transplanted into calvarial defects. Trends of greater bone regeneration were observed in CM-LYO vs. that of the CEL-treated defects, which is consistent with previous reports of using CM and the corresponding cells, i.e., human BMSC [83], stem cells from human exfoliated deciduous teeth (SHED) [84] or rat adipose MSC [85]. Indeed, the present study used an inbred/syngeneic strain of rats, where the genetic diversity between the individuals, i.e., donor and recipient rats, was limited, and therefore, the cell source may not be strictly allogeneic. Moreover, immunological reactions to allogeneic/xenogeneic cells may not be accurately reflected in simple rodent models [86]. Thus, the true efficacy of CM vs. that of allogeneic MSC should be verified in large animal models of bone regeneration.
Inherent clinical limitations of resorbable MEM are their poor dimensional stability and sub-optimal mechanical strength and stiffness, which may result in their collapse into the bony defect unless they are supported by a biomaterial scaffold [20]. To enhance their long-term rigidity and stability, the concept of in vivo “self-mineralizing” membranes has been reported [87,88]. In the present study, in vivo MEM mineralization was observed, especially in the CM-LYO-treated MEM. This phenomenon of in vivo mineralization of collagen MEM was previously described by Feher et al. and is attributed to a potentially cell-independent mechanism [38]. It is reported that the hydrophobic nature of collagen MEM can facilitate calcium binding and mineralization by enhanced protein adsorption [89]. In the present study, the CM-LYO group exhibited the greatest histological area fraction of mineralized MEM fibers and new bone (without MEM fibers). The presence of stage 2 hybrid bone around the mineralized MEM fibers clearly indicates that MEM mineralization proceeded to new bone formation within the MEM compartment. Therefore, it is reasonable to hypothesize that MEM mineralization may have contributed to the overall bone regeneration, especially in the CM-LYO group.
An interesting finding in the present study was the heterogeneous pattern of new bone formation based on whether MEM collagen fibers were incorporated or not into the newly formed bone, i.e., new bone formation within and outside the membrane compartment. Indeed, the new bone outside the membrane compartment was histologically most similar to the native calvarial bone. In contrast, the hybrid bone (within the membrane compartment) was notably different from the native bone. A further analysis revealed distinct stages of hybrid bone formation based on the degree of mineralization of the MEM fibers, i.e., stage 1 (less mineralized) and stage 2 (more mineralized). This most likely reflects the stage of maturation, since an increasing degree of mineralization is a known age-related change in mineralized tissue [90]. Consequently, stage 2 hybrid bone demonstrated significantly greater hardness—an important indicator of bone strength mainly determined by the degree of mineralization [91]—than the new bone and even the native calvarial bone did. However, both the hybrid bone (stage 1 and 2) and new bone revealed significantly lower mineral/matrix ratios vs. that of the native calvaria bone. These data are similar to previous comparisons between new and native bone in rat calvaria [92,93] and to recent studies of bone regeneration following cell [94] and/or scaffold implantation [95]. With regard to its ‘fate’, the hybrid bone, particularly stage 2, showed signs of remodeling and replacement by new bone. This could potentially explain the relatively lower area of hybrid bone and the greater area of new bone (without MEM fibers) in the CM-LYO group. Taken together, the current data suggest that the use of MEM in rat calvaria results in a combination of regular new bone and hybrid new bone characterized by the incorporation of MEM in the newly formed bone. While further time course studies are needed to determine the exact sequence of events (with regard to MEM mineralization and hybrid bone formation), it appears that the hybrid bone is ultimately remodeled and replaced by regular new bone, and this process may be accelerated in CM-LYO-treated MEM.
The proteomic composition of lyophilized pooled CM was analyzed as the potential basis for its in vivo effects. Indeed, previous studies have comprehensively described the general proteomic profile of MSC [47,96], therefore, in the present study, we focused only on bone-related processes. Several key growth factors (TGFβ1, TGFβ2, PDGFA, VEGFC, etc.) involved in biological processes relevant to bone regeneration (Table 1) were identified in MSC-CM. Correspondingly, the expressions of several osteogenesis-related genes were enhanced in human BMSC upon exposure to CM for 48 h (Supplementary Figure S3), suggesting a pro-osteogenic effect. Moreover, several proteins related to Wnt/β-catenin signaling, a key signaling pathway during osteoblastogenesis [97], and angiogenesis were enriched in CM. In the context of MEM, Wnt-related [98] and angiogenesis-related proteins [4], in addition to TGFβ [25], have been shown to adsorb to collagen, revealing the potential mechanisms of MEM-CM activity. Correspondingly, areas of active in vivo bone formation (Supplementary Figure S6) and angiogenesis were observed in the present study. However, no remarkable differences in these events could be detected between the groups, which could be due to the relatively late time point of the histological analysis (4 weeks). Therefore, no reliable correlations between the in vitro and in vivo data could be drawn. The inclusion of earlier time points (1–2 weeks) and immunohistochemical methods in future studies to detect specific cells/processes may reveal such associations and their effects on in vivo bone regeneration. Nevertheless, the present study supports the current evidence for the efficacy of MSC-CM for in vivo bone regeneration [59,60,61,62,63,64,65,99,100,101,102,103,104,105,106,107,108].
Some limitations of the present study must be acknowledged. Firstly, the proteomic analysis was performed using only pooled CM (3 MSC donors) and not CM from independent donor-MSC, thus precluding the assessment of donor related variations. Secondly, human-derived MSC-CM were compared to rat-derived MSC (from syngeneic rats) in vivo, which may have cofounded the findings. However, the use of human-derived MSC would necessitate the use of immunocompromised animals, while the use of rat-derived MSC-CM would limit the clinical relevance of the therapy. The intra-group variations in the in vivo data were relatively large, reflecting biological differences in the healing response between the animals. Nevertheless, the measures of central tendency were reliable enough to allow the detection of statistically significant differences between the groups. Finally, in vitro assessments of protein adsorption and ‘release’ from the different functionalized membranes (CM-LYO and CM-SOAK) in future studies could shed light on potential differences in their mechanisms of action and their in vivo effects on bone regeneration.
## 5. Conclusions
Application of CM-LYO-functionalized MEM revealed a trend for enhanced GBR in rat calvaria defects compared to that of conventional GBR (MEM alone) and cell therapy (MEM with rMSC). The regenerated tissues presented a combination of regular new bone and hybrid new bone characterized by bone formation within the membrane compartment and incorporation of MEM in the newly formed bone. Further research is needed to determine the functional properties of these new bone tissues in terms of supporting implant osseointegration and prosthetic loading in more clinically relevant animal models. Moreover, future refinements of the study design and methodology may reveal correlations between the proteome of CM and in vivo processes. In summary, functionalizing MEM with MSC-CM represents a clinically relevant, ‘off-the-shelf’ strategy to promote GBR.
## References
1. Benic G.I., Hammerle C.H.. **Horizontal bone augmentation by means of guided bone regeneration**. *Periodontol. 2000* (2014.0) **66** 13-40. DOI: 10.1111/prd.12039
2. Thoma D.S., Bienz S.P., Figuero E., Jung R.E., Sanz-Martin I.. **Efficacy of lateral bone augmentation performed simultaneously with dental implant placement: A systematic review and meta-analysis**. *J. Clin. Periodontol.* (2019.0) **46** 257-276. DOI: 10.1111/jcpe.13050
3. Urban I.A., Montero E., Monje A., Sanz-Sanchez I.. **Effectiveness of vertical ridge augmentation interventions: A systematic review and meta-analysis**. *J. Clin. Periodontol.* (2019.0) **46** 319-339. DOI: 10.1111/jcpe.13061
4. Omar O., Elgali I., Dahlin C., Thomsen P.. **Barrier membranes: More than the barrier effect?**. *J. Clin. Periodontol.* (2019.0) **46** 103-123. DOI: 10.1111/jcpe.13068
5. Chappuis V., Rahman L., Buser R., Janner S.F.M., Belser U.C., Buser D.. **Effectiveness of Contour Augmentation with Guided Bone Regeneration: 10-Year Results**. *J. Dent. Res.* (2018.0) **97** 266-274. DOI: 10.1177/0022034517737755
6. Gimbel M., Ashley R.K., Sisodia M., Gabbay J.S., Wasson K.L., Heller J., Wilson L., Kawamoto H., Bradley J.. **Repair of alveolar cleft defects: Reduced morbidity with bone marrow stem cells in a resorbable matrix**. *J. Craniofacial Surg.* (2007.0) **18** 895-901. DOI: 10.1097/scs.0b013e3180a771af
7. Shanbhag S., Suliman S., Pandis N., Stavropoulos A., Sanz M., Mustafa K.. **Cell therapy for orofacial bone regeneration: A systematic review and meta-analysis**. *J. Clin. Periodontol.* (2019.0) **46** 162-182. DOI: 10.1111/jcpe.13049
8. Sandor G.K., Numminen J., Wolff J., Thesleff T., Miettinen A., Tuovinen V.J., Mannerström B., Patrikoski M., Seppänen R., Miettinen S.. **Adipose stem cells used to reconstruct 13 cases with cranio- maxillofacial hard- tissue defects**. *Stem Cells Transl. Med.* (2014.0) **3** 530-540. DOI: 10.5966/sctm.2013-0173
9. Gjerde C., Mustafa K., Hellem S., Rojewski M., Gjengedal H., Yassin M.A., Feng X., Skaale S., Berge T., Rosen A.. **Cell therapy induced regeneration of severely atrophied mandibular bone in a clinical trial**. *Stem Cell Res. Ther.* (2018.0) **9** 213. DOI: 10.1186/s13287-018-0951-9
10. Haumer A., Bourgine P.E., Occhetta P., Born G., Tasso R., Martin I.. **Delivery of cellular factors to regulate bone healing**. *Adv. Drug Deliv. Rev.* (2018.0) **129** 285-294. DOI: 10.1016/j.addr.2018.01.010
11. Caplan A.I., Dennis J.E.. **Mesenchymal stem cells as trophic mediators**. *J. Cell Biochem.* (2006.0) **98** 1076-1084. DOI: 10.1002/jcb.20886
12. Gnecchi M., Danieli P., Malpasso G., Ciuffreda M.C.. **Paracrine Mechanisms of Mesenchymal Stem Cells in Tissue Repair**. *Methods Mol. Biol.* (2016.0) **1416** 123-146. PMID: 27236669
13. Pittenger M.F., Discher D.E., Péault B.M., Phinney D.G., Hare J.M., Caplan A.I.. **Mesenchymal stem cell perspective: Cell biology to clinical progress**. *NPJ Regen. Med.* (2019.0) **4** 22. DOI: 10.1038/s41536-019-0083-6
14. Weiss A.R.R., Dahlke M.H.. **Immunomodulation by Mesenchymal Stem Cells (MSCs): Mechanisms of Action of Living, Apoptotic, and Dead MSCs**. *Front. Immunol.* (2019.0) **10** 1191. DOI: 10.3389/fimmu.2019.01191
15. Bari E., Perteghella S., Di Silvestre D., Sorlini M., Catenacci L., Sorrenti M., Marrubini G., Rossi R., Tripodo G., Mauri P.. **Pilot Production of Mesenchymal Stem/Stromal Freeze-Dried Secretome for Cell-Free Regenerative Nanomedicine: A Validated GMP-Compliant Process**. *Cells* (2018.0) **7**. DOI: 10.3390/cells7110190
16. Veronesi F., Borsari V., Sartori M., Orciani M., Mattioli-Belmonte M., Fini M.. **The use of cell conditioned medium for musculoskeletal tissue regeneration**. *J. Cell Physiol.* (2018.0) **233** 4423-4442. DOI: 10.1002/jcp.26291
17. Benavides-Castellanos M.P., Garzon-Orjuela N., Linero I.. **Effectiveness of mesenchymal stem cell-conditioned medium in bone regeneration in animal and human models: A systematic review and meta-analysis**. *Cell Regen.* (2020.0) **9** 5. DOI: 10.1186/s13619-020-00047-3
18. Dahlin C., Linde A., Gottlow J., Nyman S.. **Healing of bone defects by guided tissue regeneration**. *Plast Reconstr. Surg.* (1988.0) **81** 672-676. DOI: 10.1097/00006534-198805000-00004
19. Elgali I., Omar O., Dahlin C., Thomsen P.. **Guided bone regeneration: Materials and biological mechanisms revisited**. *Eur. J. Oral Sci.* (2017.0) **125** 315-337. DOI: 10.1111/eos.12364
20. Caballe-Serrano J., Munar-Frau A., Ortiz-Puigpelat O., Soto-Penaloza D., Penarrocha M., Hernandez-Alfaro F.. **On the search of the ideal barrier membrane for guided bone regeneration**. *J. Clin. Exp. Dent.* (2018.0) **10** e477-e483. DOI: 10.4317/jced.54767
21. Turri A., Elgali I., Vazirisani F., Johansson A., Emanuelsson L., Dahlin C., Thomsen P., Omar O.. **Guided bone regeneration is promoted by the molecular events in the membrane compartment**. *Biomaterials* (2016.0) **84** 167-183. DOI: 10.1016/j.biomaterials.2016.01.034
22. Caballe-Serrano J., Sawada K., Miron R.J., Bosshardt D.D., Buser D., Gruber R.. **Collagen barrier membranes adsorb growth factors liberated from autogenous bone chips**. *Clin. Oral Implant. Res.* (2017.0) **28** 236-241. DOI: 10.1111/clr.12789
23. Kuchler U., Rybaczek T., Dobask T., Heimel P., Tangl S., Klehm J., Menzel M., Gruber R.. **Bone-conditioned medium modulates the osteoconductive properties of collagen membranes in a rat calvaria defect model**. *Clin. Oral Implant. Res.* (2018.0) **29** 381-388. DOI: 10.1111/clr.13133
24. Strauss F.J., Kuchler U., Kobatake R., Heimel P., Tangl S., Gruber R.. **Acid bone lysates reduce bone regeneration in rat calvaria defects**. *J. Biomed Mater. Res. A* (2021.0) **109** 659-665. DOI: 10.1002/jbm.a.37050
25. Di Summa F., Kargarpour Z., Nasirzade J., Stahli A., Mitulovic G., Panic-Jankovic T., Koller V., Kaltenbach C., Müller H., Panahipour L.. **TGFbeta activity released from platelet-rich fibrin adsorbs to titanium surface and collagen membranes**. *Sci. Rep.* (2020.0) **10** 10203. DOI: 10.1038/s41598-020-67167-3
26. Donos N., Dereka X., Mardas N.. **Experimental models for guided bone regeneration in healthy and medically compromised conditions**. *Periodontol 2000* (2015.0) **68** 99-121. DOI: 10.1111/prd.12077
27. Shanbhag S., Mohamed-Ahmed S., Lunde T.H.F., Suliman S., Bolstad A.I., Hervig T., Mustafa K.. **Influence of platelet storage time on human platelet lysates and platelet lysate-expanded mesenchymal stromal cells for bone tissue engineering**. *Stem Cell Res. Ther.* (2020.0) **11** 351. DOI: 10.1186/s13287-020-01863-9
28. Rojewski M.T., Lotfi R., Gjerde C., Mustafa K., Veronesi E., Ahmed A.B., Wiesneth M., Körper S., Sensebé L., Layrolle P.. **Translation of a standardized manufacturing protocol for mesenchymal stromal cells: A systematic comparison of validation and manufacturing data**. *Cytotherapy* (2019.0) **21** 468-482. DOI: 10.1016/j.jcyt.2019.03.001
29. Al-Sharabi N., Mustafa M., Ueda M., Xue Y., Mustafa K., Fristad I.. **Conditioned medium from human bone marrow stromal cells attenuates initial inflammatory reactions in dental pulp tissue**. *Dent. Traumatol.* (2017.0) **33** 19-26. DOI: 10.1111/edt.12277
30. Peng Y., Xuan M., Zou J., Liu H., Zhuo Z., Wan Y., Cheng B.. **Freeze-dried rat bone marrow mesenchymal stem cell paracrine factors: A simplified novel material for skin wound therapy**. *Tissue Eng. Part A* (2015.0) **21** 1036-1046. DOI: 10.1089/ten.tea.2014.0102
31. Mohamed-Ahmed S., Fristad I., Lie S.A., Suliman S., Mustafa K., Vindenes H., Idris S.B.. **Adipose-derived and bone marrow mesenchymal stem cells: A donor-matched comparison**. *Stem Cell Res. Ther.* (2018.0) **9** 168. DOI: 10.1186/s13287-018-0914-1
32. Shanbhag S., Suliman S., Mohamed-Ahmed S., Kampleitner C., Hassan M.N., Heimel P., Dobsak T., Tangl S., Bolstad A.I., Mustafa K.. **Bone regeneration in rat calvarial defects using dissociated or spheroid mesenchymal stromal cells in scaffold-hydrogel constructs**. *Stem Cell Res. Ther.* (2021.0) **12** 575. DOI: 10.1186/s13287-021-02642-w
33. Yassin M.A., Leknes K.N., Pedersen T.O., Xing Z., Sun Y., Lie S.A., Finne-Wistrand A., Mustafa K.. **Cell seeding density is a critical determinant for copolymer scaffolds-induced bone regeneration**. *J. Biomed Mater. Res. A* (2015.0) **103** 3649-3658. DOI: 10.1002/jbm.a.35505
34. Toriumi D.M., Raslan W.F., Friedman M., Tardy M.E.. **Histotoxicity of cyanoacrylate tissue adhesives. A comparative study**. *Arch. Otolaryngol. Head Neck. Surg.* (1990.0) **116** 546-550. DOI: 10.1001/archotol.1990.01870050046004
35. Rezende M.L., Cunha Pde O., Damante C.A., Santana A.C., Greghi S.L., Zangrando M.S.. **Cyanoacrylate Adhesive as an Alternative Tool for Membrane Fixation in Guided Tissue Regeneration**. *J. Contemp Dent. Pract.* (2015.0) **16** 512-518. DOI: 10.5005/jp-journals-10024-1714
36. An Y.Z., Strauss F.J., Park J.Y., Shen Y.Q., Thoma D.S., Lee J.S.. **Membrane fixation enhances guided bone regeneration in standardized calvarial defects: A pre-clinical study**. *J. Clin. Periodontol.* 2021. DOI: 10.1111/jcpe.13583
37. Urbaniak G., Plous S.. *Research Randomizer, version 4.0* (2013.0)
38. Feher B., Apaza Alccayhuaman K.A., Strauss F.J., Lee J.S., Tangl S., Kuchler U., Gruber R.. **Osteoconductive properties of upside-down bilayer collagen membranes in rat calvarial defects**. *Int. J. Implant Dent.* (2021.0) **7** 50. DOI: 10.1186/s40729-021-00333-y
39. Boivin G., Bala Y., Doublier A., Farlay D., Ste-Marie L.G., Meunier P.J., Delmas P.. **The role of mineralization and organic matrix in the microhardness of bone tissue from controls and osteoporotic patients**. *Bone* (2008.0) **43** 532-538. DOI: 10.1016/j.bone.2008.05.024
40. Unal M., Ahmed R., Mahadevan-Jansen A., Nyman J.S.. **Compositional assessment of bone by Raman spectroscopy**. *Analyst* (2021.0) **146** 7464-7490. DOI: 10.1039/D1AN01560E
41. Cesaratto A., Lombardi J., Leona M.. **Tracking photo-degradation of triarylmethane dyes with surface-enhanced Raman spectroscopy**. *J. Raman Spectrosc.* (2016.0) **48** 418-424. DOI: 10.1002/jrs.5056
42. Aasebo E., Brenner A.K., Hernandez-Valladares M., Birkeland E., Berven F.S., Selheim F., Bruserud Ø.. **Proteomic Comparison of Bone Marrow Derived Osteoblasts and Mesenchymal Stem Cells**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22115665
43. Bahlas S., Damiati L.A., Al-Hazmi A.S., Pushparaj P.N.. **Decoding the Role of Sphingosine-1-Phosphate in Asthma and Other Respiratory System Diseases Using Next Generation Knowledge Discovery Platforms Coupled With Luminex Multiple Analyte Profiling Technology**. *Front. Cell Dev. Biol.* (2020.0) **8** 444. DOI: 10.3389/fcell.2020.00444
44. Liao Y., Wang J., Jaehnig E.J., Shi Z., Zhang B.. **WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs**. *Nucleic Acids Res.* (2019.0) **47** W199-W205. DOI: 10.1093/nar/gkz401
45. Maffioli E., Nonnis S., Angioni R., Santagata F., Cali B., Zanotti L., Negri A., Viola A., Tedeschi G.. **Proteomic analysis of the secretome of human bone marrow-derived mesenchymal stem cells primed by pro-inflammatory cytokines**. *J. Proteomics.* (2017.0) **166** 115-126. DOI: 10.1016/j.jprot.2017.07.012
46. Baberg F., Geyh S., Waldera-Lupa D., Stefanski A., Zilkens C., Haas R., Schroeder T., Stühler K.. **Secretome analysis of human bone marrow derived mesenchymal stromal cells**. *Biochim. Biophys. Acta Proteins Proteom.* (2019.0) **1867** 434-441. DOI: 10.1016/j.bbapap.2019.01.013
47. Kehl D., Generali M., Mallone A., Heller M., Uldry A.C., Cheng P., Gantenbein B., Hoerstrup S.P., Weber B.. **Proteomic analysis of human mesenchymal stromal cell secretomes: A systematic comparison of the angiogenic potential**. *NPJ Regen. Med.* (2019.0) **4** 8. DOI: 10.1038/s41536-019-0070-y
48. Shin S., Lee J., Kwon Y., Park K.S., Jeong J.H., Choi S.J., Bang S.I., Chang J.W., Lee C.. **Comparative Proteomic Analysis of the Mesenchymal Stem Cells Secretome from Adipose, Bone Marrow, Placenta and Wharton’s Jelly**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22020845
49. Saleem R., Mohamed-Ahmed S., Elnour R., Berggreen E., Mustafa K., Al-Sharabi N.. **Conditioned Medium from Bone Marrow Mesenchymal Stem Cells Restored Oxidative Stress-Related Impaired Osteogenic Differentiation**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms222413458
50. Sagaradze G., Grigorieva O., Nimiritsky P., Basalova N., Kalinina N., Akopyan Z., Efimenko A.. **Conditioned Medium from Human Mesenchymal Stromal Cells: Towards the Clinical Translation**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20071656
51. Marolt Presen D., Traweger A., Gimona M., Redl H.. **Mesenchymal Stromal Cell-Based Bone Regeneration Therapies: From Cell Transplantation and Tissue Engineering to Therapeutic Secretomes and Extracellular Vesicles**. *Front. Bioeng. Biotechnol.* (2019.0) **7** 352. DOI: 10.3389/fbioe.2019.00352
52. Bieback K., Fernandez-Munoz B., Pati S., Schafer R.. **Gaps in the knowledge of human platelet lysate as a cell culture supplement for cell therapy: A joint publication from the AABB and the**. *Int. Soc. Cell Gene Ther. Transfus.* (2019.0) **59** 3448-3460
53. Fekete N., Rojewski M.T., Lotfi R., Schrezenmeier H.. **Essential components for ex vivo proliferation of mesenchymal stromal cells**. *Tissue Eng. Part C Methods* (2014.0) **20** 129. DOI: 10.1089/ten.tec.2013.0061
54. Shanbhag S., Suliman S., Bolstad A.I., Stavropoulos A., Mustafa K.. **Xeno-Free Spheroids of Human Gingiva-Derived Progenitor Cells for Bone Tissue Engineering**. *Front. Bioeng. Biotechnol.* (2020.0) **8** 968. DOI: 10.3389/fbioe.2020.00968
55. Madrigal M., Rao K.S., Riordan N.H.. **A review of therapeutic effects of mesenchymal stem cell secretions and induction of secretory modification by different culture methods**. *J. Transl. Med.* (2014.0) **11** 260. DOI: 10.1186/s12967-014-0260-8
56. Nikolits I., Nebel S., Egger D., Kress S., Kasper C.. **Towards Physiologic Culture Approaches to Improve Standard Cultivation of Mesenchymal Stem Cells**. *Cells* (2021.0) **10**. DOI: 10.3390/cells10040886
57. Palombella S., Guiotto M., Higgins G.C., Applegate L.L., Raffoul W., Cherubino M., Hart A., Riehle M.O., di Summa P.G.. **Human platelet lysate as a potential clinical-translatable supplement to support the neurotrophic properties of human adipose-derived stem cells**. *Stem Cell Res. Ther.* (2020.0) **11** 432. DOI: 10.1186/s13287-020-01949-4
58. Kim S.N., Lee C.J., Nam J., Choi B., Chung E., Song S.U.. **The Effects of Human Bone Marrow-Derived Mesenchymal Stem Cell Conditioned Media Produced with Fetal Bovine Serum or Human Platelet Lysate on Skin Rejuvenation Characteristics**. *Int. J. Stem Cells* (2021.0) **14** 94-102. DOI: 10.15283/ijsc20070
59. Inukai T., Katagiri W., Yoshimi R., Osugi M., Kawai T., Hibi H., Ueda M.. **Novel application of stem cell-derived factors for periodontal regeneration**. *Biochem Biophys Res. Commun.* (2013.0) **430** 763-768. DOI: 10.1016/j.bbrc.2012.11.074
60. Xu J., Wang B., Sun Y., Wu T., Liu Y., Zhang J., Lee W.Y., Pan X., Chai Y., Li G.. **Human fetal mesenchymal stem cell secretome enhances bone consolidation in distraction osteogenesis**. *Stem Cell Res. Ther.* (2016.0) **7** 134. DOI: 10.1186/s13287-016-0392-2
61. Fujio M., Xing Z., Sharabi N., Xue Y., Yamamoto A., Hibi H., Ueda M., Fristad I., Mustafa K.. **Conditioned media from hypoxic-cultured human dental pulp cells promotes bone healing during distraction osteogenesis**. *J. Tissue Eng. Regen. Med.* (2017.0) **11** 2116-2126. DOI: 10.1002/term.2109
62. Ogata K., Katagiri W., Osugi M., Kawai T., Sugimura Y., Hibi H., Nakamura S., Ueda M.. **Evaluation of the therapeutic effects of conditioned media from mesenchymal stem cells in a rat bisphosphonate-related osteonecrosis of the jaw-like model**. *Bone* (2015.0) **74** 95-105. DOI: 10.1016/j.bone.2015.01.011
63. Tsuchiya S., Ohmori M., Hara K., Fujio M., Ikeno M., Hibi H., Ueda M.. **An Experimental Study on Guided Bone Regeneration Using a Polylactide-co-glycolide Membrane-Immobilized Conditioned Medium**. *Int. J. Oral Maxillofac. Implant.* (2015.0) **30** 1175-1186. DOI: 10.11607/jomi.3915
64. Diomede F., D’Aurora M., Gugliandolo A., Merciaro I., Orsini T., Gatta V., Piattelli A., Trubiani O., Mazzon E.. **Biofunctionalized Scaffold in Bone Tissue Repair**. *Int. J. Mol. Sci.* (2018.0) **19**. DOI: 10.3390/ijms19041022
65. Qiu J., Wang X., Zhou H., Zhang C., Wang Y., Huang J.. **Enhancement of periodontal tissue regeneration by conditioned media from gingiva-derived or periodontal ligament-derived mesenchymal stem cells: A comparative study in rats**. *Stem Cell Res. Ther.* (2020.0) **11** 42. DOI: 10.1186/s13287-019-1546-9
66. Lai C.H., Zhou L., Wang Z.L., Lu H.B., Gao Y.. **Use of a collagen membrane loaded with recombinant human bone morphogenetic protein-2 with collagen-binding domain for vertical guided bone regeneration**. *J. Periodontol.* (2013.0) **84** 950-957. DOI: 10.1902/jop.2012.120415
67. Chang Y.Y., Lee J.S., Kim M.S., Choi S.H., Chai J.K., Jung U.W.. **Comparison of collagen membrane and bone substitute as a carrier for rhBMP-2 in lateral onlay graft**. *Clin. Oral Implants Res.* (2015.0) **26** e13-e19. DOI: 10.1111/clr.12320
68. Jo J.Y., Jeong S.I., Shin Y.M., Kang S.S., Kim S.E., Jeong C.M., Huh J.-B.. **Sequential delivery of BMP-2 and BMP-7 for bone regeneration using a heparinized collagen membrane**. *Int. J. Oral Maxillofac. Surg.* (2015.0) **44** 921-928. DOI: 10.1016/j.ijom.2015.02.015
69. Furuhata M., Takayama T., Yamamoto T., Ozawa Y., Senoo M., Ozaki M., Yamano S., Sato S.. **Real-time assessment of guided bone regeneration in critical size mandibular bone defects in rats using collagen membranes with adjunct fibroblast growth factor-2**. *J. Dent. Sci.* (2021.0) **16** 1170-1181. DOI: 10.1016/j.jds.2021.03.008
70. Piao Z.G., Kim J.S., Son J.S., Lee S.Y., Fang X.H., Oh J.S., You J.-S., Kim S.-G.. **Osteogenic evaluation of collagen membrane containing drug-loaded polymeric microparticles in a rat calvarial defect model**. *Tissue Eng. Part A* (2014.0) **20** 3322-3331. DOI: 10.1089/ten.tea.2013.0717
71. Nagata M., Iwasaki K., Akazawa K., Komaki M., Yokoyama N., Izumi Y., Morita I.. **Conditioned Medium from Periodontal Ligament Stem Cells Enhances Periodontal Regeneration**. *Tissue Eng. Part A* (2017.0) **23** 367-377. DOI: 10.1089/ten.tea.2016.0274
72. Steil L., Thiele T., Hammer E., Bux J., Kalus M., Völker U., Greinacher A.. **Proteomic characterization of freeze-dried human plasma: Providing treatment of bleeding disorders without the need for a cold chain**. *Transfusion* (2008.0) **48** 2356-2363. DOI: 10.1111/j.1537-2995.2008.01856.x
73. Zhao J., Wang S., Bao J., Sun X., Zhang X., Zhang X., Ye D., Wei J., Liu C., Jiang X.. **Trehalose maintains bioactivity and promotes sustained release of BMP-2 from lyophilized CDHA scaffolds for enhanced osteogenesis in vitro and in vivo**. *PLoS ONE* (2013.0) **8**. DOI: 10.1371/journal.pone.0054645
74. Bari E., Di Silvestre D., Mastracci L., Grillo F., Grisoli P., Marrubini G., Nardini M., Mastrogiacomo M., Sorlini M., Rossi R.. **GMP-compliant sponge-like dressing containing MSC lyo-secretome: Proteomic network of healing in a murine wound model**. *Eur. J. Pharm. Biopharm.* (2020.0) **155** 37-48. DOI: 10.1016/j.ejpb.2020.08.003
75. Zhang C., Wang T., Zhang L., Chen P., Tang S., Chen A., Li M., Peng G., Gao H., Weng H.. **Combination of lyophilized adipose-derived stem cell concentrated conditioned medium and polysaccharide hydrogel in the inhibition of hypertrophic scarring**. *Stem Cell Res. Ther.* (2021.0) **12** 23. DOI: 10.1186/s13287-020-02061-3
76. Kot M., Baj-Krzyworzeka M., Szatanek R., Musial-Wysocka A., Suda-Szczurek M., Majka M.. **The Importance of HLA Assessment in “Off-the-Shelf” Allogeneic Mesenchymal Stem Cells Based-Therapies**. *Int. J. Mol. Sci.* (2019.0) **20**. DOI: 10.3390/ijms20225680
77. Ankrum J.A., Ong J.F., Karp J.M.. **Mesenchymal stem cells: Immune evasive, not immune privileged**. *Nat. Biotechnol.* (2014.0) **32** 252-260. DOI: 10.1038/nbt.2816
78. Kang S.H., Chung Y.G., Oh I.H., Kim Y.S., Min K.O., Chung J.Y.. **Bone regeneration potential of allogeneic or autogeneic mesenchymal stem cells loaded onto cancellous bone granules in a rabbit radial defect model**. *Cell Tissue Res.* (2014.0) **355** 81-88. DOI: 10.1007/s00441-013-1738-z
79. Mahalingam V.D., Behbahani-Nejad N., Horine S.V., Olsen T.J., Smietana M.J., Wojtys E.M., Wellik D.M., Arruda E.M., Larkin L.M.. **Allogeneic versus autologous derived cell sources for use in engineered bone-ligament-bone grafts in sheep anterior cruciate ligament repair**. *Tissue Eng. Part A* (2015.0) **21** 1047-1054. DOI: 10.1089/ten.tea.2014.0422
80. Rapp A.E., Bindl R., Erbacher A., Kruchen A., Rojewski M., Schrezenmeier H., Müller I., Ignatius A.. **Autologous Mesenchymal Stroma Cells Are Superior to Allogeneic Ones in Bone Defect Regeneration**. *Int. J. Mol. Sci.* (2018.0) **19**. DOI: 10.3390/ijms19092526
81. Maiti S.K., Shivakumar M.U., Mohan D., Kumar N., Singh K.P.. **Mesenchymal Stem Cells of Different Origin-Seeded Bioceramic Construct in Regeneration of Bone Defect in Rabbit**. *Tissue Eng. Regen. Med.* (2018.0) **15** 477-492. DOI: 10.1007/s13770-018-0129-7
82. Lopez-Fernandez A., Barro V., Ortiz-Hernandez M., Manzanares M.C., Vivas D., Vives J., Vélez R., Ginebra M.P., Aguirre M.. **Effect of Allogeneic Cell-Based Tissue-Engineered Treatments in a Sheep Osteonecrosis Model**. *Tissue Eng. Part A* (2020.0) **26** 993-1004. DOI: 10.1089/ten.tea.2019.0339
83. Osugi M., Katagiri W., Yoshimi R., Inukai T., Hibi H., Ueda M.. **Conditioned media from mesenchymal stem cells enhanced bone regeneration in rat calvarial bone defects**. *Tissue Eng. Part A* (2012.0) **18** 1479-1489. DOI: 10.1089/ten.tea.2011.0325
84. Hiraki T., Kunimatsu R., Nakajima K., Abe T., Yamada S., Rikitake K., Tanimoto K.. **Stem cell-derived conditioned media from human exfoliated deciduous teeth promote bone regeneration**. *Oral Dis.* (2020.0) **26** 381-390. DOI: 10.1111/odi.13244
85. Sanchooli T., Norouzian M., Ardeshirylajimi A., Ghoreishi S., Abdollahifar M., Nazarian H., Piryaei A.. **Adipose Derived Stem Cells Conditioned Media in Combination with Bioceramic-Collagen Scaffolds Improved Calvarial Bone Healing in Hypothyroid Rats**. *Iran. Red Crescent Med. J.* (2017.0). DOI: 10.5812/ircmj.45516
86. Mehler V.J., Burns C., Moore M.L.. **Concise Review: Exploring Immunomodulatory Features of Mesenchymal Stromal Cells in Humanized Mouse Models**. *Stem Cells* (2019.0) **37** 298-305. DOI: 10.1002/stem.2948
87. Wang J., Qu Y., Chen C., Sun J., Pan H., Shao C., Tang R., Gu X.. **Fabrication of collagen membranes with different intrafibrillar mineralization degree as a potential use for GBR**. *Mater. Sci. Eng. C Mater. Biol. Appl.* (2019.0) **104** 109959. DOI: 10.1016/j.msec.2019.109959
88. Li J., Yan J.F., Wan Q.Q., Shen M.J., Ma Y.X., Gu J.T., Gao P., Tang X.-Y., Yu F., Chen J.-H.. **Matrix stiffening by self-mineralizable guided bone regeneration**. *Acta Biomater.* (2021.0) **125** 112-125. DOI: 10.1016/j.actbio.2021.02.012
89. Vo T.N., Ekenseair A.K., Spicer P.P., Watson B.M., Tzouanas S.N., Roh T.T., Mikos A.G.. **In vitro and in vivo evaluation of self-mineralization and biocompatibility of injectable, dual-gelling hydrogels for bone tissue engineering**. *J. Control. Release* (2015.0) **205** 25-34. DOI: 10.1016/j.jconrel.2014.11.028
90. Bailey A.J., Sims T.J., Ebbesen E.N., Mansell J.P., Thomsen J.S., Mosekilde L.. **Age-related changes in the biochemical properties of human cancellous bone collagen: Relationship to bone strength**. *Calcif Tissue Int.* (1999.0) **65** 203-210. DOI: 10.1007/s002239900683
91. Ibrahim A., Magliulo N., Groben J., Padilla A., Akbik F., Abdel Hamid Z.. **Hardness, an Important Indicator of Bone Quality, and the Role of Collagen in Bone Hardness**. *J. Funct. Biomater.* (2020.0) **11**. DOI: 10.3390/jfb11040085
92. Ahmed R., Law A.W.L., Cheung T.W., Lau C.. **Raman spectroscopy of bone composition during healing of subcritical calvarial defects**. *Biomed. Opt. Express* (2018.0) **9** 1704-1716. DOI: 10.1364/BOE.9.001704
93. Ahmed R., Wang W., Zia A.W., Lau C.. **Collagen formation observed from healing calvarial defects with principal component analysis of Raman scattering**. *Analyst* (2018.0) **143** 4614-4622. DOI: 10.1039/C8AN01021H
94. Du F., Wang Q., Ouyang L., Wu H., Yang Z., Fu X., Liu X., Yan L., Cao Y., Xiao R.. **Comparison of concentrated fresh mononuclear cells and cultured mesenchymal stem cells from bone marrow for bone regeneration**. *Stem Cells Transl. Med.* (2021.0) **10** 598-609. DOI: 10.1002/sctm.20-0234
95. Omar O., Engstrand T., Kihlstrom Burenstam Linder L., Aberg J., Shah F.A., Palmquist A., Birgersson U., Elgali I., Pujari-Palmer M., Engqvist H.. **In situ bone regeneration of large cranial defects using synthetic ceramic implants with a tailored composition and design**. *Proc. Natl. Acad. Sci. USA* (2020.0) **117** 26660-26671. DOI: 10.1073/pnas.2007635117
96. Kupcova Skalnikova H.. **Proteomic techniques for characterisation of mesenchymal stem cell secretome**. *Biochimie* (2013.0) **95** 2196-2211. DOI: 10.1016/j.biochi.2013.07.015
97. Okamoto M., Udagawa N., Uehara S., Maeda K., Yamashita T., Nakamichi Y., Kato H., Saito N., Minami Y., Takahashi N.. **Noncanonical Wnt5a enhances Wnt/beta-catenin signaling during osteoblastogenesis**. *Sci. Rep.* (2014.0) **4** 4493. DOI: 10.1038/srep04493
98. Hiraiwa T., Nakai Y., Yamada T.G., Tanimoto R., Kimura H., Matsumoto Y., Miki N., Hiroi N., Funahashi A.. **Quantitative analysis of sensitivity to a Wnt3a gradient in determination of the pole-to-pole axis of mitotic cells by using a microfluidic device**. *FEBS Open Bio.* (2018.0) **8** 1920-1935. DOI: 10.1002/2211-5463.12525
99. Linero I., Chaparro O.. **Paracrine effect of mesenchymal stem cells derived from human adipose tissue in bone regeneration**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0107001
100. Wang K.X., Xu L.L., Rui Y.F., Huang S., Lin S.E., Xiong J.H., Li Y.-H., Lee W.Y.-W., Li G.. **The effects of secretion factors from umbilical cord derived mesenchymal stem cells on osteogenic differentiation of mesenchymal stem cells**. *PLoS ONE* (2015.0) **10**. DOI: 10.1371/journal.pone.0120593
101. Katagiri W., Sakaguchi K., Kawai T., Wakayama Y., Osugi M., Hibi H.. **A defined mix of cytokines mimics conditioned medium from cultures of bone marrow-derived mesenchymal stem cells and elicits bone regeneration**. *Cell Prolif.* (2017.0) **50** e012059. DOI: 10.1111/cpr.12333
102. Katagiri W., Osugi M., Kawai T., Ueda M.. **Novel cell-free regeneration of bone using stem cell-derived growth factors**. *Int. J. Oral Maxillofac. Implant.* (2013.0) **28** 1009-1016. DOI: 10.11607/jomi.3036
103. Katagiri W., Kawai T., Osugi M., Sugimura-Wakayama Y., Sakaguchi K., Kojima T., Kobayashi T.. **Angiogenesis in newly regenerated bone by secretomes of human mesenchymal stem cells**. *Maxillofac. Plast Reconstr. Surg.* (2017.0) **39** 8. DOI: 10.1186/s40902-017-0106-4
104. Kawai T., Katagiri W., Osugi M., Sugimura Y., Hibi H., Ueda M.. **Secretomes from bone marrow-derived mesenchymal stromal cells enhance periodontal tissue regeneration**. *Cytotherapy* (2015.0) **17** 369-381. DOI: 10.1016/j.jcyt.2014.11.009
105. Wang C.Y., Yang H.B., Hsu H.S., Chen L.L., Tsai C.C., Tsai K.S., Yew T.-L., Kao Y.-H., Hung S.-C.. **Mesenchymal stem cell-conditioned medium facilitates angiogenesis and fracture healing in diabetic rats**. *J. Tissue Eng. Regen. Med.* (2012.0) **6** 559-569. DOI: 10.1002/term.461
106. Ogisu K., Fujio M., Tsuchiya S., Tsuboi M., Qi C., Toyama N., Kamio H., Hibi H.. **Conditioned media from mesenchymal stromal cells and periodontal ligament fibroblasts under cyclic stretch stimulation promote bone healing in mouse calvarial defects**. *Cytotherapy* (2020.0) **22** 543-551. DOI: 10.1016/j.jcyt.2020.05.008
107. Diomede F., Gugliandolo A., Scionti D., Merciaro I., Cavalcanti M.F., Mazzon E.. **Biotherapeutic effect of gingival stem cells conditioned medium in bone tissue restoration**. *Int. J. Mol. Sci.* (2018.0) **19**. DOI: 10.3390/ijms19020329
108. Pranskunas M., Simoliunas E., Alksne M., Martin V., Gomes P.S., Puisys A., Kaupinis A., Juodzbalys G.. **Assessment of the Bone Healing Process Mediated by Periosteum-Derived Mesenchymal Stem Cells’ Secretome and a Xenogenic Bioceramic-An In Vivo Study in the Rabbit Critical Size Calvarial Defect Model**. *Materials* (2021.0) **14**. DOI: 10.3390/ma14133512
|
---
title: Large-Scale Polymorphism Analysis of Dog Leukocyte Antigen Class I and Class
II Genes (DLA-88, DLA-12/88L and DLA-DRB1) and Comparison of the Haplotype Diversity
between Breeds in Japan
authors:
- Jiro Miyamae
- Masaharu Okano
- Fumihiko Katakura
- Jerzy K. Kulski
- Tadaaki Moritomo
- Takashi Shiina
journal: Cells
year: 2023
pmcid: PMC10001263
doi: 10.3390/cells12050809
license: CC BY 4.0
---
# Large-Scale Polymorphism Analysis of Dog Leukocyte Antigen Class I and Class II Genes (DLA-88, DLA-12/88L and DLA-DRB1) and Comparison of the Haplotype Diversity between Breeds in Japan
## Abstract
Polymorphisms of canine leukocyte antigen (DLA) class I (DLA-88 and DLA-$\frac{12}{88}$L) and class II (DLA-DRB1) genes are important for disease susceptibility studies, but information on the genetic diversity among dog breeds is still lacking. To better elucidate the polymorphism and genetic diversity between breeds, we genotyped DLA-88, DLA-$\frac{12}{88}$L, and DLA-DRB1 loci using 829 dogs of 59 breeds in Japan. Genotyping by Sanger sequencing identified 89, 43, and 61 alleles in DLA-88, DLA-$\frac{12}{88}$L, and DLA-DRB1 loci, respectively, and a total of 131 DLA-88–DLA-$\frac{12}{88}$L–DLA-DRB1 haplotypes (88-$\frac{12}{88}$L-DRB1) were detected more than once. Of the 829 dogs, 198 were homozygotes for one of the 52 different 88-$\frac{12}{88}$L-DRB1 haplotypes (homozygosity rate: $23.8\%$). Statistical modeling suggests that $90\%$ of the DLA homozygotes or heterozygotes with one or other of the 52 different 88-$\frac{12}{88}$L-DRB1 haplotypes within somatic stem cell lines would benefit graft outcome after 88-$\frac{12}{88}$L-DRB1-matched transplantation. As previously reported for DLA class II haplotypes, the diversity of 88-$\frac{12}{88}$L-DRB1 haplotypes varied remarkably between breeds but was relatively conserved within most breeds. Therefore, the genetic characteristics of high DLA homozygosity rate and poor DLA diversity within a breed are useful for transplantation therapy, but they may affect biological fitness as homozygosity progresses.
## 1. Introduction
The major histocompatibility complex (MHC) molecules play important roles in inducing acquired immunity by presenting peptides derived from foreign antigens, such as germs and viruses that T cells recognize as non-self, resulting in the elimination of these antigens. The MHC molecules are classified into class I (MHC-I) and class II (MHC-II), and regulate self- and non-self discrimination in immunity by presenting antigen peptides to CD8+ and CD4+ T cells, respectively [1,2]. The MHC genes encoding MHC-I and MHC-II molecules are composed of multigene families; each of them is extremely polymorphic in many animals. For example, so far, more than 34,000 human leukocyte antigen (HLA) alleles have been identified and reported in the IPD-IMGT database (https://www.ebi.ac.uk/ipd/imgt/hla/ (accessed on 28 November 2022)). In addition, specific HLA alleles associated with susceptibility or resistance to various diseases [3,4,5] and matching of HLA polymorphisms between donor and recipient in transplantation are important factors for suppressing alloimmune responses [6,7,8,9].
The domesticated dog (*Canis lupus* familiaris) is one of the major companion animals of humans that also is used for biomedical research, such as on the pathobiology of cancers and autoimmune diseases, whose clinical phenotypes (presentations) are similar to those in humans, and in transplantation studies as a preclinical model [10,11]. A draft of the dog genome sequence was determined in the early 2000s, and the dog leukocyte antigen (DLA) loci were located on two chromosome (chr) segments, chr 12 and chr 18 [12,13]. Overall, three DLA class I (DLA-I) loci (DLA-88, DLA-12, and DLA-64) and four DLA class II (DLA-II) loci (DLA-DRA, DLA-DRB1, DLA-DQA1, and DLA-DQB1) were mapped on chr 12, and one divergent DLA-I locus DLA-79 was mapped on chr 18.
To date, 173 DLA-I and 297 DLA-II alleles have been identified and released by the Canine MHC Nomenclature Committee into the IPD-MHC database (https://www.ebi.ac.uk/ipd/mhc/group/DLA/ (accessed on 28 November 2022)). Of the 173 DLA-I alleles identified in several polymorphism studies using more than 500 dogs in total, 139, 17, 9, and 8 alleles were identified in the DLA-88, DLA-12, DLA-64, and DLA-79 loci, respectively [14,15,16,17,18]. In contrast, the polymorphism analyses of the class II region, using more than 10,000 dogs of over 200 breeds, identified 1, 181, 30, and 86 alleles in DLA-DRA, DLA-DRB1, DLA-DQA1, and DLA-DQB1 loci, respectively [19,20,21,22]. Genetic diversity of the DLA-II haplotypes with linked DLA-DRB1, DLA-DQA1, and DLA-DQB1 alleles has been analyzed extensively in various dog breeds. Kennedy et al. reported that DLA polymorphisms are relatively limited within a dog breed, but there are significant differences in the types and frequencies of the DLA-II haplotypes between dog breeds [20,21,22]. In addition, there are reports on DLA-88–DLA-DRB1 haplotypes in the Beagle, DLA-88–DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes in the German shepherd dog and some other dog breeds [22,23,24].
Several PCR-based genotyping methods for the DLA-II genes were established, and specific DLA-II polymorphisms were associated to various diseases such as rheumatoid arthritis [25], diabetes mellitus [26,27], and chronic enteritis [28] by case-control studies in various dog breeds. In contrast, though the association between DLA-79 polymorphisms and multiple immune-mediated diseases was reported, there were no clear associations found between polymorphisms of the other DLA-I genes and disease except for an association with pancreatic acinar atrophy in German Shepherds [23,29].
A reason for this limited association of DLA-I polymorphisms with different dog diseases might be that a locus-specific DLA-I genotyping method for association studies had not been established properly because copy number variations of the DLA-I genes per haploid were unknown. In this regard, we recently found that a gene conversion event between DLA-88 and DLA-12 had generated a hybrid DLA-I locus DLA-88L, resulting in two distinct DLA-I haplotype structures, DLA-88–DLA-12–DLA-64 and DLA-88–DLA-88L–DLA-64 [18,30] (Figure 1A). We describe these two alternative haplotypes as the 88-$\frac{12}{88}$L-64 haplotypes. We also developed a polymorphism analysis method to separate the DLA-88 and DLA-$\frac{12}{88}$L alleles based on their genomic structures [30]. However, no simple technology was developed to efficiently separate the DLA-12 and DLA-88L alleles from each other (DLA-$\frac{12}{88}$L), until the present study (Figure 1B). Thus, there are few published studies on the genetic diversity of different combinations of DLA-I and DLA-II polymorphisms in various breeds.
In this study, to characterize the intra- and inter-breed DLA diversity, including both DLA-I and DLA-II genes in various breeds, we developed a new genotyping method to separate DLA-12 from DLA-88L accurately and performed polymorphism analysis of the relatively polymorphic DLA-I genes, DLA-88 and DLA-$\frac{12}{88}$L, and the most polymorphic DLA-II gene, DLA-DRB1, using 829 dogs of 59 breeds that were collected in Japan. We also estimated three-locus DLA-88–DLA-$\frac{12}{88}$L–DLA-DRB1 haplotypes (88-$\frac{12}{88}$L-DRB1) from the detected allele information and evaluated the genetic diversity within and between breeds based on the three-locus haplotype frequency. Furthermore, since the DLA-88, DLA-$\frac{12}{88}$L, and DLA-DRB1 genes have the characteristics of classical MHC genes such as HLA-A, HLA-B, and HLA-DRB1, their DLA polymorphisms are thought to play important roles for the allo-recognition mechanism during transplantation. Hence, to evaluate the possibility of 88-$\frac{12}{88}$L-DRB1-matched transplantation using somatic stem cells in the field of veterinary medicine, we simulated statistically the proportion of recipient dogs that possibly could undergo 88-$\frac{12}{88}$L-DRB1-matched transplantation of somatic stem cells if these cells were established from the 88-$\frac{12}{88}$L-DRB1 homozygotes.
## 2.1. RNA and DNA Samples
Peripheral blood samples from 829 dogs of 59 breeds were collected from the Animal Medical Center (ANMEC) at Nihon University, Marble Veterinary Medical center, and the Nippon Veterinary and Life Science University in accordance with the guidelines for animal experiments specific to each location when the dog owner approved to use the blood for research. Of these, 403 were genotyped initially using RNA samples extracted in the previous study [18,30], and the remaining 426 were genotyped using newly extracted genomic DNA samples (Table 1). We initially genotyped RNA samples (converted to cDNA for amplification) because transcribed MHC genes and alleles are detected more easily without contamination of amplicons originating from pseudogenes or duplicated genes if primer locations crossover to at least two homologous locations. Limitations of the RNA-based genotyping method [15,18] were corrected by also genotyping genomic DNA samples.
The genomic DNA was extracted from peripheral blood mononuclear cells by using TRIzol LS Reagent (Invitrogen/Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) or Kaneka Easy DNA Extraction Kit version 2 (Kaneka Corporation, Hyogo, Japan) according to the manufacturer’s protocols.
## 2.2. PCR Amplification of DLA-88 and DLA-12/88L Genes
Polymorphism analysis for DLA-88 and DLA-$\frac{12}{88}$L was performed using the genomic DNA from 426 dogs obtained for this study and 403 dogs that already were genotyped in our previous study [18].
The first PCR was performed independently using a specific primer set (88-seg-F and 88-seg-R2) to amplify the 4.0 kb genomic region, including DLA-88 and using a specific primer set (88L/12-seg-F and 88L/12-seg-R) to amplify the 5.6 kb genomic region including DLA-$\frac{12}{88}$L (Figure 1B and Supplementary Table S1A). The composition of the PCR solution was 20 ng of DNA, 0.4 unit of KOD FX DNA polymerase (TOYOBO, Osaka, Japan), 10 uL of 2× PCR buffer, 2 mM of dNTP and 0.4 uM of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 63 °C/30 s, and 68 °C/4 min for DLA-88 and 98 °C/10 s, 58 °C/30 s, and 68 °C/5 min for DLA-$\frac{12}{88}$L. After the PCR products were purified using ExoSAP-IT (GE Healthcare, Piscataway, NJ, USA) and diluted, the 2nd PCR to distinguish between DLA-12 and DLA-88L alleles was performed using a DLA-12 specific primer set (12-F and $\frac{88}{12}$/88L-R) and a DLA-88 and DLA-88L specific primer set ($\frac{88}{88}$L-F and $\frac{88}{12}$/88L-R) (Supplementary Table S1B) [15,18]. The composition of the PCR solution was 1 uL of the first PCR product diluted 1000-fold, 0.4 unit of KOD FX DNA polymerase, 10 μL of 2× PCR buffer, 2 mM of dNTP, and 0.4 μL of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 63 °C/30 s, and 68 °C/90 s.
## 2.3. PCR Amplification of DLA-DRB1 Gene
Polymorphism analysis for DLA-DRB1 was performed using RNA samples from the 403 dogs as templates by using DLA-DRB1 specific primer sets (DRB1-F and DRB1-R) (Supplementary Table S1D) [31]. The cDNA samples were synthesized with the oligo-dT primer using the RevaTra Ace reverse transcriptase reaction (TOYOBO, Osaka, Japan) after DNase I treatment using 1 μg of RNA (Invitrogen/Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) according to the manufacturer’s protocol. The polymorphism analysis was also performed using DNA samples from the 426 dogs as templates by using DLA-DRB1 specific primer sets (DRB1-g-F and DRB1-g-R) (Supplementary Table S1E) [32]. The composition of the PCR solution consisted of 20 ng of cDNA or genomic DNA, 0.4 units of KOD FX DNA polymerase, 10 uL of 2× PCR buffer, 2 mM of dNTP, and 0.4 uM of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 60 °C/30 s, and 68 °C/45 s.
## 2.4. Sanger-Sequencing
After purification of PCR products using ExoSAP-IT (GE Healthcare, Piscataway, NJ, USA), the purified PCR products were sequenced directly with Big Dye Terminator Kit Ver. 1.1 or Ver. 3.1 (Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) and ABI3130 genetic analyzer (Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA). The nucleotide sequences of the PCR products for DLA-88, DLA-12, and DLA-88L were determined using sequencing primers i1F-T and i3R-T (Supplementary Table S1C). When the DLA-88 allele sequences were difficult to determine due to sequence offsets by nucleotide insertions and deletions in intron 2 and/or exon 3 of the DLA-88 alleles [30,33], additional sequencing was performed using another primer i2F2 (Figure 1B).
## 2.5. Allele Assignment and Confirmation of Novel DLA Alleles
DLA allelic sequences were assigned using Sequencher Ver. 5.0.1 DNA sequence assembly software (Gene Code Co., Ann Arbor, MI, USA) by comparing them with known DLA-88, DLA-88L, DLA-12, and DLA-DRB1 allele sequences released in the GenBank (https://www.ncbi.nlm.nih.gov/genbank/ (accessed on 26 April 2022)) and the IPD-MHC Canines database (https://www.ebi.ac.uk/ipd/mhc/group/DLA/ (accessed on 26 April 2022)). Allele sequences from the Sanger sequencing data also were assigned using the MHC allele assignment software Assign ATF ver. 1.0.2.45 (Conexio, Western Australia, Australia). New alleles were confirmed by PCR and direct sequencing again. The PCR products were cloned into the pTA2 cloning vector with the TA cloning kit (TOYOBO, Osaka, Japan), and the nucleotide sequence in 4 to 8 clones per DNA sample was analyzed to avoid PCR and sequencing artifacts.
## 2.6. Nomenclature of Novel DLA Alleles
We defined the alleles amplified by using the DLA-88 and DLA-88L specific primer set as the DLA-88L allele and the alleles amplified by using the DLA-12 specific primer set as DLA-12 allele in the 2nd PCR of DLA-$\frac{12}{88}$L. Since all the DLA-88L alleles reported so far in the IPD-MHC Canines database have been named “DLA-88*”, we also followed the rule for the identified DLA-88L novel alleles as well as all published DLA-88 alleles. The official name of the novel allele was assigned according to the DLA nomenclature in the IPD-MHC database. Novel DLA alleles that have not been given an official allele name were named “DLA-88*nov”, “DLA-12*nov”, or “DLA-DRB1*nov” as tentative allele names.
## 2.7. Estimation of DLA-88–DLA-12/88L–DLA-DRB1 (88-12/88L-DRB1) Haplotypes
The 88-$\frac{12}{88}$L-DRB1 haplotypes for each dog, which are 88-12-DRB1 or 88-88L-DRB1, were identified and estimated manually based on genotyping data of 829 dogs as previously described [18,20]. We initially identified the 88-$\frac{12}{88}$L-DRB1 haplotypes for homozygous dogs and estimated the haplotypes for heterozygous dogs on the basis of those within the homozygous dogs. To confirm our manual haplotype estimation, we also estimated the 88-$\frac{12}{88}$L-DRB1 haplotypes in each breed by using the maximum likelihood method of the PHASE program [34].
## 2.8. Data Analysis
Calculation of expected heterozygosity (He), Hardy–*Weinberg equilibrium* (HWE) test, and principal component analysis (PCA) based on the 88-$\frac{12}{88}$L-DRB1 haplotype frequencies in each breed were performed by GenAlEx Ver. 6.5 [35]. In the PCA, to reduce the 88-$\frac{12}{88}$L-DRB1 haplotype numbers as explanatory variables, we used the haplotype frequencies composed of the field-1 level alleles. This level reflects differences in immuno-responsiveness due to changes in the amino acid sequences of the peptide-binding region and T-cell recognition region of each DLA allele [33]. The inbreeding coefficient (Fis) and haplotype richness (Hr) were calculated by FSTAT Ver. 2.9.4 (available from https://www2.unil.ch/popgen/softwares/fstat.htm (accessed on 21 June 2021). In this case, the significant deviation of FIS from zero was also tested by FSTAT Ver. 2.9.4. Pearson product-moment correlation coefficient was calculated with R ver. 3.6.3 (available from https://www.r-project.org/ (accessed on 17 March 2020)) to evaluate differences in DLA-DRB1 allele diversity in the same dog breeds from different countries, UK and Japan. A phylogenetic tree was constructed by the Neighbor Joining method and assessed using 10,000 bootstrap replicates after aligning the DLA sequences using the MEGA X software (available from https://www.megasoftware.net/ (accessed on 3 March 2020) [36]. A pairwise sequence similarity plot was displayed by a graphical user interface GenomeMatcher [37].
## 3.1. Allele Number and Frequency of DLA-88, DLA-88L, DLA-12, and DLA-DRB1
Table 2 and Supplementary Table S2 show detailed information on the types, numbers, and frequencies of DLA-88, DLA-88L, DLA-12, and DLA-DRB1 alleles identified in the 829 dogs. In total, 193 DLA alleles (89 in DLA-88, 18 in DLA-88L, 25 in DLA-12, and 61 in DLA-DRB1) were identified, and 17, 7, 5, and 6 were novel alleles of DLA-88, DLA-88L, DLA-12, and DLA-DRB1, respectively. Figure 2 shows frequencies of the 20 most frequent DLA alleles and the number of animals carrying the alleles in popular breeds in Japan. The highest frequent alleles in each DLA gene were DLA-88*006:01 (allele frequency: $8.9\%$), DLA-12*001:01:01 ($45.1\%$), and DLA-DRB1*015:01 ($14.0\%$). Although 43 DLA-$\frac{12}{88}$L alleles were identified, $65.7\%$ (545 dogs) carried DLA-12*001:01:01. In the DLA-DRB1 gene, DLA-DRB1*015 group alleles (DLA-DRB1*015:01, DLA-DRB1*015:02, DLA-DRB1*015:03, and DLA-DRB1*015:04) were the most common, and $37.7\%$ had the alleles. This result showed a similar ratio ($23.6\%$) to the previously published report [20].
## 3.2. Phylogenetic Relationships of the DLA-88, DLA-88L, and DLA-12 Alleles
A phylogenetic tree of 109 different alleles was reconstructed using the DLA-88, DLA-12, and DLA-88L nucleotide sequences of exon 2–intron 2–exon 3 without indels (746 bp: alignment length). Ninety-nine of the 109 alleles were identified in the 426 dogs that we sequenced in this study. Another 10 DLA-88 nucleotide sequences of exon 2–intron 2–exon 3 were obtained from the IPD-MHC and NCBI databases, and seven (DLA-88*028:04, DLA-88*032:02, DLA-88*045:02, DLA-88*046:01, DLA-88*047:01, DLA-88*049:01 and DLA-88*050:01) were not detected in our study. The phylogenetic tree clearly divided the DLA-88 and DLA-12 alleles into two lineages, and 16 of the 17 DLA-88L alleles were included in the DLA-88 lineage (Figure 3A). The phylogenetic tree clearly divided the DLA-88 and DLA-12 alleles into two lineages, and 16 of the 17 DLA-88L alleles were included in the DLA-88 lineage (Figure 3A). Of the 17 DLA-88L alleles, 14 were composed of two separate lineages containing two common alleles, DLA-88*017:01 and DLA-88*029:01 (Supplementary Table S2). In contrast, DLA-88*nov65, which was assigned as a DLA-88L allele, aligned with the DLA-12 lineage, and its nucleotide sequence showed a high similarity of $99.87\%$ to DLA-12*004:01 (Figure 3B). The intron 2 sequence of DLA-88*nov65 was highly different from DLA-88*501:01 and DLA-88*017:01, which grouped with the DLA-88 and DLA-88L alleles, respect ively (Figure 3B).
## 3.3. Evaluation of DLA-DRB1 Polymorphisms between Same Dog Breeds in Japan and the United Kingdom
The migration or transportation of dog breeds between different geographic locations has been shown to have a detectable effect on breed structures with the generation of genetically differentiated sub-populations [38,39,40]. Therefore, to evaluate the genetic bias of the DLA polymorphisms between the same dog breeds in Japan and another country, we compared our DLA-DRB1 genotyping data with the previously published DLA-DRB1 polymorphism data in the United Kingdom (UK) [21]. We compared the proportion of the dogs with each of the different DLA-DRB1 alleles in 10 breeds that had been analyzed in more than 12 dogs per breed in the present (Japan) and previous studies (UK). Of the 10 breeds in the UK and Japan, moderate to strong correlations with correlation coefficients ranging from 0.453 (Beagle) to 0.977 (Cavalier King Charles Spaniel), and a median value of 0.853 was confirmed in the nine breeds (Beagle, Golden Retriever, Labrador Retriever, Dachshund, Miniature Schnauzer, American Cocker Spaniel, Cavalier King Charles Spaniel, Shih Tzu, and Yorkshire Terrier) (Figure 4). The Beagles showed a moderate correlation coefficient, but a large difference was observed between the two countries in the proportion of individuals carrying DLA-DRB1*006:01 ($74.6\%$ in the UK vs. $10.8\%$ in Japan). However, for Border Collies, whereas 7 out of 10 alleles were commonly observed in both countries, the DLA-DRB1 allele frequency differed markedly, and no positive correlation was observed between the proportions (correlation coefficient r: −0.159) of alleles in each country.
## 3.4. Frequency of the 88-12/88L-DRB1 Haplotypes
To identify the two 88-$\frac{12}{88}$L-DRB1 haplotypes (88-12-DRB1 or 88-88L-DRB1) within the 829 dogs, we searched homozygous dogs with the three-loci, any two-loci (88-$\frac{12}{88}$L, 88-DRB1 and $\frac{12}{88}$L-DRB1) and one-locus from our genotyping data. Firstly, 52 different sub-haplotypes of the 88-$\frac{12}{88}$L-DRB1 haplotypes were identified within 198 dogs ($23.8\%$) that were homozygous at the three loci. Then, 54 sub-haplotypes of the 88-$\frac{12}{88}$L-DRB1 haplotypes were identified within 40 dogs and 163 dogs that were homozygous at two-loci (88-$\frac{12}{88}$L in 7 dogs, 88-DRB1 in 7 dogs, $\frac{12}{88}$L-DRB1 in 16 dogs) and at one-locus (DLA-88 in 3 dogs, DLA-$\frac{12}{88}$L in 111 dogs, and DLA-DRB1 in 49 dogs), respectively.
In total, 106 sub-haplotypes of the 88-$\frac{12}{88}$L-DRB1 haplotypes were identified. In addition, 84 sub-haplotypes of 88-$\frac{12}{88}$L-DRB1 were estimated from the haplotype estimation of the 428 remaining heterozygous dogs with reference to the 106 sub-haplotypes of the 88-$\frac{12}{88}$L-DRB1 haplotypes. Consequently, 190 88-$\frac{12}{88}$L-DRB1 sub-haplotypes in total were obtained from 803 dogs of 49 breeds (Supplementary Table S3). However, the remaining 26 dogs could not be assigned to the 88-$\frac{12}{88}$L-DRB1 haplotypes, because the combination of sub-haplotypes was not narrowed down to less than three loci. Of 190 88-$\frac{12}{88}$L-DRB1 sub-haplotypes, 131 were detected two or more times, while the other 59 sub-haplotypes were detected just once within heterozygous dogs (Supplementary Table S3B). The frequencies of 131 sub-haplotypes within the 88-12-DRB1 and the 88-88L-DRB1 haplotype structures were $79.4\%$ and $20.6\%$, respectively (Table 3).
Of the 131 different 88-$\frac{12}{88}$L-DRB1 haplotypes, 29 were high-frequency haplotypes with a frequency of $1.0\%$ or more (i.e., the detected number of the haplotype was ≥16) (Table 4). Of these 29 highly frequent haplotypes, four haplotypes (DLA-88*006:01–DLA-12*001:01–DRB1*056:01 (Haplotype(Hp)-ID 116), DLA-88*502:01–DLA-12*001:01–DRB1*001:02 (Hp-ID 6), DLA-88*001:03–DLA-12*001:01–DRB1*046:01 (Hp-ID 91), and DLA-88*511:01–DLA-12*001:03–DRB1*092:01:1 (Hp-ID 117) were observed in only one breed. The other 25 haplotypes were observed in two or more breeds, and 11 sub-haplotypes (Hp-IDs 2, 20, 22, 23, 37, 46, 51, 52, 69, 73, and 99) showed high haplotype frequencies of $70\%$ or more in specific dog breeds (Table 4). Therefore, more than $50\%$ of the high-frequency 88-$\frac{12}{88}$L-DRB1 haplotypes (15 of 29 haplotypes) were found in breeds with a large number of dogs tested. DLA-88*004:02–DLA-12*001:01–DRB1*006:01 (Hp-ID 12) was detected as the most frequent haplotype in two breeds, Pomeranian (haplotype frequency: $46.6\%$) and Yorkshire Terrier (Hp frequency: $31.7\%$) (Supplementary Table S3A). In addition, the DLA-88*003:02–DLA-88*017:01–DRB1*009:01 (Hp-ID 31), and DLA-88*501:01–DLA-12*001:01:01–DRB1*001:01 (Hp-ID 8) were commonly observed in multiple breeds with relatively similar frequencies (Supplementary Table S3A).
## 3.5. Comparison of Genetic Diversity between Dog Breeds by Haplotype Numbers and Heterozygosity
We investigated the genetic diversity of the 88-$\frac{12}{88}$L-DRB1 haplotypes using a total of 725 dogs within 24 different dog breeds (analyzed using ≥ 10 dogs/breed) and mongrels (mixed breeds). The number of different haplotypes in each breed ranged from three Shetland Sheepdogs to 27 Toy Poodles (Table 5), and up to 34 different haplotypes among the mongrels (Figure 2). Six dog breeds (Miniature Schnauzer, Shetland Sheepdog, Shiba, American Cocker Spaniel, Papillon, and Bernese Mountain Dog) had one particular 88-$\frac{12}{88}$L-DRB1 sub-haplotype at a frequency of greater than $50\%$ (Figure 5). Only two or three haplotypes represented more than $80\%$ of all the haplotypes in seven breeds (Miniature Schnauzer, Shetland Sheepdog, Shiba, American Cocker Spaniel, Golden Retriever, Miniature Pinscher, and Shih Tzu). In contrast, more than 20 different haplotypes were detected in Chihuahua and Toy Poodle, and each haplotype frequency was distributed similarly (Figure 5).
We calculated the genetic diversity indices, such as observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (Fis), and haplotype richness (Hr) to evaluate the 88-$\frac{12}{88}$L-DRB1 diversity in each breed (Table 5). The mean Ho value in the 24 dog breeds was 0.736. The Ho values deviated significantly from HWE in 8 breeds (Cavalier King Charles Spaniel, Golden Retriever, Labrador Retriever, American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle), and the Ho values were significantly lower than He values in five breeds (American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle). The Hr values in the 24 breeds ranged from 2.13 in Shetland Sheepdog to 7.79 in Toy Poodle.
## 3.6. Characteristics of Genetic Relationship of the 88-12/88L-DRB1 Haplotypes by Principal Component Analysis
To evaluate genetic relationship of the 88-$\frac{12}{88}$L-DRB1 haplotypes among different dog breeds, PCA was performed using the 24 breeds listed in Table 5. Of the 24 breeds plotted by PCA, 22 breeds were distributed closely around the centroid of the quadrants as if they were almost one population. The Shetland Sheepdog and Miniature Schnauzer breeds diverged markedly from the other 22 breeds. ( Figure 6A and Supplementary Table S3A). The positions of the Shetland Sheepdog and Miniature Schnauzer breeds within the matrix appear to have reflected the presence of their dominant haplotypes, 88*003–88*017–DRB1*002 (Hp-ID 20) in Shetland Sheepdog (Hp frequency: $67.1\%$) and 88*013–12*003–DRB1*009 (Hp-ID 23) in Miniature Schnauzer ($68.8\%$). Since the 88*003–88*017–DRB1*002 (Hp-ID 20) also were commonly observed among Welsh Corgi and Border Collie, these two breeds were located slightly outside the large group of the other breeds and closer to Shetland Sheepdog. Removing the Shetland Sheepdog and Miniature Schnauzer outliers from the PCA analysis changed the genetic relationship slightly between some of the 22 breeds on the basis of the 88-$\frac{12}{88}$L-DRB1 haplotype frequencies (Figure 6B). For example, the French Bulldog, Bulldog, Border Collie, and Yorkshire Terrier share the 88*028–88*029–DRB1*015 (Hp-IDs 24 and 25) at relatively high frequencies ($13.4\%$ to $43.4\%$), and these four breeds grouped more closely together and at some distance from the other breeds. Similarly, the Golden retriever and Labrador retriever, sharing the 88*508–12*001–DRB1*012 (Hp-ID 21), and American Cocker Spaniel and Cavalier King Charles Spaniel, sharing the 88*003–88*017–DRB1*009 (Hp-ID 31) separated further from each other and at a greater distance from the centroid [0, 0] of the PCA plot (Figure 6B).
## 3.7. Number of Potential Recipients for 88-12/88L-DRB1-Matched Transplantation, Assuming Homozygous-Derived Somatic Stem Cells as Donors
Assuming that somatic stem cells could be established from the 52 types of homozygotes of the 88-$\frac{12}{88}$L-DRB1 haplotypes and that these cells could be used as donors for 88-$\frac{12}{88}$L-DRB1-matched transplantations, we statistically modeled the number of dogs that might be recipients from the 829 individuals analyzed in this study (Figure 7). From our statistical simulation, if donor cells are established from 9, 28, and 52 types of high frequency 88-$\frac{12}{88}$L-DRB1 homozygotes, 411 ($51.2\%$), 650 ($80.7\%$), and 733 ($90.9\%$) dogs might be considered eligible for 88-$\frac{12}{88}$L-DRB1-matched transplantation as recipients. Furthermore, in 17 of 24 dog breeds ($70.8\%$) listed in Table 5, $50\%$ or more of these dogs might benefit from the 88-$\frac{12}{88}$L-DRB1-matched transplantation by using donor dogs with the most frequent 88-$\frac{12}{88}$L-DRB1 haplotypes in each breed (Table 6). Additionally, homozygotes of all haplotypes listed in Table 6, except 88*006:01–DLA-12*001:01–DRB1*015:01 (Hp-ID 37), were detected in the present study (Supplementary Table S3A).
## 4. Discussion
In this study, we genotyped the DLA-88, DLA-$\frac{12}{88}$L, and DLA-DRB1 loci by Sanger sequencing using 829 dogs of 59 breeds and identified 89, 43, and 61 alleles, respectively. We also developed a two-stage PCR method for the polymorphism analysis of the DLA-88, DLA-88L, and DLA-12 genes by separating DLA-88 and DLA-$\frac{12}{88}$L with the 1st PCR and DLA-12 and DLA-88L with the 2nd PCR (Figure 1B). This polymorphism analysis by PCR and sequencing clearly distinguished the DLA-88L allele from the DLA-88 allele, which was difficult with conventional RNA-based methods [15,18]. In fact, the previously reported DLA-88*042:02 [17] belongs to DLA-88L rather than DLA-88, and this allele along with DLA-88*008:02 and DLA-DRB1*004:01 constituted the 88-88L-DRB1 haplotype in the Maltese breed (Supplementary Table S2A). Therefore, this simpler and more accurate two-stage PCR method is an important tool to use for a better understanding of the DLA loci and haplotype differences and for evaluating various immune responses in dogs.
Overall, we identified 29 novel DLA-88, DLA-88L, and DLA-DRB1 alleles in this study. Of them, DLA-88*nov65 was newly detected as a DLA-88L allele that showed a different phylogenetic relationship from other DLA-88L alleles, and was highly similar to DLA-12*004:01 of the DLA-12 lineage (Figure 3). Interestingly, our previous study showed that DLA-12*004:01 was generated by a gene conversion event within the exon 2 region between the DLA-12 and DLA-88 alleles [30]. Therefore, the DLA-88*nov65 also might have been generated by gene conversion between the DLA-88 and DLA-12 alleles, similar to DLA-12*004:01. There may be many other unidentified DLA alleles generated by such gene conversions events.
In contrast to the DLA-I genes, the polymorphisms and diversity analyses of the DLA-II genes (DLA-DRB–DLA-DQA–DLA-DQB) have been performed previously in many different dog breeds [21,22,41,42]. Although the Japanese native species of Shiba has not been well analyzed previously, our current analysis showed that DLA-DRB1*056:01 (allele frequency: $55.4\%$) was the most frequent allele, followed by DLA-DRB1*092:01:1 ($18.9\%$), and DLA-DRB1*011:03 ($16.2\%$) (Supplementary Table S3A). These DLA-DRB1 alleles were detected only in Shiba, and therefore their detection is extremely rare even in past polymorphism analyses of the DLA-II genes, including other dog breeds of Asian origin [21,42]. The DLA polymorphism information on Japanese native breeds is extremely limited [42,43]. In this study, although we analyzed Japanese native species Shiba, Akita, Japanese Spitz, Chin, and Shikoku, the number of animals analyzed was less than 10 animals except for the 37 in the Shiba breed. Therefore, more DLA allele information is necessary for Japanese native species as well as for dog breeds that have not yet been analyzed.
Recent genomic analysis of the remains of extinct Japanese wolves (*Canis lupus* hodophilax) showed phylogenetically that after the Japanese wolf and modern dog ancestry had diverged from grey wolf lineages, gene flow occurred from the ancestor of Japanese wolves into the ancestor of Japanese dogs, including Shiba, and this flow likely continued and contributed to differentiate between the lineage of Japanese dogs and West Eurasian dogs [44]. Interestingly, DLA-DRB1*056:01 of Shiba was detected in Finnish and Russian wolves with frequencies of $4.0\%$ and $2.9\%$, respectively [45], and Shiba DLA-DRB1*092:01:1 was detected in Canadian and Croatian wolves with frequencies of $6.0\%$ and $11.0\%$, respectively [46,47]. These results indicated that Shiba might be a unique breed that shared some of its genomic sequences, including the DLA genomic region, with its ancestor in a different way than those of the European modern dog breeds.
High homozygosity of the DLA haplotypes generally implies a loss of DLA genetic diversity. The Ho values showed significantly lower values than the He values in 5 dog breeds, American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle (Table 5). This suggests a high level of inbreeding in these five breeds. The high Fis values also observed in Shih Tzu (0.209) and Papillon (0.171) strongly suggest that the DLA diversity in these breeds of our population sample is decreasing by inbreeding (Table 5). Moreover, Shetland Sheepdog showed an extremely low Ho value of 0.314 (Table 5). The loss of genetic diversity due to high homozygosity might increase the deleterious genetic variation in pure-breed dogs [48]. Also, high homozygosity of the DLA region due to both inbreeding and genetic bottlenecks by selective artificial breeding was associated with the development of autoimmune diseases in Italian Greyhounds [49]. In contrast, MHC heterogeneity of the Sea lion in wild populations appears advantageous to protect against infectious diseases [50], whereas the pregnancy rate in horses was reported to be decreased by sharing common MHC types between males and females [51]. Therefore, loss of the DLA diversity may affect biological fitness as homozygosity progresses.
The homozygous rate of DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes was $35\%$ in a previous study [22]. These three DLA-II genes are located together within 100 kb, while DLA-88 is located far from DLA-DRB1 by over 1.0 Mb [13], resulting in a much stronger linkage disequilibrium (LD) within the DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotype than the 88-$\frac{12}{88}$L-DRB1 haplotype. Therefore, the lower 88-$\frac{12}{88}$L-DRB1 homozygous rate ($23.8\%$) in this study than that of DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotype in the previous study [22] might be associated with LD and rates of recombination between different DLA gene loci. The detection of 52 types of homozygotes for the 88-$\frac{12}{88}$L-DRB1 haplotypes suggests that $90.9\%$ of the dogs analyzed in our study would have a successful 88-$\frac{12}{88}$L-DRB1-matched transplantation if their somatic stem cells were used in such a procedure. In comparison, if induced pluripotent stem cells (iPSCs) were established from homozygotes of 30 and 50 types of HLA haplotypes (HLA-A–HLA-B–HLA-DRB1) that are frequently observed in Japanese, $82.2\%$ and $90.7\%$ of Japanese would benefit from HLA-matched iPSC transplantation [52]. However, the HLA homozygosity rate for humans is relatively low at 0.5 to $1.5\%$ for the HLA-A–HLA-B–HLA-DRB1 haplotype [53,54,55]. Therefore, the HLA of 15,000 and 24,000 individuals would need to be genotyped to identify these 30 and 50 HLA homozygotes, respectively [52]. In this regard, the MHC homozygotes, preferred donors for somatic stem cell sources, would be much easier to detect in dogs than in humans due to their higher rate of MHC homozygosity. Moreover, our new data on the frequency of DLA haplotypes in various dog breeds could help in the implementation of somatic stem cell transplantation along with a recent development of clinical-grade canine iPSCs derivation [56,57] and assist with the high expectations for regenerative medicine in the veterinary field [58].
In the PCA using the 88-$\frac{12}{88}$L-DRB1 haplotype frequencies, the values of the first principal component (PC1) and the second principal component (PC2) were extremely low at around $10\%$ in both analyses, but with relatively strong diversity between the DLA haplotypes among the 24 dog breeds, which are popular in Japan (Figure 6). The Hr values of 88-$\frac{12}{88}$L-DRB1 were less than five in 11 breeds, suggesting that there were relatively few breeds with many different types of 88-$\frac{12}{88}$L-DRB1 haplotypes (Table 5). Moreover, haplotype frequency bias was confirmed for six breeds, with some haplotype frequencies at $50\%$ or more within each breed (Figure 5). These results showed that the DLA diversity is highly conserved within most breeds, but divergent between almost all breeds, as similarly observed in a previous study on the DLA-II haplotype diversity [20,22]. Therefore, the DLA allele and DLA haplotype frequencies appear to change largely depending on the breeds. Of the 829 dogs of 59 breeds analyzed in this study, $68.5\%$ (568 of 829 dogs) were from the top 20 most popular breeds registered in Japan (Table 1). In contrast, only a few dogs were analyzed from breeds that are listed in the top ten most popular breeds in the USA (American Kennel Club; https://www.akc.org/ (accessed on 28 November 2022)), such as German Shepherd and Rottweiler, which were less popular breeds in Japan. *The* genetic closeness of the DLA haplotypes among different breeds can be evaluated more accurately by enhancing the DLA genotyping data of breeds from which only a small number of dogs were analyzed in our present study. We showed that there are differences in the distribution of DLA-DRB1 alleles within the same breeds, such as Border Collie and Beagle, particularly if they are located in different countries, such as Japan and the UK (Figure 4). Since differences in the DLA diversity within a single breed between different countries have also been reported in some other breeds [49,59,60], geographical location can affect DLA diversity resulting in differences in the susceptibility for various diseases even in a single breed between different countries. From such a discussion, further DLA polymorphisms analysis for various breeds in different countries is warranted to better comprehend the intriguing features of DLA diversity.
A limitation of our study concerning DLA haplotypes containing both DLA-I and DLA-II genes was not to include DLA-DQA1 and DLA-DQB1 polymorphisms that might be linked to the 88-$\frac{12}{88}$L-DRB1 haplotypes. This was beyond the scope of our present study. The three genes of DLA-DRB1, DLA-DQA1, and DLA-DQB1 often show strong linkage disequilibrium [22], but novel DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes might be generated by the recombination between the DLA-DR and DLA-DQ genes. Although the mismatch of HLA-DQ polymorphisms in organ transplantation is associated with the production of de novo donor-specific antibodies (DSA) against the HLA-DQ molecules and contributes to poor graft outcomes [61,62], such studies are lacking in dogs. Therefore, in regard to transplantations in dogs, further studies are necessary to genotype DLA-DQA1 and DLA-DQB1 and consolidate the extended haplotypes that were identified in our study, including all DLA-I and DLA-II genes, to select the most suitable organ donors in future.
Several specific DLA-II alleles and haplotypes have been reported so far to associate with various diseases in different breeds. For example, DLA-DRB1*010:01:1 or the DLA-DRB1*010:01:1–DLA-DQA1*002:01–DLA-DQB1*015:01 haplotype is associated significantly with the risk of necrotizing meningoencephalitis in Pug [63]. DLA-DRB1*094:01 is associated significantly with acquired retinal degradation syndrome in Dachshunds [64]. These susceptible DLA-DRB1 alleles were also detected in the Pugs and Dachshunds in our study (Supplementary Table S3A). However, the association between the DLA alleles and diseases is unknown because we did not survey the medical history of the individuals used in this study. Also, we have not evaluated the association between decreased heterozygosity of the DLA haplotype and decreased biological fitness. Considering these limitations of the present study, we would like to elucidate the association between DLA polymorphisms and diseases and fertility in the future by investigating the disease history in each of the major breeds in our study population.
## 5. Conclusions
*The* genetic diversity of DLA haplotypes varied remarkably between breeds but was relatively conserved within the breed in our large-scale polymorphism analysis. *The* genetic characteristics of the high DLA homozygosity rate and poor DLA diversity within the same breed are useful for transplantation therapy, but they also may affect biological fitness negatively as homozygosity progresses. This DLA polymorphism information might be useful in future studies for the realization of canine transplantation medicine and elucidation of the pathology of various diseases and for the development of DLA-haplotype-based veterinary medicine.
## References
1. König R., Huang L.-Y., Germain R.N.. **MHC class II interaction with CD4 mediated by a region analogous to the MHC class I binding site for CD8**. *Nature* (1992) **356** 796-798. DOI: 10.1038/356796a0
2. Garcia K.C., Scott C.A., Brunmark A., Carbone F.R., Peterson P.A., Wilson I.A., Teyton L.. **CD8 enhances formation of stable T-cell receptor/MHC class I molecule complexes**. *Nature* (1996) **384** 577-581. DOI: 10.1038/384577a0
3. Shiina T., Inoko H., Kulski J.. **An update of the HLA genomic region, locus information and disease associations: 2004**. *Tissue Antigens* (2004) **64** 631-649. DOI: 10.1111/j.1399-0039.2004.00327.x
4. Shiina T., Hosomichi K., Inoko H., Kulski J.K.. **The HLA genomic loci map: Expression, interaction, diversity and disease**. *J. Hum. Genet.* (2009) **54** 15-39. DOI: 10.1038/jhg.2008.5
5. Matzaraki V., Kumar V., Wijmenga C., Zhernakova A.. **The MHC locus and genetic susceptibility to autoimmune and infectious diseases**. *Genome Biol.* (2017) **18** 76. DOI: 10.1186/s13059-017-1207-1
6. Montgomery R.A., Tatapudi V.S., Leffell M.S., Zachary A.A.. **HLA in transplantation**. *Nat. Rev. Nephrol.* (2018) **14** 558-570. DOI: 10.1038/s41581-018-0039-x
7. Eapen M., Klein J.P., Sanz G.F., Spellman S., Ruggeri A., Anasetti C., Brown M., Champlin R.E., Garcia-Lopez J., Hattersely G.. **Effect of donor–recipient HLA matching at HLA A, B, C, and DRB1 on outcomes after umbilical-cord blood transplantation for leukaemia and myelodysplastic syndrome: A retrospective analysis**. *Lancet Oncol.* (2011) **12** 1214-1221. DOI: 10.1016/S1470-2045(11)70260-1
8. Zachary A.A., Leffell M.S.. **HLA mismatching strategies for solid organ transplantation–a balancing act**. *Front. Immunol.* (2016) **7** 575. DOI: 10.3389/fimmu.2016.00575
9. Sugita S., Iwasaki Y., Makabe K., Kimura T., Futagami T., Suegami S., Takahashi M.. **Lack of T cell response to iPSC-derived retinal pigment epithelial cells from HLA homozygous donors**. *Stem Cell Rep.* (2016) **7** 619-634. DOI: 10.1016/j.stemcr.2016.08.011
10. Schoenebeck J.J., Ostrander E.A.. **Insights into morphology and disease from the dog genome project**. *Annu. Rev. Cell Dev. Biol.* (2014) **30** 535-560. DOI: 10.1146/annurev-cellbio-100913-012927
11. Graves S.S., Storb R.. **Developments and translational relevance for the canine haematopoietic cell transplantation preclinical model**. *Vet. Comp. Oncol.* (2020) **18** 471-483. DOI: 10.1111/vco.12608
12. Kirkness E.F., Bafna V., Halpern A.L., Levy S., Remington K., Rusch D.B., Delcher A.L., Pop M., Wang W., Fraser C.M.. **The dog genome: Survey sequencing and comparative analysis**. *Science* (2003) **301** 1898-1903. DOI: 10.1126/science.1086432
13. Lindblad-Toh K., Wade C.M., Mikkelsen T.S., Karlsson E.K., Jaffe D.B., Kamal M., Clamp M., Chang J.L., Kulbokas E.J., Zody M.C.. **Genome sequence, comparative analysis and haplotype structure of the domestic dog**. *Nature* (2005) **438** 803-819. DOI: 10.1038/nature04338
14. Graumann M., DeRose S., Ostrander E., Storb R.. **Polymorphism analysis of four canine MHC class I genes**. *Tissue Antigens* (1998) **51** 374-381. DOI: 10.1111/j.1399-0039.1998.tb02976.x
15. Ross P., Buntzman A.S., Vincent B.G., Grover E.N., Gojanovich G.S., Collins E.J., Frelinger J.A., Hess P.R.. **Allelic diversity at the DLA-88 locus in Golden Retriever and Boxer breeds is limited**. *Tissue Antigens* (2012) **80** 175-183. DOI: 10.1111/j.1399-0039.2012.01889.x
16. Venkataraman G.M., Geraghty D., Fox J., Graves S.S., Zellmer E., Storer B.E., Torok-Storb B.J., Storb R.. **Canine DLA-79 gene: An improved typing method, identification of new alleles and its role in graft rejection and graft-versus-host disease**. *Tissue Antigens* (2013) **81** 204-211. DOI: 10.1111/tan.12094
17. Venkataraman G.M., Kennedy L.J., Little M.T., Graves S.S., Harkey M.A., Torok-Storb B.J., Storb R.. **Thirteen novel canine dog leukocyte antigen-88 alleles identified by sequence-based typing**. *Hla* (2017) **90** 165-170. DOI: 10.1111/tan.13077
18. Miyamae J., Suzuki S., Katakura F., Uno S., Tanaka M., Okano M., Matsumoto T., Kulski J.K., Moritomo T., Shiina T.. **Identification of novel polymorphisms and two distinct haplotype structures in dog leukocyte antigen class I genes: DLA-88, DLA-12 and DLA-64**. *Immunogenetics* (2018) **70** 237-255. DOI: 10.1007/s00251-017-1031-5
19. Wagner J., DeRose S., Burnett R., Storb R.. **Nucleotide sequence and polymorphism analysis of canine DRA cDNA clones**. *Tissue Antigens* (1995) **45** 284-287. DOI: 10.1111/j.1399-0039.1995.tb02454.x
20. Kennedy L.J., Barnes A., Happ G., Quinnell R., Bennett D., Angles J., Day M., Carmichael N., Innes J., Isherwood D.. **Extensive interbreed, but minimal intrabreed, variation of DLA class II alleles and haplotypes in dogs**. *Tissue Antigens* (2002) **59** 194-204. DOI: 10.1034/j.1399-0039.2002.590303.x
21. Kennedy L., Barnes A., Short A., Brown J., Lester S., Seddon J., Fleeman L., Francino O., Brkljacic M., Knyazev S.. **Canine DLA diversity: 1. New alleles and haplotypes**. *Tissue Antigens* (2007) **69** 272-288. DOI: 10.1111/j.1399-0039.2006.00779.x
22. Kennedy L.J., Ollier W.E., Marti E., Wagner J.L., Storb R.F.. **Canine immunogenetics**. *The Genetics of the Dog* (2012) 91-135
23. Tsai K.L., Starr-Moss A.N., Venkataraman G.M., Robinson C., Kennedy L.J., Steiner J.M., Clark L.A.. **Alleles of the major histocompatibility complex play a role in the pathogenesis of pancreatic acinar atrophy in dogs**. *Immunogenetics* (2013) **65** 501-509. DOI: 10.1007/s00251-013-0704-y
24. Hardt C., Ferencik S., Tak R., Hoogerbrugge P., Wagner V., Grosse-Wilde H.. **Sequence-based typing reveals a novel DLA-88 allele, DLA-88* 04501, in a beagle family**. *Tissue Antigens* (2006) **67** 163-165. DOI: 10.1111/j.1399-0039.2006.00497.x
25. Ollier W.E., Kennedy L.J., Thomson W., Barnes A.N., Bell S.C., Bennett D., Angles J.M., Innes J.F., Carter S.D.. **Dog MHC alleles containing the human RA shared epitope confer susceptibility to canine rheumatoid arthritis**. *Immunogenetics* (2001) **53** 669-673. DOI: 10.1007/s002510100372
26. Kennedy L., Barnes A., Short A., Brown J., Seddon J., Fleeman L., Brkljacic M., Happ G., Catchpole B., Ollier W.. **Canine DLA diversity: 3. Disease studies**. *Tissue Antigens* (2007) **69** 292-296. DOI: 10.1111/j.1399-0039.2006.00781.x
27. Denyer A., Massey J., Davison L., Ollier W., Catchpole B., Kennedy L.. **Dog leucocyte antigen (DLA) class II haplotypes and risk of canine diabetes mellitus in specific dog breeds**. *Canine Med. Genet.* (2020) **7** 15. DOI: 10.1186/s40575-020-00093-9
28. Nakazawa M., Miyamae J., Okano M., Kanemoto H., Katakura F., Shiina T., Ohno K., Tsujimoto H., Moritomo T., Watari T.. **Dog leukocyte antigen (DLA) class II genotypes associated with chronic enteropathy in French bulldogs and miniature dachshunds**. *Vet. Immunol. Immunopathol.* (2021) **237** 110271. DOI: 10.1016/j.vetimm.2021.110271
29. Friedenberg S.G., Buhrman G., Chdid L., Olby N.J., Olivry T., Guillaumin J., O’Toole T., Goggs R., Kennedy L.J., Rose R.B.. **Evaluation of a DLA-79 allele associated with multiple immune-mediated diseases in dogs**. *Immunogenetics* (2016) **68** 205-217. DOI: 10.1007/s00251-015-0894-6
30. Miyamae J., Okano M., Nishiya K., Katakura F., Kulski J.K., Moritomo T., Shiina T.. **Haplotype structures and polymorphisms of dog leukocyte antigen (DLA) class I loci shaped by intralocus and interlocus recombination events**. *Immunogenetics* (2022) **74** 245-259. DOI: 10.1007/s00251-021-01234-5
31. Miyamae J., Yagi H., Sato K., Okano M., Nishiya K., Katakura F., Sakai M., Nakayama T., Moritomo T., Shiina T.. **Evaluation of alloreactive T cells based on the degree of MHC incompatibility using flow cytometric mixed lymphocyte reaction assay in dogs**. *Immunogenetics* (2019) **71** 635-645. DOI: 10.1007/s00251-019-01147-4
32. Wagner J., Burnett R., Works J., Storb R.. **Molecular analysis of DLA-DRBB1 polymorphism**. *Tissue Antigens* (1996) **48** 554-561. DOI: 10.1111/j.1399-0039.1996.tb02669.x
33. Kennedy L.J., Angles J., Barnes A., Carter S., Francino O., Gerlach J., Happ G., Ollier W., Thomson W., Wagner J.. **Nomenclature for factors of the dog major histocompatibility system (DLA), 2000: Second report of the ISAG DLA Nomenclature Committee**. *Anim. Genet.* (2001) **32** 193-199. DOI: 10.1046/j.1365-2052.2001.00762.x
34. Stephens M., Smith N.J., Donnelly P.. **A new statistical method for haplotype reconstruction from population data**. *Am. J. Hum. Genet.* (2001) **68** 978-989. DOI: 10.1086/319501
35. Peakall R., Smouse P.E.. **GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research**. *Mol. Ecol. Notes* (2006) **6** 288-295. DOI: 10.1111/j.1471-8286.2005.01155.x
36. Kumar S., Stecher G., Li M., Knyaz C., Tamura K.. **MEGA X: Molecular evolutionary genetics analysis across computing platforms**. *Mol. Biol. Evol.* (2018) **35** 1547-1549. DOI: 10.1093/molbev/msy096
37. Ohtsubo Y., Ikeda-Ohtsubo W., Nagata Y., Tsuda M.. **GenomeMatcher: A graphical user interface for DNA sequence comparison**. *BMC Bioinform.* (2008) **9**. DOI: 10.1186/1471-2105-9-376
38. Quignon P., Herbin L., Cadieu E., Kirkness E.F., Hédan B., Mosher D.S., Galibert F., André C., Ostrander E.A., Hitte C.. **Canine population structure: Assessment and impact of intra-breed stratification on SNP-based association studies**. *PLoS ONE* (2007) **2**. DOI: 10.1371/journal.pone.0001324
39. Parker H.G., Dreger D.L., Rimbault M., Davis B.W., Mullen A.B., Carpintero-Ramirez G., Ostrander E.A.. **Genomic analyses reveal the influence of geographic origin, migration, and hybridization on modern dog breed development**. *Cell Rep.* (2017) **19** 697-708. DOI: 10.1016/j.celrep.2017.03.079
40. Lampi S., Donner J., Anderson H., Pohjoismäki J.. **Variation in breeding practices and geographic isolation drive subpopulation differentiation, contributing to the loss of genetic diversity within dog breed lineages**. *Canine Med. Genet.* (2020) **7** 5. DOI: 10.1186/s40575-020-00085-9
41. Runstadler J., Angles J., Pedersen N.C.. **Dog leucocyte antigen class II diversity and relationships among indigenous dogs of the island nations of Indonesia (Bali), Australia and New Guinea**. *Tissue Antigens* (2006) **68** 418-426. DOI: 10.1111/j.1399-0039.2006.00696.x
42. Kang M., Ahn B., Youk S., Cho H.-s., Choi M., Hong K., Do J.T., Song H., Jiang H., Kennedy L.J.. **High Allelic Diversity of Dog Leukocyte Antigen Class II in East Asian Dogs: Identification of New Alleles and Haplotypes**. *J. Mamm. Evol.* (2021) **28** 773-784. DOI: 10.1007/s10914-021-09560-x
43. Niskanen A., Hagström E., Lohi H., Ruokonen M., Esparza-Salas R., Aspi J., Savolainen P.. **MHC variability supports dog domestication from a large number of wolves: High diversity in Asia**. *Heredity* (2013) **110** 80-85. DOI: 10.1038/hdy.2012.67
44. Gojobori J., Arakawa N., Xiayire X., Matsumoto Y., Matsumura S., Hongo H., Ishiguro N., Terai Y.. **The Japanese wolf is most closely related to modern dogs and its ancestral genome has been widely inherited by dogs throughout East Eurasia**. *bioRxiv* (2021) **2021** 463851
45. Niskanen A., Kennedy L., Ruokonen M., Kojola I., Lohi H., Isomursu M., Jansson E., Pyhäjärvi T., Aspi J.. **Balancing selection and heterozygote advantage in major histocompatibility complex loci of the bottlenecked Finnish wolf population**. *Mol. Ecol.* (2014) **23** 875-889. DOI: 10.1111/mec.12647
46. Arbanasić H., Huber Đ., Kusak J., Gomerčić T., Hrenović J., Galov A.. **Extensive polymorphism and evidence of selection pressure on major histocompatibility complex DLA-DRB1, DQA1 and DQB1 class II genes in Croatian grey wolves**. *Tissue Antigens* (2013) **81** 19-27. DOI: 10.1111/tan.12029
47. Kennedy L.J., Angles J.M., Barnes A., Carmichael L.E., Radford A.D., Ollier W.E., Happ G.M.. **DLA-DRB1, DQA1, and DQB1 alleles and haplotypes in North American gray wolves**. *J. Hered.* (2007) **98** 491-499. DOI: 10.1093/jhered/esm051
48. Marsden C.D., Ortega-Del Vecchyo D., O’Brien D.P., Taylor J.F., Ramirez O., Vilà C., Marques-Bonet T., Schnabel R.D., Wayne R.K., Lohmueller K.E.. **Bottlenecks and selective sweeps during domestication have increased deleterious genetic variation in dogs**. *Proc. Natl. Acad. Sci. USA* (2016) **113** 152-157. DOI: 10.1073/pnas.1512501113
49. Pedersen N.C., Liu H., Leonard A., Griffioen L.. **A search for genetic diversity among Italian Greyhounds from Continental Europe and the USA and the effect of inbreeding on susceptibility to autoimmune disease**. *Canine Genet. Epidemiol.* (2015) **2** 17. DOI: 10.1186/s40575-015-0030-9
50. Osborne A.J., Pearson J., Negro S.S., Chilvers B.L., Kennedy M.A., Gemmell N.J.. **Heterozygote advantage at MHC DRB may influence response to infectious disease epizootics**. *Mol. Ecol.* (2015) **24** 1419-1432. DOI: 10.1111/mec.13128
51. Burger D., Thomas S., Aepli H., Dreyer M., Fabre G., Marti E., Sieme H., Robinson M.R., Wedekind C.. **Major histocompatibility complex-linked social signalling affects female fertility**. *Proc. R. Soc. B Biol. Sci.* (2017) **284** 20171824. DOI: 10.1098/rspb.2017.1824
52. Nakatsuji N., Nakajima F., Tokunaga K.. **HLA-haplotype banking and iPS cells**. *Nat. Biotechnol.* (2008) **26** 739-740. DOI: 10.1038/nbt0708-739
53. Taylor C.J., Bolton E.M., Pocock S., Sharples L.D., Pedersen R.A., Bradley J.A.. **Banking on human embryonic stem cells: Estimating the number of donor cell lines needed for HLA matching**. *Lancet* (2005) **366** 2019-2025. DOI: 10.1016/S0140-6736(05)67813-0
54. Lin G., Xie Y., OuYang Q., Qian X., Xie P., Zhou X., Xiong B., Tan Y., Li W., Deng L.. **HLA-matching potential of an established human embryonic stem cell bank in China**. *Cell Stem Cell* (2009) **5** 461-465. DOI: 10.1016/j.stem.2009.10.009
55. Álvarez-Palomo B., García-Martinez I., Gayoso J., Raya A., Veiga A., Abad M.L., Eiras A., Guzmán-Fulgencio M., Luis-Hidalgo M., Eguizabal C.. **Evaluation of the Spanish population coverage of a prospective HLA haplobank of induced pluripotent stem cells**. *Stem Cell Res. Ther.* (2021) **12** 233. DOI: 10.1186/s13287-021-02301-0
56. Kimura K., Tsukamoto M., Tanaka M., Kuwamura M., Ohtaka M., Nishimura K., Nakanishi M., Sugiura K., Hatoya S.. **Efficient reprogramming of canine peripheral blood mononuclear cells into induced pluripotent stem cells**. *Stem Cells Dev.* (2021) **30** 79-90. DOI: 10.1089/scd.2020.0084
57. Yoshimatsu S., Edamura K., Yoshii Y., Iguchi A., Kondo H., Shibuya H., Sato T., Shiozawa S., Okano H.. **Non-viral derivation of a transgene-free induced pluripotent stem cell line from a male beagle dog**. *Stem Cell Res.* (2021) **53** 102375. DOI: 10.1016/j.scr.2021.102375
58. Scarfone R.A., Pena S.M., Russell K.A., Betts D.H., Koch T.G.. **The use of induced pluripotent stem cells in domestic animals: A narrative review**. *BMC Vet. Res.* (2020) **16**. DOI: 10.1186/s12917-020-02696-7
59. Angles J., Kennedy L., Pedersen N.C.. **Frequency and distribution of alleles of canine MHC-II DLA-DQB1, DLA-DQA1 and DLA-DRB1 in 25 representative American Kennel Club breeds**. *Tissue Antigens* (2005) **66** 173-184. DOI: 10.1111/j.1399-0039.2005.00461.x
60. Gershony L.C., Belanger J.M., Short A.D., Le M., Hytönen M.K., Lohi H., Famula T.R., Kennedy L.J., Oberbauer A.M.. **DLA class II risk haplotypes for autoimmune diseases in the bearded collie offer insight to autoimmunity signatures across dog breeds**. *Canine Genet. Epidemiol.* (2019) **6** 2. DOI: 10.1186/s40575-019-0070-7
61. DeVos J.M., Gaber A.O., Knight R.J., Land G.A., Suki W.N., Gaber L.W., Patel S.J.. **Donor-specific HLA-DQ antibodies may contribute to poor graft outcome after renal transplantation**. *Kidney Int.* (2012) **82** 598-604. DOI: 10.1038/ki.2012.190
62. Tikkanen J.M., Singer L.G., Kim S.J., Li Y., Binnie M., Chaparro C., Chow C.-W., Martinu T., Azad S., Keshavjee S.. **De novo DQ donor-specific antibodies are associated with chronic lung allograft dysfunction after lung transplantation**. *Am. J. Respir. Crit. Care Med.* (2016) **194** 596-606. DOI: 10.1164/rccm.201509-1857OC
63. Greer K., Wong A., Liu H., Famula T., Pedersen N.C., Ruhe A., Wallace M., Neff M.. **Necrotizing meningoencephalitis of Pug dogs associates with dog leukocyte antigen class II and resembles acute variant forms of multiple sclerosis**. *Tissue Antigens* (2010) **76** 110-118. DOI: 10.1111/j.1399-0039.2010.01484.x
64. Stromberg S.J., Thomasy S.M., Marangakis A.D., Kim S., Cooper A.E., Brown E.A., Maggs D.J., Bannasch D.L.. **Evaluation of the major histocompatibility complex (MHC) class II as a candidate for sudden acquired retinal degeneration syndrome (SARDS) in Dachshunds**. *Vet. Ophthalmol.* (2019) **22** 751-759. DOI: 10.1111/vop.12646
|
---
title: 'The Bioaccessibility of Yak Bone Collagen Hydrolysates: Focus on Analyzing
the Variation Regular of Peptides and Free Amino Acids'
authors:
- Zitao Guo
- Yuliang Yang
- Bo Hu
- Lingyu Zhu
- Chunyu Liu
- Moying Li
- Zhenghua Gu
- Yu Xin
- Zhongpeng Guo
- Haiyan Sun
- Yanming Guan
- Liang Zhang
journal: Foods
year: 2023
pmcid: PMC10001269
doi: 10.3390/foods12051003
license: CC BY 4.0
---
# The Bioaccessibility of Yak Bone Collagen Hydrolysates: Focus on Analyzing the Variation Regular of Peptides and Free Amino Acids
## Abstract
The lack of a bioaccessibility test for yak bone collagen hydrolysates (YBCH) limits their development as functional foods. In this study, simulated gastrointestinal digestion (SD) and absorption (SA) models were utilized to evaluate the bioaccessibility of YBCH for the first time. The variation in peptides and free amino acids was primarily characterized. There was no significant alteration in the concentration of peptides during the SD. The transport rate of peptides through the Caco-2 cell monolayers was 22.14 ± $1.58\%$. Finally, a total of 440 peptides were identified, more than $75\%$ of them with lengths ranging from 7 to 15. The peptide identification indicated that about $77\%$ of the peptides in the beginning sample still existed after the SD, and about $76\%$ of the peptides in the digested YBCH could be observed after the SA. These results suggested that most peptides in the YBCH resist gastrointestinal digestion and absorption. After the in silico prediction, seven typical bioavailable bioactive peptides were screened out and they exhibited multi-type bioactivities in vitro. This is the first study to characterize the changes in peptides and amino acids in the YBCH during gastrointestinal digestion and absorption, and provides a foundation for analyzing the mechanism of YBCH’s bioactivities.
## 1. Introduction
Collagen peptide is mainly produced by extraction, hydrolysis, and refining with fresh animal tissues which are rich in collagen such as skins, bones, tendons, and scales [1]. In addition to providing a nutritional function, collagen peptide possesses the ability to regulate physiological activities such as modulating immunity, reducing obesity, alleviating osteoporosis, improving bone density, promoting skin health, etc. [ 2,3,4,5,6]. With people’s the increasing attention to body health, especially since the COVID-19 outbreak, the demand for collagen peptide is increasing. It has been reported that the global collagen peptide market reached USD 598.1 million in 2020 alone. The annual projected growth rate is $5.8\%$ from 2021 to 2028 (https://www.zionmarketresearch.com/report/collagen-peptides-market, accessed on 14 January 2023). Currently, considering the product cost and the fact that peptides are the main components of protein hydrolysates, most collagen peptide in the market is sold in the form of collagen hydrolysates.
Yak bone collagen hydrolysates (YBCH) are manufactured by the enzymatic hydrolysis of yak bone collagen. They are a mixture of peptides and free amino acids. Among these, peptides account for about $88\%$ (w/w) [7]. YBCH have been widely applied in the fields of food and cosmetics due to its various physiological activities. In recent years, the studies on YBCH have mainly focused on two aspects. One is to obtain bioactive peptides (BAP) through multistage chromatography purification and separation. For example, Ye et al. isolated and identified two novel peptides (GPSGPAGKDGRIGQPG and GDRGETGPAGPAGPIGPV) with osteoblast proliferation-promoting activity by employing an ultrafiltration membrane system and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) [8]. In a similar way, it has been reported that antioxidant peptides could be isolated from YBCH [9]. In addition, many researchers and consumers are more interested in the bioactivity of YBCH rather than isolating and purifying BAP from YBCH. Therefore, the other research aim regarding YBCH is to verify its bioactivities through in vivo animal experiments. For example, the immunomodulatory effects of YBCH were tested on cyclophosphamide-induced immunosuppression BALB/c mice [6]. After the intervention, signs related to the immunity of mice indicated that the produced YBCH could effectively prevent and ameliorate immunosuppression by improving innate and adaptive immunity. Additionally, to further certificate the positive effects of YBCH on osteoporosis in vivo, the YBCH were supplemented to ovariectomy-induced osteoporotic rats [5]. After ovariectomy, the osteoporosis-related indices of the rats who received YBCH were significantly ameliorated when compared with those of the control group. The serum untargeted metabolomics revealed that YBCH intake could protect or recover ovariectomy-induced osteoporosis by regulating the amino acid metabolism and the lipid metabolism. In our previous study, the modulating effects of YBCH on the gut microbiota of mice were investigated [10]. After a 30-day intervention, the ratio of Firmicutes to Bacteroidetes in the fecal microbiota was reduced and the amount of short-chain fatty acids in the fecal matter of mice were remarkably elevated. Moreover, the anti-obesity effects of YBCH on high-fat-diet mice were investigated [11]. The joint analysis of the microbiome and untargeted metabolomics suggested that the alleviation effects of YBCH on obesity might be achieved by modulating gut microbiota amino acid metabolism.
In summary, these studies showed that YBCH possess multi-type bioactivities and can be utilized as functional foods or dietary supplements. However, different from polysaccharides, which could resist gastrointestinal digestion and absorption, peptides face severe challenges in the digestive tract. In addition to the harsh gastrointestinal digestive environment, a variety of proteases or peptidases in the digestive fluid further promote the decomposition of peptides [12]. More importantly, the instability of peptides significantly influences their function and metabolism due to the loss of key amino acids or the alteration in peptide size [1]. Therefore, whether for developing functional foods or clarifying the mechanism of their biological effects, it is necessary to evaluate the bioaccessibility of YBCH during gastrointestinal digestion and absorption. Nevertheless, there were fewer reports investigating the changes in YBCH during gastrointestinal digestion and absorption. Owing to their simplicity and universality, simulated gastrointestinal digestion and absorption have been widely implicated in the field of food to predict outcomes of in vivo digestion [13]. Moreover, with the rapid development of peptide identification technology and bioinformatics, a variety of peptide databases and in silico prediction technologies have been developed. These tools make it easier to identify bioactive peptides and analyze their bioactivities [14]. In this study, the objective was to forecast the variation regular of free amino acids and peptides of YBCH during in vivo digestion by employing simulated gastrointestinal digestion and absorption. Moreover, the peptides in the samples were identified by HPLC-MS/MS. The biological activities of the bioavailable peptides were predicted by an in silico analysis and verified by an in vitro test. This study not only provides a foundation for analyzing the mechanism of YBCH’s biological activity but also promotes the application of YBCH in the fields of food and medicine.
## 2.1. Chemical Agents and Preparation of YBCH
The preparation of YBCH was conducted as previously described [10], and the details as shown in the Supplementary Materials. The pepsin from porcine gastric mucosa (P6887) and pancreatin from porcine pancreas (P7545) were purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). Caco-2 cells and murine macrophage RAW 264.7 cells were bought from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). Angiotensin-converting enzyme (ACE), enzyme from rabbit lung, and dipeptidyl peptidase IV (DPP-IV)-inhibitor screening kit (MAK203) were purchased from Sigma Chemical Co. (St. Louis, MO, USA); Hippuryl-Histidine-Leucine (HHL) and Hippuric Acid (HA) were from Shanghai Maclean Biochemical Technology Co. (Shanghai, China). Cell counting kit (CCK-8) was bought from Dojindo Laboratories (Kumamoto, Japan). The IL-1β assay kit, IL-6 assay kit, TNF-α assay kit, and NO assay kit were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Other cell culture-related agents such as Dulbecco’s Modified Eagle Medium (DMEM), $0.25\%$ (w/v) trypsin-0.91 mM EDTA, and Hank’s balanced salt solution (HBSS) were obtained from Gibco Life Technologies (Grand Island, NY, USA). 1 nmol/µL essential amino acids standard solutions were purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). Unless stated, all other chemicals were analytical-grade and purchased from China Pharmaceutical Group Chemical Reagent Co., Ltd. (Shanghai, China).
## 2.2. Simulated Gastrointestinal Digestion (SD)
The SD was operated as previously described with a slight modification [15]. Briefly, 10 g YBCH was added in 385 mL deionized water. The solution was stirred well and adjusted to pH 2.0 by 1 M HCl. Pepsin was added to the solution in 1:50 (enzyme to the substrate (E/S), w/w), and the mixture was incubated for 2 h in a water bath shaker (37 °C, 0.01 g) to simulate the gastric digestion. The pepsin activity was terminated by adjusting the pH to 7.0 with 1.0 M NaOH. Then, pancreatin was added into the mixture at the ratio of E/S 1:25. Subsequently, the mixture was placed in the water bath shaker for 2 h (37 °C, 0.01 g) to simulate intestinal digestion. Samples were taken every 30 minutes during the whole process. The simulated digestion was inactivated by incubating the mixture in boiling water for 10 min. After cooling to room temperature and centrifugation (20,000× g, 20 min), the supernatant of samples was then lyophilized and stored at −80 °C until further analysis (for a maximum of 2 weeks).
## 2.3.1. Cell Culture
Caco-2 cells were cultured in a DMEM medium (containing $10\%$ fetal bovine serum, $1\%$ penicillin-streptomycin), and then incubated at 37 °C with $5\%$ CO2. The medium was changed every day. The cells were digested and sub-cultured with $0.25\%$ trypsin solution when the cells reached 80–$90\%$ confluence. Cells in the logarithmic growth phase were taken for the experiment. Caco-2 cells used in this experiment were 25~35 generations.
## 2.3.2. Cytotoxicity Test
The CCK-8 kit was employed to detect the survival rate of Caco-2 cells after obtaining different concentrations of simulated digested YBCH. Caco-2 cells were seeded in a 96-well microplate with a density of 1 × 105 cells/mL. After incubating for 24 h, the culture medium was removed. The Caco-2 cells were randomly divided into a blank control group (only DMEM medium) and the test groups (simulated digested YBCH concentrations were 30 mg/mL, 10 mg/mL, 3.3 mg/mL, 1.1 mg/mL, 0.37 mg/mL, and 0.12 mg/mL). The blank wells only included the same amount of phosphate buffer without cells. Each test was performed in triplicate. After incubation for 6 h, we aspirated the culture medium and added 100 μL of fresh culture medium. Then, 10 μL of CCK-8 was added to each well and incubated for 4 h under 37 °C with $5\%$ CO2. After that, the optical density (OD) value of each well was measured by a microplate reader at the wavelength of 450 nm. The cell survival rate was calculated according to the below Formula [1]:[1]Cell survival rate %=OD1−OD3OD2−OD3×100 where OD1, OD2, and OD3 are the absorbance of the test groups, blank control group, and blank wells group, respectively.
## 2.3.3. Transport Studies
The cellular transport study of the simulated gastrointestinal digested YBCH was conducted as previously described with a slight modification [16]. Briefly, the Caco-2 cells were seeded in 24-well transwell inserts (6.5 mm diameter, 0.4 µm pore size, Corning, City, NY, USA) with a density of 1 × 105 cells/cm2. The volume of medium on the apical side (AP) and basolateral side (BL) was separately 0.4 mL and 0.6 mL. During the incubation, the medium was refreshed every two days in the first week and every day in the subsequent culture until the monolayer integrity evaluation. The monolayer integrity was evaluated by measuring the transepithelial electrical resistance (TEER) value, detecting paracellular permeability of the fluorescein sodium, and determining the alkaline phosphatase (ALP) activity of the cell culture medium on the two sides of the transwell. The method was operated as previously described [17,18,19]. On the test day, the culture medium on both sides was aspirated and the cell monolayers were rinsed twice with the HBSS. After that, HBSS was added to both sides (AP, 0.4 mL; BL, 0.6 mL) and incubated for 30 min to stabilize prior to the transport studies. Samples (0.4 mL) with a nontoxic concentration (3.3 mg/mL) were added to the AP side, and the fresh blank HBSS solution (0.6 mL) was added to the BL side. After incubation for 2 h, samples on two sides were aspirated and stored at −80 °C until further analysis (for a maximum of 2 weeks). Each test was performed in triplicate.
After the SA, the concentration of peptides on the two sides of the transwell were analyzed by the OPA (o-phthalaldehyde) assay as previously described [20]. The transport rate was calculated using the below Formula [2]:[2]transport rate %=CBL×0.6 CAP×0.4+CBL×0.6×100 where the CBL and CAP are the peptide concentration (mg/mL) on the AP and BL sides after the SA, respectively.
## 2.4. Characterization of the Samples
To comprehensively understand the changes in YBCH during the SD and the SA, the YBCH before and after SD and the solutions on the AP and BL sides at the end of the cell test were harvested and separately termed initial samples (STA), samples after gastrointestinal digestion (SGID), samples on the AP side (SIA), and samples on the BL side (SIB). Then, all these samples were characterized by molecular weight distribution, peptide concentration, free amino acid concentration, and peptides sequence identification. The detecting was conducted as previously described with a slight modification [6,20,21], and the details are provided in the Supplementary Materials.
## 2.5.1. In Silico Prediction
The peptides that existed simultaneously in the STA, SGID, and SIA were defined as bioavailable peptides, and their biological activity was predicted by PepRanker (http://distilldeep.ucd.ie/PeptideRanker/, accessed on 14 January 2023) at first [22]. To reduce the number of false positives, the peptides with scores above 0.8 were further calculated and their physicochemical properties (including toxicity, allergenicity, hydrophilicity, hydrophobicity, charge, isoelectric point (pI), and molecular weight) were predicted. Moreover, the nontoxic and non-allergenic peptides were screened to predict their potential ability to be antihypertension peptides (AHTP) [23], antidiabetic peptides (ADP) [24], anti-inflammatory peptides (AIP) [25], and antioxidation peptides (AOP) [26]. The predicting websites are shown in the Supplementary Materials. The novelty of the screened peptides was checked using PepBank (Peptide Database-Search (harvard.edu), accessed on 14 January 2023) and BIOPEP-UWM (http://www.uwm.edu.pl/biochemia/index.php/pl/biopep, accessed on 14 January 2023).
## 2.5.2. In Vitro Verification
The screened peptides were synthesized by Fmoc solid-phase chemical synthesis (Shanghai RoyoBiotech Co., Shanghai, China). Their structures were verified by LC-MS and the peptide with a purity above $98\%$ was utilized for subsequently verifying their antihypertension, antidiabetic, anti-inflammatory, and antioxidation abilities. The detecting methods were performed as previously described and the details are provided in the Supplementary Materials [27,28,29].
## 2.6. Statistical Analysis
The software SPSS 25.0 (SPSS Inc., Chicago, IL, USA) was employed to analyze the data. One-way ANOVA followed by an LSD test (equal variances assumed) or a Games–Howell test (equal variances not assumed) were performed to evaluate the significant differences between samples. All tests had three replicates. Unless stated, each test was performed in triplicate and the results are presented by mean ± SD. A significant difference was accepted at $p \leq 0.05$, and $p \leq 0.01$ was a highly significant difference.
## 3.1. Molecular Weight Distribution and Concentration of Peptides
The molecular weight distribution during the SD and the SA is shown in Figure 1A. In the SD, the molecular weight distribution of peptides with a molecular weight below 1000 Da gradually increased, while that of the peptides whose molecular weight was above 5000 Da and 2000–3000 Da decreased. Interestingly, the molecular weight distribution of 3000–5000 Da (around $9\%$) and 1000–2000 Da (around $19\%$) was not significantly altered. After the 2 h SA, the molecular weight distribution on the AP side was similar to that at the beginning (SGID-4.0, Figure 1A). Only the molecular weight distribution of 189–500 Da was significantly reduced, from 23.09 ± $2.06\%$ to 13.75 ± $1.09\%$. For the BL side, the molecular weight distribution of peptides with a molecular weight below 500 Da occupied more than $75\%$, which indicated that the tripeptides, dipeptides, and free amino acids were the main components of the BL side.
The alteration in peptide concentration was also monitored during the SD and the SA (Figure 1B). The concentration of peptides in the STA was 21.95 ± 1.73 mg/mL, while it was 19.87 ± 0.40 mg/mL after the SD. Although there was a reduction in the concentration of peptides during the SD, statistical analysis indicated that there were no significant differences ($p \leq 0.05$, Games–Howell test). After the SA, the peptide concentration on the AP and BL side was 1.92 ± 0.04 mg/mL and 0.37 ± 0.04 mg/mL, respectively.
## 3.2. Free Amino Acids Alteration
The total concentration of free amino acids increased nearly one time after the SD (from 135 ± 3 × 10−2 mg/mL to 266 ± 8 × 10−2 mg/mL, Figure 2A). This might be induced by the increase in arginine, tyrosine, phenylalanine, leucine, and lysine (Figure 2A). These kinds of amino acids were the main components of the free amino acids after the SD; the total mass of these free amino acids was above 2.0 mg/mL and their increase ratios were all above $80\%$. Interestingly, all five kinds of amino acids were not significantly altered during the simulated gastric digestion. However, there was a remarkable increase in the previous 30 min of the simulated intestinal digestion. After that, only a slight increase was exhibited. This phenomenon could be also observed in the alteration in other kinds of amino acids during the SD.
Through comparing the alteration in the concentration of free amino acids before and after SA, it was found that the most increased amino acid was glycine (Figure 2B). The concentration of glycine increased from 0.98 × 10−2 mg/mL to 5.89 × 10−2 mg/mL; the increase ratio was $501.02\%$. This was followed by proline and tyrosine, whose increase ratio was $338.46\%$ and $234.41\%$, respectively. Moreover, leucine, phenylalanine, methionine, alanine, and histidine all increased by more than $100\%$. The free amino acid distribution on the two sides of the transwell is shown in Figure 2B. It indicates that $82.5\%$ of the proline was transported to the BL side. Noteworthily, only $6.40\%$ of the arginine was distributed on the BL side. These results suggest that intestinal cells might have different transport capacities for different free amino acids in YBCH.
## 3.3. Transport Study
The cell toxicity and monolayer integrity were first studied before the SA. Results indicated that the digested YBCH had no toxicity to the Caco-2 cell. The survival rate of the cell under different concentrations of the digested YBCH was above $90\%$. Notably, the cell survival rate was almost $100\%$ under the concentration of <3.33 mg/mL (Figure 3A). To avoid inaccurate results of transport and absorption due to the saturation of the peptide during transport, the concentration was set at 3.33 mg/mL for the subsequent SA test. After 21 days of incubation, the TEER value was 595 ± 18.06 Ω·cm2 (Figure 3B) and the ALP ratio of the AP to BL was 7.43 ± 0.51 (Figure 3C). Moreover, the paracellular permeability rate of fluorescein sodium was 2.63 ± $0.34\%$, which was significantly lower than that of the blank control (23.89 ± $0.6\%$, Figure 3D). These results indicate that the cell monolayer was suitable for the transport study. Calculated according to the concentration of peptides on the two sides (Figure 1B), the transport rate was 22.14 ± $1.58\%$.
## 3.4. Identification of Peptides
Amongst all samples taken, a total of 440 peptides were identified (information of 440 peptides was uploaded to Mendeley Data, https://data.mendeley.com/datasets/s3j9vpfdff/1, accessed on 23 February 2023, file name: S1-peptideSummary). The number of peptides in STA, SGID, SIA, and SIB was 251, 248, 232, and 97, respectively (Figure 4A). Among these identified peptides, one peptide (PGPAGPAGP) in the STA and two peptides (PGPAGPA and PGAVGPA) in the SIA both belonged to the collagen alpha-1(I) chain and collagen alpha-2(I) chain. The minimized peptide length was 7, while the longest one was 31 (Figure 4B). Among these peptides, the percentage of peptides with lengths ranging from 7 to 15 was more than $75\%$. The uniqueness and repeatability of peptides in each sample were displayed by the Venn diagram (Figure 4C). The number of peptides that could be identified in both STA and SGID was 193. During the SD, 55 new peptides were produced. Compared with the number of peptides in the SIA, only six new peptides appeared in the SIB after the SA. Significantly, 145 peptides simultaneously existed in the STA, SGID, and SIA. A volcano plot was employed to display the alteration in the relative content of peptides during the SD and SA. Compared with SGID, the relative content of 25 peptides in the STA was significantly upregulated, and that of 19 peptides was downregulated (Figure 4D). In addition, the relative content of 12 peptides was altered in the SIA when compared with that of SIB. Among these peptides, the upregulated and the downregulated contents account for half, respectively (Figure 4E).
## 3.5. Prediction of the Biological Activity of Bioavailable Peptides
BAP can exert their physiological activity in vivo only after they resist decomposition in the digestive tract. Therefore, the 145 peptides that simultaneously existed in the STA, SGID, and SIA deserve more attention. Moreover, 73 of these peptides were also identified in the SIB. These results suggest that the 73 peptides might be absorbed by the intestinal epithelial cells and the other 72 peptides might resist absorption. For more convenient analysis and statistics, the 73 peptides and the 72 peptides were divided into two groups and separately named anti-digestion group (AD) and anti-digestion and anti-absorption group (ADA) (the information of 145 peptides was uploaded to Mendeley Data, https://data.mendeley.com/datasets/s3j9vpfdff/1, accessed on 23 February 2023, file name: S2-basic information of AD and ADA). The bioactive activity of these peptides was predicted by employing in silico tools.
The peptides with a predicted score of biological activity above 0.8 were selected and reordered according to their intensity in the SIA (BAP score of 145 peptides was uploaded to Mendeley Data, https://data.mendeley.com/datasets/s3j9vpfdff/1, accessed on 23 February 2023, file name: S3-properties of AD and ADA). Finally, a total of 6 and 10 typical anti-digestion peptides were screened from the AD group and the ADA group, respectively (Table 1). The shortest peptide length was 7, while the longest one was 19. The molecular weight of these peptides ranged from 571.75 to 1484.94, and most of them were hydrophobic. Moreover, only five peptides were originated from the collagen alpha-2(I) chain. Furthermore, the potential bioactive activity of the nontoxic and non-allergenic peptides was predicted (Table 2). It suggested that all these peptides (except FGFDGDF) might be applied as AHTP, ADP, and AIP. In addition, the antioxidant activity of peptides in the AD and ADA group might be similar. The activity prediction scores of these peptides were very close.
## 3.6. Verification of the Biological Activity of Bioavailable Peptides
To further confirm the prediction results, the biological activities of the screened peptides were verified in vitro. The antihypertension and antidiabetic abilities of these peptides were evaluated by detecting their IC50 values on ACE and DPP-IV. For ACE inhibition, only PGPMGPSGPR had an IC50 value below 10 mM, while that of other peptides (except FGFDGDF) ranged from 11.7 ± 1.34 mM to 17.07 ± 2.45 mM (Table 3). Considering the application in vivo, such a high IC50 value of these peptides suggested their poor ability to develop as ACE inhibitors. The IC50 value on ACE of FGFDGDF was not determined due to its low predicted score as AHTP (Table 2). More importantly, we found that FGFDGDF could not be totally dissolved in the reaction solution. Among the seven peptides, GPAGPAGPIGPVG had the best inhibition ability on DPP-IV, with a low IC50 value of 0.07 ± 0.01 mM. In addition, GPPGPAGPAG, FGFDGDF, and PAGPAGPIGPV also had a good performance; their IC50 values on DPP-IV were lower than 1 mM (Table 3).
To verify the ability of these peptides as AIP, their alleviation effects on the lipopolysaccharide (LPS)-induced murine macrophage inflammation models were evaluated. Results indicated that these peptides had no cytotoxicity on macrophages and they could significantly inhibit the release of inflammatory factors (Figure S1 and Figure 5). The inhibition rates of these peptides on IL-1β and NO were all above $50\%$. Except for AGPAGPAGPAGPR, which had a low inhibition rate on TNF-α (mean value $34.96\%$) and IL-6 (mean value $25.9\%$), most peptides possessed an inhibition rate above $50\%$ on these two inflammatory factors. The hydroxyl radical scavenging activity and ferric ion chelating activity of the seven peptides were detected to characterize their antioxidation ability. Results indicated that the above two activities of the seven peptides were all below $50\%$. However, consistent with the prediction results (Table 2), there was no difference in the anti-oxidant activity of Seq1 and Seq2 in the AD group, and the analogous situation was also observed between Seq3, Seq4, Seq5, Seq6, and Seq7 in the ADA group (Figure 5).
## 4. Discussion
Bioaccessbility should be paid more attention to when developing new functional products because it is the most important factor affecting the biological activity of functional ingredients. In this study, to further expand the application of YBCH, the bioaccessbility of YBCH was evaluated by employing simulated gastrointestinal digestion and absorption. The changes in peptides and free amino acids were monitored during the SD and the SA. Results indicated that only slight changes in the concentration of peptides and free amino acids were observed (Figure 1 and Figure 2). Moreover, the identification of peptides showed that about $77\%$ of the peptides in the STA could be identified in the SGID after the SD, and about $76\%$ of the peptides in the SGID could be observed in the SIA after the SA (Figure 4C). Although 91 peptides in the SIA were also identified in the SIB, the transport rate of peptides was only 22.14 ± $1.58\%$ (Figure 1B). These results suggest that most peptides in YBCH resist gastrointestinal digestion and absorption. The structural parameters of peptides could significantly influence the bioaccessbility of peptides during digestion and absorption in vivo [12]. From the changes in peptides and free amino acids, we speculated that the stability of YBCH might be attributed to their specific physicochemical properties, including molecular weight and amino acid composition.
The low molecular weight not only promotes the absorption of peptides but also might be beneficial for the stability of peptides. It has been reported that the low-molecular-weight peptides might avoid protease enzymes due to smaller number of protease recognition and cleavage sites in their sequence [12]. In this study, the peptides with a molecular weight below 3000 Da were the major components of the STA (occupied more than $70\%$). After the SD, the molecular weight distribution of peptides whose molecular weight was above 3000 Da was finally reduced to around $13.36\%$ (Figure 1A). Thus, a large number of peptides with low molecular weight existing in YBCH might be an important reason for the stability of YBCH during the SD. Moreover, the steric hindrance of these low-molecular-weight peptides might be another important factor to avoid protease or peptidase cleavage. GPAGPPGPIGNV and NAPHMR are BAPs derived from YBCH and sea cucumber gonad, respectively [30,31]. It has been reported that they could resist simulated gastrointestinal digestion. The steric hindrance value of these two BAPs was found to be 0.57 and 0.52 by calculating with an in silico tool named AntiCP 2.0 (https://webs.iiitd.edu.in/raghava/anticp2/predict.php, accessed on 14 January 2023) [32]. However, most of the 145 peptides which were found simultaneously in the STA, SGID, and SIA possessed a higher predicted steric hindrance value than that of the above-reported BAPs (steric hindrance predict score of 145 peptides was uploaded to Mendeley Data, https://data.mendeley.com/datasets/s3j9vpfdff/1, accessed on 23 February 2023, file name: S3-properties of AD and ADA). Therefore, the steric hindrance of these low-molecular-weight peptides might increase the difficulty of the enzymatic hydrolysis process during gastrointestinal digestion.
In addition, peptides with different molecular weights might have their own characteristics during gastrointestinal digestion and absorption. For example, during simulated gastric digestion, the molecular weight distribution of the peptides with a molecular weight above 5000 Da was only reduced from 17.65 ± $1.23\%$ to 13.31 ± $0.72\%$. However, at the beginning of the simulated intestinal digestion, the molecular weight distribution of these peptides sharply decreased from 13.31 ± $0.72\%$ to 6.33 ± $0.37\%$. On the contrary, for the peptides with a molecular weight 2000–3000 Da, the molecular weight distribution was reduced by half (19.22 ± $0.91\%$ to 10.81 ± $1.15\%$) during the simulated gastric digestion, while only a slight decrease (10.08 ± $0.54\%$ to 7.45 ± $0.49\%$) was observed in the simulated intestinal digestion (Figure 1). Additionally, the molecular weight could not only influence the permeability rates of peptides but also affect their absorption pathway. Many reports have suggested that the molecular weight of peptides has a negative relationship with the permeability rates [33,34]. This might be attributed to their different path through intestinal endothelial cells. For example, most di- and tripeptides were transported via PepT1, which is a member of the H+-dependent carried family and widely distributed on intestinal endothelial cells. The larger ones might be absorbed through a paracellular route via tight junctions or transcytosis [35]. However, it should be noted that the paracellular diffusion area only occupied $0.01\%$ of the human gut surface area, and transcytosis is an energy-dependent transport route [36,37]. Therefore, in fact, most of the peptides could not be effectively absorbed by the intestinal epithelial cells. Recently, it has been difficult to accurately quantify and identify the small peptides from the protein hydrolysates, especially for di- and tripeptides [38]. In this study, limited by the detection technology, no peptides with a length below seven were identified. However, it could be speculated that most di-/tripeptides (molecular weight below 500 Da) in the YBCH were absorbed by the Caco-2 cell monolayers based on the molecular weight distribution of the peptides in the SIB and the SIA (Figure 1A). Meanwhile, the major parts of YBCH, peptides with molecular weight ranging from 500 Da to 2000 Da, were not altered significantly during the SA.
The number and location of different amino acids in the peptide sequence could significantly alter the bioavailability of the peptide. Especially for collagen-derived peptides, it is necessary to pay more attention to the effects of proline and glycine on the bioaccessibility of peptides. This is mainly because there are a large number of repeated “G-X-Y” structures in collagen alpha chains, and the X and Y usually are occupied by proline or hydroxyproline [1]. Proline and hydroxyproline have long been recognized as important factors to increase the stability of peptides during gastrointestinal digestion [35]. On the one hand, this might be due to the fact that proline and hydroxyproline are not the cleavage sites of digestive proteases and peptidases including pepsin, trypsin, and chymotrypsin, etc. [ 12]. During the SD and the SA, free proline content was very low (<0.01 mg/mL, Figure 2). Inversely, the amino acids which are the cleavage sites for most proteases, such as arginine, lysine, phenylalanine, leucine, and tyrosine, were the major components of the free amino acids (Figure 2). On the other hand, the proline has a γ-lactam structure. This structure is similar to the γ-lactam moiety in the pyroglutamyl peptides, which could increase the steric hindrance of peptides to proteases [39,40]. In this study, the composition of peptide sequences in STA showed that only 15 peptides did not contain proline. The number of peptides containing two, three, and four prolines in their sequence was 55, 86, and 50, respectively. Notably, there were seven peptides that contained eight prolines in their sequence (Supplementary Materials, Figure S2). Therefore, the high amount of proline in their sequence might enhance the steric hindrance of these peptides to protease and peptidase. In addition, prior studies have noted the effects of different amino acids located in the N- and C-terminal of peptides on their ability to resist digestion and absorption. For example, the peptides with an N-terminal containing isoleucine, lysine, methionine, proline, and valine or a C-terminal containing valine have high permeability [41]. In this study, a total of 125 peptides in STA had glysine in their N-terminal, which accounted for $49.8\%$ of all peptides in STA. In the C-terminal of the peptides in STA, the most widespread amino acid was alanine (57 peptides), followed by arginine (46 peptides) and glycine (45 peptides) (Supplementary Materials, Figure S3). Although there were 26 peptides with C-terminals containing valine, it has been reported that the amino acids in the N-terminal contribute more to bioaccessbility than those in the C-terminal [40]. Therefore, the absence of permeability-promoting amino acids in the N-terminal of peptides in YBCH might enhance the ability of these peptides to resist digestion and absorption.
Moreover, we found that the difference in molecular weight and amino acid composition might explain why the 73 peptides in the AD group might be absorbed by the intestinal epithelial cells and the other 72 peptides in the ADA group might resist absorption. Regarding molecular weight (Supplementary Materials, Figure S4A), the number of peptides with a molecular weight below 1000 Da was 44 in the AD group, while that in the ADA group was 36. Meanwhile, there was a lower number of peptides with molecular weight between 1000 Da and 1500 Da in the AD group. Regarding peptide length (Supplementary Materials, Figure S4B), it was found that the peptide length of most of the peptides in the AD group was located between 7 and 10, which accounts for about $60\%$. These results indicated that the molecular weight of peptides in the AD group might be lower than that in the ADA group with the same peptide length. Thus, the higher molecular weight in the ADA group than that in the AD group might be one reason for the peptides’ different performance during intestinal epithelial cell absorption. After statistical analysis, there were 15 peptides in the AD group with C-terminal-containing valine, which accounts for about $20.5\%$ of the total number of peptides in the group (Supplementary Materials, Figure S4C). However, that number was five in the ADA group, which accounts for only $6.9\%$. Thus, the high ratio of peptides with C-terminal-containing valine in the AD group might contribute to their absorption during SA. To sum up, the molecular weight and amino acid composition of the peptides might be two important reasons that influence their bioaccessibility during gastrointestinal digestion and intestinal epithelial cell absorption.
The traditional screening process of BAP usually requires multiple purification steps combined with in vitro activity verification to continuously narrow the search area in the protein hydrolysates. Finally, the sequence composition of the peptides was determined by LC-MS/MS. Moreover, this procedure needs a lot of time, and more important is that the obtained BAP be tested to evaluate its ability to resist digestion if it is developed as a functional ingredient. This screening method might cause the obtained BAP to be unsuitable for in vivo use. By constructing a reliable simulated in vitro digestion and absorption model, the changes in peptides during the entire digestion and absorption process can be effectively monitored. This not only helps us comprehensively know about the characteristics of peptides during digestion but also obtain results that are closer to the real situation in vivo. Then, combined with the current increasingly mature in silico tools, we can predict the desired bioactivity and further conduct in vitro and in vivo verification. This method might be more efficient and reliable. In this study, although limited by the detection technology in that the peptides below hepteptide were not identified, 440 peptides were obtained in all taken samples. This undoubtedly gives us a treasure trove of many peptides which deserve a further explore into their bioactivity. The most interesting finding was the 145 peptides that existed from the start to the end of the simulated digestion and absorption. After the theoretical calculation and in silico prediction, two typical anti-digestion BAPs and five typical anti-digestion and -absorption BAPs were separately screened (Table 1 and Table 2). After in vitro activity verification, some peptides showed that they deserved further testing in vivo. In addition, combined with our previous work, their bioactivity and their effects on the composition of gut microbiota should be further investigated and validated.
## 5. Conclusions
The bioaccessibility test of functional ingredients is very important for illustrating their mechanisms of action and promotion in the application field. Although YBCH have been developed as dietary nutrients and functional foods for a long time in China due to their various biological activities such as antioxidant and immunomodulation effects, their bioaccessibility has not been reported. Notably, fewer reports exist regarding the variation regular of free amino acids and peptides during gastrointestinal digestion and adsorption, which limits the application of YBCH. Different from previous studies focused on investigating the activity of YBCH, in this study, simulated gastrointestinal digestion and absorption were conducted to characterize the alteration in peptides and free amino acids of YBCH in the digestive tract. Results indicated that most peptides in YBCH resist digestion and absorption, which might be due to the low molecular weight of peptides, the high-frequency distribution of proline, and the absence of permeability-promoting amino acids (high glycine in the N-terminal and less valine in the C-terminal) in the terminal of peptides. Moreover, the joint utilization of in silico analysis and in vitro tests suggested that various types of BAPs could be obtained after the YBCH digestion and absorption. This study promotes new insights into the application of YBCH, and the bioactivities of the obtained peptides should be further verified in the future.
## References
1. Hong H., Fan H.B., Chalamaiah M., Wu J.P.. **Preparation of low-molecular-weight, collagen hydrolysates (peptides): Current progress, challenges, and future perspectives**. *Food. Chem.* (2019) **301** 125222. DOI: 10.1016/j.foodchem.2019.125222
2. Lee E.J., Hur J., Ham S.A., Jo Y., Lee S., Choi M.J., Seo H.G.. **Fish collagen peptide inhibits the adipogenic differentiation of preadipocytes and ameliorates obesity in high fat diet-fed mice**. *Int. J. Biol. Macromol.* (2017) **104** 281-286. DOI: 10.1016/j.ijbiomac.2017.05.151
3. Liu J., Wang Y., Song S., Wang X., Qin Y., Si S., Guo Y.. **Combined oral administration of bovine collagen peptides with calcium citrate inhibits bone loss in ovariectomized rats**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0135019
4. Zdzieblik D., Oesser S., Baumstark M.W., Gollhofer A., Konig D.. **Collagen peptide supplementation in combination with resistance training improves body composition and increases muscle strength in elderly sarcopenic men: A randomised controlled trial**. *Br. J. Nutr.* (2015) **114** 1237-1245. DOI: 10.1017/S0007114515002810
5. Ye M., Zhang C., Jia W., Shen Q., Qin X., Zhang H., Zhu L.. **Metabolomics strategy reveals the osteogenic mechanism of yak (**. *Food. Funct.* (2020) **11** 1498-1512. DOI: 10.1039/C9FO01944H
6. Gao S., Hong H., Zhang C., Wang K., Zhang B., Han Q.-a., Liu H., Luo Y.. **Immunomodulatory effects of collagen hydrolysates from yak (**. *J. Funct. Food* (2019) **60**. DOI: 10.1016/j.jff.2019.103420
7. Li F., Jia D., Yao K.. **Amino acid composition and functional properties of collagen polypeptide from Yak**. *LWT.-Food. Sci. Technol.* (2009) **42** 945-949. DOI: 10.1016/j.lwt.2008.12.005
8. Ye M., Jia W., Zhang C., Shen Q., Zhu L., Wang L.. **Preparation, identification and molecular docking study of novel osteoblast proliferation-promoting peptides from yak (**. *RSC. Adv.* (2019) **9** 14627-14637. DOI: 10.1039/C9RA00945K
9. Sun X., Wang K., Gao S., Hong H., Zhang L., Liu H., Feng L., Luo Y.. **Purification and characterization of antioxidant peptides from yak (**. *J. Food. Sci. Technol.* (2021) **58** 3106-3119. DOI: 10.1007/s13197-020-04814-7
10. Guo Z., Liu C., Hu B., Zhu L., Yang Y., Liu F., Gu Z., Xin Y., Zhang L.. **Simulated gastrointestinal digestion of yak bone collagen hydrolysates and insights into its effects on gut microbiota composition in mice**. *Food. Biosci.* (2021) **44** 101463. DOI: 10.1016/j.fbio.2021.101463
11. Guo Z., Hu B., Zhu L., Yang Y., Liu C., Liu F., Shi Y., Li M., Gu Z., Xin Y.. **Microbiome-metabolomics insights into the feces of high-fat diet mice to reveal the anti-obesity effects of yak (Bos grunniens) bone collagen hydrolysates**. *Food. Res. Int.* (2022) **156** 111024. DOI: 10.1016/j.foodres.2022.111024
12. Udenigwe C.C., Abioye R.O., Okagu I.U., Obeme-Nmom J.I.. **Bioaccessibility of bioactive peptides: Recent advances and perspectives**. *Curr. Opin. Food. Sci.* (2021) **39** 182-189. DOI: 10.1016/j.cofs.2021.03.005
13. Brodkorb A., Egger L., Alminger M., Alvito P., Assuncao R., Ballance S., Bohn T., Bourlieu-Lacanal C., Boutrou R., Carriere F.. **Infogest static in vitro simulation of gastrointestinal food digestion**. *Nat. Protoc.* (2019) **14** 991-1014. DOI: 10.1038/s41596-018-0119-1
14. Ningrum A., Wardani D., Vanidia N., Syarifudin A., Ekafitri R., Kristanti D., Setiaboma W., Siti H., Munawaroh H.. **In Silico Approach of Glycinin and Conglycinin Chains of Soybean By-Product (Okara) Using Papain and Bromelain**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27206855
15. Zhang Q., Tong X., Qi B., Wang Z., Li Y., Sui X., Jiang L.. **Changes in antioxidant activity of Alcalase-hydrolyzed soybean hydrolysate under simulated gastrointestinal digestion and transepithelial transport**. *J. Funct. Food.* (2018) **42** 298-305. DOI: 10.1016/j.jff.2018.01.017
16. Feng M., Betti M.. **Transepithelial transport efficiency of bovine collagen hydrolysates in a human Caco-2 cell line model**. *Food. Chem.* (2017) **224** 242-250. DOI: 10.1016/j.foodchem.2016.12.044
17. Fu Q.X., Wang H.Z., Xia M.X., Deng B., Shen H.Y., Ji G., Li G.W., Xie Y.. **The effect of phytic acid on tight junctions in the human intestinal Caco-2 cell line and its mechanism**. *Eur. J. Pharm. Sci.* (2015) **80** 1-8. DOI: 10.1016/j.ejps.2015.09.009
18. Trigo J.P., Engstrom N., Steinhagen S., Juul L., Harrysson H., Toth G.B., Pavia H., Scheers N., Undeland I.. **In vitro digestibility and Caco-2 cell bioavailability of sea lettuce (Ulva fenestrata) proteins extracted using pH-shift processing**. *Food Chem.* (2021) **356** 129683. PMID: 33845254
19. Turner P.C., Wu Q.K., Piekkola S., Gratz S., Mykkanen H., El-Nezami H.. **Lactobacillus rhamnosus strain GG restores alkaline phosphatase activity in differentiating Caco-2 cells dosed with the potent mycotoxin deoxynivalenol**. *Food. Chem. Toxicol.* (2008) **46** 2118-2123. DOI: 10.1016/j.fct.2008.02.004
20. Puchalska P., Concepcion Garcia M., Luisa Marina M.. **Identification of native angiotensin-I converting enzyme inhibitory peptides in commercial soybean based infant formulas using HPLC-Q-ToF-MS**. *Food. Chem.* (2014) **157** 62-69. DOI: 10.1016/j.foodchem.2014.01.130
21. Ma Y., Hou Y., Han B., Xie K., Zhang L., Zhou P.. **Peptidome comparison following gastrointestinal digesta of bovine versus caprine milk serum**. *J. Dairy. Sci.* (2021) **104** 47-60. DOI: 10.3168/jds.2020-18471
22. Mooney C., Haslam N.J., Pollastri G., Shields D.C.. **Towards the improved discovery and design of functional peptides: Common features of diverse classes permit generalized prediction of bioactivity**. *PLoS ONE* (2012) **7**. DOI: 10.1371/journal.pone.0045012
23. Manayalan B., Basith S., Shin T.H., Wei L.Y., Lee G.. **MAHTPred: A sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation**. *Bioinformatics* (2019) **35** 2757-2765. DOI: 10.1093/bioinformatics/bty1047
24. Charoenkwan P., Nantasenamat C., Hasan M.M., Moni M.A., Lio P., Manavalan B., Shoombuatong W.. **StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides**. *Methods* (2021) **204** 189-198. DOI: 10.1016/j.ymeth.2021.12.001
25. Manavalan B., Shin T.H., Kim M.O., Lee G.. **AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest**. *Front. Pharmacol.* (2018) **9** 276. DOI: 10.3389/fphar.2018.00276
26. Olsen T.H., Yesiltas B., Marin F.I., Pertseva M., Garcia-Moreno P.J., Gregersen S., Overgaard M.T., Jacobsen C., Lund O., Hansen E.B.. **AnOxPePred: Using deep learning for the prediction of antioxidative properties of peptides**. *Sci. Rep.* (2020) **10** 21471. DOI: 10.1038/s41598-020-78319-w
27. Zhao Y.Q., Zhang L., Tao J., Chi C.F., Wang B.. **Eight antihypertensive peptides from the protein hydrolysate of Antarctic krill (**. *Food Res. Int.* (2019) **121** 197-204. DOI: 10.1016/j.foodres.2019.03.035
28. Ahn C.B., Je J.Y., Cho Y.S.. **Antioxidant and anti-inflammatory peptide fraction from salmon byproduct protein hydrolysates by peptic hydrolysis**. *Food Res. Int.* (2012) **49** 92-98. DOI: 10.1016/j.foodres.2012.08.002
29. Venkatesan K., Nazeer R.A.. **Antioxidant Activity of Purified Protein Hydrolysates from Northern Whiting Fish (**. *Int. J. Pept. Res. Ther.* (2013) **20** 209-219. DOI: 10.1007/s10989-013-9384-6
30. Ye M., Zhang C., Zhu L., Jia W., Shen Q.. **Yak (**. *J. Sci. Food. Agric.* (2020) **100** 2600-2609. DOI: 10.1002/jsfa.10286
31. Zhong C., Sun L.C., Yan L.J., Lin Y.C., Liu G.M., Cao M.J.. **Production, optimisation and characterisation of angiotensin converting enzyme inhibitory peptides from sea cucumber (**. *Food. Funct.* (2018) **9** 594-603. DOI: 10.1039/C7FO01388D
32. Agrawal P., Bhagat D., Mahalwal M., Sharma N., Raghava G.P.S.. **AntiCP 2.0: An updated model for predicting anticancer peptides**. *Brief. Bioinform.* (2021) **22** bbaa153. DOI: 10.1093/bib/bbaa153
33. Hong S.M., Tanaka M., Koyanagi R., Shen W., Matsui T.. **Structural Design of Oligopeptides for Intestinal Transport Model**. *J. Agric. Food. Chem.* (2016) **64** 2072-2079. DOI: 10.1021/acs.jafc.6b00279
34. Wang B., Li B.. **Effect of molecular weight on the transepithelial transport and peptidase degradation of casein-derived peptides by using Caco-2 cell model**. *Food. Chem.* (2017) **218** 1-8. DOI: 10.1016/j.foodchem.2016.08.106
35. Xu Q.B., Hong H., Wu J.P., Yan X.H.. **Bioavailability of bioactive peptides derived from food proteins across the intestinal epithelial membrane: A review**. *Trends. Food. Sci. Technol.* (2019) **86** 399-411. DOI: 10.1016/j.tifs.2019.02.050
36. Lennernas H.. **Intestinal permeability and its relevance for absorption and elimination**. *Xenobiotica* (2007) **37** 1015-1051. DOI: 10.1080/00498250701704819
37. Komin A., Russell L.M., Hristova K.A., Searson P.C.. **Peptide-based strategies for enhanced cell uptake, transcellular transport, and circulation: Mechanisms and challenges**. *Adv. Drug. Deliver. Rev.* (2017) **110** 52-64. DOI: 10.1016/j.addr.2016.06.002
38. Christina E.L., Michèle M.I., Stan K.. **Assessment of bioavailability after In Vitro digestion and first pass metabolism of bioactive peptides from collagen hydrolysates**. *Curr. Issues. Mol. Biol.* (2021) **43** 1592-1605. PMID: 34698092
39. Sato K.. **Structure, Content, and Bioactivity of Food-Derived Peptides in the Body**. *J. Agric. Food. Chem.* (2018) **66** 3082-3085. DOI: 10.1021/acs.jafc.8b00390
40. Sato K., Nisimura R., Suzuki Y., Motoi H., Nakamura Y., Ohtsuki K., Kawabata M.. **Occurrence of indigestible pyroglutamyl peptides in an enzymatic hydrolysate of wheat gluten prepared on an industrial scale**. *J. Agric. Food. Chem.* (1998) **46** 3403-3405. DOI: 10.1021/jf980603i
41. Ding L., Wang L.Y., Yu Z.P., Ma S.T., Du Z.Y., Zhang T., Liu J.B.. **Importance of terminal amino acid residues to the transport of oligopeptides across the caco-2 cell monolayer**. *J. Agric. Food. Chem.* (2017) **65** 7705-7712. DOI: 10.1021/acs.jafc.7b03450
|
---
title: Real-World Analysis on the Characteristics, Therapeutic Paths and Economic
Burden for Patients Treated for Glaucoma in Italy
authors:
- Valentina Perrone
- Dario Formica
- Benedetta Piergentili
- Luca Rossetti
- Luca Degli Esposti
journal: Healthcare
year: 2023
pmcid: PMC10001280
doi: 10.3390/healthcare11050635
license: CC BY 4.0
---
# Real-World Analysis on the Characteristics, Therapeutic Paths and Economic Burden for Patients Treated for Glaucoma in Italy
## Abstract
This real-world analysis was performed on administrative databases to evaluate characteristics, therapies, and related economic burden of glaucoma in Italy. Adults with at least 1 prescription for ophthalmic drops (ATC class S01E: antiglaucoma preparations, miotics) during data availability period (January 2010−June 2021) were screened, then patients with glaucoma were included. First date of ophthalmic drops prescription was the index date. Included patients had at least 12 months of data availability before index-date and afterwards. Overall, 18,161 glaucoma-treated patients were identified. The most frequent comorbidities were hypertension ($60.2\%$), dyslipidemia ($29.7\%$) and diabetes ($17\%$). During available period, $70\%$ ($$n = 12$$,754) had a second-line therapy and $57\%$ ($$n = 10$$,394) a third-line therapy, predominantly ophthalmic drugs. As first-line, besides $96.3\%$ patients with ophthalmic drops, a small proportion reported trabeculectomy ($3.5\%$) or trabeculoplasty ($0.4\%$). Adherence to ophthalmic drops was found in $58.3\%$ patients and therapy persistence reached $78.1\%$. Mean total annual cost per patient was €1,725, mostly due to all-cause drug expenditure (€800), all-cause hospitalizations (€567) and outpatient services (€359). In conclusion, glaucoma-treated patients were mostly in monotherapy ophthalmic medications, with an unsatisfying adherence and persistence (<$80\%$). Drug expenditures were the weightiest item among healthcare costs. These real-life data suggest that further efforts are needed to optimize glaucoma management.
## 1. Introduction
Vision impairment represents an important public health issue, and its burden is likely to increase in the future because of ageing of the global population [1]. Glaucoma is a chronic optic neuropathy age-related and among the main causes of vision loss [2]. The characteristic progressive damage of the optic nerve leads to an irreversible, although preventable, visual field loss [3]. Generally, the symptoms are almost absent at early stages and arise at late stages with problems related to permanent visual loss [4]. To date, the only controllable factor to prevent or delay the progressive course of glaucoma is the elevated intraocular pressure (IOP), even though studies suggest other modifiable risk factor could be represented by socioeconomic status, dietary intake, poor exercise or sleeping apnea [5].
Last estimates indicate approximately 60 million individuals with glaucoma worldwide, and around 8 million for Europe, with a prevalence of $2.5\%$ [6]. In Italy around 550,000 individuals are estimated to have received a diagnosis for glaucoma [7]. The potential blindness, as well as the irreversible vision impairment, have a detrimental impact on the quality of life of glaucoma patients, that has been reported to decrease in parallel with the increment of glaucoma severity [8,9].
Glaucoma is often underdiagnosed, or diagnosis occurs at a later stage [10]. Antiglaucoma treatments aim to reduce and prevent further damage to the optic nerve and to preserve the residual visual capacity [7]. The most recent European Glaucoma Society (ESG) Guidelines advise the strategy proven to be effective focuses on lowering IOP. Treatments available are represented by medication, laser or surgery [7,10]. Pharmacological treatments, i.e., topical ophthalmic drops as monotherapy are considered to be first line therapy [11]. In case of lack of efficacy or intolerance, switching to a second drug in monotherapy or combination is advised. Among second line option, trabeculoplasty may also be considered, and the most recent guidelines recommended that trabeculoplasty should be considered as an option for initial treatment in mild or moderate phases of open angle glaucoma [11]. For patients using medication, ensuring an optimal level of adherence and persistence to treatments is essential to reduce risk of disease progression [12,13]. Indeed, good adherence and persistence are key points to obtain the beneficial effect of glaucoma therapy, by lowering IOP to prevent vision loss. Sub-optimal levels of adherence and persistence represent risk factors for disease progression, and it has been described in the literature that patients with stable visual fields were more than $75\%$ adherent to their therapy, while patients with a worsening of their condition were less than $45\%$ adherent [14]. Large evidence showed poor adherence to the prescribed topical drops for glaucoma treatment, when compared to medication adherence for other systemic chronic conditions [13,15,16]. Several methodologies for adherence evaluation have been reported, some of which are self-report, pharmacy refill reports, electronic monitoring and direct observation [15], but to date there is not a clear pattern on what method correlates best with clinically outcomes. Moreover, instrument scoring systems have been introduced and have been shown to predict the actual glaucoma medication adherence [17].
To date, little evidence is available on the drug utilization, characteristics and economic burden of patients with glaucoma in Italy. Hence, the analysis aims to evaluate the characteristics of patients with glaucoma, to describe their diagnostic and therapeutic paths, to assess the drug utilization of ophthalmic drops used by these patients and to analyze the health care resource use and related direct costs for Italian National Health Service (INHS) in clinical practice in Italy.
## 2.1. Data Source
This is a retrospective observational analysis that used data collected from the administrative health databases of Italian Local Health Units (LHUs) from Puglia, Campania, Umbria, Lazio and Veneto Regions, covering around 2.7 million health-assisted subjects. Such databases store information on all healthcare resources reimbursed by the INHS. The databases used to perform the analysis were: demographic database (to get data on age and sex), pharmaceutical database (with data related to drugs dispensed, such as Anatomical–Therapeutic Chemical [ATC] code, number of packages, number of units per package, costs and prescription date), hospitalization database (including discharge diagnosis codes classified according to the International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM], diagnosis Related Group [DRG] and DRG related charge), outpatient specialist services database (containing data on type, description activity of diagnostic tests and specialist visits for patients in analysis) and payment exemption database (containing date and type of exemption).
An anonymous univocal patient ID was assigned by the LHUs to each health-assisted subject to ensure patient privacy. This ID allowed us to perform the electronic linkage between the databases. The anonymity process was in full compliance with UE Data Privacy Regulation $\frac{2016}{679}$ (“GDPR”) and Italian D.lgs. n. $\frac{196}{2003}$, as amended by D.lgs. n. $\frac{101}{2018.}$ Aggregated results are herein reported, so that it is not possible to connect to individual patients.
## 2.2. Patient Population
All records of adult patients (≥18 years old) with at least 1 prescription for ophthalmic drops belonging to the class of ATC S01E (antiglaucoma preparations and miotics) during all data availability period, which spanned from January 2010 to June 2021, were screened for inclusion. Among them, patients with glaucoma were detected during the inclusion period January 2011−June 2020 by the presence of at least one of the following criteria (not necessarily after the ophthalmic drops prescription): (i) presence of hospitalization discharge diagnosis for glaucoma (ICD-9-CM: 365); (ii) an active exemption code for glaucoma (code 019); (iii) procedure for trabeculectomy (codes 12.64 OR 12.54) or trabeculoplasty (code 12.59) (as proxy of diagnosis). The index date was the date of the first ophthalmic drops prescription. All patients included in the analysis had at least 12 months of data availability period prior and afterward the index date, while those with missing data were excluded. Follow-up went from index date to end of data availability period or death (whichever occurred first).
## 2.3. Baseline Patient’ Characteristics
At index date, data on age and sex were analyzed. Presence of comorbidities was investigated in the year prior index date by evaluating the Charlson Comorbidity index [18], which gives a score based on the presence of specific comorbidities identified by hospitalization discharge diagnosis and/or drugs treatment (therefore, untreated/hospitalized comorbidities are not captured). Moreover, the proportion of patients affected by the following conditions has been reported: hypertension (at least one antihypertensive drugs prescription, ATC codes: C02, C03; C07; C08; C09), dyslipidemia (at least one lipid modifying agents prescription, ATC code: C10); diabetes (at least one antidiabetic drugs prescription, ATC code A10); cataract (ICD-9-CM code 366 or procedure codes 13.2, 13.3, 13.4, 13.6, 13.71); blindness (ICD-9-CM code 369 or exemption code C05); retinal/choroid disorders (ICD-9-CM code 361, 362, 363); diabetic retinopathy (DR): (ICD-9-CM code: 362.0); wet age-related macular degeneration (wAMD) (ICD-9-CM code 362.52); retinal vein occlusion (RVO) (ICD-9-CM code 362.3), Parkinson’s disease (ICD-9-CM code 332 or exemption code 038); Alzheimer’s disease (ICD-9-CM code 331.0 or exemption code 029); rheumatoid arthritis (ICD-9-CM code 714.0 or exemption code 006). Since comorbidities were identified based on hospitalization/treatment reimbursed by the INHS, they could have been underestimated.
Follow-up. Treatment line identification was performed and considered the whole analysis period. The number of lines was identified by presence of ophthalmic drops alone or in combination. Switching from one ophthalmic agent to another one was defined as change of line. Trabeculectomy and trabeculoplasty were considered as distinct treatment lines. The drug utilization was assessed by evaluating persistence, adherence and discontinuation of ophthalmic drops. Specifically, persistence was defined as presence of any ophthalmic drop prescriptions during the last quarter of 12 months follow-up. Discontinuation was identified as the absence of ophthalmic drops treatment prescriptions during the last trimester of 12 months follow-up period (interruption) or switching to another ophthalmic drops treatment (switch). Adherence to ophthalmic drops treatment was calculated during the first 12 months of follow-up by using the proportion of days covered (PDC), i.e., the ratio between the number of days of medication supplied and the observed time. Patients were classified as adherent (PDC ≥ 80), partially adherent (40 ≤ PDC < 80) and poorly adherent (PDC < $40\%$) [13]. Adherence was calculated based on prescriptions, and the actual use made by the patient is unknown.
## 2.4. Healthcare Resource Consumption and Costs
The analyses on healthcare resource consumption and costs were performed over the first year of follow-up on alive patients. Healthcare resource consumptions were reported as annual mean (and standard deviation, SD) number of all drug prescriptions, all-cause hospitalizations, all outpatient services per patient. Direct medical costs related to the healthcare resource consumption described above were reported in Euros (€) as annual mean with SD cost per patient. Drug costs were evaluated based on the INHS purchase price. Hospitalization costs were determined using DRG tariffs, which represent the reimbursement levels by the INHS to healthcare providers. Healthcare costs related to specialist visits, and diagnostic services were defined according to the tariffs of each region (called Nomenclatore tariffario regionale).
## 2.5. Statistical Analysis
All analyses were descriptive. Categorical variables have been reported as numbers and percentages, continuous variables as mean with SD. Patients with values exceeding the mean value three times the SD were excluded from the cost analysis. Following the “Opinion $\frac{05}{2014}$ on Anonymization Techniques” drafted by the “European Commission Article 29 Working Party”, the analyses involving ≤ 3 patients were not reported (NR) for data privacy, as they were potentially traceable to single individuals. All analyses have been performed using STATA SE version 12.0.
## 3. Results
From a sample population of around 2.7 million health-assisted subjects, 105,948 users of ophthalmic drops were identified, and among them 18,161 patients had evidence of glaucoma based on the criteria applied and were therefore included (Figure 1).
Characteristics were reported in Table 1: $44\%$ of patients was male and mean age was 67 years. The most populated age ranges were those 65−74 years ($28.1\%$), 75−84 years ($23.6\%$) and 55−64 years ($20.4\%$). Mean Charlson Index was 0.9, with around $22\%$ of patients showing a score ≥2 indicating a moderate-severe comorbid profile. Hypertension was the comorbidity most frequently detected ($60.2\%$) followed by dyslipidemia ($29.7\%$) and diabetes ($17\%$). Regarding eye-related diseases, cataract was observed in $8.9\%$ of patients while $0.7\%$ was blind. At index date, $30.3\%$ of patients received prostaglandin analogues, $30\%$ had fixed combination, mostly timolol-based, while $25.7\%$ received beta blocking agents, $10.8\%$ carbonic anhydrase inhibitors and $3\%$ sympatico mimetics (Figure 2).
During all the period analyzed, $11.5\%$ of patients had a trabeculectomy, $2\%$ a trabeculoplasty. Patients that underwent trabeculoplasty were older compared to those treated with drops only and showed a higher level of comorbidity profile (Table 1).
Of all the patients included ($$n = 18$$,161), by considering all available period, $70\%$ ($$n = 12$$,754) had a second line of therapy and $57\%$ ($$n = 10$$,394) a third line. Lines of therapy were mainly represented by ophthalmic drugs, and therapeutic sequences are reported in Table 2. As first line, $96.3\%$ patients had ophthalmic drops, while only a small proportion of patients reported trabeculectomy ($3.5\%$) or trabeculoplasty ($0.4\%$). The majority of patients ($66\%$) with ophthalmic drops as first line switched to another ophthalmic therapy, while $2.8\%$ had a trabeculectomy procedure and $0.4\%$ a trabeculoplasty. All patients with trabeculectomy or trabeculoplasty as first line had ophthalmic drops as second line, while as third line a second procedure was found, respectively, in $11.6\%$ and $13\%$ of patients (Table 2).
Regarding drug utilization, during first year of follow-up, $58.3\%$ were adherent to ophthalmic drops, $25.6\%$ partially adherent and $16.1\%$ poorly adherent (Table 3). Persistence to ophthalmic medication interested $78.1\%$ of patients while the remaining $21.9\%$ interrupted the therapy. Around $42\%$ of patients switched the index ophthalmic drugs during the first year of follow-up.
The analysis on mean annual resource consumption and costs during first year of follow-up revealed a mean annual number of 17 prescriptions, 6.4 outpatient specialist services and a mean of 0.3 all-cause hospitalization. The mean total annual direct cost per patients was €1,725, related mostly to all-cause drug expenditure (€800) followed by all-cause hospitalizations (€567) and outpatient services (€359) (Figure 3).
## 4. Discussion
This analysis on real-world data provided insights into characteristics of glaucoma patients, their therapeutic paths, and health care resource use and related direct costs for INHS in Italian settings of clinical practice. Among 2.4 million health-assisted individuals, almost 18,000 glaucoma patients under ophthalmic drops were included in the analysis, with a prevalence of $0.67\%$. In *Europe glaucoma* prevalence is almost $2.5\%$ [6], and in Italy 550,000 individuals are estimated to have received a diagnosis for glaucoma [7], and prevalence rates of $2.51\%$ of Primary Open Angle Glaucoma, $0.97\%$ of Primary Closed Angle Glaucoma and $0.29\%$ of secondary glaucoma were estimated [19]. The discrepancy between our data and published reports is feasibly attributable to the fact that in the present analysis glaucoma patients were identified by treatment prescription and not by a direct diagnosis.
The analysis of patient’ characteristics revealed a mean age of 66 years and almost $60\%$ being female; these data were in line with other real-world studies reporting the same mean age and a slight female predominance [6]. The comorbidity profile of these patients showed a higher frequency of hypertension and dyslipidemia in almost 20−$30\%$ of patients; data from an observational Italian study reported the most frequent comorbidities (self-reported) were systemic hypertension ($53.2\%$) and hyperlipidemia ($26.2\%$), similar to our findings [9]. All these comorbidities indicate a polypharmacy tendency for these patients, suggesting paying attention to avoid drug–drug interaction in patients prescribed multiple drugs and that an individualized management should be considered that integrates anti-glaucoma agents into the overall treatment plan [20]. In the present analysis all glaucoma patients under ophthalmic drops were included, being by definition in first-line treatment. Most of the patients ($86.7\%$) were under ophthalmic drops as monotherapy, as per guidelines [10].
It has been extensively reported that adherence to glaucoma medication could be a challenging problem [12]. Adherence to ophthalmic medication is poor, and multiple factors have been identified, including more recurrent and complex dosing, as well as patient-centered factors, such as poor disease or health consciousness, and a passive learning style [21]. Medication adherence plays an essential role alongside several factors such as clinical benefit, economic burden and quality of life of a patient [18,22,23]. In our analysis we have found that $58.3\%$ were adherent, 25.6 % partially adherent and $16.1\%$ poorly adherent. The latter value is within the rates of nonadherence with glaucoma medications found in the literature, that span from $16\%$ to $30\%$ [24]. It should be underlined that our analysis was limited to one year of observation but given that glaucoma is a chronic condition requiring a life-long treatment, studies with longer follow-up have shown that therapy adherence tends to further decrease over the years [23]. Similarly, persistence also ranged from $69\%$ to $84\%$, according to European studies [24,25]. A proper drug utilization, namely optimization of adherence and persistence to treatment, may provide a decrease in the healthcare burden of patients.
The analysis of healthcare resource consumption and cost showed that medication expenditures were found to be the main driving force, accounting for $46\%$ of total costs. In other European counties, treatment costs for patients with glaucoma has been reported to range between $42\%$−$56\%$ of total direct cost for patients in all stages of glaucoma [23]. Moreover, it has been reported that the economic burden of glaucoma increases with disease severity. An analysis performed in Europe showed an increase of around €86 on total cost for each progression in glaucoma stage, from €455 (stage 0) to €969 (stage 4) per person year [26].
The present analysis has some limitations related to its observational and retrospective design and to the data source. Indeed, administrative databases are primarily intended for administrative purpose, even if their utilization for healthcare research is increasing over the years. Some limitations are related to the lack of clinical data within the database therefore, it was not possible to retrieve information on the status of glaucoma, level of severity, nor type of glaucoma. Furthermore, the identification of patients was made by presence of ophthalmic drugs; therefore, untreated patients were not captured. The comorbidities were observed during all data availability periods before inclusion; therefore, variations and incomplete capture of these variables could have been present among patients. Drug utilization is based on drug dispenses; therefore, reasons behind choice of therapy or switch are not collected. Minimally invasive glaucoma surgery (MIGS) was not identified as, to date, there is no code for reimbursement structure for MIGS available in Italy [7].
## 5. Conclusions
This real-world analysis depicted the characteristics, therapeutic path and economic burden of glaucoma patients under ophthalmic drops in Italy by means of administrative data. The vast majority of treated patients were under ophthalmic medication in monotherapy. Drug utilization analyses reveal poor adherence and persistence below $80\%$. Results were consistent with the literature, while the low prevalence reported could be explained by the methodology applied, since the analysis focused on glaucoma patients in treatment. Patients’ management was associated with healthcare resource consumption and costs mostly related to drug prescriptions. Although this result could depend on the fact all patents were treated, this trend adds to the growing body of knowledge that treatments are a major driving force for glaucoma patients. Overall, these real-life data advise that strategies to optimize glaucoma management should be focused on ensuring a proper drug utilization; efforts to increase the adherence and persistence to ophthalmic medication has been widely reported to enhance the likelihood to get benefit from the therapy. Furthermore, we have shown a complex therapeutic pattern for these patients, that move towards multiple line of therapy, and, in addition, displayed a comorbid profile requiring a polytherapy regimen with risk of drug-drug interaction, suggesting an unmet therapeutic need that should be taken into account in the development of new treatments/techniques for glaucoma.
## References
1. Swenor B.K., Ehrlich J.R.. **Ageing and vision loss: Looking to the future**. *Lancet Glob. Health* (2021) **9** e385-e386. DOI: 10.1016/S2214-109X(21)00031-0
2. Kreft D., Doblhammer G., Guthoff R.F., Frech S.. **Prevalence, incidence, and risk factors of primary open-angle glaucoma—A cohort study based on longitudinal data from a German public health insurance**. *BMC Public Health* (2019) **19**. DOI: 10.1186/s12889-019-6935-6
3. Leske M.C.. **Open-Angle Glaucoma—An Epidemiologic Overview**. *Ophthalmic Epidemiol.* (2007) **14** 166-172. DOI: 10.1080/09286580701501931
4. Lee D.A., Higginbotham E.J.. **Glaucoma and its treatment: A review**. *Am. J. Health Pharm.* (2005) **62** 691-699. DOI: 10.1093/ajhp/62.7.691
5. Coleman A.L., Kodjebacheva G.. **Risk factors for glaucoma needing more attention**. *Open Ophthalmol. J.* (2009) **3** 38-42. DOI: 10.2174/1874364100903020038
6. Allison K., Patel D., Alabi O.. **Epidemiology of Glaucoma: The Past, Present, and Predictions for the Future**. *Cureus* (2020) **12** e11686. DOI: 10.7759/cureus.11686
7. Costagliola C., Sbordone M., Gandolfi S., Cesari L., Furneri G., Fea A.M.. **Minimally Invasive Surgery in Mild-to-Moderate Glaucoma Patients in Italy: Is It Time to Change?**. *Clin. Ophthalmol.* (2020) **14** 2639-2655. DOI: 10.2147/OPTH.S264839
8. Wang Y., Alnwisi S., Ke M.. **The impact of mild, moderate, and severe visual field loss in glaucoma on patients’ quality of life measured via the Glaucoma Quality of Life-15 Questionnaire**. *Medicine* (2017) **96** e8019. DOI: 10.1097/MD.0000000000008019
9. Floriani I., Quaranta L., Rulli E., Katsanos A., Varano L., Frezzotti P., Rossi G.C.M., Carmassi L., Rolle T., Ratiglia R.. **Health-related quality of life in patients with primary open-angle glaucoma. An Italian multicentre observational study**. *Acta Ophthalmol.* (2015) **94** e278-e286. DOI: 10.1111/aos.12890
10. Spaeth G.L.. **European Glaucoma Society Terminology and Guidelines for Glaucoma, 5th Edition**. *Br. J. Ophthalmol.* (2021) **105** 1-169. DOI: 10.1136/bjophthalmol-2021-egsguidelines
11. Li T., Lindsley K., Rouse B., Hong H., Shi Q., Friedman D.S., Wormald R., Dickersin K.. **Comparative Effectiveness of First-Line Medications for Primary Open-Angle Glaucoma**. *Ophthalmology* (2015) **123** 129-140. DOI: 10.1016/j.ophtha.2015.09.005
12. **European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition—Chapter 3: Treatment principles and options Supported by the EGS Foundation**. *Br. J. Ophthalmol.* (2017) **101** 130-195. DOI: 10.1136/bjophthalmol-2016-EGSguideline.003
13. Zaharia A.-C., Dumitrescu O.-M., Radu M., Rogoz R.-E.. **Adherence to Therapy in Glaucoma Treatment—A Review**. *J. Pers. Med.* (2022) **12**. DOI: 10.3390/jpm12040514
14. Rossi G.C., Pasinetti G.M., Scudeller L., Radaelli R., Bianchi P.E.. **Do Adherence Rates and Glaucomatous Visual Field Progression Correlate?**. *Eur. J. Ophthalmol.* (2011) **21** 410-414. DOI: 10.5301/EJO.2010.6112
15. Sleath B., Blalock S., Covert D., Stone J.L., Skinner A.C., Muir K., Robin A.L.. **The Relationship between Glaucoma Medication Adherence, Eye Drop Technique, and Visual Field Defect Severity**. *Ophthalmology* (2011) **118** 2398-2402. DOI: 10.1016/j.ophtha.2011.05.013
16. Sleath B., Blalock S.J., Stone J.L., Skinner A.C., Covert D., Muir K., Robin A.. **Validation of a short version of the glaucoma medication self-efficacy questionnaire**. *Br. J. Ophthalmol.* (2011) **96** 258-262. DOI: 10.1136/bjo.2010.199851
17. Meichenbaum D., Turk D.. *Facilitating Treatment Adherence: A Practitioner’s Guidebook* (1987) 1-55
18. Charlson M.E., Pompei P., Ales K.L., MacKenzie C.R.. **A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation**. *J. Chronic Dis.* (1987) **40** 373-383. DOI: 10.1016/0021-9681(87)90171-8
19. Cedrone C., Culasso F., Cesareo M., Zapelloni A., Cedrone P., Cerulli L.. **Prevalence of glaucoma in Ponza, Italy: A comparison with other studies**. *Ophthalmic Epidemiology* (1997) **4** 59-72. DOI: 10.3109/09286589709057098
20. Huber M., Kölzsch M., Stahlmann R., Hofmann W., Bolbrinker J., Dräger D., Kreutz R.. **Ophthalmic Drugs as Part of Polypharmacy in Nursing Home Residents with Glaucoma**. *Drugs Aging* (2012) **30** 31-38. DOI: 10.1007/s40266-012-0036-x
21. Robin A.L., Muir K.W.. **Medication adherence in patients with ocular hypertension or glaucoma**. *Expert Rev. Ophthalmol.* (2019) **14** 199-210. DOI: 10.1080/17469899.2019.1635456
22. Rouland J.-F., Le Pen C., Benhaddi H., Piriou E., Lilliu H., Kenigsberg P.-A., Abellan P., Arnoux M., Attia A., Baudouin C.. **Naturalistic, Prospective Study of Glaucoma and Ocular Hypertension Treatment in France: Strategies, Clinical Outcomes, and Costs at 2 Years**. *Eur. J. Ophthalmol.* (2005) **15** 562-580. DOI: 10.1177/112067210501500507
23. Pöhlmann J., Norrbacka K., Boye K.S., Valentine W.J., Sapin H.. **Costs and where to find them: Identifying unit costs for health economic evaluations of diabetes in France, Germany and Italy**. *Eur. J. Health Econ.* (2020) **21** 1179-1196. DOI: 10.1007/s10198-020-01229-1
24. Wilensky J., Fiscella R.G., Carlson A.M., Morris L.S., Walt J.. **Measurement of Persistence and Adherence to Regimens of IOP-Lowering Glaucoma Medications Using Pharmacy Claims Data**. *Am. J. Ophthalmol.* (2006) **141** 28-33. DOI: 10.1016/j.ajo.2005.09.011
25. Hwang D.-K., Liu C.J.-L., Pu C.-Y., Chou Y.-J., Chou P.. **Persistence of Topical Glaucoma Medication**. *JAMA Ophthalmol.* (2014) **132** 1446-1452. DOI: 10.1001/jamaophthalmol.2014.3333
26. Traverso C.E., Walt J.G., Kelly S.P., Hommer A.H., Bron A.M., Denis P., Nordmann J.P., Renard J.P., Bayer A., Grehn F.. **Direct costs of glaucoma and severity of the disease: A multinational long term study of resource utilisation in Europe**. *Br. J. Ophthalmol.* (2005) **89** 1245-1249. DOI: 10.1136/bjo.2005.067355
|
---
title: Methylglyoxal-Modified Albumin Effects on Endothelial Arginase Enzyme and Vascular
Function
authors:
- Ebaa M. Alzayadneh
- Alia Shatanawi
- R. William Caldwell
- Ruth B. Caldwell
journal: Cells
year: 2023
pmcid: PMC10001288
doi: 10.3390/cells12050795
license: CC BY 4.0
---
# Methylglyoxal-Modified Albumin Effects on Endothelial Arginase Enzyme and Vascular Function
## Abstract
Advanced glycation end products (AGEs) contribute significantly to vascular dysfunction (VD) in diabetes. Decreased nitric oxide (NO) is a hallmark in VD. In endothelial cells, NO is produced by endothelial NO synthase (eNOS) from L-arginine. Arginase competes with NOS for L-arginine to produce urea and ornithine, limiting NO production. Arginase upregulation was reported in hyperglycemia; however, AGEs’ role in arginase regulation is unknown. Here, we investigated the effects of methylglyoxal-modified albumin (MGA) on arginase activity and protein expression in mouse aortic endothelial cells (MAEC) and on vascular function in mice aortas. Exposure of MAEC to MGA increased arginase activity, which was abrogated by MEK/ERK$\frac{1}{2}$ inhibitor, p38 MAPK inhibitor, and ABH (arginase inhibitor). Immunodetection of arginase revealed MGA-induced protein expression for arginase I. In aortic rings, MGA pretreatment impaired acetylcholine (ACh)-induced vasorelaxation, which was reversed by ABH. Intracellular NO detection by DAF-2DA revealed blunted ACh-induced NO production with MGA treatment that was reversed by ABH. In conclusion, AGEs increase arginase activity probably through the ERK$\frac{1}{2}$/p38 MAPK pathway due to increased arginase I expression. Furthermore, AGEs impair vascular function that can be reversed by arginase inhibition. Therefore, AGEs may be pivotal in arginase deleterious effects in diabetic VD, providing a novel therapeutic target.
## 1. Introduction
Vascular dysfunction (VD) contributes to several diabetic complications and its pathophysiology is intricately linked to oxidative stress and inflammation. Advanced glycation end products (AGE) and arginase enzyme have been shown separately to play roles in VD; however, the relationship between these two factors in diabetic VD is not yet clear. Arginase is well demonstrated as an important enzyme in urea cycle, detoxifying ammonia by hydrolyzing L-arginine to ornithine and urea. There are two identified isoforms encoded by different genes, arginase I and II; however, they share similar mechanisms and metabolites [1,2]. Arginase is constitutively expressed in human endothelial cells in both isoforms, where arginase I is located in the cytosol, and arginase II in mitochondria of human endothelial cells [3,4]. In addition to its role in the urea cycle, arginase produces ornithine required for polyamines and L-proline synthesis involved in cell proliferation, differentiation, and repair [5]. There is a growing body of evidence indicating that constitutive levels of arginase activity in endothelium limit NO synthesis and NO-dependent vasodilatory function [6,7,8]. Arginase was shown to be induced by various stimuli such as oxidative stress, oxidized lipoproteins, tumor necrosis factor (TNFα), and hypoxia [9,10,11,12,13,14].
Upregulation of arginase was also demonstrated in cells exposed to high glucose and in diabetic animal models. High glucose increased arginase activity and limited NO production in bovine coronary endothelial cells in a Rho-kinase-dependent pathway, in which siRNA knockdown of arginase I prevented high-glucose-induced changes [15]. Arginase upregulation was shown to be mediated by reactive oxygen species (ROS) and the PKC/Rho A pathway [9]. Interestingly, both arginase and endothelial nitric oxide synthase (eNOS) contributed to high-glucose-induced superoxide production, due to uncoupling of eNOS associated with diminished availability of L-arginine [9,16]. The functional impairment associated with increased arginase expression and activity in diabetes was demonstrated in isolated vascular preparations and under in vivo conditions [17]. Both mRNA expression and activity of arginase were increased in aorta and liver of a streptozotocin-induced diabetic rat model [15]. Impaired endothelium-dependent vasorelaxation of coronary arteries from rats with type 1 diabetes was normalized by arginase inhibition [15]. Moreover, aortic and retinal endothelial dysfunction in streptozotocin-induced type 1 diabetes was linked to increased arginase expression [18,19]. The role of arginase for vascular dysfunction in vivo was investigated in type 2 diabetic rats, in which arginase inhibition improved myocardial microvascular dysfunction by increased NO availability [20]. Additionally, arginase has been identified as a key player in skeletal muscle arteriolar endothelial dysfunction in a diabetic rat model, where inhibition of arginase restored flow-induced vasodilation [21]. Arginase upregulation and vasodilation impairment were reported in cavernous tissue of diabetic rats linked to extracellular signal–regulated kinase (ERK$\frac{1}{2}$) [22]. Clinical studies on diabetic patients supported earlier findings on animal studies indicating a significant role for arginase in endothelial dysfunction. Plasma arginase activity was elevated in patients with type 2 diabetes mellitus in comparison with healthy subjects and correlated positively with fasting plasma glucose levels and glycosylated hemoglobin HbA1c levels [23]. Furthermore, arginase levels in plasma were associated with markers of oxidative stress and HbA1c [23]. Functionally, coronary arterioles obtained from patients with diabetes displayed reduced endothelium-dependent relaxation in vitro and increased expression of arginase I in endothelial cells [24]. The endothelium-dependent vasodilatation of coronary arterioles was enhanced by arginase inhibition [24]. In addition, an in vivo study demonstrated that arginase inhibition markedly improves endothelium-dependent vasodilatation in the forearm of patients with type 2 diabetes and coronary artery disease, while it does not affect endothelial function in healthy controls [25].
On the other hand, AGEs, the products of non-enzymatic glycation and oxidation of proteins and lipids that accumulate in diabetes, together with their signal transduction receptor (RAGE), are linked to both the etiology and pathological consequences of types 1 and 2 diabetes [26,27]. AGEs form to an accelerated degree in hyperglycemia and accumulate in the blood vessel wall, directly modifying proteins by the formation of cross-links primarily in the basement membrane and the extracellular matrix [26,27]. Furthermore, circulating AGEs interact with endothelial RAGEs to transduce multiple signaling pathways, which lead to perturbation of cellular functions [27]. RAGE is a member of the immunoglobulin superfamily that binds to multiple ligands such as AGEs, HMGB-1, S100 proteins, or amyloid beta peptide [28,29,30]. Engagement of RAGE to its agonists activates several pathways that result in activating NADPH oxidases, ROS production, ERK, P38 MAP-kinase, JAK/STAT pathway, phospho-inisitol-3 kinases, and NfκB pathway, which culminate in the upregulation of RAGE and other profibrotic and proinflammatory target genes [27].
Clinically, the levels of serum AGEs in patients with type 2 diabetes are inversely related to the degree of endothelium-dependent and endothelium-independent vasodilation [31]. Several mechanisms by which AGEs affect NO bioavailability were suggested in the literature and mostly relate to eNOS. AGEs may reduce the stability of eNOS or impair NO production via RAGE-induced deactivation of the eNOS enzyme [32,33]. To our knowledge, it is still not clear if AGEs directly affect arginase activity, arginase expression, or NO bioavailability in endothelial cells. Given that AGEs via RAGE induce ROS formation and ERK$\frac{1}{2}$ activation, which are also signaling pathways implicated in arginase stimulation in diabetic vasculature, as shown previously, we sought to investigate the effect of AGE (MGA) on arginase activity and expression. We hypothesized that AGEs may upregulate arginase enzymes, leading to a reduction in the availability of arginine and NO, thus causing deleterious effects on vascular function.
## 2.1. Cell Culture and Treatments
In all cell experiments, mouse aortic endothelial cells (MAECs) were utilized. Proliferating MAECs were purchased from Cell Applications, San Diego, CA, USA. Cells were cultured in Endothelial Growth Medium (Cell Applications, San Diego, CA, USA) and maintained in a humidified atmosphere at 37 °C and $5\%$ CO2. Cells were adapted to grow in M199 supplemented with 50 µM L-arginine (Invitrogen, Carlsbad, CA, USA) for 72 h before the experiment to match the normal plasma L-arginine concentration (40 to 100 µM). In addition, $10\%$ FBS (Catalog # SH30396, hyClone, GE Healthcare Life Sciences South Logan, UT, USA), $1\%$ penicillin/streptomycin, and $1\%$ L-glutamine were added to cell growth medium. Cells used for experiments are from 3 to 9 passage numbers. When cells reached $80\%$ confluency, they were serum-starved overnight in M199 supplemented with 50 µM L-arginine, $1\%$ L-glutamine, $1\%$ penicillin/streptomycin, and $0.2\%$ FBS. Glycated albumin (MGA) was prepared as described and characterized previously [34,35]. Briefly, 500 μM methylglyoxal (Sigma, Catalog #M0252, St. Louis, MO, USA) was incubated with 100 μM BSA (Sigma) dissolved in phosphate-buffered saline (PBS) for 24 h, then washed on 10 kDa filters (Macrosep® Advance Device, Pall Life Sciences, MI, USA) to remove excess methylglyoxal, reconstituted with M199 serum-free media, and passed through a 0.2 μm filter [34,35]. In subsets of cells, the inhibitors for arginase, namely boronic acids 2(S)-amino-6-boronohexanoic acid (ABH) (1 mM, ChemCruz, Catalog #221197, Dallas, TX, USA), p38 MAPK, SB-202190 (10 µM) (EMD biosciences, Catalog #S7076, San Diego, CA, USA), and mitogen-activated protein kinase kinase MEK/ERK$\frac{1}{2}$, PD98059 (EMD biosciences, Catalog #P215, San Diego, CA, USA) (10 µM), were used and added 2 h before the addition of MGA (100 µM) (Sigma-Aldrich, St. Louis, MO, USA) for 24 h; inhibitor concentrations and durations were as previously described [36]. Independent experiments (3–5) were carried out from different passages.
## 2.2. Arginase Activity
Arginase activity was measured using a colorimetric determination of urea production from L-arginine as described previously [37]. Cells were lysed in Tris buffer (50 mM Tris-HCI, 0.1 mM EDTA and EGTA, pH 7.5) containing protease inhibitors (Catalog # P8340, Sigma, St. Louis, MO, USA). These mixtures were subjected to three freeze–thaw cycles and then centrifuged for 10 min at 20,000× g. The supernatants were used for arginase activity assay. In brief, 25 µL of supernatant was heated with MnCl2 (10 mM) for 10 m at 56 °C to activate arginase. The mixture was then incubated with 50 µL L-arginine (0.5 M, pH 9.7) for one hour at 37 °C to hydrolyze the L-arginine. The hydrolysis reaction was stopped with acid and the mixture was then heated at 100 °C with 25 µL of α-isonitrosopropiophenone ($9\%$ α-ISPF in EtOH) for 45 min. The samples were kept in the dark at room temperature for 10 min; then, absorbance was measured at 540 nm.
## 2.3. Immunodetection of Arginase
Cells were lysed in RIPA buffer (#ab156034, Abcam, Boston, MA, USA) having protease and phosphatase inhibitors (Catalog #P5726 and P0044, Sigma, St. Louis, MO, USA). Cell lysates were centrifuged for 10 min at 20,000× g, and supernatants were collected for Western blotting analysis. Protein estimation was conducted in supernatants using a protein assay kit (Bio Rad, Hercules, CA, USA). Equal amounts of protein were loaded, separated by electrophoresis using $10\%$ SDS-PAGE gels, and transferred into nitrocellulose membranes. The blots were blocked using $5\%$ bovine serum albumin (Sigma, St. Louis, MO, USA), incubated with their respective primary and secondary antibodies, anti-arginase 1 (Santa Cruz, Catalog #166920, 1:1000, Dallas, TX, USA), anti-arginase-2 (Santa Cruz, Catalog #393496, 1:1000, Dallas, TX, USA), anti-GAPDH (Catalog #abx005569, 1:10,000, abbexa, Cambridge, UK), followed by the respective secondary antibodies. Signals were detected using chemiluminescence (PierceTM ECL Western, Thermophisher, IL, USA) and the ChemiDoc MP imaging system (Bio-Rad, Hercules, CA, USA). To quantify the resultant blots, individual band intensities were measured (arbitrary units) and ratios of protein to GAPDH were calculated per sample using NIH ImageJ softwareversion 1.53.
## 2.4. Histochemical Detection of Intracellular NO
For the detection of intracellular NO, endothelial cells (1.2 × 105 cells) were plated on a non-coated cover slide (18 × 18 mm) and starved for 24 h prior to treatment; cells were treated with either bovine serum albumin (100 μM) or MGA (100 μM) for 24 h. For cells with inhibition conditions, inhibitors L-NAME (Abcam, Catalog #120136, 1 mM, UK) or ABH (1 mM) were added 30 min before the addition of incubation media (DAF-2DA, Catalog #ab145283, 5 μM, for 40 min, Abcam, in serum-free media) according to the manufacturer’s instructions and as previously described [38]. To promote NO generation by NOS, subsets of cells were treated with acetylcholine (1 μM, Sigma) and L-arginine (1 mM, Sigma) to intensify the signal during the 40 min incubation. Then, cells were washed with PBS twice and fixed in $2\%$ paraformaldehyde for 3 min at 0 °C, and mounted on a slide with mounting media as reported previously [39]. Cells were directly observed under an inverted fluorescence microscope (AxioObserver. Z1; Zeiss, Jena, Germany). The quantification of fluorescence intensity of representative images from 3 independent experiments was carried out using NIH ImageJ software version 1.53.
## 2.5. Animals
Vascular function experiments were performed on aortas obtained from C57BL/6J wild-type mice aged 10 months. Protocols were approved by the Institutional Animal Care and Use Committee of the Medical College of Georgia (Animal Welfare Assurance no. D16-00197).
## 2.6. Vascular Function
Vascular function was assessed as described previously [40]. Following deep anesthesia, tissues were harvested, and mouse aortas were rapidly excised and placed immediately in ice-cold Krebs–Henseleit buffer (NaCl, 118 mM; NaHCO3, 25 mM; glucose, 5.6 mM; KCl, 4.7 mM; KH2PO4, 1.2 mM; MgSO4 7H2O, 1.17 mM and CaCl2 2H2O, 2.5 mM), cleaned, and cut into 2–3 mm segments. Thereafter, aortic rings were placed in M199 serum-free media supplied with 50 μM L-arginine with or without the addition of MGA and the arginase inhibitor (ABH, 1 mM) for 24 h at 37 °C in culture chambers. Aortic rings (3–4 for each condition) were mounted in an oxygenated wire myograph chamber (Danish Myo Technology, Ann Arbor, MI, USA). Tissues were allowed to equilibrate at a resting tension of 5 mN for 1 h with buffer changes. Following phenylephrine (1 μM) precontraction, relaxation curves were performed using progressive doses of acetylcholine (ACh, endothelium-dependent vasodilator) or sodium nitroprusside (SNP, endothelium-independent vasodilator). Changes in tension were measured by a force transducer. A 1 h equilibration was performed between subsequent relaxation curves. Vasorelaxation responses were calculated as the percentage of phenylephrine-induced contraction.
## 2.7. Statistical Analysis
Data are given as mean ± SEM. For multiple comparisons, statistical analysis was performed by one-way analysis of variance (ANOVA) with the Tukey post test. For single comparisons, statistical differences were determined by the Student T test. Differences in concentration–response curves were determined using two-way repeated measures ANOVA. Independent experiments were performed 3–6 times. All statistical analyses were performed with GraphPad Prism version 8.01 (San Diego, CA, USA). Results were considered significant when $p \leq 0.05.$
## 3.1. Arginase Activity
Treatment of endothelial cells (MAEC) with (100 μM, 24 h) MGA increased arginase activity by $64\%$ compared to the control BSA-treated cells ($p \leq 0.001$), as shown in Figure 1. This increase was abrogated when cells were pretreated with the inhibitor of p38 MAPK, SB-202190 (10 µM), or the inhibitor of MEK/ERK$\frac{1}{2}$, PD98059 (10 µM), or the inhibitor of arginase, ABH (1 mM); $$n = 5$$ independent experiments.
## 3.2. Arginase Expression
MGA treatment (100 μM, 24 h) increased arginase I immunodetected protein expression by $41.6\%$ ($p \leq 0.05$, $$n = 5$$) compared to control BSA conditions, as shown in Figure 2A; however, arginase II expression was not altered, as demonstrated in Figure 2B. These findings indicate that arginase I is the isoform that mainly contributed to the increased arginase activity shown in this study.
## 3.3. Histochemical Detection of Intracellular NO
Intracellular NO generation was assessed in MAECs utilizing the DAF-2DA marker. Subsets of cells were treated with BSA as a control (100 μM, 24 h) (Figure 3A); the addition of ACh (1 μM) to BSA-treated cells induced an increase in the DAF-2DA fluorescence, reflecting NO generation (Figure 3B) compared with no ACh in Figure 3A. Pretreatment with L-NAME (1 mM) reduced ACh-induced NO production (Figure 3C), while ACh-induced NO production increased with pretreatment with the arginase inhibitor ABH (1 mM) (Figure 3D). Another subset of cells were pretreated with MGA (100 μM, 24 h), which demonstrated nearly undetectable fluorescence without ACh stimulation (Figure 3E); NO production increased slightly after the addition of ACh in MGA-treated cells (Figure 3F), whereas the L-NAME inhibitor blunted NO production in ACh-stimulated, MGA-treated cells (Figure 3G). Interestingly, pretreatment with the ABH inhibitor rescued NO production to close to the control ACh-stimulated cells (Figure 3H). A quantification of DAF fluorescence intensity in the different treatment conditions is depicted in Figure 3I). It is noteworthy that ABH restoration of ACh-induced NO production, indicated by increased fluorescence intensity in BSA treatment, was reversed by L-NAME inhibition to a level less than when ABH was not used, while eNOS was inhibited by L-NAME, confirming that this effect of ABH is rather due to the inhibition of arginase enzyme and not the stimulation of eNOS (Figure 3I).
## 3.4. Vascular Function
To determine the effect of MGA on endothelial function in vivo, we performed vascular studies using aortas isolated from C57BL/6J healthy mice. We examined vasorelaxation responses to the endothelium-dependent vasodilator ACh and the endothelium-independent vasodilator SNP (Figure 4). Pretreatment of isolated aortas with MGA (100 μM, 24 h) induced an impairment of vasorelaxation response to ACh (maximum relaxation of 39.7 ± $5.7\%$ vs. 90.7 ± $1.7\%$ in control condition, $p \leq 0.05$, $$n = 3$$–5 independent experiments), as shown in Figure 4A. ABH largely prevented MGA-impaired vasorelaxation with a maximum relaxation of 80.4 ± $5.3\%$, $p \leq 0.05$, $$n = 3$$–5 independent experiments. Thus, blocking arginase activity reversed MGA-induced impairment. Aortic relaxation responses to SNP were not different between control, MGA-treated rings or ABH- and MGA-treated rings, as demonstrated in Figure 4B. ABH pretreatment of control rings (BSA) did not affect vasorelaxation responses to either ACh or SNP (data not shown).
## 4. Discussion
This study demonstrates for the first time that advanced glycated end products represented by methylglyoxal-modified albumin stimulates arginase enzyme activity in an ERK$\frac{1}{2}$ MEKK and p38 MAPK-dependent pathway, as summarized in Figure 5. Increased activity is mainly due to increased arginase I expression, as shown in our study. Our findings support previous reports showing that constitutive levels of arginase activity in endothelial cells limit NO synthesis and NO-dependent vasodilatory function [6,7,8]. In hyperglycemic conditions, both AGEs and arginase have been individually linked to various diabetic complications, including vascular dysfunction; however, in the literature, there is a lack of studies investigating if there is a direct influence of AGEs on arginase regulation. Previously, AGE-modified albumin was shown to have suppressive effects on NOS-3 activity and expression in HUVECs, an effect that if combined with upregulation of arginase, would aggravate limited NO bioavailability and VD [41].
Intracellular detection of NO in cultured endothelial cells in our study showed that MGA-induced increased activity and expression of arginase was accompanied by a reduction in NO bioavailability. Furthermore, we show that MGA treatment of aortic rings impaired endothelial-dependent vasodilation in response to ACh, which was reversed by the arginase inhibition (ABH) without affecting SNP-induced (endothelial-independent) vasorelaxation, suggesting a role for endothelial arginase enzyme in MGA-induced vascular impairment. In accordance with these findings, aortic rings treated with AGE demonstrated blunted endothelial-dependent vasorelaxation. These findings were consistent with a previous report by Watson’s group in which AGE treatment of rat aortic rings impaired endothelial-dependent vasodilation that was blocked by inhibition arginase, NADPH oxidase, and superoxide [42]. We showed no alteration of endothelial-independent relaxation; however, they showed increased endothelial independent vasodilation by AGE [42]. Furthermore, they reported increased arginase and NADPH oxidase mRNA expression with MGA treatment, which may not be necessarily predictive for protein expression. On the contrary, our study showed an increase in both activity and protein expression of arginase enzyme upon MGA treatment. Similar to our findings, coronary arteries obtained from diabetic patients had increased protein levels of arginase I and showed a better vasodilation response to ACh in the presence of the arginase inhibitor [24]. Moreover, we provide evidence of reduced NO production using the intracellular marker DAF-2DA, whereas arginase inhibition with ABH restored ACh-induced NO production in cultured endothelial cells treated with MGA, which explains our vascular function findings.
In concordance with our findings that arginase I expression was preferentially increased by AGEs, arginase knockout mice models suggested that arginase I is crucial in diabetes-induced vascular dysfunction. One study showed that streptozotocin-induced diabetic knockout mice lacking the arginase II with partial deletion of arginase I exhibited better endothelial-dependent vasodilation and less arginase activity compared with diabetic wild-type and knockout mice lacking the AII isoform alone [18].
A growing body of evidence indicates that AGE receptor (RAGE) engagement by its ligands including AGE stimulate NADPH oxidase, reactive oxygen species (ROS) production, ERK$\frac{1}{2}$, P38 MAP-kinase, NFκB activation, and gene transcription, culminating in microvasculature alterations manifested in diabetes [43,44,45]. Arginase expression/activity has been extensively shown to be stimulated by a wide range of stimuli involving oxidative stress when administered to cultured endothelial cells, including high glucose [15], oxidized low-density lipoprotein (LDL) [12], H2O2 [5,46,47], peroxynitrite [9], and endotoxins [10]. Additionally, in vivo studies revealed that conditions well known to be associated with elevated oxidative stress have elevated endothelial arginase expression, such as ischemia–reperfusion [48] and ageing [49].
Moreover, AGEs via RAGE receptors as well as arginase-induced eNOS uncoupling may lead to ROS formation, including superoxide (O2-) ion, which further combines with NO to form the potent oxidant peroxynitrite, limiting NO bioavailability and aggravating the oxidative injury to endothelial cells [50]. Taken together, AGE-induced arginase upregulation might result from AGE-stimulated ROS formation and might contribute to AGE-induced ROS loop at the same time.
Arginase activation was linked to protein kinase C (PKC), Rho-associated protein kinase (ROCK), and the mitogen-activated protein kinase (MAPK) pathways [9,51,52]. Post-translational modifications such as S-nitrosylation of arginase I via inducible NOS2 have been identified in age-related endothelial dysfunction [53]. In addition, the physiologic modulation of the glutathione/glutathione disulfide ratio has been suggested to play a role in the control of arginase I activity in pathological conditions of increased oxidative stress [13].
Although we show no changes in protein expression of arginase II, it may contribute to increased arginase activity by other activating mechanisms. Pandey et al. demonstrated a mechanism for rapid arginase II increased activity via translocation from mitochondria to cytoplasm in response to oxidized LDL interaction with LOX1 receptor causing NO dysregulation and vascular dysfunction [54]. AGEs were reported to bind the LOX1 receptor, presenting a compelling mechanism for arginase II contribution to increased arginase activity that requires further investigation [55,56].
In concordance to the previous evidence that hyperglycemia-induced dysregulation of NO and increased generation of ROS as well as endothelial dysfunction are maintained even after the restoration of normoglycemia, known as hyperglycemic memory phenomenon, we observed from previous studies that the degree of endothelial function improvement achieved by arginase inhibition was independent of glucose control, which can be partly explained by the role of the AGEs/RAGE axis involved in this phenomenon [57,58,59].
These intriguing observations highlight the role of AGE in arginase regulation of NO and oxidative stress, which may present a putative therapeutic target to maintain cardiovascular integrity and function in diabetes.
## 5. Conclusions
Based on our findings, we conclude that AGEs affect VD by upregulating arginase activity and expression, thus limiting NO bioavailability in endothelial cells. This study emphasizes the importance of further investigating the interaction between AGEs and arginase enzymes, particularly in diabetes.
## References
1. Haraguchi Y., Takiguchi M., Amaya Y., Kawamoto S., Matsuda I., Mori M.. **Molecular cloning and nucleotide sequence of cDNA for human liver arginase**. *Proc. Natl. Acad. Sci. USA* (1987.0) **84** 412-415. DOI: 10.1073/pnas.84.2.412
2. Morris S.M., Bhamidipati D., Kepka-Lenhart D.. **Human type II arginase: Sequence analysis and tissue-specific expression**. *Gene* (1997.0) **193** 157-161. DOI: 10.1016/S0378-1119(97)00099-1
3. Bachetti T., Comini L., Francolini G., Bastianon D., Valetti B., Cadei M., Grigolato P., Suzuki H., Finazzi D., Albertini A.. **Arginase pathway in human endothelial cells in pathophysiological conditions**. *J. Mol. Cell. Cardiol.* (2004.0) **37** 515-523. DOI: 10.1016/j.yjmcc.2004.05.004
4. Ryoo S., Berkowitz D.E., Lim H.K.. **Endothelial arginase II and atherosclerosis**. *Korean J. Anesthesiol.* (2011.0) **61** 3. DOI: 10.4097/kjae.2011.61.1.3
5. Li H., Meininger C.J., Hawker J.R., Haynes T.E., Kepka-Lenhart D., Mistry S.K., Morris S.M., Wu G.. **Regulatory role of arginase I and II in nitric oxide, polyamine, and proline syntheses in endothelial cells**. *Am. J. Physiol. Endocrinol. Metab.* (2001.0) **280** E75-E82. DOI: 10.1152/ajpendo.2001.280.1.E75
6. Berkowitz D.E., White R., Li D., Minhas K.M., Cernetich A., Kim S., Burke S., Shoukas A.A., Nyhan D., Champion H.C.. **Arginase reciprocally regulates nitric oxide synthase activity and contributes to endothelial dysfunction in aging blood vessels**. *Circulation* (2003.0) **108** 2000-2006. DOI: 10.1161/01.CIR.0000092948.04444.C7
7. Lim H.K., Lim H.K., Ryoo S., Benjo A., Shuleri K., Miriel V., Baraban E., Camara A., Soucy K., Nyhan D.. **Mitochondrial arginase II constrains endothelial NOS-3 activity**. *Am. J. Physiol. Heart Circ. Physiol.* (2007.0) **293** 3317-3324. DOI: 10.1152/ajpheart.00700.2007
8. Zhang C., Hein T.W., Wang W., Chang C., Kuo L.. **Constitutive expression of arginase in microvascular endothelial cells counteracts nitric oxide-mediated vasodilatory function**. *FASEB J.* (2001.0) **15** 1264-1266. DOI: 10.1096/fj.00-0681fje
9. Chandra S., Romero M.J., Shatanawi A., Alkilany A.M., Caldwell R.B., Caldwell R.W.. **Oxidative species increase arginase activity in endothelial cells through the RhoA/Rho kinase pathway**. *Br. J. Pharmacol.* (2012.0) **165** 506-519. DOI: 10.1111/j.1476-5381.2011.01584.x
10. Zhang W., Baban B., Rojas M., Tofigh S., Virmani S.K., Patel C., Behzadian M.A., Romero M.J., Caldwell R.W., Caldwell R.B.. **Arginase Activity Mediates Retinal Inflammation in Endotoxin-Induced Uveitis**. *Am. J. Pathol.* (2009.0) **175** 891-902. DOI: 10.2353/ajpath.2009.081115
11. Liang X., Arullampalam P., Yang Z., Ming X.F.. **Hypoxia Enhances Endothelial Intercellular Adhesion Molecule 1 Protein Level Through Upregulation of Arginase Type II and Mitochondrial Oxidative Stress**. *Front. Physiol.* (2019.0) **10** 1003. DOI: 10.3389/fphys.2019.01003
12. Ryoo S., Lemmon C.A., Soucy K.G., Gupta G., White A.R., Nyhan D., Shoukas A., Romer L.H., Berkowitz D.E.. **Oxidized Low-Density Lipoprotein-Dependent Endothelial Arginase II Activation Contributes to Impaired Nitric Oxide Signaling**. *Circ. Res.* (2006.0) **99** 951-960. DOI: 10.1161/01.RES.0000247034.24662.b4
13. Iyamu E.W.. **The redox state of the glutathione/glutathione disulfide couple mediates intracellular arginase activation in HCT-116 colon cancer cells**. *Dig. Dis. Sci.* (2010.0) **55** 2520-2528. DOI: 10.1007/s10620-009-1064-1
14. Gao X., Xu X., Belmadani S., Park Y., Tang Z., Feldman A.M., Chilian W.M., Zhang C.. **TNF-α Contributes to Endothelial Dysfunction by Upregulating Arginase in Ischemia/Reperfusion Injury**. *Arterioscler. Thromb. Vasc. Biol.* (2007.0) **27** 1269-1275. DOI: 10.1161/ATVBAHA.107.142521
15. Romero M.J., Platt D.H., Tawfik H.E., Labazi M., El-Remessy A.B., Bartoli M., Caldwell R.B., Caldwell R.W.. **Diabetes-induced coronary vascular dysfunction involves increased arginase activity**. *Circ. Res.* (2008.0) **102** 95-102. DOI: 10.1161/CIRCRESAHA.107.155028
16. Santhanam L., Christianson D.W., Nyhan D., Berkowitz D.E.. **Arginase and vascular aging**. *J. Appl. Physiol.* (2008.0) **105** 1632-1642. DOI: 10.1152/japplphysiol.90627.2008
17. Pernow J., Jung C.. **Arginase as a potential target in the treatment of cardiovascular disease: Reversal of arginine steal?**. *Cardiovasc. Res.* (2013.0) **98** 334-343. DOI: 10.1093/cvr/cvt036
18. Romero M.J., Iddings J.A., Platt D.H., Ali M.I., Cederbaum S.D., Stepp D.W., Caldwell R.B., Caldwell R.W.. **Diabetes-induced vascular dysfunction involves arginase, I**. *Am. J. Physiol. Heart Circ. Physiol.* (2012.0) **302** H159-H166. DOI: 10.1152/ajpheart.00774.2011
19. Elms S.C., Toque H.A., Rojas M., Xu Z., Caldwell R.W., Caldwell R.B.. **The role of arginase I in diabetes-induced retinal vascular dysfunction in mouse and rat models of diabetes**. *Diabetologia* (2013.0) **56** 654-662. DOI: 10.1007/s00125-012-2789-5
20. Grönros J., Jung C., Lundberg J.O., Cerrato R., Östenson C.-G., Pernow J.. **Arginase inhibition restores in vivo coronary microvascular function in type 2 diabetic rats**. *Am. J. Physiol. Circ. Physiol.* (2011.0) **300** H1174-H1181. DOI: 10.1152/ajpheart.00560.2010
21. Johnson F.K., Johnson R.A., Peyton K.J., Shebib A.R., Durante W.. **Arginase promotes skeletal muscle arteriolar endothelial dysfunction in diabetic rats**. *Front. Immunol.* (2013.0) **4** 119. DOI: 10.3389/fimmu.2013.00119
22. Nunes K.P., Toque H.A., Caldwell R.B., William Caldwell R., Clinton Webb R.. **Extracellular Signal-Regulated Kinase (ERK) Inhibition Decreases Arginase Activity and Improves Corpora Cavernosal Relaxation in Streptozotocin (STZ)-Induced Diabetic Mice**. *J. Sex. Med.* (2011.0) **8** 3335-3344. DOI: 10.1111/j.1743-6109.2011.02499.x
23. Shatanawi A., Momani M.S., Al-Aqtash R., Hamdan M.H., Gharaibeh M.N.. **L-Citrulline Supplementation Increases Plasma Nitric Oxide Levels and Reduces Arginase Activity in Patients with Type 2 Diabetes**. *Front. Pharmacol.* (2020.0) **11** 584669. DOI: 10.3389/fphar.2020.584669
24. Beleznai T., Feher A., Spielvogel D., Lansman S.L., Bagi Z.. **Arginase 1 contributes to diminished coronary arteriolar dilation in patients with diabetes**. *Am. J. Physiol. Heart Circ. Physiol.* (2011.0) **300** H777-H783. DOI: 10.1152/ajpheart.00831.2010
25. Shemyakin A., Kövamees O., Rafnsson A., Böhm F., Svenarud P., Settergren M., Jung C., Pernow J.. **Arginase Inhibition Improves Endothelial Function in Patients with Coronary Artery Disease and Type 2 Diabetes Mellitus**. *Circulation* (2012.0) **126** 2943-2950. DOI: 10.1161/CIRCULATIONAHA.112.140335
26. Goldin A., Beckman J.A., Schmidt A.M., Creager M.A.. **Advanced glycation end products: Sparking the development of diabetic vascular injury**. *Circulation* (2006.0) **114** 597-605. DOI: 10.1161/CIRCULATIONAHA.106.621854
27. Ramasamy R., Yan S.F., Schmidt A.M.. **Receptor for AGE (RAGE): Signaling mechanisms in the pathogenesis of diabetes and its complications**. *Ann. N. Y. Acad. Sci.* (2011.0) **1243** 88-102. DOI: 10.1111/j.1749-6632.2011.06320.x
28. Park H.J., Boyington J.C.. **The 1.5 Å crystal structure of human receptor for advanced glycation endproducts (RAGE) ectodomains reveals unique features determining ligand binding**. *J. Biol. Chem.* (2010.0) **285** 40762-40770. DOI: 10.1074/jbc.M110.169276
29. Koch M., Chitayat S., Dattilo B.M., Schiefner A., Diez J., Chazin W.J., Fritz G.. **Structural Basis for Ligand Recognition Activation of, R.A.G.E**. *Structure* (2010.0) **18** 1342-1352. DOI: 10.1016/j.str.2010.05.017
30. Leclerc E., Fritz G., Vetter S.W., Heizmann C.W.. **Binding of S100 proteins to RAGE: An update**. *Biochim. Biophys. Acta Mol. Cell. Res.* (2009.0) **1793** 993-1007. DOI: 10.1016/j.bbamcr.2008.11.016
31. Tan K.C.B., Chow W.-S., Ai V.H.G., Metz C., Bucala R., Lam K.S.L.. **Advanced glycation end products and endothelial dysfunction in type 2 diabetes**. *Diabetes Care* (2002.0) **25** 1055-1059. DOI: 10.2337/diacare.25.6.1055
32. Rojas A., Romay S., González D., Herrera B., Delgado R., Otero K.. **Regulation of endothelial nitric oxide synthase expression by albumin-derived advanced glycosylation end products**. *Circ. Res.* (2000.0) **86** e50-e54. DOI: 10.1161/01.RES.86.3.e50
33. Biao X., Chibber R., Ruggiero D., Kohner E., Ritter J., Ferro A.. **Impairment of vascular endothelial nitric oxide synthase activity by advanced glycation end products**. *FASEB J.* (2003.0) **17** 1289-1291. DOI: 10.1096/fj.02-0490fje
34. Alzayadneh E.M., Chappell M.C.. **Angiotensin-(1–7) abolishes AGE-induced cellular hypertrophy and myofibroblast transformation via inhibition of ERK1/2**. *Cell. Signal.* (2014.0) **26** 3027-3035. DOI: 10.1016/j.cellsig.2014.09.010
35. Westwood M.E., Argirov O.K., Abordo E.A., Thornalley P.J.. **Methylglyoxal-modified arginine residues—A signal for receptor-mediated endocytosis and degradation of proteins by monocytic THP-1 cells**. *Biochim. Biophys. Acta Mol. Cell Res.* (1997.0) **1356** 84-94. DOI: 10.1016/S0167-4889(96)00154-1
36. Mazrouei S., Sharifpanah F., Caldwell R.W., Franz M., Shatanawi A., Muessig J., Fritzenwanger M., Schulze P.C., Jung C.. **Regulation of MAP kinase-mediated endothelial dysfunction in hyperglycemia via arginase I and eNOS dysregulation**. *Biochim Biophys. Acta Mol. Cell Res.* (2019.0) **1866** 1398-1411. DOI: 10.1016/j.bbamcr.2019.05.004
37. Corraliza I.M., Campo M.L., Soler G., Modolell M.. **Determination of arginase activity in macrophages: A micromethod**. *J. Immunol. Methods* (1994.0) **174** 231-235. DOI: 10.1016/0022-1759(94)90027-2
38. Rajapakse A.G., Yepuri G., Carvas J.M., Stein S., Matter C.M., Scerri I., Ruffieux J., Montani J.-P., Ming X.-F., Yang Z.. **Hyperactive S6K1 Mediates Oxidative Stress and Endothelial Dysfunction in Aging: Inhibition by Resveratrol**. *PLoS ONE* (2011.0) **6**. DOI: 10.1371/journal.pone.0019237
39. Sugimoto K., Fujii S., Takemasa T., Yamashita K.. **Detection of intracellular nitric oxide using a combination of aldehyde fixatives with 4,5-diaminofluorescein diacetate**. *Histochem. Cell Biol.* (2000.0) **113** 341-347. DOI: 10.1007/s004180000151
40. Shatanawi A., Romero M.J., Iddings J.A., Chandra S., Umapathy N.S., Verin A.D., Caldwell R.B., Rodriguez P.C., Toque H.A., Narayanan S.P.. **Angiotensin II-induced vascular endothelial dysfunction through RhoA/Rho kinase/p38 mitogen-activated protein kinase/arginase pathway**. *Am. J. Physiol. Cell Physiol.* (2011.0) **300** 1181-1192. DOI: 10.1152/ajpcell.00328.2010
41. Xu B., Ji Y., Yao K., Cao Y.X., Ferro A.. **Inhibition of human endothelial cell nitric oxide synthesis by advanced glycation end-products but not glucose: Relevance to diabetes**. *Clin. Sci.* (2005.0) **109** 439-446. DOI: 10.1042/CS20050183
42. El-Bassossy H.M., Neamatallah T., Balamash K.S., Abushareb A.T., Watson M.L.. **Arginase overexpression and NADPH oxidase stimulation underlie impaired vasodilation induced by advanced glycation end products**. *Biochem. Biophys. Res. Commun.* (2018.0) **499** 992-997. DOI: 10.1016/j.bbrc.2018.04.036
43. Perrone A., Giovino A., Benny J., Martinelli F.. **Advanced Glycation End Products (AGEs): Biochemistry, Signaling, Analytical Methods, and Epigenetic Effects**. *Oxid. Med. Cell Longev.* (2020.0) **2020**. DOI: 10.1155/2020/3818196
44. Yan S., Du Schmidt A.M., Anderson G.M., Zhang J., Brett J., Zou Y.S., Pinsky D., Stern D.. **Enhanced cellular oxidant stress by the interaction of advanced glycation end products with their receptors/binding proteins**. *J. Biol. Chem.* (1994.0) **269** 9889-9897. DOI: 10.1016/S0021-9258(17)36966-1
45. Wautier M.-P., Chappey O., Corda S., Stern D.M., Schmidt A.M., Wautier J.-L.. **Activation of NADPH oxidase by AGE links oxidant stress to altered gene expression via RAGE**. *Am. J. Physiol. Metab.* (2001.0) **280** E685-E694. DOI: 10.1152/ajpendo.2001.280.5.E685
46. Wautier M.P., Guillausseau P.J., Wautier J.L.. **Activation of the receptor for advanced glycation end products and consequences on health**. *Diabetes Metab. Syndr Clin. Res. Rev.* (2017.0) **11** 305-309. DOI: 10.1016/j.dsx.2016.09.009
47. Thengchaisri N., Hein T.W., Wang W., Xu X., Li Z., Fossum T.W., Kuo L.. **Upregulation of arginase by H**. *Arterioscler. Thromb. Vasc. Biol.* (2006.0) **26** 2035-2042. DOI: 10.1161/01.ATV.0000233334.24805.62
48. Hein T.W., Zhang C., Wang W., Chang C.-I., Thengchaisri N., Kuo L.. **Ischemia-reperfusion selectively impairs nitric oxide-mediated dilation in coronary arterioles: Counteracting role of arginase**. *FASEB J.* (2003.0) **17** 2328-2330. DOI: 10.1096/fj.03-0115fje
49. Shin W., Berkowitz D.. **Medicine SR-E& Molecular, 2012 Undefined. Increased Arginase II Activity Contributes to Endothelial Dysfunction through Endothelial Nitric Oxide Synthase Uncoupling in Aged Mice**
50. Caldwell R.W., Rodriguez P.C., Toque H.A., Priya Narayanan S., Caldwell R.B.. **Arginase: A Multifaceted Enzyme Important in Health and Disease**. *Physiol. Rev.* (2018.0) **98** 641. DOI: 10.1152/physrev.00037.2016
51. Toque H.A., Romero M.J., Tostes R.C., Shatanawi A., Chandra S., Carneiro Z.N., Inscho E.W., Webb R.C., Caldwell R.B., Caldwell R.W.. **p38 Mitogen-activated protein kinase (MAPK) increases arginase activity and contributes to endothelial dysfunction in corpora cavernosa from angiotensin-II-treated mice**. *J. Sex. Med.* (2010.0) **7** 3857-3867. DOI: 10.1111/j.1743-6109.2010.01996.x
52. Yao L., Chandra S., Toque H., Bhatta A., Rojas M., Caldwell R.B.. **Prevention of diabetes-induced arginase activation and vascular dysfunction by Rho kinase (ROCK) knockout**. *Cardiovasc. Res.* (2012.0) **97** 509-519. DOI: 10.1093/cvr/cvs371
53. Santhanam L., Lim H.K., Miriel V., Brown T., Patel M., Balanson S., Ryoo S., Anderson M., Irani K., Khanday F.. **Inducible NO synthase–dependent S-nitrosylation and activation of arginase1 contribute to age-related endothelial dysfunction**. *Circ. Res.* (2007.0) **101** 692-702. DOI: 10.1161/CIRCRESAHA.107.157727
54. Pandey D., Bhunia A., Oh Y.J., Chang F., Bergman Y., Kim J.H., Serbo J., Boronina T.N., Cole R.N., Van Eyk J.. **OxLDL Triggers Retrograde Translocation of Arginase2 in Aortic Endothelial Cells via ROCK and Mitochondrial Processing Peptidase**. *Circ. Res.* (2014.0) **115** 450-459. DOI: 10.1161/CIRCRESAHA.115.304262
55. Yoshimoto R., Fujita Y., Kakino A., Iwamoto S., Takaya T., Sawamura T.. **The discovery of LOX-1, its ligands and clinical significance**. *Cardiovasc. Drugs Ther.* (2011.0) **25** 379-391. DOI: 10.1007/s10557-011-6324-6
56. Shiu S.W.M., Tan K.C.B., Wong Y., Leng L., Bucala R.. **Glycoxidized LDL increases lectin-like oxidized low density lipoprotein receptor-1 in diabetes mellitus**. *Atherosclerosis* (2009.0) **203** 522-527. DOI: 10.1016/j.atherosclerosis.2008.07.012
57. Khalid M., Petroianu G., Adem A.. **Advanced Glycation End Products and Diabetes Mellitus: Mechanisms and Perspectives**. *Biomolecules* (2022.0) **12**. DOI: 10.3390/biom12040542
58. Mahdi A., Kövamees O., Checa A., Wheelock C.E., von Heijne M., Alvarsson M., Pernow J.. **Arginase inhibition improves endothelial function in patients with type 2 diabetes mellitus despite intensive glucose-lowering therapy**. *J. Intern. Med.* (2018.0) **284** 388-398. DOI: 10.1111/joim.12785
59. Costantino S., Paneni F., Battista R., Castello L., Capretti G., Chiandotto S., Tanese L., Russo G., Pitocco D., Lanza G.A.. **Impact of Glycemic Variability on Chromatin Remodeling, Oxidative Stress, and Endothelial Dysfunction in Patients with Type 2 Diabetes and with Target HbA1c Levels**. *Diabetes* (2017.0) **66** 2472-2482. DOI: 10.2337/db17-0294
|
---
title: Impact of Air-Drying Temperature on Antioxidant Properties and ACE-Inhibiting
Activity of Fungal Fermented Lentil Flour
authors:
- Janaina Sánchez-García
- Sara Muñoz-Pina
- Jorge García-Hernández
- Ana Heredia
- Ana Andrés
journal: Foods
year: 2023
pmcid: PMC10001291
doi: 10.3390/foods12050999
license: CC BY 4.0
---
# Impact of Air-Drying Temperature on Antioxidant Properties and ACE-Inhibiting Activity of Fungal Fermented Lentil Flour
## Abstract
Solid-state fermentation (SSF) with *Pleurotus ostreatus* enhances the nutritional value of legumes. However, drying can cause significant changes in physical and nutritional properties of the final products. Thus, this work studies the impact of air-drying temperature (50, 60, and 70 °C) on relevant properties (antioxidant properties, ACE-inhibitory capacity, phytic acid, colour, and particle size) of two fermented lentils flour (Pardina and Castellana) using freeze-drying as a reference method. Castellana variety is a better substrate for Pleurotus, generating four times more biomass. In addition, an almost total reduction of phytic acid from 7.3 to 0.9 mg/g db is achieved in this variety. Air-drying significantly decreased the particle size and the final colour with ΔE > 20; nonetheless, the temperature does not play a crucial role. SSF decreased the total phenolic content and the antioxidant capacity regardless of the variety, however, drying at 70 °C increased total phenolic content ($186\%$) in fermented Castellana flour. Comparing drying methods, freeze-drying implied a higher decrease in those parameters, reducing the TPC from 2.4 to 1.6 and from 7.7 to 3.4 mg gallic acid/g db in Pardina and Castellana dried flours. Finally, the flours inhibit the angiotensin I-converting-enzyme, and fermentation and drying increased their potential cardiovascular benefits.
## 1. Introduction
Legumes have been a cornerstone in Mediterranean cuisine and gastronomy; they are rich in proteins (usually $20\%$, reaching $30\%$ in some varieties), carbohydrates (around $60\%$), vitamins, minerals, healthy fats ($5\%$), and dietary fibres, but there has been a downward trend in their consumption [1]. Legume consumption has demonstrated a positive effect on health [2]. Furthermore, environmentally, legume cultivation promotes sustainable agriculture and contributes to climate change mitigation, and their ability to fix nitrogen can improve soil fertility and reduce the carbon footprint [3]. Moreover, they are also a more sustainable source of proteins (CO2 emissions of 1 kg of legume protein is 0.9 kg, while emissions of 1 kg of beef is 27 kg). However, its presence in the Mediterranean people’s diets has fallen to as low as 60–80 g/week, while the estimated recommendations are 200–250 g/week, according to the FAO.
Solid-state fermentation has been recently explored as a strategy to preserve/enhance the nutritional value of legumes with promising results in obtaining new ingredients [4].
This kind of biotreatment has been recently applied in lentils, peas, soybeans, or quinoa, among others [5]. Up-to-date results show increased protein (18–$23\%$) and antioxidant contents (30–$53\%$) in the fermented flours, along with increased protein digestibility (12–$17\%$) and a decrease in the carbohydrate content (6–$29\%$). A novel approach to do so relates to the use of edible fungi as starter cultures (which has been usually applied for agro-residual materials valorisation) [6,7]. In a previous study, solid-state fermentation (SSF) with *Pleurotus ostreatus* proved to be an efficient way to enhance the nutritional profile of Pardina lentils and white quinoa [8]. Additionally, the potential health benefits due to the presence of fungal biomass support the bioconversion of legumes and pseudo-cereals by SSF. In fact, mushrooms have been proved to have good antioxidant capacities, and have demonstrated their inhibitory effects towards angiotensin I-converting enzyme (ACE) [9]. The use of natural antioxidants in the production of new foods is critical to maintaining adequate levels of antioxidants and ensuring balance regarding the prevention of pathologies. The inhibition of the ACE enzyme would reduce blood pressure, lowering the risk of hypertension complications [10]. This kind of bioprocessing could also be employed as a part of a multi-stage process, including stabilization unit operations (such as drying or milling, among others), to obtain promising flours from lentils. Lentil flours have been commonly used to nutritionally enhance food products as an ingredient used as a thickener, binder, gelling agent, and/or stabilizer [11,12]. Nowadays, there are different drying methods to obtain flour such as hot air recirculation, oven drying, tunnel drying, freeze-drying, spray drying, and different temperatures of drying. However, the drying process and temperature may have mixed influences on the final product due to the varied compositions and types of raw products. Drying processes can cause significant changes in the physical and nutritional properties of the final products [13,14]. Some drying methods might improve the quality, preservation, and value of raw materials, while others may provoke a significant decline [15]. Thus, the appropriate technology and conditions used for obtaining new dry products must be established separately for each type of food, based on the specific properties of the final product [16].
To this day, according to authors’ knowledge, there are no lentil fermented flours with Pleurotus in the bibliography, and no studies of the effect of drying on its physico-chemical properties. Thus, the aim of this work is to study the impact of fungal solid-fermentation and air-drying temperature (50, 60, and 70 °C) on the final properties of fermented lentils flours (*Lens culinaris* var. Pardina and Castellana) with Pleurotus ostreatus. Relevant properties such as antioxidant properties, ACE-inhibitory capacity, phytic acid, colour, and particle size have been analysed to provide recommendations for the usage of fermented lentil flours as prospective food ingredients.
## 2.1. Materials
Lentils (Lens culinaris) of Pardina and Castellana varieties (Hacendado®, Valencia, Spain were purchased at local stores in Valencia (Spain). The *Pleurotus ostreatus* strain was obtained from the Spanish Type Culture Collection (CECT20311).
Phytic acid sodium salt hydrated from rice (C6H18O24P6·xNa+·yH2O), 2,2′-bipyridine (C10H8N2), thioglycolic acid (C2H4O2S,), sulphuric acid (H2SO4), sodium hydroxide (NaOH), sodium chloride (NaCl), 4-dimethylamino benzaldehyde (C9H11NO), acetylacetone (C5H8O2,), ascorbic acid (C6H8O6), ethyl acetate (C4H8O2), formic acid (CH2O2), 3,5-dinitrosalicylic acid (DNS) (C7H4N2O7), potassium sodium tartrate tetrahydrate (KNaC4H4O6·4H2O), folin ciocalteu reagent (C6H6O), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) (C18H16N4O6S4), 2,2-diphenyl-1-picrylhydrazyl (DPPH) (C18H12N5O6), 2,4,6-tripyridyl-s-triazine (TPTZ) (C18H12N6), (±)-6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox) (C14H18O4), gallic acid (C7H6O5), Angiotensin Converting Enzyme (ACE) from rabbit lung (≥2.0 units/mg protein) (A6778-25UN), N-Hippuric-His-Leu hydrate (HHL), glucose (C6H12O6), mycopeptone, chloramphenicol, and tryptone bile X-glucuronide agar (TBX chromogenic selective medium) were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). For HPLC analysis, vanillic acid (C8H8O4), 4-hydroxybezoic acid (C7H6O3), rutin (C27H30O16), quercetin 3-glucoside (C21H20O12), quercitrin (C21H20O11), epicatechin (C15H14O6), quercetin (C15H10O7), trans-cinnamic acid (C9H8O2), naringenin (C15H12O5), 4-O-caffeoylquinic (C16H18O9), caffeic acid (C9H8O4), p-coumaric acid (C9H8O3), sinapic acid (C11H12O5), ferulic acid (C10H10O4), apigenin-7-glucoside (C21H20O10), and kaempferol (C15H10O6) were obtained also from Sigma-Aldrich Co. (St. Louis, MO, USA), all per analytical standards (HPLC grade).
Ethanol absolute (C2H6O), concentrated hydrochloric acid (HCl), acetic acid glacial (C2H4O2), diethyl ether (C2H5OC2H5), ammonium iron (III) sulphate dodecahydrate (NH4Fe(SO4)2.12H2O), sodium carbonate (Na2CO3), and EDTA Calcium Disodium Salt (C10H12CaN2Na2O8) were obtained from Panreac AppliChem (Barcelona, Spain). Methanol (CH4O, HPLC grade), acetonitrile (C2H3N, HPLC grade), iron (III) chloride hexahydrate (FeCl3·6H2O), potassium persulphate (K2S2O8), and sodium acetate trihydrate (C2H3NaO2·3H2O) were obtained from Honeywell Fluka (Morris Plains, NJ, USA). Malt extract, agar, Plate-count agar, and Sabouraud dextrose agar were obtained from Scharlau (Barcelona, Spain).
## 2.2.1. Starter Culture Preparation
Pleurotus ostreatus mycelium from the stock culture was inoculated on malt agar petri dishes made with $2\%$ malt extract, $2\%$ glucose, $0.1\%$ mycopeptone, and $1.5\%$ agar, and then placed in an incubator (2001249, J.P. Selecta, Barcelona, Spain) at 28 °C for 14 days. The grown mycelium was inoculated in a culture broth made with $2\%$ malt extract, $2\%$ glucose, and $0.1\%$ mycopeptone, and incubated at 28 °C for 14 days. A portion of the grown mycelium was taken and inoculated in a culture broth made with $2\%$ malt extract, $2\%$ glucose, and $0.1\%$ mycopeptone, and incubated at 28 °C for 14 days.
For starter culture preparation, 10 g of Pardina and Castellana flour each was placed in petri dishes, hydrated to $65\%$ moisture, and sterilised in an autoclave (4002136, J.P. Selecta, Barcelona, Spain) at 121 °C for 20 min. Finally, 1 mL of the culture broth containing the grown fungal mycelium was inoculated and incubated at 28 °C for 14 days.
## 2.2.2. Fermentation Process
Solid-state fermentation was performed by placing 35 g of lentils (Pardina and Castellana) humidified to $65\%$ moisture in glass jars and then sterilizing them at 121 °C for 20 min. The substrates were inoculated into the glass jars by adding $\frac{1}{8}$ of the starter culture (petri dishes containing 10 g of colonised substrate divided into 8 portions) and then incubated at 28 °C for 14 days. Several glass jars were inoculated to obtain enough fermented substrate to perform all assays. Fermented substrates contained in glass jars were mixed to obtain a homogeneous sample for the subsequent drying process and analysis.
## 2.3. Drying and Milling of Fermented Grains/Seeds
Samples were dried by hot-air drying and lyophilization methods using a load of 500 g. Hot-air drying was performed using a convective dryer (Pol-Eko-Aparatura, CLW 750 TOP+, Kokoszycka, Poland) at three different drying temperatures (50, 60, and 70 °C), air velocity was 10.5 ± 0.2 m/s and air humidity percentage was 23.2 ± 2.9, 14.2 ± 1.7 and 8.7 ± 1.2 for 50, 60, and 70 °C, respectively. Lyophilization was conducted in a freeze dryer (Telstar, Lyoquest-55, Terrassa, Spain) at −45 °C and 0.8 mBar for 48 h. Milling was carried out with a food processor (Thermomix®, TM6-1, Wuppertal, Germany), applying 10,000 rpm at 15 s intervals for 1 min.
## 2.4. Drying Kinetics and Modelling
Drying kinetics was determined with a balance (Mettler Toledo, MS4002S, Greifensee, Zurich), by measuring the mass variation of the samples at each drying temperature (50, 60, and 70 °C) in a determined time interval, making several measurements over time until a constant weight was obtained. Drying curves of the samples were obtained, and an adjustment was made using the Lewis model equation (Equation [1]) for thin layer drying, which is one of the most common mathematical models used in the drying process of agricultural products [17,18]. [ 1]XtwX0w=e−k · t where: *Xtw is* the moisture at a determined time, X0w is the moisture at time 0, k is the model constant, and t is the time (min).
## 2.5.1. Proximal Substrate Composition
Proximal composition of the substrate was conducted according to standardised methodologies of the Association of Official Analytical Chemists (AOAC) [19]. Moisture, protein, lipid, ash, and carbohydrate contents were analysed, the last one by difference. Total fibre, soluble, and insoluble fibre contents were determined according to the AOAC Method 991.43 and AACC Method 32-07.01. Results were expressed in g/100 g dry basis.
## 2.5.2. Reducing Sugars
The methodology proposed by Miller [20] and Sansano et al. [ 21] for reducing sugars determination was used. An amount of 0.3 g of sample was mixed with 2 mL of $80\%$ ethanol, vortexed, and allowed to stand for 30 min. It was centrifuged at 5000× g for 5 min (5810R, Eppendorf, Hamburg, Germany). The extraction was repeated twice, and the extracts were pooled. An aliquot of 500 µL was taken and mixed with 1 mL of DNS reagent (10 g/L of 3,5-dinitrosalicylic acid, containing 300 g potassium sodium tartrate tetrahydrate and 16 g sodium hydroxide). It was heated in a water bath (J.P.Selecta, Barcelona, Spain) at 100 °C for 5 min and then cooled to room temperature. The sample was diluted with 6 mL of distilled water and the absorbance was measured at 546 nm (Helios Zeta UV-VIS Spectrophotometer, Thermo Fisher Scientific, Waltham, MA, USA). Results were expressed as g glucose/100 g dry basis using a standard curve.
## 2.5.3. Fungus Biomass
Biomass produced by the fungus was determined according to the method published by Aidoo et al. [ 22] and Tomaselli Scotti et al. [ 23]. Briefly, 100 mg of dry sample was mixed with 2.4 mL of $72\%$ sulphuric acid at 25 °C for 24 h. Samples were diluted with 55 mL of distilled water and sterilised at 121 °C for 2 h. Then, the hydrolysate was neutralised to pH 7 with 10 M and 0.5 M sodium hydroxide with a pH meter (Mettler-Toledo, SevenCompact S210, Greifensee, Zurich). Erhlick’s reagent was prepared by dissolving 2.67 g of 4-dimethylamino benzaldehyde in 100 mL of a 1:1 mixture of ethanol reagent grade and concentrated hydrochloric acid. A 1 mL aliquot of previously neutralised hydrolysate was mixed with 1 mL of acetylacetone reagent (1 mL of acetylacetone and 50 mL of 0.5 M sodium carbonate) in a glass tube with a cap and heated in a boiling water bath for 20 min, then cooled to room temperature. Then, 6 mL of ethanol was added followed by 1 mL of Erhlick’s reagent. The mixture was incubated at 65 °C for 10 min, cooled to room temperature, and the absorbance was measured at 530 nm in a spectrophotometer (Beckman Coulter, DU 730, Brea, CA, USA). Results were expressed as mg glucosamine/g dry basis.
## 2.5.4. pH and Water Activity (aw)
A $10\%$ dilution of the samples was prepared and measured for pH determination. Water activity was measured with a dew point water activity meter (Decagon Devices Inc., Aqualab 4TE, Cervera, Spain) at 25 °C.
## 2.5.5. Colour
Colour measurements of the different flours were carried out using a spectro-colourimeter (Minolta, CM-3600D, Tokyo, Japan), considering a standard illuminant D65 and a standard observer of 10°. The CIE-L*a*b*colour coordinates were measured. Tone (h) and chroma (C*) values were automatically calculated by the device with the a* and b* coordinates, and colour differences (ΔE) were calculated according to the following equation (Equation [2]):[2]ΔE=(ΔL*)2+(Δa*)2+(Δb*)2
## 2.5.6. Particle Size
Particle size was measured with the dry method, using laser diffraction equipment (Mastersizer 2000, Malvern Instruments Limited, Malvern, UK). Results were reported as the equivalent volume mean diameter D[4.3] and percentile particle size d(0.5).
## 2.5.7. Phytic Acid Content
Phytate content was determined using the method described by Haug and Lantzsch [24], and modified by Peng et al. [ 25]. A stock solution with 1.3 mg/mL phytic acid concentration was prepared and diluted with 0.2 M hydrochloric acid in the range of 0.1–1 mL (3.16–31.6 µg/mL phytate phosphorus). Ferric solution was prepared by dissolving 0.2 g of ammonium iron (III) sulphate dodecahydrate in 100 mL of 2 M hydrochloric acid and increased up to 1 L using distilled water. The 2,2′-bipyridine solution was prepared by dissolving 10 g of 2,2′-bipyridine and 10 mL of thioglycolic acid in distilled water and increased to 1 L. An amount of 50 mg of sample was mixed with 10 mL of 0.2 M hydrochloric acid and left overnight at 4 °C to prepare the extract. An amount of 1 mL of ferric solution and 500 µL of the extract were added in a tube. It was incubated in a boiling water bath for 30 min and then cooled to room temperature. The tube was centrifuged for 30 min at 3000× g and 1 mL of the supernatant was taken and mixed with 1.5 mL of 2,2′-bipyridine solution. The absorbance was measured at 519 nm against distilled water. A calibration curve was performed using a phytate reference solution. Assays were done in triplicate, and the results were expressed as mg phytic acid/g dry basis.
## 2.5.8. Phenolic Compounds by HPLC Analysis
Phenolic compounds were extracted according to the methodology proposed by Caprioli et al. [ 26] and Giusti et al. [ 27]. To perform acid hydrolysis, 2.5 g of sample was weighed, and 7.5 mL of the extraction solvent (70:30 mixture of ethanol and bi-distilled water) was added. pH was adjusted with 2 M hydrochloric acid to pH 2 and subjected to an ultrasonic bath (J.P. Setecta, 3000840) for 2 h at room temperature. Samples were centrifuged at 8000× g for 15 min. The extraction was repeated twice. Both extracts obtained were pooled and filtered with a 0.45 µm PTFE filter, and subsequently, the free phenolic fraction was analysed by HPLC.
To conduct the alkaline hydrolysis, 14 mL of a mixture of 2 M sodium hydroxide with $0.01\%$ 10 mM EDTA and $0.1\%$ ascorbic acid was added to the acid hydrolysis residue (sediment) and left overnight to release bound phenolic esters or ethers. The pH was adjusted to 2 with 6 M hydrochloric acid and centrifuged at 8000× g for 15 min. Afterward, 15 mL of a mixture of ethyl acetate and diethyl ether in a 50:50 ratio was centrifuged at 5400× g for 10 min, and repeated twice. Both organic phases were pooled and concentrated in a rotary evaporator (Heidolph, Kelheim, Germany) at 25 °C. The concentrate was reconstituted with 10 mL of methanol, filtered with a 0.45 µm PTFE filter, and analysed by HPLC.
The obtained extracts were analysed using an HPLC 1200 Series Rapid Resolution coupled to a diode detector Serie (Agilent, Palo Alto, Santa Clara, CA, USA) following the methodology explained by Tanleque-Alberto et al. [ 28]. Phenolic compounds were separated on a Brisa-LC 5 µm C18 column (250 × 4.6 mm) (Teknokroma, Barcelona, Spain). Mobile phase A was $1\%$ formic acid, and mobile phase B was acetonitrile (ACN). The following gradient program was established: 0 min, $90\%$ A; 25 min, $40\%$ A; 26 min, $20\%$ A; holding until 30 min; 35 min, $90\%$ A; holding until 40 min. The column working temperature, the flow rate and the injection volume were 30 °C, 0.5 mL/min, and 10 µL, respectively. Unknown compounds were identified by comparing the resulting chromatographic retention times with those of reference standards at the following wavelengths for each compound: 250 nm for vanillic acid; 260 nm for 4-hydroxibezoic acid, rutin, quercitin 3-glucoside, and quercitrin; 280 nm for gallic acid, epicatechin, quercetin, and trans-cinnamic acid; 290 nm for naringenin; 320 nm for 4-O-caffeoylquinic, caffeic acid, p-coumaric acid, sinapic acid, ferulic acid, and apigenin-7-glucoside; and 380 nm for kaempferol. Quantification of the identified compounds was carried out using a calibration curve by linear regression analysis of the area under the curve versus their concentration, and the results were calculated in µg/g dry weight.
## 2.5.9. Total Phenolic Content
The total phenol content was determined with the Folin-Ciocalteu method described by Chang et al. [ 29], using the same extract described above for acid extraction in Section 2.5.8. An aliquot of 125 µL of the extract was taken, and 500 µL of bi-distilled water was added, followed by 125 µL of folin ciocalteu reagent, and left to react for 6 min. An amount of 1.25 mL of $7\%$ sodium carbonate solution and 1 mL of bi-distilled water were added to reach a final volume of 3 mL. The mixture was incubated at room temperature in the dark for 30 min, and the absorbance was measured at 760 nm in a UV/Vis spectrophotometer. A standard curve for gallic acid was used, and the results were expressed as mg gallic acid/g dry basis.
## 2.5.10. Antioxidant Activity
Antioxidant activity of the samples was determined by three different methods: ABTS, DPPH, and FRAP according to the methodology proposed by Thaipong et al. [ 30] with some modifications. The same acid extract described above was used.
For ABTS assay, stock solutions of 7.4 mM ABTS and 2.6 mM potassium persulphate were prepared, and then the working solution was prepared by mixing the two stock solutions in a 1:1 ratio and leaving them to react for 12 h at room temperature in darkness. After reaction, 1 mL of the fresh working solution is diluted with 60 mL of methanol to obtain an absorbance close to 1.1 at 734 nm in a UV/Vis spectrophotometer. An aliquot of 150 µL of the acidic extract was reacted with 2.85 mL of ABTS working solution for 2 h in the dark and the absorbance was measured at 734 nm.
For DPPH assay, a fresh working solution of 0.039 g/L DPPH in pure methanol was prepared to obtain an absorbance close to 1.1 at 515 nm in a UV/Vis spectrophotometer. An aliquot of 75 µL of the extract was reacted with 2.925 mL of DPPH working solution for 30 min in the dark and the absorbance was measured at 515 nm.
For FRAP assay, stock solutions of 300 mM acetate buffer (3.1 g sodium acetate trihydrate and 16 mL acetic acid glacial in 1 L water), pH 3.6, 10 mM TPTZ (2,4,6-tripyridyl-s-triazine) solution in 40 mM hydrochloric acid, and 20 mM iron (III) chloride hexahydrate solution were prepared. A fresh working solution was prepared by mixing acetate buffer, TPTZ solution, and iron (III) chloride hexahydrate solution in a 10:1:1 ratio, respectively, and incubated at 37 °C before use. An amount of 150 µL of the extract was reacted with 2.85 mL of FRAP working solution for 30 min in the dark, and the absorbance was measured at 593 nm.
In all three methods for antioxidant activity determination, a trolox standard curve was used, and the results were expressed as mg trolox/g dry basis.
The overall antioxidant potency composite index (APCI) was determined by assigning all antioxidant activity assays (ABTS, DPPH, and FRAP) an equal weight by assigning an antioxidant index value of 100 to the highest sample score in each assay, and then calculating an antioxidant index for the other samples in each assay according to the following equation (Equation [3]) [31]:[3]Antioxidant index (%)=(sample scorehighest sample score)×100 Finally, the APCI was calculated by averaging each antioxidant activity assay’s antioxidant index (%) for each sample.
## 2.5.11. Angiotensin-Converting Enzyme Inhibitory Activity (ACE ia (%))
ACE ia (%) of the samples was determined according to the method described by Akillioǧlu and Karakaya [32] and Hernández-Olivas et al. [ 33]. A double extraction of the protein was performed by mixing 5 g of the sample with 45 mL of distilled water, and the pH was adjusted to 11 and centrifuged at 10,000× g at 4 °C for 20 min. Supernatants were pooled, and the pH was adjusted to the isoelectric point (4.5), kept in gentle agitation for 1.5 h at 4 °C, and centrifuged at 10,000× g at 4 °C for 20 min. The sediment was dissolved in 50 mM phosphate buffer, pH = 7. Extracts were analysed immediately, otherwise, they were stored at −40 °C.
ACE reactive (25 mU/mL) and the substrate Hip-His-Leu (5 mM) were dissolved in 0.15 M Tris base buffer, containing 0.3 M sodium chloride, and the pH was adjusted at 8.3. Three controls (100 μL ACE + 40 μL distilled water; 140 μL distilled water; 40 μL sample extract +100 μL distilled water) were included together with the samples (100 μL ACE + 40 μL sample extract) and then incubated at 37 °C for 5 min. An amount of 100 μL substrate to each tube was added, and the incubation was continued for 30 min at the same temperature. An amount of 150 μL of 1 M hydrochloric acid was added to stop the reaction. An amount of 1 mL ethyl acetate was added and mixed vigorously in a vortex mixer. Samples were centrifuged 1200× g for 10 min, and 750 μL of the supernatant was collected and placed into clean tubes. Ethyl acetate contained in the supernatant was evaporated by gentle shaking at 80 °C. Solid hippuric acid contained in the tubes was dissolved in 1 mL of distilled water, and the absorbance was measured at 228 nm. ACE ia (%) was calculated according to the following equation (Equation [4]):[4]ACE ia (%)=100−{100×(C−D)/(A−B)} where: A, B, C, and D are the absorbance of ACE + distilled water, distilled water, ACE + sample extract, and sample extract + distilled water, respectively.
## 2.5.12. Microbiological Analysis
Samples of the different flours were collected aseptically to perform the corresponding microbiological analysis. For this, 1 g of each flour was diluted in 9 mL of sterile distilled water, and serial decimal dilutions were made. For the investigation of total aerobic mesophilic bacteria counts, mould, and yeast count, and *Escherichia coli* count, 0.1 mL of each serial dilution were plated onto Plate-count agar, Sabouraud dextrose agar with 50 mg/L of Chloramphenicol, and Tryptone Bile X-Glucuronide agar (TBX chromogenic selective medium), and incubated at 30 °C for 72 h, 25 °C for 5–7 days, and 44 °C for 24 h, respectively. Listeria monocytogenes and Salmonella spp. detection analyses were performed according to ISO 6579-1:2017 and ISO 11290-1:2017.
## 2.5.13. Statistical Analysis
Experiments were carried out in triplicate and data were reported as mean ± standard deviation. One-way ANOVA and multiple range tests by the LSD procedure (least significant difference) of the Fisher test was performed to study possible differences between different drying temperatures using the statistical program Statgraphics Centurion version XV (Rockville, MD, USA) with a confidence level of $95\%$ (p-value < 0.05).
## 3.1. Changes in Proximal Composition Induced by Fungal Solid-State Fermentation of Lentils
It is well known that legumes can be nutritionally modified by processing such as soaking, cooking, or dehydration, among others. Processed legumes show increases in protein digestibility, available starch, and soluble fibre, or important decreases in antinutritional factors such as phytic acid [34]. Solid-state fermentation processing implies a sequence of unit operations such as soaking and sterilising the substrate prior to the fermentation itself, so the nutritional properties of fermented legumes are expected to be different. Fungal solid-state fermentation of lentils with *Pleurotus ostreatus* provokes different changes depending on the characteristics of the initial substrates, mainly the lentil cultivar, which implies significant differences in composition, structure, and seed coat, to cotyledon ratio or seed size [5,35,36,37]. Table 1 shows the proximal composition of the substrates prior to and after the SSF process of both cultivars, as well as the biomass production, pH, and water activity. Since moisture content is the most affected component due to the conditioning process prior to inoculation and fermentation, the results are shown on a dry basis. Despite the slight differences between Pardina and Castellana cultivars in terms of initial composition, a clear difference in terms of biomass production was observed, being 14.1 and 61.8 mg of glucosamine/g dry basis, respectively, although the impact on the overall protein is not significant. These results point out that Castellana lentil is a better substrate for SSF with Pleurotus ostreatus, and the explanation could be found in the morphometric characteristics of these two cultivars. Plaza et al. [ 38] characterised the main Spanish lentil cult. They found that all of them, including the Castellana cultivar, showed a medium elliptic shape except for the cultivar Pardina which was classified as wide elliptic. The same authors reported the average weight (Pardina: 3.58 ± 0.24 and Castellana: 5.91 ± 0.15) and diameter (Pardina: 4.43 ± 0.09 and Castellana: 5.86 ± 0.09), which are morphometric properties affecting the density and porosity of the fermentation bed and the growing ability of mycelium.
Pleurotus is a lignocellulosic fungus, which means that it can depolymerize a complex structure made of cellulose, hemicelluloses, and lignin. That explains the reduction of total fibre observed in fermented samples of both cultivars (Table 1), especially the insoluble fraction. In addition, this process is also reflected in the increase in reducing sugars in both samples. In the case of the Castellana lentil, this increase is much higher, probably due to the greater amount of biomass present. The increase in the soluble fraction could be attributed to a solubilization process provoked by soaking and heating the substrates before inoculation. Aguilera et al. [ 34] observed similar results in Pardina lentils submitted to industrial dehydration processing, including previous soaking and cooking steps. The same authors observed lower ash values in processed flours than in raw lentils, coinciding with the ash reduction observed in fermented lentils, probably due to mineral losses during thermal processing in both cases.
## 3.2. Air Drying Kinetics of Fermented Lentils
Fermented lentils were dried at different inlet air temperatures for modelling purposes. The drying curves obtained for both cultivars (Figure 1) revealed faster drying kinetics for Castellana lentils than Pardina. These results are in accordance with the results observed during soaking treatment before fermentation, in which water uptake was more rapid in Castellana lentils, revealing the higher moisture content of Castellana fermented lentils (Table 1) despite the same soaking time for both cultivars. Additionally, the impact of the air-drying temperature is higher in Pardina lentils.
In the case of the Pardina lentil, drying with air at 50 °C demands approximately 9 h until the product reaches a constant weight. This time is reduced to 7 and 5.5 h when drying at 60 °C and 70 °C, respectively. In the case of the Castellana variety, the same tendency occurs, needing 8 and 6 h to get a constant weight after drying at the lowest temperatures. Increasing the temperature to 70 °C decreased the time required to 4.5 h. However, when obtaining a functional food ingredient, it is necessary to consider the effect of air-drying temperature on bioactive compound (phenols and antioxidants) content, processing time, and total energy consumption, which are key factors when setting up an industrial process. In this sense, drying at 70 °C would provide faster drying.
Air-drying curves were fitted following the Lewis model, and the constants and statistical coefficients of the model are shown in Table 2. In all cases, the R squared is superior to 0.97. The parameter K, related to the drying rate, shows a linear correlation with the air temperature. In the case of the Pardina lentil, the slope was 0.012, the intersection −0.348, and the coefficient of determination was 0.9974. On the other hand, for Castellana lentils, the slope was 0.010, the intersection −0.240, and the coefficient of determination was 0.9601.
These parameters were used to estimate the drying time needed at each temperature to obtain fermented lentil flours with $7\%$ final moisture content.
On the other hand, drying affects the microbiological quality of the product regardless of the air-drying properties. In fact, dehydration of the product reduces the amount of water available for microbial growth leading to microbial inhibition or even death. However, the temperature and speed of air drying can affect the rate of destruction of microbes. The microbiological quality parameters (Table 3) demonstrate that the obtained flours fit the minimal safety requirements.
## 3.3. Impact of Processing on Particle Size, Colour, and Phytic Acid of Fermented Flours
The drying conditions frequently affect the structure of dried foodstuffs [39], which is why the particle sizes of the resulting flours were affected by the drying process used during the previous dehydration step. Figure 2 shows the particle size distribution of fermented flours obtained by different drying processes for Pardina and Castellana cultivars.
Those flours obtained from fermented and dried lentils have a smaller particle size than raw lentil flours. Choe et al. [ 40] found the same results in thermally treated common bean flours, attributing this change to decreased seed hardness and less cohesiveness than the raw samples. In both cultivars, the same effect of fermentation and drying on the distribution and size of particles is observed. The drying method, air-drying or freeze-drying, significantly impacts the particle size, although the air-drying temperature does not affect this parameter. Raw lentil flour and fermented freeze-dried lentil flour present a monomodal distribution with an average particle size of 284 µm and 76.4 µm, respectively. Fermentation and hot-air drying shifted the particle size distributions towards smaller sizes, exhibiting a multimodal pattern. The reduction in particle size due to fermentation and drying was slightly more significant in the Castellana lentil flour. Nonetheless, Martini et al. [ 11] studied the influence of different particle sizes in red lentil flour for its use in bakery products. Although they found a slight influence on the water holding capacity—decreasing as the particle size is smaller—the multivariable statistics demonstrated that the particle size of the lentil flours is not the major factor affecting the rheology. In this case, technologically speaking, the reduction in particle size due to fermentation and drying would not be a problem.
One of the parameters in the assessment of flour colour is the L*a*b* difference (ΔE). This parameter significantly increased with the combined process of SSF and drying (Table 4). It can be observed that the substantial decrease in the L* value as well as the increase of the a* value compares to the unfermented flours in all cases. Changes in L* value and a* value could be related to Maillard reactions, caramelization, and/or pigment degradation [41] during the drying process. The drying method also significantly impacts the final colour of fermented lentil flours, with the freeze-dried fermented samples having fewer colour changes compared with the unprocessed one.
The effect of SSF and drying on the final colour of flours was higher in Castellana variety than in Pardina, especially in the a* coordinate, which increases significantly. Similar results were found for dried apples [42], where the colour differences varied between 11.37 in the case of the freeze-dried apple and 21.11 in the case of air-drying at 60 °C. These differences in colour may be due to non-enzymatic Maillard reactions. Furthermore, the porous structure of dried products also differs, affecting mainly lightness of material due to the presence of air voids and pores [43].
## 3.4. Impact of Processing on Antioxidant and Anti- Hypertensive Properties of Fermented Flours
The potential health benefits of lentils have been attributed to secondary metabolites such as phenolic compounds, which exhibit antioxidant properties. These compounds can reduce the activity of reactive oxygen species by different mechanisms (scavenging the free radicals generated, complexing pro-oxidant metals, and quenching singlet oxygen) [34,44,45]. Table 5 shows the antioxidant activity evaluated by ABTS, DPPH, and FRAP, together with each antioxidant index, the APCI, and total phenol content.
The results demonstrate significant differences between the phenolic compounds due to the processing (Table 5). It was found that processed Pardina samples were characterised by reduced phenolic content. At the same time, this parameter was higher for the hot-air dried Castellana variety samples without a significant effect on temperature.
Comparing drying methods, freeze-drying implied a higher decrease in total phenol content than hot-air drying, regardless the variety. Similar results are found in literature when studying different legumes. In the case of bean sprouts, the total phenol content after being treated by hot-air drying was always significantly higher than that found when treated by freeze-drying either at low temperatures (20 °C) or high temperatures (80 °C) [46]. The same tendency was also found for pinto beans [47]. This reduction has been observed in other processes, such as boiling, and could be attributed to compound destruction, oxidation, or chemical rearrangement involving binding with other compounds [31]. Aguilera et al. [ 34] reported similar results under ordinary boiling for Pardina lentils. On the other hand, heat treatments were expected to increase TPC compounds due to the partial destruction of cellular structure and the subsequent release of bound compounds and/or the formation of Maillard reaction products with phenol and reducing agents above 40 °C [48]. Hot-air drying better retained phenolic compounds or underwent a higher release of those than freeze-drying in lentils. Regarding the antioxidant activity (ABTS, DPPH, and FRAP assays), similar trends were found exhibiting the relationship between these parameters and TPC (Table 5). Thus, fermentation and drying led to significant ($p \leq 0.05$) reduction in these activities with some exceptions. Only for Castellana lentils, an increase of FRAP and ABTS antioxidant activity was found in fermented flours due to hot-air drying. DPPH antioxidant activity, however, was the only parameter that rose because of fermentation for subsequently being negatively affected by drying. Que et al. 2008 [49] studied the influence of hot-air drying and freeze-drying on the antioxidant activities of pumpkin flours. They showed significantly higher total antioxidant activity in hot-air dried pumpkin flour than in freeze-dried flour. The authors attributed this to the creation of Maillard products or their intermediates with potent antioxidant activity. In our case, the initial content of reducing sugars is much higher in fermented Castellana than in fermented Pardina samples (see Table 1). This high concentration of reducing sugars can generate a greater number of compounds with greater antioxidant capacity after undergoing the Maillard reaction. Finally, among the processed flours, the highest APCI was obtained for fermented Castellana and Pardina flours air-dried at 70 °C with an APCI of 78.1 and $23.7\%$, respectively.
Results of chromatogram profiles and concentrations of phenolic compounds identified, phenolic acids and flavonoids, are shown in Table 6 and Table 7 for Pardina and Castellana samples, respectively. In all cases, two extractions were performed to obtain phenolic compounds taking part in the free and bound fractions. However, in the case of Pardina samples, only unquantifiable traces were found for p-Coumaric acid, Epicatechin, and Ferulic acid for the second extraction (bound fraction). In the case of the Castellana lentils, Gallic acid, 4-Hydroxibezoic acid, and p-Coumaric acid as bonded phenols were detected. In this last case, both results were summed. Therefore, it can be said that most of the phenols are in their free form in the samples because of their native form, or due to a release along processing, fermentation and/or drying. This fact and the reduction in most phenols revealed that a destruction of these compounds is the main mechanism that occurred instead of rearrangement with other molecules or chemical conversion into other phenolic derivates in Pardina. The losses in Pardina samples are a consequence of the great reduction of 4-O-caffeoylquinic and Epicatechin, the most abundant phenolic acid and flavonoid, respectively, in raw Pardina lentils, through fermentation and further drying. Only a significant increase of vanillic acid and generation of trans-Cinnamic acid were found due to fermentation and hot-air drying. For Castellana samples, a notable increase in free gallic acid was seen during fermentation and drying, together with a drastic decrease in rutin and disappearing of epicatechin, among the most abundant compounds. As a result of these variations, a net increase of phenols was found in fermented hot-air dried samples, increasing as the drying temperatures increases.
It has been reported that some fungus genera, such as Rhizopus or Aspergillus, can synthesise microbial enzymes like tannases, and are able to release low-molecular weight phenolic acids from tanninic complex molecules [50,51]. This fact, together with the heating-induced depolymerization of condensed tannins, could also be responsible for the increase of some phenolic acids found in Pardina and Castellana fermented and dried samples.
On the other hand, legumes are rich in phytates, which have traditionally been considered a disadvantage since these compounds (present in legumes, whole grains, seeds, and nuts) hinder the absorption of specific vitamins and minerals (especially calcium, iron, and zinc). However, processing legumes are usually used to reduce antinutrients in legumes, and in this study, phytic acid content was analysed to assess the impact of SSF and drying temperature (Table 5). The initial content of phytic acid in the raw flours was within the range of the values published for these two cultivars [52], and in both cases, they were much lower than the harmful range of 10–60 mg/g [53].
The reduction in phytic acid can be attributed to hydrolysis by the endogenous phytase enzyme, which can be activated during processing. However, the reduction of these compounds because of processing (soaking, cooking, fermentation, drying, etc.) depends on the type of legume and the processing conditions [31]. In this study, significant differences between cultivars were observed in phytic acid reduction because of SSF. In contrast, the phytic acid content is almost negligible in fermented Castellana lentils; no significant differences were observed between raw and fermented Pardina lentils. This result reveals that the endogenous phytase enzyme in Pardina lentils is quickly inactivated by the autoclaving of the substrate before the inoculation and incubation for fermentation. The impact of drying on phytic acid content in the fermented flour was also analysed (Table 5). No significant differences can be attributed to the drying process, temperature, or drying type. The early inactivation of endogenous phytases in the Pardina during the processing before drying could explain these results, while Castellana’s endogenous phytase seems more resistant to processing inactivation.
However, it should be noted that a moderate–small amount of phytates in the diet is even beneficial, since these compounds, in preclinical studies, have been shown to inhibit the proliferation of colon cancer cells [54]. Phytic acid, specifically hexaphosphate (inositol), is receiving special attention, because in cell and animal studies, it has been shown to have an anticancer action [55,56] in colon cancer, prostate cancer [57], and leukemia cells, among others [58]. Much remains to be investigated in this regard, but this shows that in cooked or germinated legumes, there is no problem with a certain amount of phytates, since it will hardly influence the absorption of nutrients and, on top of that, seems to bring us additional benefits.
Regarding the capacity of the samples to inhibit the angiotensin I-converting-enzyme (ACE), fermentation plus drying increased the potential cardiovascular benefits of the samples compared to their unprocessed lentils flours (Figure 3).
The impact of processing was more notable on the Pardina than the Castellana variety; even though Castellana fermented and its dried flours exhibited a slightly, but statistically significant, higher ACE-inhibitory activity than Pardina samples. According to the literature, the ACE-inhibitory activity would correspond to protein fractions and mainly to low molecular weight peptides. Some plant proteins have been reported to be a source of various bioactive ACE-inhibitory peptides with anti-hypertensive activity. This is the case for some protein hydrolysates or isolates from soybean, bean, pea, sesame, rice and zein [59]. Moreover, increased dietary intake of plant protein was reported to exert a more beneficial effect on blood pressure compared to protein from animals [59]. It is also noticeable how only the fermentation with Pleurotus significantly increases the ACE-inhibitory capacity in both varieties of lentils by about five percentage points. Pleurotus ostreatus is well known for its potential in ACE inhibition. Abdullah et al. [ 9] reported that all Pleurotus fungi studied performed ACE inhibition, mainly due to its protein content. The effect of the type of drying on ACE capacity also performs an influence. In our case, the lyophilization performs lesser activity than the lentils dried at 70 °C. Piskov et al. [ 10] studied various drying methods on the ACE inhibition activity of Pleurotus Ostreatus and concluded that P.Ostreatus dried using freeze-drying exhibited a lower ACE inhibitory capacity than the same mushroom dried with hot air in agreement with our data.
On the other hand, Mohamad Ansor et al. [ 60] reported the capability of some mycelia such as of G. lucidum in lowering blood pressure levels. Apparently, four proteins (cystathionine beta synthase-like protein, DEAD/DEAH box helicase-like protein, paxillin-like protein, and alpha/beta hydrolase-like protein) derived from edible mushrooms would be responsible for the ACE inhibition. Therefore, the partial hydrolysis of native proteins along fermentation together with the mycelium could be responsible for an improvement in the benefits to cardiovascular health of the samples. Several studies have established a relationship between the chemical structure of peptides and their ability to inhibit ACE [61]. Peptides with hydrophobic or aromatic terminal amino acids are more likely to interact with the active site of ACE and present highest ACE inhibitory activity. The presence of C-terminal aromatic amino acid residues and N-terminal hydrophobic amino acid residues can also enhance the peptide’s activity in inhibiting ACE [61].
## 4. Conclusions
In the present study, the implications of solid-state fermentation (SSF) together with stabilization (by hot-air or freeze-drying) on some functional and technological properties, which play a relevant role in ingredients’ quality, were evaluated in fermented lentil flours. The obtained results demonstrated the Castellana variety was the most suitable substrate for fungal solid-state fermentation using edible fungus Pleurotus ostreatus, with a significant reduction in antinutrient phytic acid (from 7.3 to 0.9 mg/g db) as well as insoluble fibre (15 to 11 g/100 g db). Additionally, the SSF increased the ACE inhibitory capacity in both varieties of lentils by about five percentage points. With respect to dehydration and hot-air drying, specifically at 70 °C, there was an increase in total phenolic content, inhibition of ACE, and lower phytic acid content in fermented Castellana lentils compared to freeze-drying. Drying significantly decreased the particle size from around 270 µm to less than 150 µm, reaching only 76.4 µm after lyophilization. Fermented freeze-dried lentil flour presents a monomodal distribution in both varieties of lentil, while air-drying flours shifted the particle size distributions towards smaller sizes, exhibiting a multimodal pattern. Colour changes have also been notable after fermentation and drying (ΔE > 20), being more pronounced when lyophilizing. Regarding the phenolic profile, it has been observed how fermentation changes the profile, decreasing some compounds and increasing others, such as p-coumaric acid, vanillic acid, and quercetin for the Pardina variety and gallic acid, trans-cinnamic acid, and naringenin for the Castellana variety. Regarding the antioxidant capacity, the SSF negatively affected the antioxidant capacity of lentils, however, air-drying processing at 70 °C significantly increased the values obtained by ABTS and FRAP in the case of Castellana lentil flour. Therefore, fermented Castellana flours obtained by solid-state fermentation with P.ostreatus and air-dried at 70 °C might be considered a promising rich protein ingredient with improved functionality and new optical properties. In vitro digestion evaluation, however, could be important to go deeper into the healthy benefits of these flours.
## References
1. Maphosa Y., Jideani V.A.. **The Role of Legumes in Human Nutrition**. *Functional Food-Improve Health through Adequate Food* (2017) **Volume 1** 103-122
2. Polak R., Phillips E.M., Campbell A.. **Legumes: Health Benefits and Culinary Approaches to Increase Intake**. *Clin. Diabetes* (2015) **33** 198-205. DOI: 10.2337/diaclin.33.4.198
3. Stagnari F., Maggio A., Galieni A., Pisante M.. **Multiple Benefits of Legumes for Agriculture Sustainability: An Overview**. *Chem. Biol. Technol. Agric.* (2017) **4** 2. DOI: 10.1186/s40538-016-0085-1
4. Calvo-Lerma J., Asensio-Grau A., García-Hernández J., Heredia A., Andrés A.. **Exploring the Impact of Solid-State Fermentation on Macronutrient Profile and Digestibility in Chia (**. *Foods* (2022) **11**. DOI: 10.3390/foods11030410
5. Espinosa-Páez E., Alanis-Guzmán M.G., Hernández-Luna C.E., Báez-González J.G., Amaya-Guerra C.A., Andrés-Grau A.M.. **Increasing Antioxidant Activity and Protein Digestibility in**. *Molecules* (2017) **22**. DOI: 10.3390/molecules22122275
6. Hoogeveen G.W.M., Hoogeveen H.W.. **System for Solid State Fermentation and Use Thereof**. (2010)
7. Lyons M.P., Hoskins B.J.. **Compositions and Methods for Conversion of Lignocellulosic Material to Fermentable Sugars and Products Produced Therefrom**. (2014)
8. Sánchez-García J., Asensio-Grau A., García-Hernández J., Heredia A., Andrés A.. **Nutritional and antioxidant changes in lentils and quinoa through fungal solid-state fermentation with**. *Bioresour. Bioprocess.* (2022) **9** 1-12. DOI: 10.1186/s40643-022-00542-2
9. Abdullah N., Ismail S.M., Aminudin N., Shuib A.S., Lau B.F.. **Evaluation of Selected Culinary-Medicinal Mushrooms for Antioxidant and ACE Inhibitory Activities**. *Evidence-Based Complement. Altern. Med.* (2012) **2012** 1-12. DOI: 10.1155/2012/464238
10. Tuck M.. **Management of Hypertension in the Patient with Diabetes Mellitus: Focus on the Use of Angiotensin-Converting Enzyme Inhibitors**. *Am. J. Hypertens.* (1988) **1** 384S-388S. DOI: 10.1093/ajh/1.4.384S
11. Marchini M., Carini E., Cataldi N., Boukid F., Blandino M., Ganino T., Vittadini E., Pellegrini N.. **The use of red lentil flour in bakery products: How do particle size and substitution level affect rheological properties of wheat bread dough?**. *LWT-Food Sci. Technol.* (2021) **136** 110299. DOI: 10.1016/j.lwt.2020.110299
12. Romano A., Gallo V., Ferranti P., Masi P.. **Lentil flour: Nutritional and technological properties, in vitro digestibility and perspectives for use in the food industry**. *Curr. Opin. Food Sci.* (2021) **40** 157-167. DOI: 10.1016/j.cofs.2021.04.003
13. Patrón-Vázquez J., Baas-Dzul L., Medina-Torres N., Ayora-Talavera T., Sánchez-Contreras A., García-Cruz U., Pacheco N.. **The Effect of Drying Temperature on the Phenolic Content and Functional Behavior of Flours Obtained from Lemon Wastes**. *Agronomy* (2019) **9**. DOI: 10.3390/agronomy9090474
14. González M., Vernon-Carter E., Alvarez-Ramirez J., Carrera-Tarela Y.. **Effects of dry heat treatment temperature on the structure of wheat flour and starch in vitro digestibility of bread**. *Int. J. Biol. Macromol.* (2020) **166** 1439-1447. DOI: 10.1016/j.ijbiomac.2020.11.023
15. Duan J.-L., Xu J.-G.. **Effects of Drying Methods on Physico-Chemical Properties and Antioxidant Activity of Shiitake Mushrooms (**. *Agric. Food Sci. Res.* (2015) **2** 51-55
16. Piskov S., Timchenko L., Grimm W.-D., Rzhepakovsky I., Avanesyan S., Sizonenko M., Kurchenko V.. **Effects of Various Drying Methods on Some Physico-Chemical Properties and the Antioxidant Profile and ACE Inhibition Activity of Oyster Mushrooms (**. *Foods* (2020) **9**. DOI: 10.3390/foods9020160
17. Chkir I., Balti M.A., Ayed L., Azzouz S., Kechaou N., Hamdi M.. **Effects of air drying properties on drying kinetics and stability of cactus/brewer’s grains mixture fermented with lactic acid bacteria**. *Food Bioprod. Process.* (2015) **94** 10-19. DOI: 10.1016/j.fbp.2014.12.003
18. Lewis W.K.. **The Rate of Drying of Solid Materials**. *J. Ind. Eng. Chem.* (1921) **13** 427-432. DOI: 10.1021/ie50137a021
19. 19.
Association of Official Analysis Chemists
AOAC Official Methods of Analysis of AOAC InternationalAssociation of Official Analysis Chemists InternationalArlington, VA, USA20000935584544. *AOAC Official Methods of Analysis of AOAC International* (2000)
20. Miller G.L.. **Use of Dinitrosalicylic Acid Reagent for Determination of Reducing Sugar**. *Anal. Chem.* (1959) **31** 426-428. DOI: 10.1021/ac60147a030
21. Sansano M., Juan-Borrás M., Escriche I., Andrés A.M., Heredia A.. **Effect of Pretreatments and Air-Frying, a Novel Technology, on Acrylamide Generation in Fried Potatoes**. *J. Food Sci.* (2015) **80** T1120-T1128. DOI: 10.1111/1750-3841.12843
22. Aidoo K.E., Hendry R., Wood B.J.B.. **Estimation of fungal growth in a solid state fermentation system**. *Eur. J. Appl. Microbiol. Biotechnol.* (1981) **12** 6-9. DOI: 10.1007/BF00508111
23. Scotti C., Vergoignan C., Feron G., Durand A.. **Glucosamine measurement as indirect method for biomass estimation of**. *Biochem. Eng. J.* (2001) **7** 1-5. DOI: 10.1016/S1369-703X(00)00090-5
24. Haug W., Lantzsch H.-J.. **Sensitive method for the rapid determination of phytate in cereals and cereal products**. *J. Sci. Food Agric.* (1983) **34** 1423-1426. DOI: 10.1002/jsfa.2740341217
25. Wu P., Zhao T., Tian J.-C.. **Phytic Acid Contents of Wheat Flours from Different Mill Streams**. *Agric. Sci. China* (2010) **9** 1684-1688. DOI: 10.1016/S1671-2927(09)60266-2
26. Caprioli G., Nzekoue A.F.K., Giusti F., Vittori S., Sagratini G.. **Optimization of an extraction method for the simultaneous quantification of sixteen polyphenols in thirty-one pulse samples by using HPLC-MS/MS dynamic-MRM triple quadrupole**. *Food Chem.* (2018) **266** 490-497. DOI: 10.1016/j.foodchem.2018.06.049
27. Giusti F., Capuano E., Sagratini G., Pellegrini N.. **A comprehensive investigation of the behaviour of phenolic compounds in legumes during domestic cooking and in vitro digestion**. *Food Chem.* (2019) **285** 458-467. DOI: 10.1016/j.foodchem.2019.01.148
28. Tanleque-Alberto F., Juan-Borrás M., Escriche I.. **Antioxidant characteristics of honey from Mozambique based on specific flavonoids and phenolic acid compounds**. *J. Food Compos. Anal.* (2019) **86** 103377. DOI: 10.1016/j.jfca.2019.103377
29. Chang C.-H., Lin H.-Y., Chang C.-Y., Liu Y.-C.. **Comparisons on the antioxidant properties of fresh, freeze-dried and hot-air-dried tomatoes**. *J. Food Eng.* (2006) **77** 478-485. DOI: 10.1016/j.jfoodeng.2005.06.061
30. Thaipong K., Boonprakob U., Crosby K., Cisneros-Zevallos L., Hawkins Byrne D.. **Comparison of ABTS, DPPH, FRAP, and ORAC assays for estimating antioxidant activity from guava fruit extracts**. *J. Food Compos. Anal.* (2006) **19** 669-675. DOI: 10.1016/j.jfca.2006.01.003
31. Sharma S., Kataria A., Singh B.. **Effect of thermal processing on the bioactive compounds, antioxidative, antinutritional and functional characteristics of quinoa (**. *LWT* (2022) **160** 113256. DOI: 10.1016/j.lwt.2022.113256
32. Akıllıoğlu H.G., Karakaya S.. **Effects of heat treatment and in vitro digestion on the Angiotensin converting enzyme inhibitory activity of some legume species**. *Eur. Food Res. Technol.* (2009) **229** 915-921. DOI: 10.1007/s00217-009-1133-x
33. Hernández-Olivas E., Muñoz-Pina S., García-Hernández J., Andrés A., Heredia A.. **Impact of common gastrointestinal disorders in elderly on in vitro meat protein digestibility and related properties**. *Food Biosci.* (2022) **46** 101560. DOI: 10.1016/j.fbio.2022.101560
34. Aguilera Y., Dueñas M., Estrella I., Hernández T., Benitez V., Esteban R.M., Martín-Cabrejas M.A.. **Evaluation of Phenolic Profile and Antioxidant Properties of Pardina Lentil as Affected by Industrial Dehydration**. *J. Agric. Food Chem.* (2010) **58** 10101-10108. DOI: 10.1021/jf102222t
35. Chawla P., Bhandari L., Sadh P.K., Kaushik R.. **Impact of Solid-State Fermentation (**. *Cereal Chem.* (2017) **94** 437-442. DOI: 10.1094/CCHEM-05-16-0128-R
36. Mora-Uzeta C., Cuevas-Rodríguez E., López-Cervantes J., Milán-Carrillo J., Gutiérrez-Dorado R., Reyes-Moreno C.. **Improvement Nutritional/Antioxidant Properties of Underutilized Legume Tepary Bean (**. *Agrociencia* (2019) **53** 987-1003
37. Garrido-Galand S., Asensio-Grau A., Calvo-Lerma J., Heredia A., Andrés A.. **The potential of fermentation on nutritional and technological improvement of cereal and legume flours: A review**. *Food Res. Int.* (2021) **145** 110398. DOI: 10.1016/j.foodres.2021.110398
38. Plaza J., Morales-Corts M., Pérez-Sánchez R., Revilla I., Vivar-Quintana A.. **Morphometric and Nutritional Characterization of the Main Spanish Lentil Cultivars**. *Agriculture* (2021) **11**. DOI: 10.3390/agriculture11080741
39. Guiné R.P.F.. **The Drying of Foods and Its Effect on the Physical-Chemical, Sensorial and Nutritional Properties**. *ETP Int. J. Food Eng.* (2018) **2** 93-100. DOI: 10.18178/ijfe.4.2.93-100
40. Choe U., Osorno J.M., Ohm J.-B., Chen B., Rao J.. **Modification of physicochemical, functional properties, and digestibility of macronutrients in common bean (**. *Food Chem.* (2022) **382** 132570. DOI: 10.1016/j.foodchem.2022.132570
41. Yi J.-Y., Lyu J., Bi J.-F., Zhou L.-Y., Zhou M.. **Hot air drying and freeze drying pre-treatments coupled to explosion puffing drying in terms of quality attributes of mango, pitaya, and papaya fruit chips**. *J. Food Process. Preserv.* (2017) **41** e13300. DOI: 10.1111/jfpp.13300
42. Djekic I., Tomic N., Bourdoux S., Spilimbergo S., Smigic N., Udovicki B., Hofland G., Devlieghere F., Rajkovic A.. **Comparison of three types of drying (supercritical CO**. *LWT* (2018) **94** 64-72. DOI: 10.1016/j.lwt.2018.04.029
43. Nowak D., Jakubczyk E.. **The Freeze-Drying of Foods—The Characteristic of the Process Course and the Effect of Its Parameters on the Physical Properties of Food Materials**. *Foods* (2020) **9**. DOI: 10.3390/foods9101488
44. Ranilla L.G., Genovese M.I., Lajolo F.M.. **Effect of Different Cooking Conditions on Phenolic Compounds and Antioxidant Capacity of Some Selected Brazilian Bean (**. *J. Agric. Food Chem.* (2009) **57** 5734-5742. DOI: 10.1021/jf900527v
45. Madhujith T., Shahidi F.. **Beans: A Source of Natural Antioxidants**. *Phenolic Compounds in Foods and Natural Health Products* (2005) 83-93. DOI: 10.1021/bk-2005-0909.ch008
46. Gan R.-Y., Lui W.-Y., Chan C.-L., Corke H.. **Hot Air Drying Induces Browning and Enhances Phenolic Content and Antioxidant Capacity in Mung Bean (**. *J. Food Process. Preserv.* (2016) **41** e12846. DOI: 10.1111/jfpp.12846
47. Anton A.A., Ross K.A., Beta T., Fulcher R.G., Arntfield S.D.. **Effect of pre-dehulling treatments on some nutritional and physical properties of navy and pinto beans (**. *LWT* (2008) **41** 771-778. DOI: 10.1016/j.lwt.2007.05.014
48. Zou Y., Gao Y., He H., Yang T.. **Effect of roasting on physico-chemical properties, antioxidant capacity, and oxidative stability of wheat germ oil**. *LWT* (2018) **90** 246-253. DOI: 10.1016/j.lwt.2017.12.038
49. Que F., Mao L., Fang X., Wu T.. **Comparison of hot air-drying and freeze-drying on the physicochemical properties and antioxidant activities of pumpkin (**. *Int. J. Food Sci. Technol.* (2008) **43** 1195-1201. DOI: 10.1111/j.1365-2621.2007.01590.x
50. Bajpai B., Patil S.. **A New Approach to Microbial Production of Gallic Acid**. *Braz. J. Microbiol.* (2008) **39** 708-711. DOI: 10.1590/S1517-83822008000400021
51. Aguilar-Zárate P., Cruz-Hernández M., Montañez J., Belmares-Cerda R., Aguilar C.. **Bacterial tannases: Production, properties and applications, Tanasas bacterianas: Producción, propiedades y aplicaciones**. *Rev. Mex. Ing. Química* (2014) **13** 63-74
52. Thavarajah P., Thavarajah D., Vandenberg A.. **Low Phytic Acid Lentils (**. *J. Agric. Food Chem.* (2009) **57** 9044-9049. DOI: 10.1021/jf901636p
53. Farinde E.O., Olanipekun O.T., Olasupo R.B.. **Nutritional Composition and Antinutrients Content of Raw and Processed Lima Bean (**. *Ann. Food Sci. Technol.* (2018) **19** 250-264
54. Barahuie F., Dorniani D., Saifullah B., Gothai S., Hussein M.Z., Pandurangan A.K., Arulselvan P.. **Sustained release of anticancer agent phytic acid from its chitosan-coated magnetic nanoparticles for drug-delivery system**. *Int. J. Nanomed.* (2017) **12** 2361-2372. DOI: 10.2147/IJN.S126245
55. Shamsuddin A.M.. **Anti-cancer function of phytic acid**. *Int. J. Food Sci. Technol.* (2002) **37** 769-782. DOI: 10.1046/j.1365-2621.2002.00620.x
56. Schröterová L., Hašková P., Rudolf E., Cervinka M.. **Effect of phytic acid and inositol on the proliferation and apoptosis of cells derived from colorectal carcinoma**. *Oncol. Rep.* (2010) **23** 787-793. PMID: 20127021
57. Gu M., Roy S., Raina K., Agarwal C., Agarwal R.. **Inositol Hexaphosphate Suppresses Growth and Induces Apoptosis in Prostate Carcinoma Cells in Culture and Nude Mouse Xenograft: PI3K-Akt Pathway as Potential Target**. *Cancer Res.* (2009) **69** 9465-9472. DOI: 10.1158/0008-5472.CAN-09-2805
58. Vucenik I., Shamsuddin A.M.. **Protection Against Cancer by Dietary IP**. *Nutr. Cancer* (2006) **55** 109-125. DOI: 10.1207/s15327914nc5502_1
59. Hong F., Ming L., Yi S., Zhanxia L., Yongquan W., Chi L.. **The Antihypertensive Effect of Peptides: A Novel Al-ternative to Drugs?**. *Peptides* (2008) **29** 1062-1071. DOI: 10.1016/j.peptides.2008.02.005
60. Ansor N.M., Abdullah N., Aminudin N.. **Anti-angiotensin converting enzyme (ACE) proteins from mycelia of**. *BMC Complement. Altern. Med.* (2013) **13**. DOI: 10.1186/1472-6882-13-256
61. Lin Z., Lai J., He P., Pan L., Zhang Y., Zhang M., Wu H.. **Screening, ACE-inhibitory mechanism and structure-activity relationship of a novel ACE-inhibitory peptide from**. *Food Biosci.* (2023) **52** 102374. DOI: 10.1016/j.fbio.2023.102374
|
---
title: 'Fast Eating Speed Could Be Associated with HbA1c and Salt Intake Even after
Adjusting for Oral Health Status: A Cross-Sectional Study'
authors:
- Satsuki Watanabe
- Yuhei Matsuda
- Yui Nanba
- Mayu Takeda
- Takafumi Abe
- Kazumichi Tominaga
- Minoru Isomura
- Takahiro Kanno
journal: Healthcare
year: 2023
pmcid: PMC10001298
doi: 10.3390/healthcare11050646
license: CC BY 4.0
---
# Fast Eating Speed Could Be Associated with HbA1c and Salt Intake Even after Adjusting for Oral Health Status: A Cross-Sectional Study
## Abstract
This study aimed to examine the relationship between eating speed and hemoglobin A1c (HbA1c), considering the number of teeth, using cross-sectional health examination data from community-dwelling older individuals in Japan. We used data from the Center for Community-Based Healthcare Research and Education Study in 2019. We collected data on gender, age, body mass index, blood test results, Salt intake, bone mineral density, body fat percentage, muscle mass, basal metabolic rate, number of teeth, and lifestyle information. Eating speed was evaluated subjectively as fast, normal, or slow. Overall, 702 participants were enrolled in the study and 481 participants were analyzed. Multivariate logistic regression analysis revealed a significant association between fast eating speed and being a male (odds ratio [$95\%$ confidence interval]: 2.15 [1.02–4.53]), HbA1c (1.60 [1.17–2.19]), salt intake (1.11 [1.01–1.22]), muscle mass (1.05 [1.00–1.09]), and enough sleep (1.60 [1.03–2.50]). Fast eating may be associated with overall health and lifestyle. The characteristics of fast eaters, after taking oral information into consideration, tended to increase the risk of type 2 diabetes, renal dysfunction, and hypertension. Dental professionals should provide dietary and lifestyle guidance to fast eaters.
## 1. Introduction
In a large cohort study in Japan, unhealthy dietary habits were reported to affect obesity and other health conditions [1]. Examples of unhealthy eating habits are snack food consumption, late-night meal consumption, and fast eating [2,3]. Of these, fast eating has been noted to have the potential for widespread health effects [4]. *In* general, fast eating has been reported to be associated with systemic diseases such as obesity, type 2 diabetes, non-alcoholic fatty liver disease, and renal dysfunction [5,6,7]. Although there is no fixed definition of eating speed, in many studies, a patient’s subjective eating speed is important in the assessment of time [8]. The assessment of self-reported eating speed is also included in the self-administered diet history questionnaire (DHQ), which was developed to evaluate the dietary habits of the Japanese population and has been suggested to be related to eating habits and obesity levels [9]. A meta-analysis has reported a higher body mass index (BMI) when eating faster [8]. Eating speed has also been reported to be significantly associated with a higher risk of metabolic syndrome, elevated blood pressure, and obesity [10]. In addition, eating speed may be associated with proinflammatory cytokines (IL-1β) in Japanese men without metabolic diseases [11]. Conversely, slower eating speeds have been reported to reduce excess food and energy intake [12]. Therefore, eating speed, which is a lifestyle habit, is highly likely to have an impact on health. Focusing on type 2 diabetes, which is strongly associated with metabolic syndrome, it has been reported that fast eating speed is associated with a rapid increase in blood glucose levels [13,14]. Eating speed has also been implicated as an intermediate factor in obesity, and may be associated with diabetes [15]. In addition, some reports suggest that fast eating speeds double the increased risk of type 2 diabetes [16]. However, while gender, age, and BMI have been adjusted for as confounding factors in many studies, few studies have considered oral and dental health status.
Whether oral and dental health status influences eating speed is controversial. The association between masticatory function and eating speed is contradicted by reports that people with fewer dental prostheses eat faster, while a study of 30,938 Japanese adults reported that masticatory difficulty was associated with higher hemoglobin A1c (HbA1c) [17]. Some studies reported that increasing the number of times of mastication decreased the speed of eating, whereas others reported that there was no relationship [18,19]. Next, regarding the relationship between the number of teeth and eating speed, a report found that the probability of having metabolic syndrome was 2.5 times higher in those with a small number of remaining teeth and fast eating than in those with a large number of remaining teeth and slow eating [20]. Additionally, a higher number of remaining teeth and slower eating speed have been reported to reduce the likelihood of metabolic syndrome in the older population [20].
Overall, previous studies suggest that eating speed and oral and dental health status may be related, but few studies have considered such oral and dental health-related factors in studies of eating speed [20,21].
Therefore, two hypotheses were formulated: first, eating speed is associated with oral and dental health-related status, and second, even after adjusting for oral and dental health-related status, systemic health conditions such as type 2 diabetes together with lifestyle conditions and eating speed are associated [20]. This study aimed to examine the relationship between eating speed and systemic health conditions, considering oral and dental health status, using cross-sectional health examination data from community-dwelling older individuals in Japan.
## 2.1. Data Collection
This study used the same dataset as other reports because it is based on datasets obtained from health examinations [22,23]. However, the variables of interest for the analysis and the analysis methods were different. This study was approved by the Medical Research Ethics Committee of Shimane University Faculty of Medicine (number: 20220622-1). Written informed consent was obtained from all participants, and data were collected.
## 2.2. Center for Community-Based Healthcare Research and Education (CoHRE) Study
The CoHRE *Study is* a cohort study conducted by the Shimane University Center for Community-based Healthcare Research and Education to predict and prevent lifestyle-related diseases in Ohnan-cho, Shimane Prefecture [22,24]. Surveys on health and medical information, various clinical examination information, lifestyle information, human relationship information, social resource information, and medical care cost information are ongoing.
## 2.3. Study Design
This study used cross-sectional data from the 2019 Shimane CoHRE Study; the 2019 data are the most recent version of the dataset because surveys have not been conducted after 2019 due to the COVID-19 pandemic [22].
## 2.4. Inclusion Criteria
The inclusion criteria were as follows: residents covered by Japan National Health Insurance; residents of Ohnan-cho, a mid-mountainous area in Shimane Prefecture, Japan; and residents who participated in the 2019 survey [24].
## 2.5. Exclusion Criteria
Data from residents with missing values were excluded, and complete data were analyzed [22,23].
## 2.6.1. Background Data
In the CoHRE Study, data were collected annually through standardized questionnaires and physical measurements, blood tests, and urine analysis [22]. We collected data on the following variables: gender (male/female), age, body mass index, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride, γ-glutamyl transpeptidase, glycemic index, HbA1c, estimated glomerular filtration rate, creatinine, sodium, potassium, salt intake, bone mineral density, body fat percentage, muscle mass, basal metabolic rate, number of teeth, smoking status, physical activity, walking speed (yes/no), sleeping status, and alcohol consumption (every day, sometimes, none). Salt intake was measured by estimating salt intake from spot urine specimens (Tanaka method) [25]. Walking speed and sleep status were binarized as “yes/no”; “yes” corresponded to a walking speed faster than that of an average individual of the same gender at about the same age and when the respondents were well rested, respectively [26].
## 2.6.2. Eating Speed
As in previous studies, a self-reported subjective assessment method of food intake speed was applied [10,27]. Eating speed was evaluated subjectively on a three-point scale: fast, normal, and slow.
For the analysis, eating speed was treated by dividing the group into two groups: fast and normal/slow.
## 2.7. Statistical Analysis
After confirming the normality of participant data using the Shapiro–Wilk test, continuous data were expressed as means and standard deviations, while categorical data were expressed as numbers (%).
Logistic regression analysis (backward stepwise) was used to control for possible confounding variables related to eating speed. Partial regression coefficients for each eating speed outcome were estimated after adjusting for all other variables included in the model. Adjustment items included gender, age, body mass index, HDL cholesterol, LDL cholesterol, triglyceride, γ-glutamyl transpeptidase, HbA1c, estimated glomerular filtration rate, smoking, physical activity, walking speed, sleeping, alcohol consumption, creatinine, sodium, potassium, salt intake, bone mineral density, body fat percentage, muscle mass, basal metabolic rate, and the number of teeth. All statistical analyses were performed using SPSS version 26 (IBM, Armonk, NY, USA). Two-tailed p-values were calculated for all analyses.
## 3.1. Participant Characteristics
The participants’ characteristics are summarized in Table 1. Overall, 702 participants were enrolled in the study, and 220 were excluded due to missing data. Ultimately, 481 participants were included in the analysis. Of the participants, 223 ($46.4\%$) were male, and the mean age was 66.7 (SD: 7.4) years. The mean body mass index was 66.7 (SD: 7.4) kg/m2. The mean HDL cholesterol level was 61.7 (SD: 15.1) mg/dL. The mean LDL cholesterol level was 121.8 (SD: 27.4) mg/dL. The mean triglyceride level was 101.9 (SD: 65.3) mg/dL. The mean gamma-glutamyl transpeptidase level was 37.7 (SD: 54.1) IU/L. The mean HbA1c was 6.0 (SD: 7.0). The mean estimated glomerular filtration rate was 69.4 (SD: 13.1) mL/min/1.73 m2. The mean creatinine was 85.9 (SD: 55.9) mL/min. The mean sodium was 119.7 (SD: 56.3) mEq/day. The mean potassium was 54.5 (SD: 30.7) mEq/day. The mean salt intake was 9.5 (SD: 2.1) g/day. The mean bone mineral density was 88.3 (SD: 12.2). The mean body fat percentage was $24.2\%$ (SD: $8.9\%$). The mean muscle mass was $41.2\%$ (SD: $8.5\%$). The mean basal metabolic rate was 1208.3 (SD: 230.8) kcal/day. The mean number of teeth was 23.5 (SD: 7.8). There were 41 ($8.5\%$) smokers. From the questionnaire, 261 ($54.3\%$) participants answered that they do physical exercises on a daily basis, 209 ($43.5\%$) answered that their walking speed was fast, and 350 (72.8) respondents answered that they slept well. Eating speed was fast in 136 ($28.3\%$) patients, normal in 301 ($62.6\%$), and slow in 44 ($9.1\%$). There were 133 ($27.7\%$) participants that drank alcohol every day, 105 ($21.8\%$) who did sometimes, and 243 ($50.5\%$) who never did.
## 3.2. Univariate and Multivariate Logistic Regression Analysis
The results of univariate and multivariate logistic regression analysis are shown in Table 2. There were no significant associations in univariate analysis between eating speed and gender (odds ratio [$95\%$ confidence interval]: 1.00 [0.67–1.49]), age (0.99 [0.97–1.02]), HDL cholesterol (0.99 [0.97–1.00]), LDL cholesterol (1.00 [0.99–1.01]), triglyceride (1.00 [1.00–1.00]), gamma-glutamyl transpeptidase (1.00 [1.00–1.01]), estimated glomerular filtration rate (1.00 [0.98–1.01]), creatinine (1.00 [1.00–1.00]), sodium (1.00 [1.00–1.01]), potassium (1.00 [0.99–1.00]), bone mineral density (1.00 [0.99–1.02]), body fat percentage (1.01 [0.99–1.04]), muscle mass (1.02 [1.00–1.04]), basal metabolic rate (1.00 [1.00–1.00]), number of teeth (1.01 [0.98–1.04]), smoking (1.08 [0.53–2.23]), physical activity (0.95 [0.64–1.42]), walking speed (0.89 [0.59–1.32]), or drinking alcohol (1.15 [0.91–1.46]). However, univariate analysis revealed significant correlations between eating speed and body mass index (odds ratio [$95\%$ confidence interval]: 1.07 [1.02–1.13]), HbA1c (1.60 [1.18–2.18]), and salt intake (1.14 [1.04–1.26]). Because this study used the variable reduction method during logistic regression analysis, each variable was used once as an explanatory variable in the multiple analysis, but the following variables were finally extracted as strongly related items. Multivariate logistic regression analysis revealed significant correlations between eating speed and gender (2.15 [1.02–4.53]), HbA1c (1.60 [1.17–2.18]), salt intake (1.11 [1.01–1.22]), muscle mass (1.05 [1.00–1.09]), and sleeping (1.60 [1.03–2.50]).
## 4. Discussion
Our major findings in this study are that fast eating might lead to systemic diseases such as type 2 diabetes, renal dysfunction, and hypertension even after adjusting for the number of teeth. A 5-year cohort study analyzing diabetes incidence in 4853 Japanese participants reported a hazard rate of 2.08 for diabetes incidence at fast eating speeds compared with slow eating speeds [7]. In a study of Japanese adults that investigated the relationship between eating speed and poor glycemic control, several reports indicated that fast eating was associated with poor glycemic control, a measure of postprandial blood glucose [28,29]. In addition, fast eating speed may be independently associated with insulin resistance in the Japanese population [30]. Since the results of this study are consistent with previous reports, we believe that eating speed is also associated with glycemic control in healthy older people living in the area we studied. However, because many of the reported studies were based on data from Japan, the results of this study may differ depending on the effect of race. In fact, as a Japanese-specific report, impaired glucose tolerance, which results in postprandial hyperglycemia, is common among young, thin Japanese women with a BMI < 18.5 kg/m2, and is associated with insulin resistance and adipose tissue abnormalities as the cause of such impaired glucose tolerance [31]. Therefore, whether the results of this study can be applied to populations other than the Japanese population should be carefully determined. In basic research, histamine neurons have been reported to be involved in regulating masticatory function, particularly eating speed, in experiments using rats. Although histamine neurons in the brain are often explained as being activated by slower eating speeds to facilitate visceral fat burning via the sympathetic nervous system, the detailed mechanism is still unclear [32]. However, the strength of this study is that we reported results adjusted for masticatory function as a confounding factor. Although a relationship between masticatory function and eating speed has been reported, the masticatory function has not been considered a factor related to eating speed in many studies [19]. Therefore, it is important to note that the results of this study are based on an analysis that considers oral and dental health status. However, whether masticatory function positively or negatively correlates with eating speed is a controversial issue that must be considered.
A review of the literature was conducted on the relationship between eating speed and salt intake, but no data were obtained to show a relationship between the two. However, it has been reported that sensitivity to salt increases as the number of chews increases with the inoculation of hard foods [33]. Since mastication speed is generally considered to slow down as the number of chews increases, someone with a slower eating speed may have increased sensitivity to salt intake and may have suppressed salt intake beyond what is necessary [19]. A review has reported that lower salt intake is associated with a lower risk of cardiovascular disease, all-cause mortality, kidney disease, stomach cancer, and osteoporosis [34]. Therefore, while health guidance generally teaches the limitation of salt intake, correction of fast eating may be more effective as early preventive health guidance.
Multiple reports on gender differences in eating speed indicate that males are generally faster than females [35]. The presence of gender-related differences in eating speed is consistent with our findings, as eating speed is thought to be dependent on body size and bite size. Regarding muscle mass, slow eating speed has been reported to decrease muscle mass in people with type 2 diabetes [36]. In addition, slow eating has been reported to increase the likelihood of sarcopenia and undernutrition as well as loss of muscle mass, and the results of our study were similar [27,37]. However, more detailed cohort studies are needed to determine the causal relationship between muscle mass and eating speed, whether muscle mass is reduced because of slower eating speed, or whether the eating speed is reduced due to decreased function of masticatory muscle groups and muscle groups related to swallowing caused by reduced muscle mass. The relationship between sleep and eating speed was difficult to logically examine because no previous studies have pointed to a link between the two.
One of the concerns in this study was whether oral status was related to the speed of food intake. Whether oral status would increase or decrease the speed of food intake was also difficult to predict. This is because if the number of teeth is high, the person can bite well and thus may eat more slowly; on the other hand, the person may bite more efficiently and thus may eat more quickly. At least for these two conflicting hypotheses, our results suggest that the number of teeth is not related to the food intake speed and that other factors (e.g., swallowing function, cognitive function, etc.) may be involved as determining factors. The central pattern generator (CPG) in the medulla oblongata of the brainstem is believed to contribute to the formation of the motor patterns of masticatory swallowing movements [38]. Because the cerebral cortex is believed to trigger swallowing and regulate the sequential activity of the brainstem, aging may alter the speed of food intake by disrupting this regulation [39]. In fact, it has been reported that patients with amyotrophic lateral sclerosis, a neurological disease, have problems with food intake due to dysfunction of the CPG in dysphagia, which prevents smooth swallowing [40]. Therefore, based on the results of this study, we hypothesized that the factors that influence the speed of food intake can be divided into three categories: background factors (gender, age, and body size), oral status (masticatory function, swallowing function, tongue pressure, oral dryness, oral hygiene, and tongue and lip motor function), and neural functions connecting the brain and the oral cavity, which were not investigated in this study. Mouthful volume may also be a factor that influences the food intake speed as a separate factor from oral function. Past reports indicate that there is a negative correlation between mouthful volume and the number of bites and that the number of bites per mouthful volume decreased as the mouthful volume increased [41]. New studies are needed to investigate the determinants of food intake speed in more detail.
Several intervention studies have been conducted to control fast eating speed. The first was a randomized crossover design study in which participants were instructed to consume food under two dietary conditions, with slow eating resulting in significantly lower dietary energy intake [42]. The two conditions were a fast eating group (eat as quickly as possible: take large bites, chew quickly, and refrain from pausing and putting the spoon down between bites) and a slow eating group (eat as slowly as possible: take small bites, chew each bite thoroughly, and pause and put the spoon down between bites) [42]. The second study, which showed an association between fast eating speed and elevated blood glucose levels in a randomized crossover controlled trial on a Japanese female population, used an intervention method that set a time frame (fast group eating the test food in 10 min and slow group in 20 min) [13]. Another randomized controlled trial in a Japanese population also used a time frame intervention method (fast group in 5 min and slow group in 15 min) [43]. Considering these past studies, dental professionals should contribute to the prevention of systemic diseases by providing the following dietary guidance after restoring normal eating function by improving oral and swallowing conditions. The instructional approach to food intake should include chewing with small mouth openings, chewing thoroughly when chewing, and placing the spoon down on the table as often as possible to increase the spacing between food transports. It would be better to instruct them that each meal should take at least 20 min [13]. The key point here is the determination of oral and swallowing status. All previous studies are based on healthy individuals, albeit of different genders. In other words, the benefit of this slow eating is based on the assumption that there are no oral or systemic problems. It is important for dentists and dental hygienists to demonstrate their expertise in making this determination.
In the Japanese healthcare system, dental hygienists can provide practical guidance on oral and dental hygiene under the direction of dentists. *In* general, the main purpose of oral and dental hygiene instruction is to prevent dental diseases, and nutritional and dietary guidance are rarely provided so far [44]. Moreover, dental hygienists have rarely provided instruction or guidance that focuses on fast eating. However, considering the potential for widespread systemic effects of fast eating as suggested in the present study, it is necessary to provide guidance on eating too fast, as well as on dietary balance. Eating slowly, or chewing well not only increases saliva production and improves digestive efficiency, but also has social significance, such as the enjoyment of taste [45]. Therefore, such dietary and lifestyle guidance carried out by dental hygienists, along with the restoration of oral function by dentists, could be considered important in contributing to overall systemic healthcare and preventing systemic diseases such as type 2 diabetes, renal dysfunction, and hypertension. In Japan, the collaboration between medicine and dentistry has been strengthened since 2012, with dentists and dental hygienists playing an increasingly active role in both clinical practice and research [46]. It is hoped that studies with even larger sample sizes will become possible in the future and that the factors that make up fast eating speed and its effects on the whole body will be clarified.
This study has four main limitations. First, the lack of detailed data on medications and pre-existing treatment status in this study limits adjustment in multivariate analysis.
Second, the causal relationship between the relevant items is unknown because this was a cross-sectional study. Third, dietary speed was self-reported, which might lead to low objectivity and reliability. Finally, there was methodological bias such as the examinee and reporting bias. Future studies are needed to prove this causal relationship through a prospective cohort study that incorporates data on oral function.
## 5. Conclusions
Fast eating may be associated with overall health and lifestyle. The characteristics of fast eaters, after taking oral and dental status into consideration, tended to increase the risk of type 2 diabetes, renal dysfunction, and hypertension. Dental professionals should provide dietary and lifestyle guidance to fast eaters. Additionally, the number of teeth may not be associated with fast eating.
## References
1. Ishida Y., Yoshida D., Honda T., Hirakawa Y., Shibata M., Sakata S., Furuta Y., Oishi E., Hata J., Kitazono T.. **Influence of the Accumulation of Unhealthy Eating Habits on Obesity in a General Japanese Population: The Hisayama Study**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12103160
2. Yoshida J., Eguchi E., Nagaoka K., Ito T., Ogino K.. **Association of night eating habits with metabolic syndrome and its components: A longitudinal study**. *BMC Public Health* (2018) **18**. DOI: 10.1186/s12889-018-6262-3
3. Duffey K.J., Popkin B.M.. **Energy density, portion size, and eating occasions: Contributions to increased energy intake in the United States, 1977–2006**. *PLoS Med.* (2011) **8**. DOI: 10.1371/journal.pmed.1001050
4. Karl J.P., Young A.J., Rood J.C., Montain S.J.. **Independent and combined effects of eating rate and energy density on energy intake, appetite, and gut hormones**. *Obesity* (2013) **21** E244-E252. DOI: 10.1002/oby.20075
5. Takahashi F., Hashimoto Y., Kawano R., Kaji A., Sakai R., Kawate Y., Okamura T., Ushigome E., Kitagawa N., Majima S.. **Eating Fast Is Associated with Nonalcoholic Fatty Liver Disease in Men But Not in Women with Type 2 Diabetes: A Cross-Sectional Study**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12082174
6. Argyrakopoulou G., Simati S., Dimitriadis G., Kokkinos A.. **How Important Is Eating Rate in the Physiological Response to Food Intake, Control of Body Weight, and Glycemia?**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12061734
7. Fujii H., Funakoshi S., Maeda T., Satoh A., Kawazoe M., Ishida S., Yoshimura C., Yokota S., Tada K., Takahashi K.. **Eating Speed and Incidence of Diabetes in a Japanese General Population: ISSA-CKD**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10091949
8. Kolay E., Bykowska-Derda A., Abdulsamad S., Kaluzna M., Samarzewska K., Ruchala M., Czlapka-Matyasik M.. **Self-Reported Eating Speed Is Associated with Indicators of Obesity in Adults: A Systematic Review and Meta-Analysis**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9111559
9. Kobayashi S., Honda S., Murakami K., Sasaki S., Okubo H., Hirota N., Notsu A., Fukui M., Date C.. **Both comprehensive and brief self-administered diet history questionnaires satisfactorily rank nutrient intakes in Japanese adults**. *J. Epidemiol.* (2012) **22** 151-159. DOI: 10.2188/jea.JE20110075
10. Tao L., Yang K., Huang F., Liu X., Li X., Luo Y., Wu L., Guo X.. **Association between self-reported eating speed and metabolic syndrome in a Beijing adult population: A cross-sectional study**. *BMC Public Health* (2018) **18**. DOI: 10.1186/s12889-018-5784-z
11. Mochizuki K., Misaki Y., Miyauchi R., Takabe S., Shimada M., Kuriki K., Ichikawa Y., Goda T.. **A higher rate of eating is associated with higher circulating interluekin-1beta concentrations in Japanese men not being treated for metabolic diseases**. *Nutrition* (2012) **28** 978-983. DOI: 10.1016/j.nut.2011.12.001
12. Saenz-Pardo-Reyes E., Housni F.E., Lopez-Espinoza A., Martinez Moreno A.G., Padilla Galindo M.D.R., Velazquez Saucedo G.. **Effect of eating speed modification techniques and strategies on food or energy intake: A systematic review and meta-analysis**. *Nutr. Hosp.* (2021) **38** 631-644. DOI: 10.20960/nh.03467
13. Saito Y., Kajiyama S., Nitta A., Miyawaki T., Matsumoto S., Ozasa N., Kajiyama S., Hashimoto Y., Fukui M., Imai S.. **Eating Fast Has a Significant Impact on Glycemic Excursion in Healthy Women: Randomized Controlled Cross-Over Trial**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12092767
14. Teo P.S., van Dam R.M., Whitton C., Tan L.W.L., Forde C.G.. **Association Between Self-Reported Eating Rate, Energy Intake, and Cardiovascular Risk Factors in a Multi-Ethnic Asian Population**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12041080
15. Sakurai M., Nakamura K., Miura K., Takamura T., Yoshita K., Nagasawa S.Y., Morikawa Y., Ishizaki M., Kido T., Naruse Y.. **Self-reported speed of eating and 7-year risk of type 2 diabetes mellitus in middle-aged Japanese men**. *Metabolism* (2012) **61** 1566-1571. DOI: 10.1016/j.metabol.2012.04.005
16. Radzeviciene L., Ostrauskas R.. **Fast eating and the risk of type 2 diabetes mellitus: A case-control study**. *Clin. Nutr* (2013) **32** 232-235. DOI: 10.1016/j.clnu.2012.06.013
17. Paz-Graniel I., Babio N., Mendez I., Salas-Salvado J.. **Association between Eating Speed and Classical Cardiovascular Risk Factors: A Cross-Sectional Study**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11010083
18. Zhu Y., Hollis J.H.. **Increasing the number of chews before swallowing reduces meal size in normal-weight, overweight, and obese adults**. *J. Acad. Nutr. Diet.* (2014) **114** 926-931. DOI: 10.1016/j.jand.2013.08.020
19. Paphangkorakit J., Kanpittaya K., Pawanja N., Pitiphat W.. **Effect of chewing rate on meal intake**. *Eur. J. Oral Sci.* (2019) **127** 40-44. DOI: 10.1111/eos.12583
20. Saito M., Shimazaki Y., Nonoyama T., Tadokoro Y.. **Number of Teeth, Oral Self-care, Eating Speed, and Metabolic Syndrome in an Aged Japanese Population**. *J. Epidemiol.* (2019) **29** 26-32. DOI: 10.2188/jea.JE20170210
21. Sonoda C., Fukuda H., Kitamura M., Hayashida H., Kawashita Y., Furugen R., Koyama Z., Saito T.. **Associations among Obesity, Eating Speed, and Oral Health**. *Obes. Facts* (2018) **11** 165-175. DOI: 10.1159/000488533
22. Ikebuchi K., Matsuda Y., Takeda M., Takeda M., Abe T., Tominaga K., Yano S., Isomura M., Nabika T., Kanno T.. **Relationship between Masticatory Function and Bone Mineral Density in Community-Dwelling Elderly: A Cross-Sectional Study**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9070845
23. Takeda M., Matsuda Y., Ikebuchi K., Takeda M., Abe T., Tominaga K., Isomura M., Nabika T., Kanno T.. **Relationship between Oral Health Status and Bone Mineral Density in Community-Dwelling Elderly Individuals: A Cross-Sectional Study**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9040432
24. Hamano T., Takeda M., Tominaga K., Sundquist K., Nabika T.. **Is Accessibility to Dental Care Facilities in Rural Areas Associated with Number of Teeth in Elderly Residents?**. *Int. J. Environ. Res. Public Health* (2017) **14**. DOI: 10.3390/ijerph14030327
25. Tanaka T., Okamura T., Miura K., Kadowaki T., Ueshima H., Nakagawa H., Hashimoto T.. **A simple method to estimate populational 24-h urinary sodium and potassium excretion using a casual urine specimen**. *J. Hum. Hypertens.* (2002) **16** 97-103. DOI: 10.1038/sj.jhh.1001307
26. Takeda M., Hamano T., Kohno K., Yano S., Shiwaku K., Nabika T.. **Association Between Geographic Elevation, Bone Status, and Exercise Habits: The Shimane CoHRE Study**. *Int. J. Environ. Res. Public Health* (2015) **12** 7392-7399. DOI: 10.3390/ijerph120707392
27. Nakamura T., Nakamura Y., Takashima N., Kadota A., Miura K., Ueshima H., Kita Y.. **Eating Slowly Is Associated with Undernutrition among Community-Dwelling Adult Men and Older Adult Women**. *Nutrients* (2021) **14**. DOI: 10.3390/nu14010054
28. Iwasaki T., Hirose A., Azuma T., Ohashi T., Watanabe K., Obora A., Deguchi F., Kojima T., Isozaki A., Tomofuji T.. **Association between eating behavior and poor glycemic control in Japanese adults**. *Sci. Rep.* (2019) **9** 3418. DOI: 10.1038/s41598-019-39001-y
29. Iwai K., Azuma T., Yonenaga T., Ekuni D., Watanabe K., Obora A., Deguchi F., Kojima T., Morita M., Tomofuji T.. **Association between Self-Reported Chewing Status and Glycemic Control in Japanese Adults**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18189548
30. Otsuka R., Tamakoshi K., Yatsuya H., Wada K., Matsushita K., OuYang P., Hotta Y., Takefuji S., Mitsuhashi H., Sugiura K.. **Eating fast leads to insulin resistance: Findings in middle-aged Japanese men and women**. *Prev. Med.* (2008) **46** 154-159. DOI: 10.1016/j.ypmed.2007.07.031
31. Sato M., Tamura Y., Nakagata T., Someya Y., Kaga H., Yamasaki N., Kiya M., Kadowaki S., Sugimoto D., Satoh H.. **Prevalence and Features of Impaired Glucose Tolerance in Young Underweight Japanese Women**. *J. Clin. Endocrinol. Metab.* (2021) **106** e2053-e2062. DOI: 10.1210/clinem/dgab052
32. Sakata T.. **Histamine receptor and its regulation of energy metabolism**. *Obes. Res.* (1995) **3** 541S-548S. DOI: 10.1002/j.1550-8528.1995.tb00225.x
33. Santa R., Miyamoto M., Hosono N., Homma C., Hoshi M., Goto A., Sato N., Suzuki K., Inaba H., Shibuya K.. **Mastication of Hard Gumi Decreases the Gustatory Threshold for Sodium Chloride**. *J. Nutr. Sci. Vitaminol.* (2020) **66** 587-590. DOI: 10.3177/jnsv.66.587
34. He F.J., Tan M., Ma Y., MacGregor G.A.. **Salt Reduction to Prevent Hypertension and Cardiovascular Disease: JACC State-of-the-Art Review**. *J. Am. Coll. Cardiol.* (2020) **75** 632-647. DOI: 10.1016/j.jacc.2019.11.055
35. Shiozawa K., Mototani Y., Suita K., Ito A., Matsuo I., Hayakawa Y., Kiyomoto K., Tsunoda M., Nariyama M., Umeki D.. **Gender differences in eating behavior and masticatory performance: An analysis of the Three-Factor-Eating Questionnaire and its association with body mass index in healthy subjects**. *J. Oral Biosci.* (2020) **62** 357-362. DOI: 10.1016/j.job.2020.09.005
36. Kobayashi G., Hashimoto Y., Takahashi F., Kaji A., Sakai R., Okamura T., Okada H., Kitagawa N., Nakanishi N., Majima S.. **Impact of Eating Speed on Muscle Mass in Older Patients With Type 2 Diabetes: A Prospective Study of KAMOGAWA-DM Cohort**. *Front. Nutr.* (2022) **9** 919124. DOI: 10.3389/fnut.2022.919124
37. Hashimoto Y., Takahashi F., Kaji A., Sakai R., Okamura T., Kitagawa N., Okada H., Nakanishi N., Majima S., Senmaru T.. **Eating Speed Is Associated with the Presence of Sarcopenia in Older Patients with Type 2 Diabetes: A Cross-Sectional Study of the KAMOGAWA-DM Cohort**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14040759
38. Arshavsky Y.I., Deliagina T.G., Orlovsky G.N.. **Pattern generation**. *Curr. Opin. Neurobiol.* (1997) **7** 781-789. DOI: 10.1016/S0959-4388(97)80136-5
39. Ertekin C., Aydogdu I.. **Neurophysiology of swallowing**. *Clin. Neurophysiol.* (2003) **114** 2226-2244. DOI: 10.1016/S1388-2457(03)00237-2
40. Aydogdu I., Tanriverdi Z., Ertekin C.. **Dysfunction of bulbar central pattern generator in ALS patients with dysphagia during sequential deglutition**. *Clin. Neurophysiol.* (2011) **122** 1219-1228. DOI: 10.1016/j.clinph.2010.11.002
41. Nakamichi A., Matsuyama M., Ichikawa T.. **Relationship between mouthful volume and number of chews in young Japanese females**. *Appetite* (2014) **83** 327-332. DOI: 10.1016/j.appet.2014.08.009
42. Shah M., Copeland J., Dart L., Adams-Huet B., James A., Rhea D.. **Slower eating speed lowers energy intake in normal-weight but not overweight/obese subjects**. *J. Acad. Nutr. Diet.* (2014) **114** 393-402. DOI: 10.1016/j.jand.2013.11.002
43. Toyama K., Zhao X., Kuranuki S., Oguri Y., Kashiwa Kato E., Yoshitake Y., Nakamura T.. **The effect of fast eating on the thermic effect of food in young Japanese women**. *Int. J. Food Sci. Nutr.* (2015) **66** 140-147. DOI: 10.3109/09637486.2014.986069
44. DiMaria-Ghalili R.A., Mirtallo J.M., Tobin B.W., Hark L., Van Horn L., Palmer C.A.. **Challenges and opportunities for nutrition education and training in the health care professions: Intraprofessional and interprofessional call to action**. *Am. J. Clin. Nutr.* (2014) **99** 1184S-1193S. DOI: 10.3945/ajcn.113.073536
45. Okuma N., Saita M., Hoshi N., Soga T., Tomita M., Sugimoto M., Kimoto K.. **Effect of masticatory stimulation on the quantity and quality of saliva and the salivary metabolomic profile**. *PLoS ONE* (2017) **12**. DOI: 10.1371/journal.pone.0183109
46. Sekiya H., Kurasawa Y., Kaneko K., Takahashi K.I., Maruoka Y., Michiwaki Y., Takeda Y., Ochiai R.. **Preventive Effects of Sustainable and Developmental Perioperative Oral Management Using the “Oral Triage” System on Postoperative Pneumonia after Cancer Surgery**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18126296
|
---
title: New Route to the Production of Almond Beverages Using Hydrodynamic Cavitation
authors:
- Cecilia Faraloni
- Lorenzo Albanese
- Graziella Chini Zittelli
- Francesco Meneguzzo
- Luca Tagliavento
- Federica Zabini
journal: Foods
year: 2023
pmcid: PMC10001306
doi: 10.3390/foods12050935
license: CC BY 4.0
---
# New Route to the Production of Almond Beverages Using Hydrodynamic Cavitation
## Abstract
Perceived as a healthy food, almond beverages are gaining ever-increasing consumer preference across nonalcoholic vegetable beverages, ranking in first place among oilseed-based drinks. However, costly raw material; time and energy consuming pre- and posttreatments such as soaking, blanching and peeling; and thermal sterilization hinder their sustainability, affordability and spread. Hydrodynamic cavitation processes were applied, for the first time, as a single-unit operation with straightforward scalability, to the extraction in water of almond skinless kernels in the form of flour and fine grains, and of whole almond seeds in the form of coarse grains, up to high concentrations. The nutritional profile of the extracts matched that of a high-end commercial product, as well as showing nearly complete extraction of the raw materials. The availability of bioactive micronutrients and the microbiological stability exceeded the commercial product. The concentrated extract of whole almond seeds showed comparatively higher antiradical activity, likely due to the properties of the almond kernel skin. Hydrodynamic cavitation-based processing might represent a convenient route to the production of conventional as well as integral and potentially healthier almond beverages, avoiding multiple technological steps, while affording fast production cycles and consuming less than 50 Wh of electricity per liter before bottling.
## 1. Introduction
The consumption of plant-based beverages has rapidly grown in recent years, partially replacing dairy products in the diet for a variety of reasons including health (lactose intolerance, cholesterol, and blood glucose level issues), lifestyle choices, or ethical and environmental concerns. A $10.4\%$ increase in worldwide sales of plant-based beverages is expected from 2018 to 2023, reaching USD 26 billion per year [1]. Plant-based beverages are aqueous extracts of cereals, legumes, nuts, seeds, and pseudo-cereals [2], showing a wide variety of nutritional properties and micronutrients. Almond-based beverages, along with rice-based beverages, contributed most to the $380\%$ volume increase in the consumption of rice/grain/nut/seed-based beverages in Europe from 2012 to 2015 [3]. Additionally, almond (*Prunus dulcis* L.) has been the most produced nut worldwide in recent years [4], gaining economic significance in the global food supply chain.
Soy and almond beverages have received special consideration due to their good nutritional profile and potential biological functions [5]. Almond beverages showed interesting compositional characteristics in terms of monounsaturated fatty acids content and balanced composition in the content of proteins, fat, fibers, and vitamins [6], although the specific abundance of macro- and micronutrients, in the absence of any additives other than water, depends on the composition of raw materials, i.e., skinless almond kernels, which show a large variability across varieties, climates, growing practices, and harvesting season [7]. Almond beverages can have a remarkable content in vitamin E, a fat-soluble antioxidant that can protect cells from the harmful effects of free radicals towards cancer and cardiovascular diseases [8]. Almonds are indeed important sources of mono- and unsaturated fatty acids, minerals, vitamin E, polyphenols, and phytosterols, with antioxidant properties that can have beneficial effects on human health [9]. Robust evidence exists about the association of almond consumption with various health benefits [10], including improvements to the metabolic system [11,12], microbiota [13], and cardiovascular system [14,15], as well as antioxidant, anti-inflammatory, anticancer, antimicrobial [4], and antidiabetic activity [16].
While kernels, representing around $52\%$ of the total fresh weight, are by far the most used component of almond for human consumption, other parts (skin, shell, hull, etc.) are often discarded, despite their interesting properties and their disposal representing an important environmental burden [4]. Almond skin, representing around $4\%$ of the total weight of the almond, was shown to possess beneficial properties. Phytochemicals and polyphenols contained in almond skin were associated with antibacterial and antiviral effects [17,18] to the scavenging of free radicals, and were proven to induce quinone reductase [19].
Although the specific processing steps allowing the manufacturing of plant-based beverages depend on the physiology of the particular vegetable matrix, they aim invariably at the maximum possible yield of soluble extract. For this purpose, almond seeds, sometimes after roasting, require peeling as a basic step, since skin removal allows an efficient release of kernel’s nutrients and micronutrients into water, despite the loss of important skin micronutrients [20].
For the purpose of peeling, further industrial steps are required such as soaking in water and hot water blanching, followed by wet milling, homogenization, and pasteurization or sterilization [20]. To the best knowledge of the authors, no substantial innovation has been applied in recent times to this production process, whose steps are described in greater detail in Section 2, where new technologies, aimed at replacing thermal treatments on resulting almond extracts, are also introduced.
This study presents the first evidence of the possibility of adopting controlled hydrodynamic cavitation (HC), an emerging green, efficient, and scalable method for the extraction of natural products [21], as a single-unit operation, thus replacing all the other traditional production steps in the extraction in water only of almond kernels, including whole seeds (seeds including the skin), to produce beverages at concentrations matching the market standards, as well as more concentrated extracts ready for further dilution. Performance data, including extraction yields and comparison with a high-end market product, process time, and specific energy consumption, are provided in order to allow both replication and comparison with other methods.
## 2. Technological Overview
As mentioned in Section 1, traditional industrial production steps of almond beverages are consolidated and did not substantially change in the last few decades, including [1,20,22,23] A few of the above-listed processing steps, such as roasting, hot water blanching and skin removal (peeling), and sterilization/homogenization by means of UHT or UHPH, are particularly energy intensive, and may negatively affect rheological and nutritional characteristics of products, the latter, for example, through the partial denaturation of almond proteins and the change of the profile of fatty acids, and might be harmful to valuable micronutrients, such as polyphenols of both almond skin and skinless kernel [24,25,26].
The peeling step deserves a special mention, also due to the high value of almond skin. As an alternative method working at room or moderate temperatures, ultrasound-assisted extraction (UAE) was tested and validated on the laboratory scale [25], but not yet at the preindustrial scale. UAE, whose effectivity is largely based on cavitation phenomena induced in the irradiated medium, was also applied successfully to enhance the extraction rate of nutrients and micronutrients from few vegetable materials and the stability of the aqueous extracts, including almonds [27,28]. However, intrinsic limitations of UAE, also due to the rapid attenuation of ultrasound waves in liquid media, make its full scalability hard to achieve, in fact leaving HC as the only feasible option for large-scale applications, across methods based on cavitation processes [29]. With similar outcomes to UAE [30], HC shows key advantages, including easy scale-up, lower capital cost, and higher efficiency, such as in the case of peanut milk production [31].
More in general, the evolution of consumption toward greater attention to the healthy properties of food and environmental sustainability stimulated an accelerated search for more effective and efficient technological solutions in the food supply chain. Process time, energy consumption, reduction of food waste, and preservation of healthy components have been the steering factors of this search since at least mid-1990s’, including plant-based beverages and, in particular, oilseed beverages, which have long been perceived since as potentially healthy products and are usually composed of more than $90\%$ water [2].
New technologies have been developed and tested, aimed at overcoming the shortfalls affecting conventional production processes, while ensuring enhanced chemical-physical stability and microbiological safety. Table 1 lists and shortly describes the most relevant emerging technologies with special focus on almond beverages.
To the best knowledge of the authors, not only none of the above studies, but the relevant emerging technologies in general, have ever been applied to the extraction of almond seeds, but only to finished almond beverages manufactured according to conventional methods. Moreover, in all the studies cited in Table 1, as well as in any other studies, almond seeds were peeled before manufacturing the almond beverages.
Hydrodynamic cavitation (HC) technologies and related methods are emerging among the most effective, efficient, and straightforwardly scalable in the field of the extraction of natural products, not only in comparison to newest green technologies but also to conventional methods [21]. The most important properties of HC-based extraction methods derive from the unique capability of concentrating the energy of mixed liquid-solid fluxes into microscopic hot spots with extremely high energy density, in turn released at the collapse of cavitation bubbles in the form of mechanical and thermal energy, as well as from the relatively straightforward design and set-up [37]. Thus, HC methods have been proposed as important tools to help achieving the sustainability development goals in few different technical fields [38]. HC methods have shown high process yields as single-unit operation systems applied to the extraction of natural products in water only at the preindustrial scale [39], such as in the brewing field (extraction of cereals and hops), involving starch, proteins, and polyphenols as the main constituents released into the water phase [40], conifer tree parts, involving polyphenols and volatiles [41], waste citrus peel involving pectin, polyphenols and volatiles [42], and soybean, involving proteins and fat [43].
The application of HC methods to the manufacturing of almond beverages, with perspectives up to the industrial scale, was already devised by Meneguzzo et al. in 2020 [21]. The rest of this study aims at providing the first proof at the pilot scale of such a new route to the production of almond beverages.
## 3.1. Production of Aqueous Almond Extracts
Almond (Prunus Dulcis) skinless kernels in the form of flour (<1 mm in size) and fine grain (1–2 mm in size), both from the same batch of almond seeds, and whole seeds (including the skin) in the form of coarse grain (about 3–5 mm in size) were supplied by the company Dolceamaro S.r.l. ( Monteroduni, IS, Italy). All materials came from the variety Lauranne®Avijor grown in Italy, which is a recently released late-flowering cultivar [44], with kernels characterized at least since 2010 [45]. Figure 1 shows a picture of the whole almond seeds in the form of coarse grain.
The almond materials were extracted in tap water only, with different concentrations. The details of the batch hydrodynamic cavitation-based extractor, comprising a closed hydraulic circuit of total volume around 200 L, with a centrifugal pump and a Venturi-shaped reactor with circular section as the key components, and electricity as the only energy source, were described in a previous study about the extraction of waste orange peel [42]. Pump’s impellers transferred mechanical energy to the liquid–solid mixture, in turn converting into heat during the process, and no heat dissipation method was used. Absorbed power and energy consumption, in the form of electricity supplied to the centrifugal pump, were measured by means of a three-phase digital power meter (IME, Milan, Italy, model D4-Pd, power resolution 1 W, energy resolution 10 Wh, accuracy according to the norm EN/IEC 62053-21, class 1).
Table 2 shows for each extraction test the test ID, the type, mass and concentration (% of total weight) of almond material, the overall process time, and the temperature range of the process. Each almond batch was preserved at room temperature for no more than 2 days after reception. Almond materials were inserted into the extraction system all together at the beginning of each process (initial temperature levels 26–34 °C).
Tests MFP1 and MGP1 were performed with a concentration of $7.4\%$, which is comparable with a high-end product available on the market, “Valdibella al naturale” (Valdibella agricultural cooperative, Camporeale, PA, Italy), hereinafter also referred to as the “commercial product”. The commercial product had a concentration of $8\%$ and, at the time of the tests MFP1 and MGP1, had been packaged in Tetra Pak®—Tetra Brik® Aseptic about 1 month before and showed an expiration date after about 9 months. The commercial product was manufactured with organic almonds from the typical Sicilian (Italy) varieties Tuono, Genco, Supernova, Pizzuta, and Fascionello, few of which were characterized with regards to the phenolic content, fatty acids, proteins, and volatiles [4]. The commercial product was manufactured based only on almonds cream from skinless kernels and water, according to a patented procedure (text in Italian only) [46]. The analytical figures derived for the commercial product were normalized according to the ratio of the concentration used in tests MFP1 and MGP1 to that of the commercial product, i.e., each content was multiplied by the factor $\frac{7.4}{8.0}$ = 0.925. Finally, it is noteworthy that the comparison among the samples collected from the experimental tests and the commercial product was aimed at a preliminary check of the compliance of the experimental products with a high-end market standard, while too many uncertainties remain about the commercial product, including the almond varieties actually used, the respective harvesting season, and the details of the manufacturing process, preventing further investigation.
Tests MGP2 (from the same batch of almond seeds as test MGP1) and MGP3 were performed at higher concentration ($27.2\%$ and $18\%$, respectively), aimed at showing both the possibility of obtaining concentrated extracts than could be subsequently diluted to create beverages suitable for the market, and, with test MGP3, to verify the possibility of extracting whole coarsely ground almond seeds, the latter in turn with the double purpose of avoiding the peeling step before the extraction and exploiting the bioactive properties of almond skins.
## 3.2. Sampling and Microbiological, Nutritional, Total Polyphenols, and Antiradical Activity Analyses
Table 3 shows the types of analyses performed for each extraction test and the high-end commercial almond beverage. Storage issues with samples collected from the test MGP2, except those collected for the assessment of polyphenols and antiradical activity, prevented the respective microbiological and nutritional analyses.
## 3.2.1. Sampling
For test MFP1, the aqueous extracts were sampled at the temperatures of 40 °C, 47 °C, 58 °C, 68 °C, and 74 °C. For test MGP1, the aqueous extracts were sampled at the temperatures of 40 °C, 47 °C, 58 °C, 68 °C, 78 °C, and 86 °C. After filtering with a 200 µm sieve (stainless steel mesh), three samples were collected at each point in sterile bottles, each 500 mL in volume. The sterile bottles were stored at −20 °C until analysis. For tests MGP2 and MGP3, the aqueous extracts were sampled only at the end of the process, at the temperature of 82 °C. The same volumes of the commercial beverage were collected from the respective packaging at the beginning of the analyses. For test MGP3, three further samples, each one in a Falcon test tube of volume 50 mL, were collected at the end of the process without filtration (integral extract), aimed at the assessment of the mass balance. Moreover, 0.5 kg of all raw materials used in the extraction tests were preserved for further analyses.
## 3.2.2. Microbiological, Nutritional, and Vitamin Analyses
Microbiological, nutritional, and vitamin analyses were performed by laboratories accredited by Accredia, The Italian Accreditation Body (https://www.accredia.it/en/, accessed on 6 January 2023; laboratory No. 0069 L and laboratory No. 0792l), complying with standards UNI CEI EN ISO/IEC 17025 and ISO 9001.
The colony count of total microorganisms at 30 °C was measured according to UNI EN 12822:2000 (https://store.uni.com/en/uni-en-iso-4833-1-2013, accessed on 6 January 2023); the concentrations of yeasts and molds were measured according to UNI EN 21527-1:2008 (https://store.uni.com/en/iso-21527-1-2008, accessed on 6 January 2023).
Each nutritional quantity was analyzed according to a specific method:Energy level: EU Regulation No. $\frac{1169}{2011}$ of the European Parliament and of the Council of 25 October 2011 on the provision of food information to consumers (https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32011R1169, accessed on 2 January 2023);Total and unsaturated fat: Istisan report No. $\frac{1996}{34}$ “Methods of analysis for the chemical control of foods”, pages 39 and 47, respectively (https://www.iss.it/en/rapporti-istisan, accessed on 6 January 2023);Total carbohydrates and sugar: Italian Ministerial Decree 03 February 1989 (https://www.gazzettaufficiale.it/eli/id/$\frac{1989}{07}$/$\frac{20}{089}$A3049/sg, text in Italian, accessed on 6 January 2023);Protein: Istisan report No. $\frac{1996}{34}$ “Methods of analysis for the chemical control of foods”, page 17 (https://www.iss.it/en/rapporti-istisan, accessed on 6 January 2023);Fiber: Istisan report No. $\frac{1996}{34}$ “Methods of analysis for the chemical control of foods”, page 73 (https://www.iss.it/en/rapporti-istisan, accessed on 6 January 2023);Vitamin B2 and vitamin PP: AOAC 2015.14-2015 (http://www.aoacofficialmethod.org/index.php?main_page=product_info&cPath=1&products_id=2990, accessed on 6 January 2023);Vitamin E: UNI EN 12822:2000 (https://store.uni.com/en/uni-en-12822-2000, accessed on 6 January 2023).
## 3.2.3. Total Polyphenols and Antiradical Activity
The total phenolic content (TPC) was determined based on the Folin–Ciocalteau method [47], modified according to the AOAC SMPR 2015.009 (https://www.aoac.org/resources/smpr-2015-009/, accessed on 6 January 2023), using gallic acid (Sigma-Aldrich) as standard. The analyses were performed in triplicate. However, it was anticipated that the recovery and quantification of polyphenols from raw almond materials during laboratory analysis is critically dependent on the details of the method and could be affected by greater systematic uncertainties than declared [24], an issue that does not affect the analysis of clear aqueous extracts. As further pointed out in Section 4, such concern was confirmed by the results obtained in this study, involving also vitamins; thus, the measurements of the content of polyphenols and vitamins performed on raw almond materials were discarded from the analysis.
The antiradical activity was performed according to [48]. DPPH (2,2-diphenyl-1-picrylhydrazyl) (Sigma-Aldrich, St. Louis, MO, USA) is a stable radical that can be reduced by reaction with an antiradical hydrogen–donor compound. A spectrophotometer (Beckman DU-640, Fullerton, CA, USA) was used to measure this colorimetric reaction at 517 nm, when the color of the DPPH radical changes from violet to yellow. The methanolic extracts were diluted at different proportion to find the concentration at which $50\%$ of initial absorbance value of sample with added DPPH is obtained. In order to determine the absorbance at 517 nm, 1 mL of diluted extract was added to 1 mL of methanol DPPH solution (63 M), mixed, and measured right away. After 20 min, the absorbance was tested again. A decrease of $50\%$ in the initial DPPH concentration is referred to as IC50, which is the concentration inhibiting 50 % of DPPH radicals.
For each extract, the IC50 was calculated with the following formula: % inhibition = [100 − (Ax/As)] × 100,[1] where *As is* the initial absorbance of the extract sample in DPPH solution ($t = 0$) and *Ax is* the absorbance of the same sample after 20 min. At least 4 different concentrations of the extracts were used to determinate the IC50. The analyses were performed in triplicates.
## 3.2.4. Potential Contents
Based on nutritional contents measured for raw materials, rough estimates of the respective potential contents in the aqueous extracts were computed, calculating the total amount of each relevant quantity (concentration multiplied by mass of the raw material) and dividing the result by the volume of water used in the extraction tests. Potential contents were used for a preliminary assessment of the extraction yield of each considered relevant quantity.
## 3.3. Mass Balance
The integral extract (without filtration), collected at the end of test MGP3 in three Falcon test tubes, each of volume 50 mL, was preserved at –20 °C until analysis, then thawed at room temperature and centrifuged at 9980 g for 1 h at 15 °C (ALC multispeed refrigerate centrifuge mod PK131R), to obtain a solid fraction (pellet), and an aqueous fraction containing smaller particles (supernatant). Centrifugation time longer than 1 h did not increase the sedimentation rate of small particles, which remained as suspension in the supernatant. Pellet and supernatant were dried in an oven (Type M40-VN, MPM Instruments S.r.l., Bernareggio, Italy) at 70 °C until constant weight (almost 4 days). The analysis was performed in triplicate.
## 4.1. Processes and Energy Consumption
Figure 2 shows the processes in terms of time, temperature, and specific energy consumption (assessed as the energy consumption per liter of extracted beverage), including the indication of sampling points.
Based on the comparison of tests MGP1, MGP2, and MGP3, it is notable that the specific energy consumption is not practically sensitive to the raw material concentration; thus, since the dilution step would not add relevant energy consumption, the production of concentrated extracts in operational environments would be effective for energy saving.
## 4.2. Microbiological Stability
Figure 3 summarizes the results of the microbiological analyses at time zero and after preservation for 7 days at the temperature of 4 °C in sterile bottles (shelf life), limited to the colony count of microorganisms at 30 °C (hereinafter also referred to as “microorganisms”). The concentrations of yeasts and molds (not shown) were very low, and in any case below the limit of detection of 9 UFC/g in the samples collected at process temperature of 68 °C or higher, both at time zero and at shelf life, while in the commercial product the concentration of yeasts was below the limit of detection at time zero and 27 (7–110) UFC/g at shelf life. The results for tests MFP1 and MGP1 are compared with the commercial product (red lines).
Almond kernel flour used in MFP1 and fine grain used in MGP1 had counts of microorganisms at the level of 104 (range 2.2 × 103 to 4.6 × 104 CFU/g) and 2.5 × 104 CFU/g (range 5.6 × 103 to 1.1 × 105 CFU/g), respectively, while counts of microorganisms for coarse grain of whole almonds, used in test MGP3, was at the level of just 3.5 × 102 CFU/g (range 2.5 × 102 to 4.9 × 102 CFU/g).
For samples collected from tests MFP1 and MGP1, the counts of microorganisms were never significantly higher than that of the commercial products, both at time zero and at shelf life, when the count of microorganisms for the commercial product increased by about 1.8 logs compared to time zero. For the same samples, the count of microorganisms decreased with the process temperature at the sampling point, in particular monotonically, with temperature for test MFP1 at shelf life, reaching a level significantly lower than the commercial products for the sample collected at 74 °C, almost identical to the level measured at time zero (on average 1800 CFU/g). For test MGP1, a remarkable decrease in the count of microorganisms occurred at the sampling temperature of 78 °C, which is especially visible at the shelf life, when count levels fell below that of the commercial product (about 4 × 104 CFU/g compared with about 2 × 105 CFU/g, although with large uncertainties).
For test MGP3, the count of microorganisms at time zero in the sample collected at 82 °C was just 41 CFU/g (range 30 to 56 CFU/g), reflecting the very low contamination level in the raw material.
## 4.3.1. Tests MFP1 and MGP1
Figure 4 summarizes the results of the nutritional analyses at time zero for test MFP1, compared with the potential contents (green lines) and the contents measured for the commercial product (red lines).
Figure 5 summarizes the results of the nutritional analyses at time zero for test MGP1, compared with the potential contents (green lines) and the contents measured for the commercial product (red lines).
Energy contents increased with time and temperatures for both tests MFP1 and MGP1; however, there was a more definite trend for test MGP1, where they eventually reached the potential content and the energy content of the commercial product, which were practically indistinguishable. Since fat represents by far the most abundant macronutrient of almonds, it is not surprising that the fat contents followed the same trend as the energy contents, reaching the potential contents and the energy content of the commercial product at the end of the processes. Contents of the saturated fat were very low, matching the potential content and the content of the commercial product at the end of the processes.
The content of carbohydrates in the samples from both tests MFP1 and MGP1 remained approximately constant across time and temperature, on average close to $80\%$ of both the content observed for the commercial product and the potential content. The content of sugars is not shown due to very high uncertainties; however, the only sugar above the level of detection ($0.10\%$) was sucrose, which remained practically constant in all the samples (in test MGP1, starting from the sample collected at the temperature of 47 °C).
The protein content for test MFP1 remained practically constant starting from the first collected sample, indistinguishable from the content observed for the commercial product and approximately $75\%$ of the potential content. The protein content for test MGP1 increased slightly over time, eventually reaching at the end of the process on average $80\%$ and $65\%$ of the content of the commercial product (from which it was indistinguishable) and the potential content, respectively.
The content of fibers in the samples from both tests MFP1 and MGP1 remained almost constant, in particular starting from the samples collected at the temperature of 58 °C, at much lower levels (on average between $18\%$ and $25\%$) than in the commercial product, however strictly matching the respective potential contents.
## 4.3.2. Test MGP3
Table 4 summarizes the results of the nutritional analyses at time zero for test MGP3, performed on the sample collected at the end of the process at the temperature of 82 °C. The potential contents are also shown.
Extraction yields for all the nutritional quantities were substantially lower, on average half, than for test MGP1 at comparable temperatures, with the exception of proteins (extraction yield of $58\%$ against $65\%$). The extraction yield for fibers was particularly low, about $20\%$.
## 4.4.1. TPC in Tests MFP1 and MGP1
Figure 6 summarizes the results of the TPC analyses for tests MFP1 and MGP1, which are compared with the commercial product (red lines).
In the samples collected during test MFP1, TPC was about $55\%$ of the content found in the commercial product up to the sample collected at the temperature of 58 °C, then increased to $60\%$ at 68 °C and 74 °C.
In test MGP1, TPC increased to $73\%$ of the content observed in the commercial product in the sample collected at the temperature of 68 °C, then exceeded the commercial product on average by 1.5 times in the last sample collected at the temperature of 86 °C, although with a large uncertainty.
TPC was also measured at shelf life, i.e., after preservation for 7 days at the temperature of 4 °C (data not shown). No significant changes occurred, either in the commercial product or in the test samples, except a reduction of TPC in the sample collected from test MGP1 at the temperature of 86 °C, which decreased on average by $23\%$ from the initial content, however remaining about $8\%$ higher than in the commercial product, from which it was practically indistinguishable.
## 4.4.2. TPC and Antiradical Activity in Tests MGP2 and MGP3
Table 5 summarizes the results of the TPC analyses for the samples collected at the end of the tests MGP2 and MGP3, along with the respective IC50 levels for the antiradical activity of the aqueous extracts, assessed according to the DPPH essay.
The ratio of average TPC observed for test MGP2 to the same quantity observed for test MGP3 was 1.37, which is far lower than the ratio of the respective almond mass contents, i.e., 1.70 (the water volume being the same). Thus, the polyphenols released in the water phase in test MGP3 were higher than in test MGP2, on average by about $24\%$ after normalizing to the respective almond mass contents.
The IC50 level showed even more interesting and surprising results. In fact, in test MGP3 it was almost $35\%$ lower than in test MGP2, possibly suggesting that whole almond seeds used in test MGP3 provided not only more polyphenols but also more active ones with regards to the antiradical activity. A hypothesis that might explain this experimental evidence is advanced in Section 5.
## 4.5. Vitamins
Figure 7 summarizes the results of the analyses of vitamin B2 (Riboflavin), vitamin PP (Niacin + Niacinamide), and vitamin E, for samples collected from tests MFP1 and MGP1, which are compared with the commercial product (red lines).
The content of vitamin B2 in samples collected from both tests matched the content observed for the commercial product already from the first sample collected at 40 °C, remaining around the same content until the end of the process.
The content of vitamin PP for test MFP1 increased during the process up to about $60\%$ of the content found in the commercial product for the sample collected at the temperature of 74 °C. For test MGP1, the content of vitamin PP increased up to a content indistinguishable from that of the commercial product at the temperatures of 78 °C and 86 °C.
The measurements of the content of vitamin E were affected by large uncertainties; thus, little can be said about the trends. However, the samples collected from both tests MFP1 and MGP1 starting at the temperatures of 74 °C and 78 °C, respectively, showed contents definitely above the detection level of 10 mg/kg and reaching about 14 mg/kg in test MGP1, thus suggesting the occurrence of its extraction in the water phase. In the commercial product, the content of vitamin E was below the level of detection.
## 4.6. Mass Balance for Test MGP3
Mass balance analysis was performed on the sample collected at the end of test MGP3 at the temperature of 82 °C. Table 6 summarizes the results.
As a proof of consistency, it can be noticed that the total dry biomass content (8.73 ± 0.42 g) accurately represents the content of the original raw material (concentration of $18\%$), as shown by the dry to fresh biomass ratio of $18.5\%$.
In an adequately clarified extract of whole almond seeds in the form of coarsely ground grain, i.e., the supernatant in the mass balance assessment shown in Table 6, more than $27\%$ of the original dry biomass was transferred to the aqueous extract (2.38 g out of 8.73 g of dry biomass), while about $72\%$ of the original dry biomass turned into dry pellet, i.e., the dry residue of the process (6.29 g out of 8.73 g). Such pellet had a moisture content of about $74\%$ (6.29 g of dry biomass out of 24 g of fresh biomass), which could be reduced at the filtration/separation step in operational environments, thus increasing the mass yield of the clarified extract.
## 5. Discussion
This study provided the first evidence of the feasibility and potential advantages of a HC-based extraction system as a single-unit operation with industrial perspectives, for the production of almond beverages and concentrated aqueous extracts, both from skinless kernels and whole seeds.
Based on the results presented in Section 4.2, HC processes allowed achieving microbiological stability at much lower temperatures in comparison to conventional heat treatments, such as UHT, as shown also by means of the comparison with a commercial high-end product that undergone UHT treatment. A peak temperature level of 74 °C was found to be sufficient to ensure a total count of microorganisms in the shelf-life analysis below that observed for the commercial product, as well as the absence of molds and yeasts also at shelf life (preservation for 7 days at 4 °C). The microbiologic stability of the last sample collected at 74 °C from test MFP1 was surprising, showing a count of microorganisms at shelf life even lower than at time zero and suggesting that microorganism cells were no longer viable.
In principle, this achievement would allow for higher protection to thermolabile compounds in the manufacturing process of almond beverages, as well as for avoiding the sterilization step in the industrial production chain, with consequent savings in energy consumption. The use of whole almond seeds in test MGP3, with a count of microorganisms in the raw material much lower than in the other raw materials, allowed the production of an extract practically free of microorganisms. This was likely due to the long-known properties of the almond kernel skin, which represents a protective layer that prevents the oxidation and microbial contamination of the kernel [4].
Based on the results presented in Section 4.4.1, the substantial retention of TPC in the tests MFP1 and MGP1 at shelf life suggests the effective inactivation of the polyphenol oxidase enzymes during the process, as observed with HC-based treatment of blueberries [49] and sugarcane juice [50], thus contributing to the stability of the product. Rancidification can be an important issue for foods, such as almonds and derived products, which are rich also in polyunsaturated fatty acids [1]. While not measured objectively, no visual or olfactive sign of rancidification emerged at shelf life for the samples collected at peak temperatures during the tests, possibly suggesting an effective inactivation of the lipoxygenase enzymes, which was observed in the case of other emerging food treatment methods [23].
Overall, the results about the stability of HC-derived products represent an original achievement of this study, as well as the foundation for all the other results.
Based on the results presented in Section 4.3.1, the evolution of the energy content in the tests MFP1 and MGP1 closely followed the fat content, which is consistent because fat dominated over the other nutritional quantities. Potential fat contents and their contents in the commercial product were indistinguishable. Fat was extracted very fast in MFP1, so much that, after less than 20 min and at the temperature of 40 °C, its content was more than $70\%$ the potential content, after which it barely changed until 68 °C, then increasing towards the potential content. The larger size of almond kernel grains used in test MGP1 likely delayed the extraction of fat, with a yield of about $55\%$ after less than 20 min at 40 °C, after which it increased regularly up to 78 °C, then suddenly accelerated up to a fat concentration exactly matching the potential content.
The saturated fraction of fat followed approximately the same evolution as total fat, matching the potential content, along with the corresponding content of the commercial product (about $8.5\%$ of the total fat content), at the same time and temperature. The retention of the original partition of the fat into the saturated and the unsaturated fractions is an important achievement, since unsaturated fatty acids, among other constituents of almond seeds, were attributed major health effects, such as decreasing blood lipid concentrations and neuroprotection [20].
It took about 2 h of process time and a peak temperature of 86 °C for the complete extraction of fat; although, based on the obtained results, higher initial temperatures, for example, following heat recovery at the end of the process, should not affect substantially the extraction of fat, provided that the peak temperature reaches at least the level of about 80 °C. Further research is recommended towards the optimization of the process.
The concentration of carbohydrates in the raw almond materials was about ten times lower than fat; thus, these macronutrients had comparatively lower relevance to the composition of the obtained extracts. However, the extraction of carbohydrates, along with the sugar fraction, was quite fast and practically indistinguishable from the potential level starting at the temperature of 58 °C.
The extraction rate of proteins in tests MFP1 and MGP1 closely resembled that of fat, showing very fast extraction with almond kernel flour and slightly slower with fine grains, however eventually converging around the level of the commercial product, at about $65\%$ of the potential level. A hypothesis for such an incomplete extraction rate could be advanced, about a dynamical balance between the extraction and degradation rate of the proteins during the hybrid HC and thermal extraction processes. Such hypothesis is supported by previous research, which showed that partial heat-induced almond protein denaturation occurs already at temperatures between 45 °C and 55 °C and at an accelerated pace above 65–75 °C, while such proteins, although water soluble and thus in principle easily extractable by HC processes, are embedded in oleosins surrounding the oil droplets, making them harder to extract [51]. The matching of the obtained protein concentration levels with the commercial product also appears to support the above hypothesis, which might represent a general limitation in the production of almond beverages. However, further research on this topic is necessary, also following recent findings and recommendations [52].
Fibers were quickly extracted and, starting at the temperature of 58 °C, their concentrations strictly matched the potential levels for both tests MFP1 and MGP1; however, they were more than three times lower than in the commercial product, likely due to either a greater content of fibers in the raw material used to manufacture the commercial product, or the retention in that product of the entire content of almonds cream, as per the relevant patent [46].
Overall, with concentrations of almond skinless kernel material, in the form of flour (<1 mm in size) and fine grain (1–2 mm in size), typical of commercial almond beverages, the single-unit operation HC processing showed the ability to produce extracts that were microbiologically stable and preserved practically all the nutritional properties of the raw material, although some improvements could be tried for the extraction of proteins. Based on data shown in Section 4.1, the entire process from the mixture of water at room temperature and almond material to the output of the extract ready for filtration and packaging, lasted between about 100 and 120 min, with a specific energy consumption between about 60 Wh/L and 100 Wh/L.
Based on the results presented in Section 4.3.2, the extraction rates for test MGP3, using a concentrated mixture ($18\%$) with whole almond seeds in the form of coarse grains (about 3–5 mm in size), were on average half of those achieved for the test MGP1 at comparable temperatures. The limiting factors might be either the coarser size, which took more time to HC processes for grinding and pulverization and left less time for extraction, or the protection offered by the skin to the extraction of substances embedded in the kernel, or the higher concentration of the almond raw material itself, which limited the frequency of interactions of solid particles with pressure shockwaves and mechanical jets generated at the collapse of the cavitation bubbles, or the combination of all the above factors. However, it is noticeable that the extraction rate of proteins in test MGP3 was only slightly lower than in test MGP1, which supports the above-presented hypothesis about the complex extraction/denaturation kinetics, i.e., late extraction of proteins in MGP3 might have limited their denaturation. Further experiments and theoretical research are recommended on this topic, for example to investigate the effects of isothermal steps on the extraction rate of nutritional compounds, especially at temperatures below the above-mentioned threshold for protein denaturation (45–55 °C) [51], or by using reactors able to generate more aggressive and effective cavitation regimes [37].
On the sensorial side, beyond the subjective judgment of the authors about the good taste of both the beverage-like extracts (tests MFP1 and MGP1) and the concentrated extract (test MGP3), the retention of the kernel skin in the latter test did not alter too much the usual creamy white color that consumers are used to, as shown in Figure 8.
Overall, the possibility of generating high concentration aqueous extracts from whole almond seeds, ready for further dilution and production of almond beverages, by means of HC-based processes as a single-unit operation, was successfully demonstrated, which is another original result of this study. The obtained extract was practically free of microbial contamination, although endowed with about half of the potential nutritional properties, thus requiring further research and process optimization.
On the sustainability side, based on Figure 2b, the consumption of specific energy of about 100 Wh/L at the end of the test MGP3 (concentration of $18\%$) would translate, after dilution, in a specific energy consumption for the almond beverage (concentration around $8\%$) of 50 Wh/L or even lower.
Finally, the existence of a critical gelation temperature, estimated at 87.5 °C, while representing an upper limit for the production of acceptable almond beverages or concentrated extracts ready for dilution, might offer the chance to generate new products by means of HC processes, such as almond tofu or cheese, which require higher concentrations of raw almond material than used for the manufacturing of commercial beverages [51].
The analyses performed on micronutrients extracted in the aqueous phase offer further elements to assess the performance of the HC-based processing system and the nutraceutical quality of the products.
Based on the results presented in Section 4.5, the HC-based extraction of vitamin B2 extremely was fast and effective, so much that its contents for both tests MFP1 and MGP1 matched the respective level in the commercial product already in the samples extracted at the temperature of 40 °C. The extraction rate of vitamin PP was quite fast, too, with contents close to the commercial product at 58 °C and matching it in test MGP1 at the temperatures of 78 °C and 86 °C. Such efficient HC-driven extraction was not surprising, due to the well-known high water-solubility of vitamins B2 and PP.
Contrary to the other considered vitamins, vitamin E is fat soluble and in principle harder to extract in water only. However, although delayed, its concentration in samples collected from tests MFP1 and MGP1 eventually exceeded the detection limit at the temperatures of 74 °C and 78 °C, respectively, while it was not detected in the commercial product. Based on the well-known heat sensitivity of vitamin E and its degradation beginning already at 40 °C [53], a hypothesis similar to the one presented for proteins can be advanced. In particular, a dynamical balance might occur between the extraction rate, hindered by the lipophilic nature of vitamin E, and its degradation rate, which might be partially corroborated by the late extraction and higher levels achieved in test MGP1, as well as by the slight decrease in its concentration in test MGP1 from 78 °C to 86 °C. Due to the relevance of vitamin E for human health, further experimental and theoretical research on this topic is recommended.
Based on the results presented in Section 4.4.1, TPC in tests MFP1 and MGP1 were practically indistinguishable already in the first sample collected at the temperature of 40 °C, and no further change occurred up to the temperature of 74 °C in test MFP1 and 68 °C in test MGP1, with contents in the range 50 to 60 mg/kg. The extraction rate accelerated in test MGP1 at 78 °C and even more at 86 °C, extracting about $60\%$ of TPC in in the temperature range 68 to 86 °C and eventually exceeding the content observed in the commercial product. The bimodal structure of the extraction rate, with two peaks at the beginning of the processes up to 40 °C, and in the later phase (68 °C to 86 °C), is likely to reflect the complex composition of polyphenols of almond kernels, a few tenths of which were identified and characterized [9]. The extraction rate of those polyphenols from almond kernels, and thus their identification and quantification, was found to be remarkably dependent on the extraction method, such as the used solvent, temperature, etc. [ 9,24], hence the complex pattern of the extraction rate emerging from tests MFP1 and MGP1.
Based on the results presented in Section 4.4.2 and Table 5, not only was the average TPC in the samples collected at the end of the tests MGP3, after normalization of the almond seed mass, higher than in test MGP2, but the DPPH IC50 level of the MGP3 sample was far lower than for test MGP2, despite lower absolute TPC. It has long been known that almond skin, beyond representing a protective layer that prevents from the oxidation and microbial contamination of the kernel [4], is particularly rich in polyphenols and other bioactive compounds with remarkable antiradical activity [19,54], as well as antimicrobial and antiviral activities [4,17,18], which has prompted further studies aimed at exploiting the potential of almond skin as a byproduct of the peeling step of almond seeds [25]. These properties of the almond kernel skin are the most likely candidates to explain the substantial superiority of the antiradical activity of the aqueous extract from whole almond seeds.
Overall, the effective extraction of bioactive micronutrients by means of HC processes, both the main vitamins and polyphenols available in almond kernels was successfully demonstrated. Based also on previous and extensive evidence, especially concerning the HC-based extraction of polyphenols [55], this topic appears quite consolidated. However, the findings about the bimodal extraction rate of polyphenols (higher extraction rates early in the process at moderate temperatures and later at relatively high temperatures), the effective extraction of polyphenols from whole almond seeds, which was associated with a substantially higher level of antiradical activity, and the effective extraction of vitamin E, along with the hypothesis on the related mechanisms, represent further original results of this study. Future research might investigate, in particular, the topic of antiradical activity, which brings an important contribution to the healthy properties of the product, including more biologically relevant essays than the DPPH.
Finally, the mass balance information about test MGP3, provided in Section 4.6, could be economically relevant, both to derive the potential mass yield of clarified extracts (almond beverages or concentrated extracts), and because the pellet could be reused as a filler for food or feed products, possibly still endowed with a residual content of insoluble fibers and proteins, or conveyed to biodigesters for energy generation. Further research is suggested on the analysis of the pellet resulting as a byproduct from HC-based processing of almond raw materials.
Figure 9 shows a summary scheme of the developed method for the production of almond beverage, along with basic information about the quality of the produced beverage (macronutrients and micronutrients), its stability at shelf life, and energy consumption.
This study was affected by some important limitations, which are listed and shortly discussed below.
The design of experiments was not optimized due to limitations in the availability of raw material and other resources; information available from the performed tests was exploited as much as possible, and more structured experiments are planned. This study did not investigate the rheological properties of the aqueous almond extracts, which are fundamental to the physical stability (for example, sedimentation and phase separation) and acceptability of the products [1,6,51], while, based on previous research, it can be only hypothesized that HC processes help creating stable nanoemulsions, allowing to overcome physical stability issues even without any further additives [29].
This study neither investigated the effectivity of the extraction of amino acids, which have primary relevance to the nutritional quality of any food including almonds [10], nor the presence and activity of almond-derived allergenic compounds in the aqueous extracts, which can represent an important issue [56].
Further research is recommended on all the above-discussed topics, which were not investigated in this study.
## 6. Conclusions
Hydrodynamic cavitation-based processing of almond skinless kernels or whole seeds, as a single-unit operation, showed potential advantages over conventional techniques in the production of almond beverages or concentrated aqueous extracts. The same process was able to deal with different types of almond materials, i.e., skinless kernels in the form of flour or fine grain, and whole almond seeds, including the skin and in the form of coarse grain, to produce microbiologically safe and stable extracts, to effectively extract the most important macronutrients, although with margins of improvement in the case of whole seeds, and to very effectively extract all the most important bioactive micronutrients, with special advantages in the case of whole seeds, likely due to the bioactive properties of the almond kernel skin. The nutritional composition of the extracts was comparable with a high-end organic commercial product, while showing comparable or better microbiological stability and generally superior availability of bioactive micronutrients.
The proposed method, which is straightforwardly scalable to any production capacity, would allow replacing with a single operation the complex processing chain used in conventional manufacturing methods for almond beverages, including roasting (optional), soaking in water, blanching and peeling, wet milling, the addition of stabilizers (optional), homogenization, and sterilization. Additionally, the proposed method would allow for an advantageous processing of whole almond seeds, also reducing the environmental burden due to the removal and disposal of almond kernel skin. All the available figures, including process time, specific energy consumption, and mass balance, were provided, in order to allow for meaningful comparisons with other techniques, whether well-established or emerging ones.
## References
1. Bocker R., Silva E.K.. **Innovative technologies for manufacturing plant-based non-dairy alternative milk and their impact on nutritional, sensory and safety aspects**. *Futur. Foods* (2022) **5** 100098. DOI: 10.1016/j.fufo.2021.100098
2. Sethi S., Tyagi S.K., Anurag R.K.. **Plant-based milk alternatives an emerging segment of functional beverages: A review**. *J. Food Sci. Technol.* (2016) **53** 3408-3423. DOI: 10.1007/s13197-016-2328-3
3. Jeske S., Zannini E., Arendt E.K.. **Past, present and future: The strength of plant-based dairy substitutes based on gluten-free raw materials**. *Food Res. Int.* (2018) **110** 42-51. DOI: 10.1016/j.foodres.2017.03.045
4. Barral-Martinez M., Fraga-Corral M., Garcia-Perez P., Simal-Gandara J., Prieto M.. **Almond By-Products: Valorization for Sustainability and Competitiveness of the Industry**. *Foods* (2021) **10**. DOI: 10.3390/foods10081793
5. Valencia-Flores D.C., Hernandez M., Guamis B., Ferragut V.. **Comparing the Effects of Ultra-High-Pressure Homogenization and Conventional Thermal Treatments on the Microbiological, Physical, and Chemical Quality of Almond Beverages**. *J. Food Sci.* (2013) **78** E199-E205. DOI: 10.1111/1750-3841.12029
6. Maghsoudlou Y., Alami M., Mashkour M., Shahraki M.H.. **Optimization of Ultrasound-Assisted Stabilization and Formulation of Almond Milk**. *J. Food Process. Preserv.* (2015) **40** 828-839. DOI: 10.1111/jfpp.12661
7. Beltrán Sanahuja A., Maestre Pérez S.E., Grané Teruel N., Valdés García A., Prats Moya M.S.. **Variability of Chemical Profile in Almonds (**. *Foods* (2021) **10**. DOI: 10.3390/foods10010153
8. Chalupa-Krebzdak S., Long C.J., Bohrer B.M.. **Nutrient density and nutritional value of milk and plant-based milk alternatives**. *Int. Dairy J.* (2018) **87** 84-92. DOI: 10.1016/j.idairyj.2018.07.018
9. Özcan M.M.. **A review on some properties of almond: Impact of processing, fatty acids, polyphenols, nutrients, bioactive properties, and health aspects**. *J. Food Sci. Technol.* (2022) 1-12. DOI: 10.1007/s13197-022-05398-0
10. Barreca D., Nabavi S.M., Sureda A., Rasekhian M., Raciti R., Silva A.S., Annunziata G., Arnone A., Tenore G.C., Süntar I.. **Almonds (Prunus Dulcis Mill. D. A. Webb): A Source of Nutrients and Health-Promoting Compounds**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12030672
11. Jenkins D.J.A., Kendall C.W.C., Josse A.R., Salvatore S., Brighenti F., Augustin L.S.A., Ellis P.R., Vidgen E., Rao A.V.. **Almonds Decrease Postprandial Glycemia, Insulinemia, and Oxidative Damage in Healthy Individuals**. *J. Nutr.* (2006) **136** 2987-2992. DOI: 10.1093/jn/136.12.2987
12. Liu Y., Hwang H.-J., Kim H.-S., Park H.. **Time and Intervention Effects of Daily Almond Intake on the Changes of Lipid Profile and Body Composition Among Free-Living Healthy Adults**. *J. Med. Food* (2018) **21** 340-347. DOI: 10.1089/jmf.2017.3976
13. Holscher H.D., Taylor A.M., Swanson K.S., Novotny J.A., Baer D.J.. **Almond Consumption and Processing Affects the Composition of the Gastrointestinal Microbiota of Healthy Adult Men and Women: A Randomized Controlled Trial**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10020126
14. Chen C.-Y.O., Holbrook M., Duess M.-A., Dohadwala M.M., Hamburg N.M., Asztalos B.F., Milbury P.E., Blumberg J.B., Vita J.A.. **Effect of almond consumption on vascular function in patients with coronary artery disease: A randomized, controlled, cross-over trial**. *Nutr. J.* (2015) **14** 61. DOI: 10.1186/s12937-015-0049-5
15. Jenkins D.J., Kendall C.W., Marchie A., Parker T.L., Connelly P.W., Qian W., Haight J.S., Faulkner D., Vidgen E., Lapsley K.G.. **Dose Response of Almonds on Coronary Heart Disease Risk Factors: Blood Lipids, Oxidized Low-Density Lipoproteins, Lipoprotein(a), Homocysteine, and Pulmonary Nitric Oxide**. *Circulation* (2002) **106** 1327-1332. DOI: 10.1161/01.CIR.0000028421.91733.20
16. Li S.-C., Liu Y.-H., Liu J.-F., Chang W.-H., Chen C.-M.. **Almond consumption improved glycemic control and lipid profiles in patients with type 2 diabetes mellitus**. *Metabolism* (2011) **60** 474-479. DOI: 10.1016/j.metabol.2010.04.009
17. Musarra-Pizzo M., Ginestra G., Smeriglio A., Pennisi R., Sciortino M.T., Mandalari G.. **The Antimicrobial and Antiviral Activity of Polyphenols from Almond (Prunus dulcis L.) Skin**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11102355
18. Mandalari G., Bisignano C., D’Arrigo M., Ginestra G., Arena A., Tomaino A., Wickham M.. **Antimicrobial potential of polyphenols extracted from almond skins**. *Lett. Appl. Microbiol.* (2010) **51** 83-89. DOI: 10.1111/j.1472-765X.2010.02862.x
19. Chen C.-Y.O., Blumberg J.B.. **In Vitro Activity of Almond Skin Polyphenols for Scavenging Free Radicals and Inducing Quinone Reductase**. *J. Agric. Food Chem.* (2008) **56** 4427-4434. DOI: 10.1021/jf800061z
20. Aydar E.F., Tutuncu S., Ozcelik B.. **Plant-based milk substitutes: Bioactive compounds, conventional and novel processes, bioavailability studies, and health effects**. *J. Funct. Foods* (2020) **70** 103975. DOI: 10.1016/j.jff.2020.103975
21. Meneguzzo F., Albanese L., Zabini F.. **Hydrodynamic Cavitation in Beer and Other Beverage Processing**. *Reference Module in Food Science* (2020) 369-394
22. McClements D.J., Newman E., McClements I.F.. **Plant-Based Milks: A Review of the Science Underpinning Their Design, Fabrication, and Performance**. *Compr. Rev. Food Sci. Food Saf.* (2019) **18** 2047-2067. DOI: 10.1111/1541-4337.12505
23. Reyes-Jurado F., Soto-Reyes N., Dávila-Rodríguez M., Lorenzo-Leal A., Jiménez-Munguía M., Mani-López E., López-Malo A.. **Plant-Based Milk Alternatives: Types, Processes, Benefits, and Characteristics**. *Food Rev. Int.* (2021) 1-32. DOI: 10.1080/87559129.2021.1952421
24. Bolling B.W.. **Almond Polyphenols: Methods of Analysis, Contribution to Food Quality, and Health Promotion**. *Compr. Rev. Food Sci. Food Saf.* (2017) **16** 346-368. DOI: 10.1111/1541-4337.12260
25. Tabib M., Tao Y., Ginies C., Bornard I., Rakotomanomana N., Remmal A., Chemat F.. **A One-Pot Ultrasound-Assisted Almond Skin Separation/Polyphenols Extraction and its Effects on Structure, Polyphenols, Lipids, and Proteins Quality**. *Appl. Sci.* (2020) **10**. DOI: 10.3390/app10103628
26. Oliveira I., Meyer A.S., Afonso S., Sequeira A., Vilela A., Goufo P., Trindade H., Gonçalves B.. **Effects of Different Processing Treatments on Almond (**. *Plants* (2020) **9**. DOI: 10.3390/plants9111627
27. Panda D., Manickam S.. **Cavitation Technology—The Future of Greener Extraction Method: A Review on the Extraction of Natural Products and Process Intensification Mechanism and Perspectives**. *Appl. Sci.* (2019) **9**. DOI: 10.3390/app9040766
28. Rojas M.L., Kubo M.T., Miano A.C., Augusto P.E.. **Ultrasound processing to enhance the functionality of plant-based beverages and proteins**. *Curr. Opin. Food Sci.* (2022) **48** 100939. DOI: 10.1016/j.cofs.2022.100939
29. Panda D., Saharan V.K., Manickam S.. **Controlled Hydrodynamic Cavitation: A Review of Recent Advances and Perspectives for Greener Processing**. *Processes* (2020) **8**. DOI: 10.3390/pr8020220
30. Zheng H., Zheng Y., Zhu J.. **Recent Developments in Hydrodynamic Cavitation Reactors: Cavitation Mechanism, Reactor Design, and Applications**. *Engineering* (2022). DOI: 10.1016/j.eng.2022.04.027
31. Salve A.R., Pegu K., Arya S.S.. **Comparative assessment of high-intensity ultrasound and hydrodynamic cavitation processing on physico-chemical properties and microbial inactivation of peanut milk**. *Ultrason. Sonochemistry* (2019) **59** 104728. DOI: 10.1016/j.ultsonch.2019.104728
32. Iorio M.C., Bevilacqua A., Corbo M.R., Campaniello D., Sinigaglia M., Altieri C.. **A case study on the use of ultrasound for the inhibition of Escherichia coli O157:H7 and Listeria monocytogenes in almond milk**. *Ultrason. Sonochemistry* (2018) **52** 477-483. DOI: 10.1016/j.ultsonch.2018.12.026
33. Dhakal S., Giusti M.M., Balasubramaniam V.. **Effect of high pressure processing on dispersive and aggregative properties of almond milk**. *J. Sci. Food Agric.* (2016) **96** 3821-3830. DOI: 10.1002/jsfa.7576
34. Briviba K., Gräf V., Walz E., Guamis B., Butz P.. **Ultra high pressure homogenization of almond milk: Physico-chemical and physiological effects**. *Food Chem.* (2016) **192** 82-89. DOI: 10.1016/j.foodchem.2015.06.063
35. Manzoor M.F., Siddique R., Hussain A., Ahmad N., Rehman A., Siddeeg A., Alfarga A., Alshammari G.M., Yahya M.A.. **Thermosonication effect on bioactive compounds, enzymes activity, particle size, microbial load, and sensory properties of almond (Prunus dulcis) milk**. *Ultrason. Sonochem.* (2021) **78** 105705. DOI: 10.1016/j.ultsonch.2021.105705
36. Ferragut V., Hernández-Herrero M., Veciana-Nogués M.T., Borras-Suarez M., González-Linares J., Vidal-Carou M.C., Guamis B.. **Ultra-high-pressure homogenization (UHPH) system for producing high-quality vegetable-based beverages: Physicochemical, microbiological, nutritional and toxicological characteristics**. *J. Sci. Food Agric.* (2014) **95** 953-961. DOI: 10.1002/jsfa.6769
37. Meneguzzo F., Zabini F., Albanese L., Crisci A.. **Novel Affordable, Reliable and Efficient Technologies to Help Addressing the Water-Energy-Food Nexus**. *Eur. J. Sustain. Dev.* (2019) **8** 1-17. DOI: 10.14207/ejsd.2019.v8n4p1
38. Meneguzzo F., Zabini F.. *Agri-Food and Forestry Sectors for Sustainable Development* (2021)
39. Gallina L., Cravotto C., Capaldi G., Grillo G., Cravotto G.. **Plant Extraction in Water: Towards Highly Efficient Industrial Applications**. *Processes* (2022) **10**. DOI: 10.3390/pr10112233
40. Albanese L., Ciriminna R., Meneguzzo F., Pagliaro M.. **Beer-brewing powered by controlled hydrodynamic cavitation: Theory and real-scale experiments**. *J. Clean. Prod.* (2017) **142** 1457-1470. DOI: 10.1016/j.jclepro.2016.11.162
41. Albanese L., Bonetti A., D’Acqui L., Meneguzzo F., Zabini F.. **Affordable Production of Antioxidant Aqueous Solutions by Hydrodynamic Cavitation Processing of Silver Fir (**. *Foods* (2019) **8**. DOI: 10.3390/foods8020065
42. Meneguzzo F., Brunetti C., Fidalgo A., Ciriminna R., DeLisi R., Albanese L., Zabini F., Gori A., dos Santos Nascimento L.B., De Carlo A.. **Real-Scale Integral Valorization of Waste Orange Peel via Hydrodynamic Cavitation**. *Processes* (2019) **7**. DOI: 10.3390/pr7090581
43. Preece K.E., Hooshyar N., Krijgsman A.J., Fryer P.J., Zuidam N.J.. **Intensification of protein extraction from soybean processing materials using hydrodynamic cavitation**. *Innov. Food Sci. Emerg. Technol.* (2017) **41** 47-55. DOI: 10.1016/j.ifset.2017.01.002
44. Pica A.L., Silvestri C., Cristofori V.. **Cultivar-Specific Assessments of Almond Nutritional Status through Foliar Analysis**. *Horticulturae* (2022) **8**. DOI: 10.3390/horticulturae8090822
45. Piscopo A., Romeo F., Petrovicova B., Poiana M.. **Effect of the harvest time on kernel quality of several almond varieties (**. *Sci. Hortic.* (2010) **125** 41-46. DOI: 10.1016/j.scienta.2010.02.015
46. Montalbano L.M., Solano M., Vaccaro P.. **Vegetable Beverage Based on Almonds Cream Ready to Use**. *Italian Patent* (2006)
47. Folin O., Ciocalteu V.. **On Tyrosine and Tryptophane Determinations in Proteins**. *J. Biol. Chem.* (1927) **73** 627-650. DOI: 10.1016/S0021-9258(18)84277-6
48. Romani A., Vignolini P., Isolani L., Ieri F., Heimler D.. **HPLC-DAD/MS Characterization of Flavonoids and Hydroxycinnamic Derivatives in Turnip Tops (**. *J. Agric. Food Chem.* (2006) **54** 1342-1346. DOI: 10.1021/jf052629x
49. Martynenko A., Chen Y.. **Degradation kinetics of total anthocyanins and formation of polymeric color in blueberry hydrothermodynamic (HTD) processing**. *J. Food Eng.* (2016) **171** 44-51. DOI: 10.1016/j.jfoodeng.2015.10.008
50. Bhukya J., Mohapatra D., Naik R.. **Hydrodynamic cavitation processing of ascorbic acid treated precooled sugarcane juice for physiochemical, bioactive, enzyme stability, and microbial safety**. *J. Food Process. Eng.* (2022) e14209. DOI: 10.1111/jfpe.14209
51. Devnani B., Ong L., Kentish S., Gras S.. **Heat induced denaturation, aggregation and gelation of almond proteins in skim and full fat almond milk**. *Food Chem.* (2020) **325** 126901. DOI: 10.1016/j.foodchem.2020.126901
52. Kamal H., Ali A., Manickam S., Le C.F.. **Impact of cavitation on the structure and functional quality of extracted protein from food sources—An overview**. *Food Chem.* (2023) **407** 135071. DOI: 10.1016/j.foodchem.2022.135071
53. Domínguez Avila J.A., Wall Medrano A., Ruiz Pardo C.A., Montalvo González E., González Aguilar G.A.. **Use of nonthermal technologies in the production of functional beverages from vegetable ingredients to preserve heat-labile phytochemicals**. *J. Food Process. Preserv.* (2018) **42** e13506. DOI: 10.1111/jfpp.13506
54. Monagas M., Garrido I., Lebrón-Aguilar R., Bartolome B., Gómez-Cordovés C.. **Almond (**. *J. Agric. Food Chem.* (2007) **55** 8498-8507. DOI: 10.1021/jf071780z
55. More P.R., Jambrak A.R., Arya S.S.. **Green, environment-friendly and sustainable techniques for extraction of food bioactive compounds and waste valorization**. *Trends Food Sci. Technol.* (2022) **128** 296-315. DOI: 10.1016/j.tifs.2022.08.016
56. Dhakal S., Liu C., Zhang Y., Roux K.H., Sathe S.K., Balasubramaniam V.. **Effect of high pressure processing on the immunoreactivity of almond milk**. *Food Res. Int.* (2014) **62** 215-222. DOI: 10.1016/j.foodres.2014.02.021
|
---
title: Relationship between Oxidative Stress and Left Ventricle Markers in Patients
with Chronic Heart Failure
authors:
- Aušra Mongirdienė
- Agnė Liuizė
- Dovilė Karčiauskaitė
- Eglė Mazgelytė
- Arūnas Liekis
- Ilona Sadauskienė
journal: Cells
year: 2023
pmcid: PMC10001312
doi: 10.3390/cells12050803
license: CC BY 4.0
---
# Relationship between Oxidative Stress and Left Ventricle Markers in Patients with Chronic Heart Failure
## Abstract
Oxidative stress is proposed in the literature as an important player in the development of CHF and correlates with left ventricle (LV) dysfunction and hypertrophy in the failing heart. In this study, we aimed to verify if the serum oxidative stress markers differ in chronic heart failure (CHF) patients’ groups depending on the LV geometry and function. Patients were stratified into two groups according to left ventricular ejection fraction (LVEF) values: HFrEF (<$40\%$ ($$n = 27$$)) and HFpEF (≥$40\%$ ($$n = 33$$)). Additionally, patients were stratified into four groups according to LV geometry: NG–normal left ventricle geometry ($$n = 7$$), CR–concentric remodeling ($$n = 14$$), cLVH–concentric LV hypertrophy ($$n = 16$$), and eLVF–eccentric LV hypertrophy ($$n = 23$$). We measured protein (protein carbonyl (PC), nitrotyrosine (NT-Tyr), dityrosine), lipid (malondialdehyde (MDA), oxidizes (HDL) oxidation and antioxidant (catalase activity, total plasma antioxidant capacity (TAC) markers in serum. Transthoracic echocardiogram analysis and lipidogram were also performed. We found that oxidative (NT-Tyr, dityrosine, PC, MDA, oxHDL) and antioxidative (TAC, catalase) stress marker levels did not differ between the groups according to LVEF or LV geometry. NT-Tyr correlated with PC (rs = 0.482, $$p \leq 0.000098$$), and oxHDL (rs = 0.278, $$p \leq 0.0314$$). MDA correlated with total (rs = 0.337, $$p \leq 0.008$$), LDL (rs = 0.295, $$p \leq 0.022$$) and non-HDL (rs = 0.301, $$p \leq 0.019$$) cholesterol. NT-Tyr negatively correlated with HDL cholesterol (rs = -0.285, $$p \leq 0.027$$). LV parameters did not correlate with oxidative/antioxidative stress markers. Significant negative correlations were found between the end-diastolic volume of the LV and the end-systolic volume of the LV and HDL-cholesterol (rs = –0.935, $p \leq 0.0001$; rs = –0.906, $p \leq 0.0001$, respectively). Significant positive correlations between both the thickness of the interventricular septum and the thickness of the LV wall and the levels of triacylglycerol in serum (rs = 0.346, $$p \leq 0.007$$; rs = 0.329, $$p \leq 0.010$$, respectively) were found. In conclusions, we did not find a difference in serum concentrations of both oxidant (NT-Tyr, PC, MDA) and antioxidant (TAC and catalase) concentrations in CHF patients’ groups according to LV function and geometry was found. The geometry of the LV could be related to lipid metabolism in CHF patients, and no correlation between oxidative/antioxidant and LV markers in CHF patients was found.
## 1. Introduction
Depending on its pathogenesis, heart failure is classified as either HF with preserved left ventricle ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF), with HFpEF accounting for almost half of all cases of HF [1]. The exact sequence of events that contribute to the development and progression of chronic HF (CHF) remains to be elucidated. HFrEF is known to be the result of myocardial ischemia and infarction, while HFpEF is associated with older age, dysregulated metabolism, and chronic hypertension, which contribute to oxidative stress and myocardial remodelling and dysfunction [2]. Oxidative stress (an imbalance between the increased formation of reactive oxygen species (ROS) and the elimination or neutralization of ROS by an antioxidant system [3]) is proposed as an important player in the development of CHF [4]. It correlates with left ventricle (LV) dysfunction and hypertrophy in the failing heart [5] and is involved in ventricular remodelling [6].
In a systemic review, Martins. et al. concluded that antioxidants have the potential to become of a therapeutic strategy against this important pathological condition [7,8]. However, evidence for the benefit of antioxidant therapies in clinical trials is mainly from animal models and is sparse [7,9]. Furthermore, the reason for the discrepancy in antioxidative stress therapies was raised because it could be that only specific patient groups benefit from antioxidative stress treatments [10]. Therefore, more studies are needed to better understand the role of oxidative stress as a therapeutic target for cardiac remodelling.
There are data suggesting that in the case of HFrEF, myocardial remodelling is stimulated by ROS formed in heart cells, while in the case of HFpEF, it is stimulated by ROS that have increased in the myocardium due to external changes [9]. Metabolic syndrome, chronic hypertension, and other reasons are implied to dysregulate the human antioxidant system [8] and result in increased and oxidised proteins and lipids in the blood. ROS is revealed to directly activate GTP-binding proteins in myocytes and promote both hypertrophic growth signalling and apoptosis [11,12].
Nitrotyrosine (NT-Tyr) and dityrosine are products of tyrosine oxidation [13]. NT-*Tyr is* presented as a potential marker of oxidative stress associated with tissue damage and has a potential role as an inflammatory mediator in coronary artery disease (CAD) [14]. Protein carbonylation is the most well-known type of protein oxidation resulting in irreversible loss of protein function [15]. Catalase is an enzyme of the antioxidant system [16,17]. Malondialdehyde (MDA) is presented as an important indicator of lipid peroxidation [18]. The total antioxidant capacity (TAC) reflects an estimation of the ability of different antioxidants and shows the net results of the complex interaction between oxidants and antioxidants [19].
Concentrations of the carbonyl oxidative stress markers protein (PC) and MDA in serum were shown to be higher in left ventricle (LV) hypertrophy of haemodialysis patients compared to a healthy person and were correlated with the geometry of the LV [20]. Therefore, we aimed to verify two hypotheses: 1) in HFpEF there should be more oxidised proteins and lipids in the blood than in HFrEF, 2) oxidative stress markers in serum could differ in patients depending on the LV geometry. In this case, the reduction in oxidative stress for patients of the specific group would be more appropriate. If the hypothesis is confirmed, it would be possible to study the possibility of reducing the effect of oxidants in the specific CHF group according to left ventricle ejection fraction (LVEF) or in the group according to the geometry of the LV to inhibit harmful remodelling and failure progression in specific patients with CHF.
## 2.1. Study Population
A total of 60 patients diagnosed with CHF, admitted to the Department of Cardiology at Kaunas Clinical Hospital of Lithuanian University of Health Sciences between 1 January 2016 and 1 March 2018, were included in the study. All patients gave their written consent. Inclusion criteria included no changes in functional class according to the New York Heart Association (NYHA) or medical treatment in the past 3 to 4 weeks and no new HF symptoms. The diagnosis of CHF was made following the guidelines for the diagnosis and treatment of heart failure approved by the European Society of Cardiology [21]. Patients with kidney failure (glomerular filtration rate (GFG) < 60 mL/min.), acute or chronic infection, acute coronary syndromes, diabetes mellitus, connective tissue disease, or smoking were excluded from the study.
The study group consisted of 26 ($43.3\%$) women and 34 ($56.7\%$) men whose age median (IQR) was 67.5 (22.5) years. First of all, patients were stratified into two groups according to left ventricular ejection fraction (LVEF) values: HFrEF (<$40\%$ ($$n = 27$$)) and HFpEF (≥$40\%$ ($$n = 33$$)). There was an equal distribution of patients in the NYHA classification categories (Class II-IV). Comparison of sociodemographic and clinical characteristics between the groups based on left ventricular ejection fraction values (reduced vs. midrange and preserved LVEF) showed a significantly higher number of men in a group of reduced (<$40\%$) LVEF. Furthermore, in a group of mid-range and preserved LVEF, most subjects were assigned to NYHA Class II or Class III, while in a group of reduced LVEF, the majority of patients were assigned to NYHA Class IV, and these differences were statistically significant. The sociodemographic and clinical characteristics of the study group are presented in Table 1.
## 2.2. Tests and Blood Sampling
Transthoracic echocardiogram analysis and complete blood count test were performed after admission of the patients to the hospital. Blood samples were drawn from the forearm vein with a 20 G needle into 4.5 mL vacuum tubes with ethylendiamintetraacetic acid (EDTA) and without additives. Complete blood count testing was performed on a COULTER LH 780 hematological analyzer (Brea, CA, USA). Blood serum samples for oxidative (nitrotyrosine, dityrosine, protein carbonyl, malondialdehyde, oxidised HDL) and antioxidative (total plasma antioxidant capacity, catalase (CAT)) stress biomarkers were frozen at −80 °C until analysis.
Oxidative and antioxidative markers were measured in serum using commercial reagent kits: Human total antioxidant capacity ELISA Kit abx053643 (abbexa, United Kingdom), Carbonyl Protein ELISA K7870 (immune diagnostic AG, Stubenwald-Allee 8a, 64625 Bensheim, Germany), Human oxidised high-density lipoprotein (Ox-HDL0 ELISA Kit CSB-E16552h (Cusabio biotech Co, Wuhan, China) and Nitrotyrosin ELISA K7829 (immune diagnostic AG). CAT activity in serum was evaluated according to the method described in [22]. CAT activity was measured by hydrogen peroxide reaction with ammonium molybdate, which produces a complex that absorbs at a wavelength of light of 410 nm. The results were expressed in U/mg protein. Under these conditions, one unit of catalase (U) decomposes 1 mol of hydrogen peroxide per 1 min. The protein concentration in serum was measured using the Lowry method [23]. Samples for MDA were prepared and analysed according to the methodology of Khoschosorur et al. [ 24], using the HPLC method with fluorescence detection. Chromatographic separation was performed on the HPLC system (Shimadzu Nexera X2, Kyoto, Japan). A 20-μL sample was injected onto the HPLC column (Agilent Poroshell, Santa Clara, California, 120 EC–C18, 3 × 100 mm, 2.7 μm). The chromatographic isocratic separation was carried out with a binary mobile phase of methanol and 50 mM phosphate buffer, pH 6.8 (2:3, v/v). Fluorescence detection was performed at 230 nm excitation and 430 nm emission wavelengths. The average retention time of the malondialdehyde-thiobarbituric acid adduct was 1.63 min.
All investigations were approved and conducted in accordance with the guidelines of the local Bioethics Committee and adhered to the principles of the Declaration of Helsinki and Title 45, US Code of Federal Regulations, Part 46, Protection of Human Subjects (revised 15 January 2009, effective 14 July 2009). The study was approved by the Regional Bioethics Committee of the Lithuanian University of Health Sciences (No. BE-2-102, 20 December 2018).
## 2.3. Statistical Analysis
Statistical analysis was performed using the R software, version 4.2.2, R Core Team, 2021. The Shapiro–Wilk test was used to test the normality of the variables. Quantitative variables are presented as mean ± standard deviation (SD) for normally distributed variables, or median (interquartile range) (IQR) for non-normally distributed variables. For comparison of the median (IQR) or average ± SD values between the two groups, the Mann–Whitney U test or Student’s t-test were used. Comparison of median (IQR) values among three or more groups was performed using the Kruskal–Wallis test. Dunn’s post hoc test with Bonferroni correction was used for multiple comparisons. Pearson’s Chi-square test and post hoc analysis or Fisher’s exact test (when any expected frequency was less than or equal to 5) were used to compare categorical variables between the groups based on values of left ventricular ejection fraction or left ventricular geometry. Spearman’s rank coefficient (for non-normally distributed variables) or Pearson’s correlation analysis (for normally distributed variables) was used to quantify the strength of the correlation between continuous variables. The level of statistical significance was set at 0.05 for the two-tailed test.
## 3.1. Characteristics of Study Groups according to LVEF
Data on drug use showed that more than half ($56.7\%$) of patients receive angiotensin converting enzyme (ACE) inhibitors, $48.3\%$ used β-blockers and $30\%$ diuretics. Comparison of medication usage between groups based on left ventricular ejection fraction values revealed that there was no statistically significant difference in prescribed medications (Table 2).
## 3.2. Oxidative/Antioxidant Stress Markers in Groups according to LVEF
Oxidative (nitrotyrosine, dityrosine, protein carbonyl, malondialdehyde, oxidized HDL) and antioxidative (total plasma antioxidant capacity, catalase) stress biomarker levels were not significantly different in the two groups of different LVEF values (Table 3).
Additionally, we divided the entire study sample into two groups based on the median values of malondialdehyde, protein carbonyl, and oxidised HDL levels. No statistically significant differences were found in serum oxidative/antioxidant stress markers and values of LVEF between groups with different concentrations of malondialdehyde concentration (≤114.29 µg/L vs. >114.29 µg/L) (Table 4).
Comparison of oxidative/antioxidant stress markers and values of the LVEF between groups of different levels of protein carbonyl and oxidised HDL showed that the median concentration of NT-Tyr in serum was significantly higher in a group of patients with increased levels of PC (3.16 (2.09) nM vs. 4.46 (2.12) nM, $$p \leq 0.008$$) and oxHDL (3.42 (1.38) pg/L vs. 4.51 (2.45) pg/L, $$p \leq 0.004$$) levels. No significant differences in the levels of other oxidative stress parameters were found among participants with different PC and oxHDL levels (Table 5 and Table 6).
## 3.3. Characteristics of Study Groups according to LV Geometry
Comparison of sociodemographic and clinical parameters among the left ventricular geometry groups showed statistically significant differences in the proportion of men and women in the different left ventricular geometry groups. However, post hoc pairwise comparisons for the Chi-squared test revealed that these differences were not significant. The Kruskal–*Wallis analysis* indicated statistically significant differences in age and HDL cholesterol levels between the groups. Dunn’s post hoc test with Bonferroni correction for multiple comparisons showed significant differences in the age of subjects between the NG and cLVH groups, as well as between the eLVH and cLVH groups. Post hoc analysis of HDL cholesterol levels between left ventricular geometry groups yielded nonsignificant results (Table 7). Furthermore, the differences in medication usage among the different left ventricular geometry groups were not significant (Table 8).
## 3.4. Oxidative/Antioxidative Stress Markers in Groups according to the Geometry of the Left Ventricle
Additionally, we analysed the levels of oxidative/antioxidative stress markers in the groups based on the geometry of the left ventricle. However, no statistically significant differences were found between the groups in both oxidative parameters (nitrotyrosine, dityrosine, protein carbonyl, malondialdehyde, oxidised HDL) and antioxidative parameters (total plasma antioxidant capacity, catalase) (Table 9).
## 3.5. Correlation Analysis
The correlation analysis revealed a statistically significant negative association between PC concentration and LVEF (rs = 0.257, $$p \leq 0.047$$). However, since a significant difference was found in the distribution of men and women between the groups of reduced and mid-range or preserved LVEF, we calculated the partial Spearman correlation adjusted for gender. As a result, the previously observed association between LVEF and PC concentration fell to a nonsignificant level (rs = 0.240, $$p \leq 0.067$$) (Table 10). In the current study, no significant correlation was found between oxidative/antioxidative stress biomarkers and subjects’ age, systolic and diastolic blood pressure.
Analysis of the relationship between the levels of biomarkers of oxidative/antioxidative stress revealed a moderate and statistically significant association between the concentration of NT-Tyr and PC levels (rs = 0.482, $$p \leq 0.000098$$), as well as statistically significant correlation between NT-Tyr and oxHDL levels serum (rs = 0.278, $$p \leq 0.0314$$) (Table 11).
Spearman’s correlation analysis revealed weak but statistically significant positive associations between the age of the subjects and the HDL cholesterol level (rs = 0.315, $$p \leq 0.014$$) and between systolic blood pressure and total cholesterol concentration levels (rs = 0.255, $$p \leq 0.049$$). Investigation of the relationship between oxidative stress measures and lipid metabolism biomarkers showed significant positive correlations between MDA concentration and total cholesterol levels (rs = 0.337, $$p \leq 0.008$$), LDL cholesterol levels (rs = 0.295, $$p \leq 0.022$$), and non-HDL cholesterol levels (rs = 0.301, $$p \leq 0.019$$), as well as a negative correlation between nitrotyrosine and HDL cholesterol concentrations (rs = 0.285, $$p \leq 0.027$$) in the study group (Table 12).
The results showed that none of the echocardiographic characteristics were significantly correlated with oxidative/antioxidative stress markers (Table 13). However, most of the echocardiographic parameters, such as left ventricular end-diastolic dimension (LVEDD), interventricular septum thickness (IVST), posterior wall thickness (PWT), left ventricle wall thickness (LVWT), and relative wall thickness (RWT), correlated significantly with the age of the participants (rs = –0.335, $$p \leq 0.009$$; rs = 0.448, $$p \leq 0.0003$$; rs = 0.383, $$p \leq 0.003$$; rs = 0.434, $$p \leq 0.0005$$; rs = 0.461, $$p \leq 0.0002$$, respectively). Additionally, a significant negative association was observed between the end-diastolic dimension of the left ventricle and HDL cholesterol (rs = −0.256, $$p \leq 0.048$$). However, after adjustment for age, the correlation was found to be non-significant (rs = −0.168, $$p \leq 0.202$$). Furthermore, very strong statistically significant negative correlations were found between the end-diastolic volume of the left ventricular or the end-systolic volume of the left ventricular and HDL-cholesterol in the study group (rs = –0.935, $p \leq 0.0001$; rs = –0.906, $p \leq 0.0001$, respectively). Furthermore, the results showed significant positive associations between the thickness of the interventricular septum or the thickness of the left ventricle wall and the levels of triacylglycerol in blood serum (rs = 0.346, $$p \leq 0.007$$; rs = 0.329, $$p \leq 0.010$$, respectively) (Table 14).
## 4.1. The Difference in Oxidative Stress/Antioxidant Markers between the CHF Groups
Regardless of our theoretical assumptions, there was no difference in oxidative/antioxidant markers between the groups according to the LVEF and the LV geometry. In the group of patients with higher levels of PC, the median concentration of NT-Tyr was significantly higher (4.46 (2.12) nM vs. 3.16 (2.09) nM, $$p \leq 0.008$$), as well as oxidised HDL (4.51 (2.45) pg/L vs. 3.42 (1.38) pg/L, $$p \leq 0.004$$). A moderate and statistically significant correlation was found between NT-Tyr concentration and PC levels (rs = 0.482, $$p \leq 0.000098$$). Therefore, our work supplements the knowledge that oxidants in the blood oxidise both proteins (as shown by markers of NT-Tyr and PC) and lipids (as shown by markers of MDA and HDL).
Research of oxidative stress/antioxidative status in CVD is mainly focused on markers of oxidation of proteins and lipids, antioxidant enzymes and antioxidant capacity.
To date, there are little data on serum NT-Tyr levels in CVD. Shishehbor et al. [ 14] revealed significantly increased levels of NT-Tyr in patients with CAD ($$n = 100$$, median 9.1 μmol/mol, $p \leq 0.001$) compared to healthy controls ($$n = 108$$, median 5.2 μmol/mol, $p \leq 0.001$). Ferlazzo et al. did not find a difference in plasma NT-Tyr levels between CHF patients and healthy people [25]. In our previous work, we found that dityrosine concentration was significantly higher in CHF patients compared to healthy individuals ($$n = 67$$, average 1.54 (0.48) relative units of fluorescence vs. $$n = 31$$, average 1.27 (0.53) relative units of fluorescence, $p \leq 0$,05) and increased with increasing serum hypochlorous acid concentration [26]. Grzegorz et al. showed that plasma NT-Tyr concentration was higher in 60 year old morbidly obese people than in obese 20–39 year old individuals [27]. Despite the difference in age between the groups according to the LV geometry, we did not find any differences in the NT-Tyr concentration between our CHF groups.
The concentration of MDA (a lipid oxidation marker) and PC (a protein oxidation mas) in serum has been shown to be higher, and the serum TAC concentration lower, in patients on hemodialysis with LV hypertrophy ($$n = 92$$) compared to normal LV geometry (NG, $$n = 12$$) [20]. MDA was found to be higher in cLVH ($$n = 33$$) compared to eLVH ($$n = 45$$), and PC was found to be higher in eLVH compared to cLVH. TAC was significantly lower in the eLVH, cLVH, and CR ($$n = 14$$; averages 2.23 (0.42), 2.38 (0.28), and 2.38 (0.2)) group compared to NG patients (2.90 (0.32); $p \leq 0.001$) [20]. According to these data, the authors suggest that oxidative damage of proteins is more important for the pathogenesis of eLVH, while oxidative damage of lipids could be more important to cLVH. However, our findings in the CHF groups do not agree with the findings of Zorica et al. in hemodialysis patients with LV hypertrophy.
Radovanovic et al. investigated lipid (8-epi-prostaglandin F2α, 8-epi-PGF2α, and MDA) and protein (protein thiol group group (P-SH)) oxidative stress markers in patients with different severities of ischaemic heart failure based on NYHA class and a control group. The authors found that MDA was significantly higher in NYHA class III and IV [28]. The levels of 8-epi-PGF2α concentration in 24 h urine samples were found to be significantly higher in patients with NYHA classes III and IV than in healthy subjects and patients with NYHA I and II. Significant differences in 8-epi-PGF2α excretion were also found between NYHA classes III and IV ($p \leq 0.01$), with the rise more pronounced in NYHA class IV patients. The P-SH content was significantly lower in patients with NYHA III and IV classes compared to controls [28]. Therefore, the results of MDA, 8-epi-PGF2α and P-SH could show the importance of lipid and protein oxidation in the worsening of LV function. In another study, Radovanovic et al. found a significant increase in plasma reactive carbonyl derivatives (RCD), indicating protein oxidation, in all groups of CHF patients compared to the control group [29]. P-SH levels were significantly lower in NYHA IV patients ($$n = 10$$) compared to controls ($$n = 69$$) and NYHA I/II ($$n = 11$$/71) in this study. As the functional class increased, the MDA increased steadily. The enzyme of the antioxidant system, glutathione peroxidase (GPX) activity in plasma from patients with severe CHF (NYHA III/IV class) was significantly lower compared to NYHA I/II, as well as in healthy individuals. Another enzyme of the antioxidant system, superoxide dismutase (SOD), was shown to increase in all groups of patients with CHF compared to healthy controls [29]. The authors concluded that their results showed a relationship between plasma markers of oxidative damage and stage-dependent progression of CHF and that the presence of oxidative products could define oxidative damage in the myocardium undergoing the remodelling process. Carbonyl stress, they stated, could be implicated in the LV remodelling. However, our findings do not confirm any differences in oxidative/antioxidant markers between the groups, according to both the LV parameters and the LV function.
Šaric et al. investigated markers of protein oxidation, including advanced oxidation protein products (AOPP) and P-SH concentrations in serum. They found that these markers were higher in patients with CHF ($$n = 81$$) compared to healthy subjects ($$n = 68$$) (AOPP averages were 98.52 (26.52) µmol/mg and 49.83 (22.34) µmol/mg, respectively, $p \leq 0.001$) [30]; P-SH–295.24 (94.87) µmol/L and 215.24 (94.01) µmol/L). Markers of lipid peroxidation, reactive thiobarbituric acid substances (TBARS), were also found to be higher in the CHF group compared to healthy controls (TBARS averages were 19.47 (7.37) µmol/L and 17.06 (9.56) µmol/L, respectively, $p \leq 0.005$). Consequently, the concentration of TBARS was higher in cLVH compared to NG (TBARS averages were 23.35 (8.24) µmol/L and 16.91 (8.70) µmol/L, respectively, $p \leq 0.05$) [32x]. These findings are consistent with the results of Zorica et al., who found that MDA was higher in patients with hemodialysed cLVH [20]. Catalase activity did not differ between CHF and controls in this study but was higher in eLVH compared to cLVH (catalase averages were 53.03 (20.40) U/L and 74.05 (31.29) U/L, respectively, $p \leq 0.05$) [30]. GSH activity was significantly lower in the NYHA III/IV class (with $$n = 28$$ and $$n = 10$$, respectively) compared to patients with asymptomatic CHF (NYHA I with $$n = 11$$ and NYHA II with $$n = 71$$), as well as to healthy people ($$n = 60$$). SOD was found to increase in all CHF groups compared to healthy in another study [29]. Once again, in our CHF group, both protein and lipid oxidation products, as well as catalase activity and TAC, did not differ between the groups according to LV geometry or function.
In summary, the results of oxidative stress/antioxidant markers in CVD are obtained from different groups of patients using different methodologies (including not standardised methods for TBARS, AOPP, P-SH), as well as the small number of cases examined. Despite these differences, most studies have found increased protein and lipid oxidation in CVD patients and deterioration of the activity of enzymes of the antioxidant system. However, it is difficult to compare oxidative stress/antioxidant markers in CHF patients based on changes in the geometry of the LV and function, as we only found a few works related to it, and only one of them analysed CHF patients. Our work revealed no differences in serum concentrations of both oxidant (NT-Tyr, PC, MDA) and antioxidant (TAC and catalase) concentrations in CHF patients’ groups according to LV function and geometry. It should be mentioned that in our work, we used standardised methods and a homogeneous group of patients with CHF. Our results cannot confirm both our hypothesis that in HFpEF, there should be more oxidised proteins and lipids in the blood than in HFrEF, and that the concentration of oxidative stress markers in the serum could differ in the groups of patients according to the geometry of the LV.
## 4.2. Relationship between Oxidative Stress and Lipid Metabolism Markers
Investigation of the relationship between oxidative stress and markers of lipid metabolism showed interesting findings. Significant positive associations were found between MDA concentration and total cholesterol (rs = 0.337, $$p \leq 0.008$$), LDL cholesterol (rs = 0.295, $$p \leq 0.022$$), and non-HDL cholesterol (rs = 0.301, $$p \leq 0.019$$) levels, demonstrating that higher cholesterol concentration are associated with higher lipid peroxidation levels.
In our study group, NT-Tyr concentration negatively correlated with HDL cholesterol concentration (rs = −0.285, $$p \leq 0.027$$) and positively correlated with oxHDL (rs = 0.278, $$p \leq 0.0314$$). This finding could suggest that HDL is important for inhibiting protein oxidation.
Previous works in rats and apoB-100 transgenic mice have shown that hypercholesterolemia increases nitro-oxidative stress leading to myocardial function deterioration [31]. The same authors found in their other works that in patients with CAD ($$n = 36$$), the concentration of NT-Tyr in the blood was positively correlated with triglyceride (TAG) $r = 0.47$; $p \leq 0.05$), total ($r = 0.58$; $p \leq 0.01$), and LDL cholesterol levels ($r = 0.45$, $p \leq 0.05$), and negatively with HDL cholesterol (r = −0.46; $p \leq 0.05$) [32]. It is partially in agreement with our study, where in CHF patients, we found that NT-Tyr was negatively correlated with HDL and positively correlated with oxHDL cholesterol concentrations. The results, which are in agreement with those of other studies, could lead to the conclusion that the concentration of lipids in the blood may be related to the oxidation of proteins there. More detailed studies are needed to clarify this relationship.
## 4.3. Correlations between Lipid Metabolism Markers and LV Parameters
Some correlations were found in the CHF patients’ groups between lipid metabolism and LV markers. LVEDD, LVEDV, and LVESV were negatively correlated with HDL cholesterol concentration (r = −0.256, $p \leq 0.05$, r = −0.935, $p \leq 0.0001$, r = –0.906, $p \leq 0.00001$, respectively). IVST and LVWT were positively correlated with TAG concentration ($r = 0.346$, $p \leq 0.01$; $r = 0.329$, $p \leq 0.05$, respectively).
There are only a few publications on the relationship between lipid metabolism and LV parameters. Bencsikwith et al. did not observe any significant correlation between serum cholesterol and TAG levels with LVEF in the coronary artery disease group ($$n = 36$$) [32]. Wang et al. [ 33] described a positive correlation of LVEF with serum HDL cholesterol ($r = 0.49$, $p \leq 0.0001$) in patients with angina ($$n = 114$$). Dabas et al. reported no correlation between LV parameters and lipids in type one diabetes ($$n = 30$$) [34]. Al-Daydamony et al. found a significant positive correlation between TAG levels and LVMI in patients with metabolic syndrome [35].
The pathophysiological mechanism of these results cannot be explained simply. Hypercholesterolemia could adversely influence LV systolic function through its atherogenic effect on the restriction of coronary circulation. HDL cholesterol has been shown to reduce the risk of CAD at any concentration of LDL cholesterol [36] and remains a risk factor even for people with low serum total cholesterol and TAG concentrations [37]. This could explain why HDL cholesterol is more significant for LVEDD, LVEDV and LVESV than LDL in our study. Additionally, some studies revealed that lipids can accumulate directly around myocytes and act through other pathways. VLDL were presented to promote aldosterone overproduction that may interfere with LV remodelling [38]. Increased plasma aldosterone concentration was shown to be associated with increased TAG and decreased HDL cholesterol concentration in people with metabolic syndrome [39]. We did not measure both VLDL and aldosterone levels, so we cannot evaluate the potential pathway activated by VLDL that leads to overproduction of aldosterone and the development of LVH [38]. It should be mentioned that some research has reported the possibility of aldosterone to trigger activation of nicotinamide riboside kinase (nRK$\frac{1}{2}$) and p38 mitogen-activated protein kinase (MAPK), which have been involved in the signal transduction pathway associated with cardiac hypertrophy [40]. VLDLs are known to be a major transporter of TAGs, making up about $85\%$ of their weight.
In summary, it could be stated that LV geometry could be related to lipid metabolism in patients with CHF, but future investigations are needed to reveal the possible TAG and HDL action pathways in the myocardium.
## 4.4. Correlations between Oxidative/Antioxidative and LV Markers
PC was found to be significantly correlated with LVEDD and LVEDV ($p \leq 0.001$ and $p \leq 0.05$, respectively), as well as TAC negatively correlated with both cLVH and eLVH phenotypes ($p \leq 0.05$ in both cases) in hemodialysis patients ($$n = 104$$) [20]. A weak positive correlation ($r = 0.329$; $p \leq 0.05$) was found between the levels of the P-SH group and the LVEF ($$n = 73$$) [28]. A significant association was found between plasma reactive carbonyl derivative levels (RCD) and the degree of LV remodelling (LVEDV and LVESV) only in patients with symptomatic CHF ($r = 0.469$; $p \leq 0.008$ and $r = 0.452$; $p \leq 0.011$, respectively) in another study [29]. Zarica et al. discussed that associations between ROS and LV indices prove that protein damage takes place in structural changes of the myocardium. Oxidized proteins and advanced glycation end products (AGE) has been stated as structural homology, and thereby oxidized proteins may serve as ligands for AGE receptors (RAGE) [41]. RAGE signalling activating the TGF-β pathway [42] activates cardiac remodelling [43].
The most important protein groups have been presented according to their functions related to nitrative modifications in heart tissue cells [44]. They involve proteins of the Tricarboxylic acid cycle ($23\%$), lipid metabolism ($9\%$), apoptotic process ($9\%$), muscle contraction ($9\%$), ATP biosynthetic process ($9\%$) and others. The nitration of tyrosine residues in proteins was shown to inhibit protein catalytic activity [9] and damage the energy metabolism pathway [45] resulting in damage to heart function [46]. It was revealed that one of the enzymes of the intracellular antioxidant system, catalase, protects mouse hearts against cardiac remodelling by suppressing the intracellular NF-ĸB signalling pathway associated with protein nitration [44]. Additionally, Zorica et al. summarised that ROS entering the cell triggers the NF-kB pathway, resulting in increased cyclooxygenase 2 synthesis leading to cardiovascular inflammation and remodelling [47].
MDA was found to be significantly correlated with LVEDD and LVEDV ($p \leq 0.001$ and $p \leq 0.05$, respectively) in hemodialysed patients [20]. MDA and 8-epi-PGF2α significantly correlated with LVEF (r = –0.476; $p \leq 0.001$ and r= –0.787; $p \leq 0.001$, respectively) in Radovanovic’s CHF study [28]. Both results show a clear relationship between the level of oxidative lipids and the severity of myocardial dysfunction [28]. However, no significant correlation was found between antioxidant enzyme activities and ventricular remodelling indices in the research by Radovanovic et al. [ 29] and in ours. Unfortunately, in our CHF patients’ group, we did not find any correlation between oxidative stress and LV geometry or function markers. The reason for the discrepancies obtained may be the too small number of cases in our study or the different groups of patients investigated.
## 4.5. Limitations of the Study
Our work has some limitations. Because the study was pilot, we chose to investigate a small number of cases. We measured only a few markers of oxidative damage of proteins (NT-Tyr, dityrosine, protein carbonyl), lipids (MDA, oxHDL), and antioxidative (TPA, catalase). Investigation of 8-epi-PGF2α, P-SH, RCD concentrations, and antioxidant enzymes glutathione peroxidase and superoxide dismutase activity should be useful for a more complete picture. Measurements of serum aldosterone and TNF-α concentration would also have been useful. We did not evaluate diastolic function (mitral inflow E- and A-wave velocities). However, this is the first study investigating possible relationships in the pathogenesis of LV remodelling and oxidative/antioxidant markers. Future studies with a large sample size and involving markers of endocrine (aldosterone)-inflammation (TNF-α) markers in CHF developed due to ischaemic heart disease are required to explore the relationship.
## 5. Conclusions
Our work revealed no difference in serum concentrations of oxidant markers (NT-Tyr, PC, MDA) and antioxidant markers (TAC and catalase) in CHF patients’ groups according to LV function and geometry. While the geometry of the LV could be related to lipid metabolism in CHF patients, future investigations are needed to reveal the pathways through which TAG and HDL affect cardiomyocytes. Additionally, we did not find any correlation between oxidative/antioxidant markers and LV markers in CHF patients. Our hypothesis that in HFpEF there should be more oxidised proteins and lipids in the blood than in HFrEF patients, and that the concentration of oxidative stress markers in serum could differ in the groups of patients according to the geometry of the LV cannot be confirmed.
## References
1. Glezeva N., Baugh J.A.. **Role of inflammation in the pathogenesis of heart failure with preserved ejection fraction and its potential as a therapeutic target**. *Heart Fail. Rev.* (2014) **19** 681-694. DOI: 10.1007/s10741-013-9405-8
2. James S., Barton D., O’Connell E., Voon V., Murtagh G., Watson C., Murphy T., Prendiville B., Brennan D., Hensey M.. **Life expectancy for community-based patients with heart failure from time of diagnosis**. *Int. J. Cardiol.* (2015) **178** 268-274. DOI: 10.1016/j.ijcard.2014.09.131
3. Tsutsui H., Kinugawa S., Matsushima S.. **Oxidative stress and heart failure**. *Am. J. Physiol. Heart Circ. Physiol.* (2011) **301** H2181-H2190. DOI: 10.1152/ajpheart.00554.2011
4. Bertero E., Maack C.. **Calcium Signaling and Reactive Oxygen Species in Mitochondria**. *Circ. Res.* (2018) **122** 1460-1478. DOI: 10.1161/CIRCRESAHA.118.310082
5. Sorescu D., Griendling K., Sorescu D., Griendling K.. **Reactive Oxygen Species, Mitochondria, and NAD(P)H Oxidases in the Development and Progression of Heart Failure**. *Congest. Heart Fail.* (2002) **8** 132-140. DOI: 10.1111/j.1527-5299.2002.00717.x
6. Takano H., Hasegawa H., Nagai T., Komuro I.. **Implication of cardiac remodeling in heart failure: Mechanisms and therapeutic strategies**. *Intern. Med.* (2003) **42** 465-469. DOI: 10.2169/internalmedicine.42.465
7. Martins D., Garcia L.R., Queiroz D.A.R., Lazzarin T., Tonon C.R., Balin P.d.S., Polegato B.F., de Paiva S.A.R., Azevedo P.S., Minicucci M.F.. **Oxidative Stress as a Therapeutic Target of Cardiac Remodeling**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11122371
8. Senoner T., Dichtl W.. **Oxidative Stress in Cardiovascular Diseases: Still a Therapeutic Target?**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11092090
9. Mongirdienė A., Skrodenis L., Varoneckaitė L., Mierkytė G., Gerulis J.. **Reactive Oxygen Species Induced Pathways in Heart Failure Pathogenesis and Potential Therapeutic Strategies**. *Biomedicines* (2022) **10**. DOI: 10.3390/biomedicines10030602
10. van der Pol A., van Gilst W.H., Voors A.A., van der Meer P.. **Treating oxidative stress in heart failure: Past, present and future**. *Eur. J. Heart Fail.* (2019) **21** 425-435. DOI: 10.1002/ejhf.1320
11. Hirotani S., Otsu K., Nishida K., Higuchi Y., Morita T., Nakayama H., Yamaguchi O., Mano T., Matsumura Y., Ueno H.. **Involvement of nuclear factor-kappaB and apoptosis signal-regulating kinase 1 in G-protein-coupled receptor agonist-induced cardiomyocyte hypertrophy**. *Circulation* (2002) **105** 509-515. DOI: 10.1161/hc0402.102863
12. Kwon S.H., Pimentel D.R., Remondino A., Sawyer D.B., Colucci W.S.. **H2O2 regulates cardiac myocyte phenotype via concentration-dependent activation of distinct kinase pathways**. *J. Mol. Cell. Cardiol.* (2003) **35** 615-621. DOI: 10.1016/S0022-2828(03)00084-1
13. Pacher P., Szabó C.. **Role of peroxynitrite in the pathogenesis of cardiovascular complications of diabetes**. *Curr. Opin. Pharmacol.* (2006) **6** 136-141. DOI: 10.1016/j.coph.2006.01.001
14. Shishehbor M.H., Aviles R.J., Brennan M.L., Fu X., Goormastic M., Pearce G.L., Gokce N., Keaney J.F., Penn M.S., Sprecher D.L.. **Association of nitrotyrosine levels with cardiovascular disease and modulation by statin therapy**. *JAMA* (2013) **289** 1675-1680. DOI: 10.1001/jama.289.13.1675
15. Sun Y.. **Myocardial repair/remodelling following infarction: Roles of local factors**. *Cardiovasc. Res.* (2009) **81** 482-490. DOI: 10.1093/cvr/cvn333
16. Dubois-Deruy E., Peugnet V., Turkieh A., Pinet F.. **Oxidative Stress in Cardiovascular Diseases**. *Antioxidants* (2020) **9**. DOI: 10.3390/antiox9090864
17. Dardi P., Perazza L.R., Couto G.K., Campos G.P., Capettini L.D.S.A., Rossoni L.V.. **Vena cava presents endothelial dysfunction prior to thoracic aorta in heart failure: The pivotal role of nNOS uncoupling/oxidative stress**. *Clin. Sci.* (2021) **135** 2625-2641. DOI: 10.1042/CS20210810
18. Zhang Y., Chen S.-Y., Hsu T., Santella R.M.. **Immunohistochemical detection of malondialdehyde-DNA adducts in human oral mucosa cells**. *Carcinogenesis* (2002) **23** 207-211. DOI: 10.1093/carcin/23.1.207
19. Dennis K.E., Hill S., Rose K.L., Sampson U.K., Hill M.F.. **Augmented cardiac formation of oxidatively -induced carbonylated proteins accompanies the increased functional severity of post—Myocardial infarction heart failure in the setting of type 1 diabetes mellitus**. *Cardiovasc. Pathol.* (2013) **22** 473-480. DOI: 10.1016/j.carpath.2013.03.001
20. Dimitrijevic Z.M., Martinovic S.S.S., Nikolic V.N., Cvetkovic T.P.. **Protein Carbonyl Content Is a Predictive Biomarker of Eccentric Left Ventricular Hypertrophy in Hemodialysis Patients**. *Diagnostics* (2019) **9**. DOI: 10.3390/diagnostics9040202
21. Dickstein K., Cohen-Solal A., Filippatos G., McMurray J.V., Ponikowski P., Poole-Wilson P.A., Stromberg A., van Veldhuisen D.J., Atar D., Hoes A.V.. **ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: The Task Force for the diagnosis and treatment of acute and chronic heart failure 2008 of the European Society of Cardiology**. *Eur. Heart J.* (2008) **29** 2388-2442. DOI: 10.1016/j.ejheart.2008.08.005
22. Sadauskiene I., Liekis A., Bernotiene R., Sulinskiene J., Kasauskas A., Zekonis G.. **The effects of buckwheat leaf and flower extracts on antioxidant status in mouse organs**. *Oxid. Med. Cell. Longev.* (2018) **2018** 6712407. DOI: 10.1155/2018/6712407
23. Lowry O.H., Rosebrough N.J., Farr A.L., Randall R.J.. **Protein measuremen with folinphenol reagent**. *J. Biol. Chem.* (1951) **193** 265-275. DOI: 10.1016/S0021-9258(19)52451-6
24. Khoschsorur G.A., Winklhofer-Roob B.M., Rabl H., Auer T., Peng Z., Schaur R.J.. **Evaluation of a sensitive HPLC method for the determination of malondialdehyde, and application of the method to different biological materials**. *Chromatographia* (2000) **52** 181-184. DOI: 10.1007/BF02490453
25. Ferlazzo N., Currò M., Isola G., Maggio S., Bertuccio M.P., Trovato-Salinaro A., Matarese G., Alibrandi A., Caccamo D., Ientile R.. **Changes in the biomarkers of oxidative/nitrosative stress and endothelial dysfunction are associated with cardiovascular risk in periodontitis patients**. *Curr. Issues Mol. Biol.* (2021) **43** 704-715. DOI: 10.3390/cimb43020051
26. Mongirdienė A., Laukaitienė J., Skipskis V., Kašauskas A.. **The Effect of Oxidant Hypochlorous Acid on Platelet Aggregation and Dityrosine Concentration in Chronic Heart Failure Patients and Healthy Controls**. *Medicina* (2019) **55**. DOI: 10.3390/medicina55050198
27. Jakubiak G.K., Cieślar G., Stanek A.. **Nitrotyrosine, Nitrated Lipoproteins, and Cardiovascular Dysfunction in Patients with Type 2 Diabetes: What Do We Know and What Remains to Be Explained?**. *Antioxidants* (2022) **11**. DOI: 10.3390/antiox11050856
28. Radovanovic S., Krotin M., Simic D.V., Mimic-Oka J., Savic-Radojevic A., Pljesa-Ercegovac M., Matic M., Ninkovic N., Ivanovic B., Simic T.. **Markers of oxidative damage in chronic heart failure: Role in disease progression**. *Redox Rep.* (2008) **13** 109-116. DOI: 10.1179/135100008X259204
29. Radovanovic S., Savic-Radojevic A., Pljesa-Ercegovac M., Djukic T., Suvakov S., Krotin M., Simic D.V., Matic M., Radojicic Z., Pekmezovic T.. **Markers of Oxidative Damage and Antioxidant Enzyme Activities as Predictors of Morbidity and Mortality in Patients With Chronic Heart Failure**. *J. Card. Fail.* (2012) **18** 493-501. DOI: 10.1016/j.cardfail.2012.04.003
30. Šarić S., Cvetković T., Petrović D., Mitić V., Stojanović S., Stoiljković V., Deljanin-Ilić M.. **Correlation between oxidative stress parameters and left ventricular geometry in patients with chronic heart failure**. *Acta Fac. Med. Naissensis* (2020) **37** 241-251. DOI: 10.5937/afmnai2003241S
31. Csont T., Bereczki E., Bencsik P., Fodor G., Görbe A., Zvara Á., Csonka C., Puskás L.G., Sántha M., Ferdinandy P.. **Hypercholesterolemia increases myocardial oxidative and nitrosative stress thereby leading to cardiac dysfunction in apoB-100 transgenic mice**. *Cardiovasc. Res.* (2007) **76** 100-109. DOI: 10.1016/j.cardiores.2007.06.006
32. Bencsik P., Sasi V., Kiss K., Kupai K., Kolossváry M., Maurovich-Horvat P., Csont T., Ungi I., Merkely B., Ferdinandy P.. **Serum lipids and cardiac function correlate with nitrotyrosine and MMP activity in coronary artery disease patients**. *Eur. J. Clin. Investig.* (2015) **45** 692-701. DOI: 10.1111/eci.12458
33. Wang T.-D., Lee C.-M., Wu C.-C., Lee T.-M., Chen W.-J., Chen M.-F., Liau C.-S., Sung F.-C., Lee Y.-T.. **The effects of dyslipidemia on left ventricular systolic function in patients with stable angina pectoris**. *Atherosclerosis* (1999) **146** 117-124. DOI: 10.1016/S0021-9150(99)00108-2
34. Dabas A., Yadav S., Gupta V.K.. **Lipid profile and correlation to cardiac risk factors and cardiovascular function in type 1 adolescent diabetics from a developing country**. *Int. J. Pediatr.* (2014) **2014** 513460. DOI: 10.1155/2014/513460
35. Al-Daydamony M., El-Tahlawi M.. **What Is the Effect of Metabolic Syndrome without Hypertension on Left Ventricular Hypertrophy?**. *Echocardiography* (2016) **33** 1284-1289. DOI: 10.1111/echo.13247
36. Grundy S.M., Goodman D.W., Rifkind B.M., Cleeman J.I.. **The place of HDL in cholesterol management. A perspective from the National Cholesterol Educational Program**. *Arch. Intern. Med.* (1989) **149** 505-510. DOI: 10.1001/archinte.1989.00390030011003
37. Lien W.P., Lai L.P., Shyu K.G., Hwang J.J., Chen J.J., Lei M.H., Cheng J.J., Huang P.J., Tsai K.S.. **Low-serum high-density lipoprotein cholesterol concentration is an important coronary risk factor in Chinese patients with low serum levels of total cholesterol and triglyceride**. *Am. J. Cardiol.* (1996) **77** 1112-1115. DOI: 10.1016/S0002-9149(96)00144-0
38. Tsai Y.Y., Rainey W.E., Bollag W.B.. **Very low-density lipoprotein (VLDL)-induced signals mediating aldosterone production**. *J. Endocrinol.* (2017) **232** R115-R129. DOI: 10.1530/JOE-16-0237
39. Hannich M., Wallaschofski H., Nauck M., Reincke M., Adolf C., Völzke H., Rettig R., Hannemann A.. **Physiological aldosterone concentrations are associated with alterations of lipid metabolism: Observations from the general population**. *Int. J. Endocrinol.* (2018) **2018** 4128174. DOI: 10.1155/2018/4128174
40. Unger T., Li J.. **The role of the renin-angiotensin-aldosterone system in heart failure**. *J. Renin Angiotensin Aldosterone Syst.* (2004) **5** S7-S10. DOI: 10.3317/jraas.2004.024
41. Ramasamy R., Schmidt A.M.. **Receptor for Advanced Glycation End Products (RAGE) and Implications for the Pathophysiology of Heart Failure**. *Curr. Heart Fail. Rep.* (2012) **9** 107-116. DOI: 10.1007/s11897-012-0089-5
42. Yamazaki K.G., Gonzalez E., Zambon A.C.. **Crosstalk Between the Renin–Angiotensin System and the Advance Glycation End Product Axis in the Heart: Role of the Cardiac Fibroblast**. *J. Cardiovasc. Transl. Res.* (2012) **5** 805-813. DOI: 10.1007/s12265-012-9405-4
43. Frangogiannis N.G.. **Regulation of the Inflammatory Response in Cardiac Repair**. *Circ. Res.* (2012) **110** 159-173. DOI: 10.1161/CIRCRESAHA.111.243162
44. Qin F., Lennon-Edwards S., Lancel S., Biolo A., Siwik D.A., Pimentel D.R., Dorn G.W., Kang Y.J., Colucci W.S.. **Cardiac-Specific Overexpression of Catalase Identifies Hydrogen Peroxide-Dependent and -Independent Phases of Myocardial Remodeling and Prevents the Progression to Overt Heart Failure in Gαq-Overexpressing Transgenic Mice**. *Circ. Heart Fail.* (2010) **3** 306-313. DOI: 10.1161/CIRCHEARTFAILURE.109.864785
45. Castro L., Demicheli V., Tórtora V., Radi R.. **Mitochondrial protein tyrosine nitration**. *Free. Radic. Res.* (2010) **45** 37-52. DOI: 10.3109/10715762.2010.516254
46. Cucu I.. **Signaling Pathways in Inflammation and Cardiovascular Diseases: An Update of Therapeutic Strategies**. *Immuno* (2022) **2** 630-650. DOI: 10.3390/immuno2040039
47. Duncan J.G., Finck B.N.. **The PPARalpha-PGC-1alpha axis controls cardiac energy metabolism in healthy and diseased myocardium**. *PPAR Res.* (2008) **2008** 253817. DOI: 10.1155/2008/253817
|
---
title: Geographical Disparities in Esophageal Cancer Incidence and Mortality in the
United States
authors:
- Yeshwanth Vedire
- Navpreet Rana
- Adrienne Groman
- Beas Siromoni
- Sai Yendamuri
- Sarbajit Mukherjee
journal: Healthcare
year: 2023
pmcid: PMC10001323
doi: 10.3390/healthcare11050685
license: CC BY 4.0
---
# Geographical Disparities in Esophageal Cancer Incidence and Mortality in the United States
## Abstract
Background: *Our previous* research on neuroendocrine and gastric cancers has shown that patients living in rural areas have worse outcomes than urban patients. This study aimed to investigate the geographic and sociodemographic disparities in esophageal cancer patients. Methods: We conducted a retrospective study on esophageal cancer patients between 1975 and 2016 using the Surveillance, Epidemiology, and End Results database. Both univariate and multivariable analyses were performed to evaluate overall survival (OS) and disease-specific survival (DSS) between patients residing in rural (RA) and urban (MA) areas. Further, we used the National Cancer Database to understand differences in various quality of care metrics based on residence. Results: $$n = 49$$,421 (RA [$12\%$]; MA [$88\%$]). The incidence and mortality rates were consistently higher during the study period in RA. Patients living in RA were more commonly males ($p \leq 0.001$), Caucasian ($p \leq 0.001$), and had adenocarcinoma ($p \leq 0.001$). Multivariable analysis showed that RA had worse OS (HR = 1.08; $p \leq 0.01$) and DSS (HR = 1.07; $p \leq 0.01$). Quality of care was similar, except RA patients were more likely to be treated at a community hospital ($p \leq 0.001$). Conclusions: Our study identified geographic disparities in esophageal cancer incidence and outcomes despite the similar quality of care. Future research is needed to understand and attenuate such disparities.
## 1. Introduction
Esophageal cancer is the 8th most common cancer globally, with an age-standardized incidence rate (ASR) of 6.3 per 100,000 persons in 2020 [1]. As of 2022, the lifetime risk of developing esophageal cancer is 1 in 125 men and 1 in 417 women for the US population [2]. While the incidence and mortality trends of esophageal cancer in the US are decreasing, the global trends are reportedly increasing [3]. Age, gender, race, socioeconomic status, and geographical location have been reported to play a role in esophageal cancer incidence and mortality [3].
Males, Blacks people, people of lower socioeconomic status, and patients in low-income areas have been reported to be at a higher risk of developing and dying from esophageal cancer [3,4,5]. In contrast, a study in Brazil found an inverse relationship between esophageal cancer incidence and the level of urbanization [6]. A similar study utilizing the North American Association of Central Cancer Registries found no significant difference between overall cancer incidence rates between urban and rural areas. However, esophageal cancer incidence rates were higher in rural areas in the US [7].
A possible explanation for these disparities may be the difference in the quality of care. It has been well documented that Black patients are more likely to be diagnosed at a later stage and not receive timely definitive treatment resulting in poorer survival compared to Asian and White patients [8,9,10]. Other patient factors such as socioeconomic status, insurance status, and distance required to travel for medical care can influence the quality-of-care [11,12]. Interestingly, Clark et al. found that patients at high-volume academic centers had better outcomes than low-volume community centers [12].
Our group has previously used the Surveillance Epidemiology and End Results (SEER) and the National Cancer Database (NCDB) databases to explore trends and disparities in neuroendocrine [13] and gastric cancers [14] between urban and rural populations in the US. We sought to assess if any such disparities exist for esophageal cancer by analyzing data from the SEER and NCDB databases.
## 2.1. Data Source
The data for this retrospective analysis were extracted from the Surveillance, Epidemiology, and End Results Program (SEER) database from 1975 to 2016 and National Cancer Data Base (NCDB) from 2006 to 2017. The SEER database is a National Cancer Institute program that collects cancer-related data from various population-based registries, which cover approximately $47.9\%$ of the US population [15]. The SEER database collects patient demographics, primary tumor site, tumor morphology, stage at diagnosis, course of treatment, insurance status, patient location, vital status, and survival data. The data on cancer rates and mortality are received from the Census Bureau and Nations Center for Health Statistics.
The NCDB is a joint effort by the American College of Surgeons and the American Cancer Society to collect data from hospital cancer registries to evaluate cancer trends and treatment patterns [16]. The NCDB captures data from approximately 1500 commission-on cancer-accredited facilities covering nearly $70\%$ of newly diagnosed cancer patients.
## 2.2. Study Population
We used the International Classification of Diseases of Oncology, 3rd Edition (ICD-O-3) diagnostic codes to identify and include all esophageal cancer patients from NCDB and SEER databases for our analysis. Patients from all stages (AJCC 6th and 7th editions) were included in the analysis.
The residential area of patients was classified as urban or rural based on the Rural-Urban Continuum Code available (RUCC) in the NCDB and SEER databases. The RUCC codes were used to categorize geographical localities into metropolitan and non-metropolitan by the Office of Management and Budget based on population. Consistent with our previous research, we categorized counties as urban if they were considered metropolitan (MA) as per RUCC coding (RUCC 1–3) and counties as rural if they were considered non-metropolitan (RA) as per RUCC coding (RUCC 4–9) [14].
## 2.3. Statistical Analysis
The SEER database was utilized to identify and analyze data on patient demographics such as age, race, sex, insurance (insured, uninsured, and unknown), residence (metro [MA], and rural [RA]), marital status, tumor characteristics (histology, grade, and stage), period of diagnosis (1975–1989, 1990–2000, 2001–2010, and 2011–1016) patient vital status, and disease-specific (DSS) overall survival (OS). The incidence and mortality rates from the various time periods were calculated to analyze esophageal cancer trends and evaluate the difference in trends between the rates and survival outcomes in RA and MA populations.
Similar sociodemographic data were collected for patients in the NCDB database, which included patient age, ethnicity (Hispanic and non-Hispanic), race, sex, insurance provider (government, private, and uninsured), county median income (≤USD 50,353 and ≥USD 50,354), residence (MA, and RA), facility at which treated (academic/Integrated, Community, and unknown), distance traveled for care (miles), tumor characteristics (histology, grade, and stage), period of diagnosis (2006–2011 and 2012–2017), OS data, and quality of care indicators such as the number of regional lymph nodes examined (<15 and ≥15), time from diagnosis to start of treatment, adjuvant and neoadjuvant therapy received (yes, and no), chemotherapy received (none, single agent, multiagent, unknown regimen, unknown if chemotherapy was received), surgical margins checked (yes or no), length of inpatient stay, 30-day readmission (planned and unplanned), and 30- and 90-day mortality.
Association between the place of residence and various sociodemographic variables, tumor characteristics, and quality of care metrics were assessed using Wilcoxon Rank Sum (continuous variable) and Chi-square tests (categorical variables). The study’s primary goals were to evaluate incidence and mortality trends in the rural and urban population between 1975 and 2016 and to estimate the OS and DSS using univariate and multivariate Cox proportional modeling. The multivariate model adjusted survival for age, sex, stage, grade, year of diagnosis, insurance status, marital status, race, and area of residence. Using the log-rank test, Kaplan Meir survival analysis was used to compare long-term outcomes between urban and rural areas. Incidence rates were calculated for each residence (MA and RA) and decade using the SEER population database. Data regarding RUCC codes were available for 676 and 2718 cases in the SEER and NCDB databases, respectively. These cases were excluded from all analyses. Statistical significance was indicated by $p \leq 0.05.$ All statistical analyses were performed using SAS, version 9.4, statistical software (SAS Institute Inc., Cary, NC, USA).
## 2.4. Reporting Guidelines
This study is reported as per Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies (Table S1).
## 3.1. SEER Database
A total of 49,421 esophageal cancer patients with RUCC codes were identified in our retrospective analysis of the SEER database between 1975 and 2016. The mean age of the cohort was 65.4 years. Most of the patients were males ($78.6\%$), Caucasian ($75\%$), and had an urban residence ($87.5\%$). A total of 44,048 ($87.9\%$) of the patients died in 41 years follow-up period.
Descriptive characteristics of patients residing in an MA ($87.5\%$) and RA ($12.5\%$) are compared and summarized in Table 1. Patients in RA were more likely to be males (RA vs. MA, $82.1\%$ vs. $78.1\%$; Chi-Square test, $p \leq 0.001$), Caucasian ($86.4\%$ vs. $74.2\%$; $p \leq 0.001$), married ($60.5\%$ vs. $55.4\%$; $p \leq 0.001$), and have adenocarcinoma ($64.2\%$ vs. $56.9\%$; $p \leq 0.001$). Although there was a statistically significant difference in patient insurance status and tumor grade at diagnosis between people residing in RA and MA, data were not known for a significant portion of the population for both characteristics. There was no significant difference in patient age, tumor stage, and the number of patient deaths.
Chi Square and Wilcoxon Rank Sum tests were performed to compare sociodemographic and clinicopathological variables between urban and rural esophageal cancer patients. All significantly different ($p \leq 0.05$) are highlighted in bold.
Esophageal cancer patients residing in an MA had consistently lower age-adjusted incidence rates between 1975 and 2016 than patients with rural residences. The incidence rates in patients from an RA showed an upward trend with a rate of 4.66 cases/100,000 people between 1975 and 1989 to 6.40 cases/100,000 people between 2011 and 2016, whereas the rate in MA was relatively stable with 2.39 cases/100,000 people between 1975 and 1989 to 3.07 cases/100,000 people between 2011 and 2016. Similar to incidence rates, age-adjusted mortality rates were also consistently higher in RA patients. However, unlike incidence rates, mortality rates were relatively stable in both RA and MA patients. Incidence and mortality rates in RA and MA populations are shown in Figure 1 and Table S2.
In addition to comparing the trends between rural and urban populations, we performed attributable risk percentage and population attributable risk percent calculations between these two populations. The attributable risk percentage and the population attributable risk percent for esophageal cancer incidence ranged from 30.20 to 61.90 and from 1.39 to 6.98 between 1975 and 2016, respectively. The table with attributable risk percentage and population attributable risk for every year between 1975 and 2016 is presented in supplementary material as Table S3.
We performed univariate (Table 2) and multivariable survival analyses (Table 3) for OS and DSS for esophageal cancer patients. On univariate analysis for OS, increasing age (HR [$95\%$ CI], 1.01 [1.01–1.01]; Wald $p \leq 0.001$), African American race (1.37 [1.33–1.40]; $p \leq 0.001$), single (1.27 [1.25–1.30]; $p \leq 0.001$), and uninsured (1.43 [1.32–1.54]; $p \leq 0.001$) patients were associated with poor outcomes. In addition, tumors with squamous cell carcinoma histology (1.30 [1.28–1.33]; $p \leq 0.001$), grade III/IV (1.29 [1.27–1.32]; $p \leq 0.001$), and regional (1.32 [1.29–1.36]; $p \leq 0.001$) and distant (2.76 [2.70–2.83]; $p \leq 0.001$) spread were also associated with poorer outcomes. Patient sex and location of residence were not significant predictors of OS. Similar to OS, worse DSS was associated with patient age (1.01 [1.01–1.01]; $p \leq 0.001$), African American race (1.37 [1.33–1.41]; $p \leq 0.001$), single marital status (1.26 [1.23–1.28]; $p \leq 0.001$), uninsured status (1.44 [1.32–1.56]; $p \leq 0.001$), and tumors with squamous cell carcinoma histology (1.28 [1.25–1.31]; $p \leq 0.001$). Higher grade (II/IV) (1.35 [1.32–1.38]; $p \leq 0.001$), regional (1.48 [1.44–1.52]; $p \leq 0.001$), and distant (3.28 [3.19–3.37]; $p \leq 0.001$) stages were found to be poor indicators on univariate analysis (Table 3). The location of the residence was not associated with either OS or DSS (Figures S1 and S2).
Univariate cox proportional modeling for OS and DSS with HR and $95\%$ CI for sociodemographic and clinicopathological variables available in the SEER database are shown. All statistically significant ($p \leq 0.05$) outcomes are highlighted in bold.
For multivariable analysis, age, sex, race, marital status, insurance status, tumor histology, grade and stage, residence, and year of diagnosis were used as covariates. Multivariable analysis confirmed the results of univariate analysis for age, race, marital and insurance status, tumor histology, stage and grade, and year of diagnosis as significant prognostic indicators for both OS and DSS. Additionally, the female sex was found to be associated with better OS (0.87 [0.85–0.90]; $p \leq 0.001$) and DSS (0.90 [0.87–0.92]; $p \leq 0.001$). In contrast to univariate analysis, patients residing in RA had a significantly poorer OS (1.07 [1.04–1.10]; $p \leq 0.001$) and DSS (1.08 [1.04–1.11]; $p \leq 0.001$) on multivariable analysis (Table 4; Figure 2).
Multivariate cox proportional modeling for OS and DSS with HR and $95\%$ CI for sociodemographic and clinicopathological variables available in the SEER database are shown. All variables collected from the SEER database were used as covariates in multivariate model. All statistically significant outcomes ($p \leq 0.05$) are highlighted in bold.
## 3.2. NCDB
To better understand the difference in incidence and mortality rates and survival analyses observed in the SEER data between patients residing in RA and MA, we analyzed the quality-of-care variables available in the NCDB database to try and explain these differences. A total of 72,226 esophageal cancer patients with RUCC codes were identified in our retrospective analysis; 12,930 ($17.9\%$) had a rural residence; and 59,296 ($82.1\%$) of the patients resided in an urban area. Data about treatment facility, time from diagnosis to treatment, type of chemotherapy, sequence of radiation therapy, time from diagnosis to surgery, number of lymph nodes examined, surgical margin status, length of stay, planned or unplanned 30-day readmission, and 30- and 90-day mortality were evaluated as a measure of the quality of care from NCDB for patients diagnosed between 2006 and 2017.
We saw a statistically significant difference between RA and MA patients for most of the quality-of-care variables, as shown in Table 4. However, a clinically significant difference was found only for county median income, type of insurance, distance traveled for treatment, and type of treatment facility. Patients in RA were more likely to live in counties with a median income ≤ USD 50,353 ($72.5\%$ vs. $36.9\%$; $p \leq 0.001$) and were insured by a government entity ($61.6\%$ vs. $57.2\%$; $p \leq 0.001$). Furthermore, they traveled further to receive care (Mean in miles [Std. Deviation], 67.1 [130.1] vs. 27.0 [107.4]; Wilcoxon rank Sum test $p \leq 0.001$) and received care at a community facility ($57.3\%$ vs. $43.9\%$; $p \leq 0.001$).
Chi Square and Wilcoxon Rank Sum tests were performed to compare quality of care variables between urban and rural esophageal cancer patients. All significantly different ($p \leq 0.05$) are highlighted in bold.
## 4. Discussion
In this population-based retrospective analysis of the SEER and NCDB databases, we found that esophageal cancer incidence and mortality rates steadily increased from 1975 to 2016 in both rural and urban areas. Over this period, patients residing in RA consistently had higher incidence and mortality rates. Interestingly, DSS and OS were not associated with residence on univariate analysis. However, on multivariable analysis for DSS and OS, RA patients had an HR of 1.08 (1.04–1.11) and 1.07 (1.04–1.10), respectively. This suggested that other variables and factors may contribute to the differences in survival. To possibly explore these factors, we analyzed differences in variables that reflected the quality of care between RA and MA patients and found that RA patients received a similar quality and type of treatment as MA patients. This suggests that a combination of factors may explain these discrepancies.
Our study shows that the age adjusted incidence and mortality rates in both urban and rural populations increased consistently between 1975 and 2016; this is in contrast to the study performed by Ulhenhopp et al. This study shows a downward trend for both the incidence and mortality rates between a similar time period using the SEER database. A possible explanation for this could be that our study included patients in SEER and NCDB for whom RUCC codes were available.
We found that patients residing in RA were more likely to be males. Sociodemographic factors have been reported to play a significant role in esophageal cancer incidence, treatment, and survival [17]. Studies have reported a male-to-female incidence ratio of 9:1 for esophageal adenocarcinoma [18,19] and a higher incidence of high-grade disease in males [20]. Differences in hormonal levels of estrogen and insulin, growth factors such as IGF-1, and inflammatory mediators have been proposed as possible explanations for these differences in esophageal and other cancer incidence and survival [18,21].
A study describing costs of care at various stages of treatment for different cancers reported that an initial and end-of-life care in esophageal cancer patients was USD 20,433 and USD 18,760, respectively, one of the highest across various cancers [22]. A study examining colorectal, lung, cervical, and breast cancer trends in the US found that uninsured patients with decreased or no physician contact were less likely to undergo age-appropriate screening for cancer [23]. While insured patients showed better outcomes than uninsured patients, insurance type is also a significant predictor of survival [24]. In our analysis, we found that RA patients were more likely to have a lower income and more likely to be either uninsured or insured by a government agency which could explain the worse survival in the rural population.
Quality of care disparities can explain the differences observed across socioeconomic strata. Patients having a lower socioeconomic status are more likely to be victims of these disparities and have poorer outcomes [25]. These disparities may stem from decreased availability of high-quality care or increased difficulty accessing such care. In our study, we found that patients residing in RA were more likely to travel farther and receive care at a community center than their MA counterparts who received care at integrated academic institutions. Although RA patients were more likely to be treated at community centers, they had a similar 30-day unplanned readmission and 30-day mortality as urban patients, but 90-day mortality was higher. This observation was similar to the study reported by Boffa et al. They found that patients treated at affiliate hospitals had better surgical margins, a similar 30-day mortality rate, and a higher 90-day mortality rate [26]. These results are hypothesis generating that immediate peri-operative care always do not translate into long term outcomes in esophageal cancer. Another advantage commonly stated with surgical treatment at academic centers is the improved mortality rates with increased annual hospital and surgeon volumes, as seen in a meta-analysis by Brusselaers et al. [ 27].
The Leapfrog Group, an advocacy organization, suggested a minimum hospital volume and surgeon volume of 20 and seven, respectively, for esophagectomies [28]. While adopting such standards might not decrease the average cost of an esophagectomy, higher hospital and surgeon volumes have decreased complications and length of stay, which are the biggest drivers of cost [12,29,30]. Although no federal mandate exists in the US towards regionalization, there has been a $12.4\%$ decline in the number of centers offering esophagectomy between 2004 and 2012 [31]. This consolidation of esophagectomy centers was associated with fewer patients treated at low-volume centers, improved 90-day mortality rate, lymph node harvest, and decreased length of stay and positive margin rate. While regionalization brings improved outcomes and decreased medical costs, robust structures and strategies must be implemented to decrease the risk of further marginalizing socioeconomically disadvantaged sections of society from accessing quality care.
To our knowledge this is the first population-based study investigating the disparities in incidence and mortality trends of esophageal cancer between RA and MA populations using national databases in the US. We also evaluated how sociodemographic variables impact patients’ overall and disease-specific survival. Additionally, we used the NCDB database to identify differences in quality-of-care metrics, which might explain the difference in survival observed between the two populations. However, our study has limitations, including missing and unknown data, most notably for the stage, grade, insurance status and treatment specifics, and positive margin rate. Secondly, selection and misclassification bias may have impacted the study, given its retrospective nature. Although we used previously reported definitions for rural and urban areas, the differing definitions of rurality may cause a misclassification bias [13,14].
## 5. Conclusions
Our SEER-based analysis found significant sociodemographic differences between esophageal cancer patients in RA vs. MA. We found that despite the advances in diagnostic and treatment techniques, the incidence and mortality rates increased between 1975 and 2016. Additionally, the rate of increase and the absolute rates were higher in RA consistently over this period. Multivariable survival analysis showed significantly poor overall and disease-specific survival in RA patients. Although quality of care metrics were similar between the two populations, a larger proportion of the population being males, lower median income, and socioeconomic status, difficulty accessing care, and treatment at community centers amongst rural patients could be some of the possible explanations for the observed disparities in incidence, mortality rates, and survival between the two populations in the US. Our results are consistent with similar studies in other countries and studies in the US evaluating other cancers [4,5,6,13,14]. Our findings suggest future research with more robust datasets are required to understand the underpinnings of the observed disparities. This understanding can be used to develop tailored healthcare policies needed to improve the quality of care for all esophageal cancer patients in the US.
## References
1. **World Cancer Research Fund Oesophageal Cancer Statistics**
2. **American Cancer Society Key Statistics for Esophageal Cancer**
3. Uhlenhopp D.J., Then E.O., Sunkara T., Gaduputi V.. **Epidemiology of esophageal cancer: Update in global trends, etiology and risk factors**. *Clin. J. Gastroenterol.* (2020.0) **13** 1010-1021. DOI: 10.1007/s12328-020-01237-x
4. Kou K., Baade P.D., Gatton M., Cramb S.M., Sun J., Lu Z., Fu Z., Chu J., Xu A., Guo X.. **Individual- and Area-Level Socioeconomic Inequalities in Esophageal Cancer Survival in Shandong Province, China: A Multilevel Analysis**. *Cancer Epidemiol. Biomark. Prev.* (2019.0) **28** 1427-1434. DOI: 10.1158/1055-9965.EPI-19-0203
5. Li B., Liu Y., Peng J., Sun C., Rang W.. **Trends of Esophageal Cancer Incidence and Mortality and Its Influencing Factors in China**. *Risk Manag. Healthc. Policy* (2021.0) **14** 4809-4821. DOI: 10.2147/RMHP.S312790
6. Amorim C.A., De Souza L.P., Moreira J.P., Luiz R.R., De V.C.A.J., De Souza H.S.P.. **Geographic distribution and time trends of esophageal cancer in Brazil from 2005 to 2015**. *Mol. Clin. Oncol.* (2019.0) **10** 631-638. DOI: 10.3892/mco.2019.1842
7. Zahnd W.E., James A.S., Jenkins W.D., Izadi S.R., Fogleman A.J., Steward D.E., Colditz G.A., Brard L.. **Rural-Urban Differences in Cancer Incidence and Trends in the United States**. *Cancer Epidemiol. Biomark. Prev.* (2018.0) **27** 1265-1274. DOI: 10.1158/1055-9965.EPI-17-0430
8. Zhang C., Zhang C., Wang Q., Li Z., Lin J., Wang H.. **Differences in Stage of Cancer at Diagnosis, Treatment, and Survival by Race and Ethnicity Among Leading Cancer Types**. *JAMA Netw. Open* (2020.0) **3** e202950. DOI: 10.1001/jamanetworkopen.2020.2950
9. Abbas A., Madison Hyer J., Pawlik T.M.. **Race/Ethnicity and County-Level Social Vulnerability Impact Hospice Utilization Among Patients Undergoing Cancer Surgery**. *Ann. Surg. Oncol.* (2021.0) **28** 1918-1926. DOI: 10.1245/s10434-020-09227-6
10. Salehi O., Vega E.A., Lathan C., James D., Kozyreva O., Alarcon S.V., Kutlu O.C., Herrick B., Conrad C.. **Race, Age, Gender, and Insurance Status: A Comparative Analysis of Access to and Quality of Gastrointestinal Cancer Care**. *J. Gastrointest. Surg.* (2021.0) **25** 2152-2162. DOI: 10.1007/s11605-021-05038-6
11. Okereke I.C., Westra J., Tyler D., Klimberg S., Jupiter D., Venkatesan R., Brooks K., Kuo Y.F.. **Disparities in esophageal cancer care based on race: A National Cancer Database analysis**. *Dis. Esophagus* (2022.0) **35** doab083. DOI: 10.1093/dote/doab083
12. Clark J.M., Boffa D.J., Meguid R.A., Brown L.M., Cooke D.T.. **Regionalization of esophagectomy: Where are we now?**. *J. Thorac. Thorac. Dis.* (2019.0) **11** S1633-S1642. DOI: 10.21037/jtd.2019.07.88
13. Gosain R., Ball S., Rana N., Groman A., Gage-Bouchard E., Dasari A., Mukherjee S.. **Geographic and demographic features of neuroendocrine tumors in the United States of America: A population-based study**. *Cancer* (2020.0) **126** 792-799. DOI: 10.1002/cncr.32607
14. Rana N., Gosain R., Lemini R., Wang C., Gabriel E., Mohammed T., Siromoni B., Mukherjee S.. **Socio-Demographic Disparities in Gastric Adenocarcinoma: A Population-Based Study**. *Cancers* (2020.0) **12**. DOI: 10.3390/cancers12010157
15. **About the SEER Program**
16. 16.
American Cancer Society
American College of Surgeons
Available online: https://www.facs.org/quality-programs/cancer-programs/national-cancer-database/(accessed on 10 November 2022)
17. Delman A.M., Ammann A.M., Turner K.M., Vaysburg D.M., Van Haren R.M.. **A narrative review of socioeconomic disparities in the treatment of esophageal cancer**. *J. Thorac. Dis.* (2021.0) **13** 3801-3808. DOI: 10.21037/jtd-20-3095
18. Argyrakopoulou G., Dalamaga M., Spyrou N., Kokkinos A.. **Gender Differences in Obesity-Related Cancers**. *Curr. Obes. Rep.* (2021.0) **10** 100-115. DOI: 10.1007/s13679-021-00426-0
19. Nobel T.B., Livschitz J., Eljalby M., Janjigian Y.Y., Bains M.S., Adusumilli P.S., Jones D.R., Molena D.. **Unique Considerations for Females Undergoing Esophagectomy**. *Ann. Surg.* (2020.0) **272** 113-117. DOI: 10.1097/SLA.0000000000003202
20. Allen J.E., Desai M., Roumans C.A.M., Vennalaganti S., Vennalaganti P., Bansal A., Falk G., Lieberman D., Sampliner R., Thota P.. **Low Risk of Progression of Barrett’s Esophagus to Neoplasia in Women**. *J. Clin. Gastroenterol.* (2021.0) **55** 321-326. DOI: 10.1097/MCG.0000000000001362
21. Heo J.W., Kim S.E., Sung M.K.. **Sex Differences in the Incidence of Obesity-Related Gastrointestinal Cancer**. *Int. J. Mol. Sci.* (2021.0) **22**. DOI: 10.3390/ijms22031253
22. Kaye D.R., Min H.S., Herrel L.A., Dupree J.M., Ellimoottil C., Miller D.C.. **Costs of Cancer Care Across the Disease Continuum**. *Oncologist* (2018.0) **23** 798-805. DOI: 10.1634/theoncologist.2017-0481
23. Hall I.J., Tangka F.K.L., Sabatino S.A., Thompson T.D., Graubard B.I., Breen N.. **Patterns and Trends in Cancer Screening in the United States**. *Prev. Chronic. Dis.* (2018.0) **15** E97. DOI: 10.5888/pcd15.170465
24. Osazuwa-Peters N., Simpson M.C., Rohde R.L., Challapalli S.D., Massa S.T., Adjei Boakye E.. **Differences in Sociodemographic Correlates of Human Papillomavirus-Associated Cancer Survival in the United States**. *Cancer Control* (2021.0) **28** 10732748211041894. DOI: 10.1177/10732748211041894
25. Chen K.A., Strassle P.D., Meyers M.O.. **Socioeconomic factors in timing of esophagectomy and association with outcomes**. *J. Surg. Oncol.* (2021.0) **124** 1014-1021. DOI: 10.1002/jso.26606
26. Boffa D.J., Mallin K., Herrin J., Resio B., Salazar M.C., Palis B., Facktor M., McCabe R., Nelson H., Shulman L.N.. **Survival After Cancer Treatment at Top-Ranked US Cancer Hospitals vs. Affiliates of Top-Ranked Cancer Hospitals**. *JAMA Netw. Open* (2020.0) **3** e203942. DOI: 10.1001/jamanetworkopen.2020.3942
27. Brusselaers N., Mattsson F., Lagergren J.. **Hospital and surgeon volume in relation to long-term survival after oesophagectomy: Systematic review and meta-analysis**. *Gut* (2014.0) **63** 1393-1400. DOI: 10.1136/gutjnl-2013-306074
28. Group T.L.. **Complex Adult and Pediatric Surgery**
29. Jiang R., Liu Y., Ward K.C., Force S.D., Pickens A., Sancheti M.S., Javidfar J., Fernandez F.G., Khullar O.V.. **Excess Cost and Predictive Factors of Esophagectomy Complications in the SEER-Medicare Database**. *Ann. Thorac. Surg.* (2018.0) **106** 1484-1491. DOI: 10.1016/j.athoracsur.2018.05.062
30. Fischer C., Lingsma H., Klazinga N., Hardwick R., Cromwell D., Steyerberg E., Groene O.. **Volume-outcome revisited: The effect of hospital and surgeon volumes on multiple outcome measures in oesophago-gastric cancer surgery**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0183955
31. Arnold B.N., Chiu A.S., Hoag J.R., Kim C.H., Salazar M.C., Blasberg J.D., Boffa D.J.. **Spontaneous regionalization of esophageal cancer surgery: An analysis of the National Cancer Database**. *J. Thorac. Dis.* (2018.0) **10** 1721-1731. DOI: 10.21037/jtd.2018.02.12
|
---
title: Differential Effects of Nonsteroidal Anti-Inflammatory Drugs in an In Vitro
Model of Human Leaky Gut
authors:
- Michele d’Angelo
- Laura Brandolini
- Mariano Catanesi
- Vanessa Castelli
- Cristina Giorgio
- Margherita Alfonsetti
- Mara Tomassetti
- Mara Zippoli
- Elisabetta Benedetti
- Maria Candida Cesta
- Sandro Colagioia
- Pasquale Cocchiaro
- Annamaria Cimini
- Marcello Allegretti
journal: Cells
year: 2023
pmcid: PMC10001324
doi: 10.3390/cells12050728
license: CC BY 4.0
---
# Differential Effects of Nonsteroidal Anti-Inflammatory Drugs in an In Vitro Model of Human Leaky Gut
## Abstract
The intestinal barrier is the main contributor to gut homeostasis. Perturbations of the intestinal epithelium or supporting factors can lead to the development of intestinal hyperpermeability, termed “leaky gut”. A leaky gut is characterized by loss of epithelial integrity and reduced function of the gut barrier, and is associated with prolonged use of Non-Steroidal Anti-Inflammatories. The harmful effect of NSAIDs on intestinal and gastric epithelial integrity is considered an adverse effect that is common to all drugs belonging to this class, and it is strictly dependent on NSAID properties to inhibit cyclo-oxygenase enzymes. However, different factors may affect the specific tolerability profile of different members of the same class. The present study aims to compare the effects of distinct classes of NSAIDs, such as ketoprofen (K), Ibuprofen (IBU), and their corresponding lysine (Lys) and, only for ibuprofen, arginine (Arg) salts, using an in vitro model of leaky gut. The results obtained showed inflammatory-induced oxidative stress responses, and related overloads of the ubiquitin-proteasome system (UPS) accompanied by protein oxidation and morphological changes to the intestinal barrier, many of these effects being counteracted by ketoprofen and ketoprofen lysin salt. In addition, this study reports for the first time a specific effect of R-Ketoprofen on the NFkB pathway that sheds new light on previously reported COX-independent effects, and that may account for the observed unexpected protective effect of K on stress-induced damage on the IEB.
## 1. Introduction
As evidence accumulates linking the health of the gut with a variety of acute and chronic illnesses, understanding the mechanisms underlying gut injury and protection is of paramount importance. The intestinal barrier is the main contributor to gut homeostasis, modulating the absorption of water, electrolytes, and nutrients from the lumen while restricting the passage of noxious luminal elements and microorganisms [1]. Alterations in the permeability of the intestinal epithelium disrupts gastrointestinal (GI) function, leading to damage with far-reaching consequences for the pathophysiology of not only GI tract disorders, but also autoimmune diseases, among others [2]. The front line of this barrier is preserved by only a single layer of specialized epithelial cells that are connected by tight junction (TJ) proteins. However, various factors support the barrier, among them mucins, antimicrobial molecules, immunoglobulins, and cytokines. Perturbations of the intestinal epithelium or supporting factors can lead to the development of intestinal hyperpermeability, termed “leaky gut” [3]. A leaky gut is characterized by loss of epithelial integrity and reduced function of the gut barrier and is associated with the absorption of pathogens or toxic and inflammatory substances in the bloodstream [3]. Intestinal permeability is an established feature of numerous inflammatory and autoimmune disorders impacting the digestive system, such as inflammatory bowel disease and celiac disease, for which intestinal permeability is considered a symptom, not a cause of disease. Other disorders have been indicated as possible long-term consequences of leaky gut. In fact, toxins from the intestine may release into the bloodstream and cause a chronic low-grade inflammatory response that may represent a risk factor in many diseases, including dysmetabolism, long-term disorders such as arthritis, chronic fatigue syndrome [4], chronic liver diseases [5], diabetes [6], multiple sclerosis [7], and even cognitive disorders [8].
The established causes of increased intestinal permeability include systematic erosion of the intestinal lining. The intestinal lining has numerous layers of defense and can sustain temporary injury because it is constructed to continually repair and replace itself. However, significant and recurring damage associated with chronic drug use, alcohol abuse, or radiation therapy may erode the barrier, leading to deep and penetrating ulcers. Ethanol (EtOH) exposure can affect intestinal permeability by modulating TJ proteins [9] and gut flora, leading to inflammation [10].
A leaky gut has also been associated with prolonged use of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) that may be toxic to the intestinal epithelium, causing erosions, perforations, and longitudinal ulcers in the gut [11,12]. Both acute and chronic ingestion of NSAIDs by healthy volunteers and patients have been reported to promote altered intestinal barrier dysfunction and hypermotility [13].
The harmful effect of NSAIDs on intestinal and gastric epithelial integrity is considered an adverse effect that is common to all drugs belonging to this class, and it is strictly dependent on NSAID properties to inhibit cyclo-oxygenase (COX) enzymes. However, factors such as drug distribution, potency, unrelated drug actions, and salt form can influence the specific tolerability profile of different members of the same class [3,14,15,16]. For instance, we previously compared the effects of ketoprofen L-lysine salt (KLS) and ketoprofen sodium salt in an in vitro model of gastric epithelium stressed with ethanol (EtOH) [17], and demonstrated a specific action of L-lysine (Lys) in counteracting the harmful effect of the drug on the gastric mucosa layers, due to the amino acid ability to scavenge 4-Hydroxynonenal (HNE)-protein adducts and balance oxidative stress levels [17].
The present study aims to compare the effects of distinct classes of NSAIDs, such as ketoprofen (K), Ibuprofen (IBU), and their corresponding lysine (Lys) and, only for IBU, arginine (Arg) salts, in an in vitro model of leaky gut with focus on the inflammatory-induced oxidative stress responses and related consequences on the ubiquitin-proteasome system (UPS), in order to evaluate differential effects in the epithelial damage recovery phase.
## 2.1. Experimental Model: Caco-2 EtOH-Stressed
To perform analyses on an intestinal epithelium in vitro model, the Caco-2 cell line, purchased from American Type Culture Collection (ATCC, HTB-37), was used. Caco-2 are colorectal carcinoma cells. Caco-2 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) containing of $10\%$ fetal calf serum (FCS), 100 U/mL penicillin, 2 mM glutamine (all from Corning, New York, NY, USA), and $1\%$ non-essential amino acid solution (MEM) (Gibco, New York, NY, USA), that was changed every 2 to 3 days. Cells were passaged after partial digestion with $0.25\%$ trypsin-EDTA (Corning, New York, NY, USA) and were seeded at the density of 3 × 105 cells/cm2 (passages 3–9). In vitro experiments were performed for 20 days (20 Day in vitro, DIV) apart from the MTS assay because the metabolism of the control (CTR) cells does not allow for reading of the formazan produced (OVER signal, see paragraph relative to MTS assay for further details). Once the intestinal epithelial model was obtained, cells were exposed to EtOH to induce leaky gut conditions. In this regard, the epithelial model was pre-treated for 24 h with EtOH (concentrations tested include $2\%$, $4\%$, $6\%$, and $8\%$. The $6\%$ concentration was chosen for the subsequent experiments) (Sigma, St. Louis, MO, USA). The medium containing EtOH was replaced with the medium containing NSAIDs, or control (culture medium w/o NSAIDs), and maintained in culture for 72 h. NSAID stock was prepared at the concentration of 50 mM in 1 ml of sterile water (Corning, New York, NY, USA). Regarding IBU and K, 20 µL of NaOH 5N were added. The final concentrations of the tested NSAIDs in the preliminary experiments were 0.25, 0.5, and 1 mM. For the subsequent experiments, we focused on 1 mM.
Different sets of in vitro experiments were performed:CTREtOHEtOH + IBUEtOH + IBU-LysEtOH + IBU-ArgEtOH + KEtOH + KLSEtOH + ArgEtOH + Lys
## 2.2. Activated Macrophages Model
The murine macrophage cell line RAW 264.7 (ATCC TIB-71) was purchased from ATCC, and cells were cultured as indicated by the manufacturer. For the experiment, cells were seeded 10,000/cm2 for 24 h, starved for 4 h (w/o serum), and activated with lipopolysaccharides (LPS 10 ng/mL for 24 h; Sigma Aldrich, St. Louis, MO, USA). Then, activated macrophages were treated with 1 mM of the R or S enantiomer of K or KLS for 24 h to evaluate NFkB translocation (as reported in the Subcellular protein fractionation kit paragraph).
## 2.3. Cell Viability Assay
Cells were seeded 3 × 105 cells/cm2 in a 96 multiwell plate, and after 48 h, cells were exposed to different EtOH concentrations ($2\%$, $4\%$, $6\%$, $8\%$) for 24 h, to find a sub-toxic condition (40–$50\%$ of cell death) avoiding high cellular mortality. Cell viability was determined by using the Cell Titer 96 Aqueous One Solution kit MTS assay (Promega, Madison, WI, USA). Specifically, 20 µL of MTS was added to each well, and incubated for 1 h at 37 °C. the absorbance was read at 492 nm using a microplate reader (Spark, Tecan, CH). Based on the MTS assay results, the concentration of $6\%$ EtOH was chosen for the subsequent experiments. Then, the medium was replaced with 3 different concentrations of the tested NSAIDs (0.25 mM, 0.5 mM, and 1 mM) for 72 h. The results were expressed as the ratio between the absorbance of treated cells and the absorbance of CTR cells and reported as a percentage of viable cells.
## 2.4. Cell Index
Cells were seeded on a 16-well E-plate at the density of 3 × 105 cell/cm2 cultured for 20 days post-confluence and then treated with $6\%$ EtOH (24 h), followed by 3 different concentrations of the tested NSAIDs for 72 h (0.25 mM, 0.5 mM, and 1 mM). The dimensionless parameter cell index (CI) was evaluated using the xCELLIgence system (Roche Applied Science, DE) and used to quantify cell status based on the measured cell-electrode impedance. Normalized cell index at a certain point is obtained by dividing the CI value by the value at a reference time point.
## 2.5. Measurement of Transepithelial Electrical Resistance
Cells were seeded on PET membrane inserts (1.1 cm2 area) at a density of 3 × 105 cells/cm2, maintained for 20 days in a complete medium, and then treated with EtOH $6\%$ (24 h), followed by 3 different concentrations of NSAIDs (0.25 mM, 0.5 mM, and 1 mM) for 72 h. The permeability of the cellular junctions was determined by measuring the transepithelial electrical resistance (TEER) of cell monolayers using a Millicell ERS (Millipore Company, Darmstadt, Germany) employing Ag-AgCl electrodes, following the manufacturer’s protocol. The final values are expressed as Ohm*cm2 based on the following equation: TEER = (R − Rb) × A, where R is the resistance of filter insert with cells, *Rb is* the resistance of the filter alone, and A is the filter’s growth area. For subsequent experiments, the NSAID concentration of 1 mM after EtOH damage was chosen.
## 2.6. Measurement of Interleukin-8 Concentration
The human interleukin-8 (IL-8) ELISA kit (ab46032, Abcam, Cambridge, UK) is designed for the quantitative measurement of IL-8 concentrations in the supernatants. Cells were seeded at the seeding density of 3 × 105 cells/cm2 and maintained in a complete medium in a 100 mm dish for 20 days post-confluence. After 20 days of culture, cells were exposed to EtOH ($6\%$) for 24 h and NSAIDs (1 mM) for 72 h. Then, the media was collected, and a gentle centrifugation was performed. For every condition, 100 µL of each sample or 100 µL of each standard were added into the appropriate well. The plate was then incubated for 1 h at room temperature. After aspirating the liquid from each well, 3 washes with 300 µL of 1X wash Buffer were performed. Then, 50 µL of 1X Biotinylated anti-IL-8 were added to each well and incubated for 1 h at room temperature. After incubation with anti-IL-8, 3 washes were performed. At this point, 100 µL of 1X Streptavidin-HRP solution was added to each well, and the plate was incubated for 30 min at room temperature. After further washes, 100 µL of chromogen 3,3′,5,5′-Tetramethylbenzidine (TMB) substrate solution was added, and the plate was incubated in the dark for 15 min at room temperature. Finally, the reaction was stopped with 100 µL of stop reagent, and the absorbance was read at 450 nm.
## 2.7. Measurement of TNF-α Concentration
A human tumor necrosis factor alpha (TNF-α) ELISA kit (ab181421, Abcam, Cambridge, UK) for the quantitative measurement of TNF-α was used. Cells were plated in a 100 mm dish (seeding density 3 × 105 cells/cm2) in a complete medium and cultured as described above. The supernatants were collected and gently centrifuged to remove cell debris. For every condition, 50 µL of sample or standard and 50 µL of antibody cocktail were added to each well and incubated for 1 h on a plate shaker. After extensive washes, the TMB development solution was added to each well and incubated for 10 min in the dark on a plate shaker; then the reaction was stopped with the addition of 100 µL of stop solution to each well. The optical density was recorded at 450 nm.
## 2.8. Measurement of GST Activity
Glutathione-S-transferase (GST) Assay Kit (ab65326, Abcam, Cambridge, UK) is a colorimetric assay used to detect GST activity in cell lysates in order to quantify GST-tagged fusion proteins. Cells were plated (seeding density 3 × 105 cells/cm2) into a culture-treated dish (100 mm) and cultured as described above. The EtOH $6\%$ was added, and cells were incubated for 24 h, followed by 72 h in the different NSAID treatments upon EtOH removal. Cells were lysed in 100 µL of GST Assay Buffer for each condition and centrifuged at 10,000× g at 4 °C for 15 min. Supernatants were collected. In total, 25 µL of sample for all the conditions plus 25 µL of GST assay buffer was added to each well. For positive control wells, 5 µL of positive control with 45 µL of GST assay buffer was combined. At this point, 5 µL of glutathione was added to each sample and control wells, and 50 µL of the reaction mix, comprised of 49 µL of GST assay buffer and 1 µL of GST substrate solution, was added to each well. The plate was read at 340 nm in optical density.
## 2.9. Western Blotting Analysis and Protein Extraction
Protein concentration was determined with the BCA protein assay kit (Thermo Scientific, Waltham, MA, USA). This assay uses a detergent-compatible formulation based on bicinchoninic acid (BCA) for the colorimetric quantification of protein concentration. Cells were plated (seeding density 3 × 105 cells/cm2) in a complete medium into a 100 mm dish and cultured for 20 days post-confluence. After 20 days, cells were exposed for 24 h to EtOH ($6\%$) and for 72 h to NSAIDs (1 mM), collected and lysed in an ice-cold RIPA buffer (with freshly added protease and phosphatase inhibitors). Lysates were then diluted in sample buffer 4X (Biorad, Hercules, CA, USA). Protein samples (30–50 µg) were run in 8–$12\%$ SDS–polyacrylamide gel and electroblotted onto a polyvinyl difluoride membrane (PVDF; Sigma Aldrich, USA). Nonspecific binding sites were blocked by Every Blocking Buffer (EBB; Biorad, USA) for 5 min. Membranes were then incubated overnight at 4 °C with the following primary antibodies diluted in EBB: anti-NFκB (1:1000, ab32536, Abcam, UK), anti-IKBα (1:1000, ab32518, Abcam, UK), anti-pIKK (1:500, 2697, Cell Signaling, Danvers, MA, USA), anti-PPARγ (1:1000, ab178860, Abcam, UK), anti-4-HNE (1:1000, ab46545, Abcam, UK), anti-GSTA-4 (1:1000, ab134919, Abcam, UK), anti-Ubiquitin (1:500, GTX128826, GeneTex, Irvine, CA, USA), and Ubiquitin K48 (1:500, ab140601, Abcam, USA). As secondary antibodies, peroxidase-conjugated anti-mouse and anti-rabbit (1:20,000; Cod. 115-035-003; Cod.111-035-033; Jackson Immuno-research, Newmarket, UK) were used. Membranes were incubated with luminol (Thermo Scientific, USA) according to the manufacturer’s instructions. Bands were obtained using UVITEC (Cambridge, UK) and analyzed by ImageJ software. The relative densities of the immunoreactive bands were normalized to HRP-conjugated anti-Actin (1:10,000, 8584, Cell Signaling, MA, USA) or anti-GAPDH (1:4000, sc-32233, Santa Cruz Biotechnology, Dallas, TX, USA). Values were given as Relative Units (RU).
## 2.10. Subcellular Protein Fractionation Kit
For the subcellular protein fractionation, kit #78840, Thermo Scientific, USA was used. Caco-2 cells were plated (seeding density 3 × 105 cells/cm2) in a complete medium into a 100 mm dish and maintained for 20 days post-confluence. After 20 days of culture, cells were exposed for 24 h to EtOH ($6\%$) and 72 h to NSAIDs (1 mM).
RAW 264.7 cells were seeded at 10,000 cells/cm2 and activated with LPS. Then, activated macrophages were treated with 1 mM of the R or S enantiomer of K or KLS for 24 h.
For both Caco-2 and RAW 264.7 cells, the procedure of extraction of different components was performed as previously reported [18]. The protein fraction extracts were separated on a $10\%$ SDS–polyacrylamide gel and electroblotted onto PVDF. Nonspecific binding sites were blocked by EBB for 5 min. Membranes were then incubated overnight at 4 °C with anti-NFκB and anti-PPARγ. As secondary antibodies, peroxidase-conjugated anti-rabbit, and anti-mouse were used. Bands were detected using Alliance 4.7 UVITEC (Cambridge, UK) and analyzed by ImageJ software. The relative densities of the immunoreactive bands were normalized to anti-Actin or anti-laminin using ImageJ software. Values were given as RU.
## 2.11. OxyBlot Assay Kit
OxyBlot protein oxidation detection kits (S7150, Millipore Company, Burlington, MA, USA) were used according to the manufacturer’s instructions. Briefly, cells were plated (seeding density 3 × 105 cells/cm2) in a complete medium in a T75 flask and cultured for 20 days post-confluence. After 20 days, cells were exposed for 24 h to EtOH ($6\%$) and for 72 h to NSAIDs (1 mM). OxyBlot assay was then performed as previously reported [19].
## 2.12. Morphological Analysis: Phalloidin iFluor 488 Staining
Cells were seeded on sterile glass coverslips at a density of 3 × 105 cells/cm2 in a complete medium and cultured as described above. Cells were then fixed with $3.7\%$ paraformaldehyde (Sigma, USA) in PBS for 15 min at room temperature and then permeabilized with $0.1\%$ Triton X-100 (Sigma, USA) in PBS for 5 min. After further washes with PBS, phalloidin 1000X (ab176753, Abcam, UK) was diluted to 1X in $1\%$ BSA in PBS, and the cells were incubated for 90 min at room temperature (according to the manufacturer’s instructions). Finally, coverslips were mounted on microscope slides with Vectashield Mounting Medium H2000 with DAPI and cells were observed using a Leica SP5 confocal microscope. For each condition, $$n = 3$$, 5 fields/slide were analyzed. A representative picture is shown.
## 2.13. Morphological Analysis: Immunofluorescence for ZO-1
Cells were seeded on sterile glass coverslips at a density of 3 × 105 cells/cm2 in a complete medium and cultured for 20 days post-confluence. Cells were then treated with $6\%$ EtOH (24 h) and then with 1 mM NSAIDs (72 h). Cells were fixed in $3.7\%$ paraformaldehyde in PBS for 15 min at room temperature, rinsed with PBS, and permeabilized with $0.1\%$ Triton X-100 in PBS for 5 min. After further washes with PBS, cells were incubated for 30 min with BSA $4\%$ in PBS and then incubated overnight at 4 °C with the primary antibody zonulin-1 (ZO-1) (33-910, Thermo Scientific, MA, USA rabbit anti-human, dilution 1:100). Cells were rinsed with PBS, then incubated with secondary antibodies Alexa Fluor-488 (anti-rabbit, dilution 1:2000) and diluted in $4\%$ BSA in PBS. Finally, coverslips were mounted on microscope slides with Vectashield Mounting Medium with DAPI and cells were observed using a Leica SP5 confocal microscope.
## 2.14. Proteosome Trypsin-like and Caspase-like Activity Assay
Trypsin-like and caspase-like activities were determined using AMC-tagged peptide substrates (R&D system, Minneapolis, MN, USA), which release free highly fluorescent 7-amido-4-methyl coumarin (AMC) in the presence of proteasome proteolytic activity. The assay was performed in the presence or absence of bortezomib (Santa Cruz Biotechnology, Dallas, TX, USA), a selective proteasome inhibitor. Cells were seeded in T75 flasks and were exposed to EtOH for 24 h ($6\%$), and then NSAIDS (1 mM for 72 h). Cells were then trypsinized and centrifuged for 6 min at 250× g. Cell pellets were rinsed with cold phosphate buffer saline, transferred into 1.5 mL tubes, then centrifuged for 6 min at 250× g. Then, cell pellets were resuspended in a proteasome lysis buffer and extracted as previously described [20]. Extract samples and AMC standards (1–10 μM) were placed in 96 black well plates with a final volume of 100 μL.
In all sample wells, the fluorescent substrate AMC (final concentration 200 μM, trypsin-like) [Boc-LRR-AMC] (R&D System, MN, USA) and caspase-like [Z-LLE-AMC] (R&D System, MN, USA) was added with or without bortezomib (final concentration 100 μM). To quantify specific proteasome activity at each T (T0 or T120), the fluorescence values of the wells without inhibitors were subtracted from the fluorescence values of the wells with inhibitors. Values of proteasome activities read using a microplate reader (Spark, Tecan) correspond to the difference between fluorescence obtained in the absence of inhibitors and presence of bortezomib and the results were expressed as a percentage of CTR activity.
## 2.15. Proteosome Chymotrypsin-Like Activity Assay
Chymotrypsin activity was evaluated using a specific kit (MAK172, Sigma Aldrich, MO, USA) following the manufacturer’s protocols. Protein crude extracts were obtained and quantified as described in the previous section. Values of proteasome activities read using a microplate reader (Spark, Manneford, CH, Tecan) correspond to the difference between the fluorescence obtained in the absence of an inhibitor and the presence of bortezomib. Results were expressed in percentage of control (CTR).
## 2.16. DCFDA Cellular Reactive Oxygen Species (ROS) Assay Kit
2′–7′-dichlorofluorescein diacetate (DCFDA, ab113851, Abcam, UK) cellular reactive oxygen species (ROS) detection assay kit was used to analyze ROS production in Caco-2 in vitro models for treatments over 48 h following manufacturer’s protocols. Cells were treated after 48 h from seeding in a 96 black well plate in a complete medium w/o phenol red (seeding density 3 × 105 cell/cm2) and then exposed to EtOH ($6\%$) for 24 h and then NSAIDs (1 mM for 72 h). After 72 h of NSAID exposure, cells were washed with 1X buffer and incubated with DCFDA 10 μM for 30 min at 37 °C protected from the light. H2O2 (800 μM) was used as a positive control. ROS production was instantly evaluated by determining the formation of fluorescent dichlorofluorescein at the endpoint using a microplate reader at Ex-485 nm and Em-535 nm. Data were obtained by subtracting blank readings from all measurements.
## 2.17. Crosslink Immunoprecipitation Kit
The Pierce crosslink immunoprecipitation kit (IP, 26147, Thermo Fischer, USA) allows extremely effective and efficient antigen immunoprecipitations by covalently crosslinking antibodies onto Protein A/G resin. Cells were plated (seeding density 3 × 105 cells/cm2) in complete medium in a 100 mm dish and cultured as described above. Then, cells were collected and incubated for 5 min with 500 μL IP lysis/wash buffer for each condition. Lysates were centrifugated at 13,000× g for 10 min and supernatants were transferred to a new tube for protein concentration determination. The A/G plus agarose was linked with anti-NFκB (5 μg) or anti-IKBα (5 μg) for all the tested conditions according to the manufacturer’s instructions. The immunoprecipitation was performed with 1000 μg of cellular extract for all the conditions and antibodies linked to A/G plus agarose (anti-NFκB, Abcam ab32536)/anti-IKBα, Abcam ab32518). The columns were incubated overnight at 4 °C. The eluted antigen was diluted in 5X lane marker sample buffer DTT (1,4-Dithiothreitol) and then heated at 95 °C for 5 min. Eluates for NFκB or IKBα were separated on $10\%$ SDS–polyacrylamide gel and electroblotted onto PVDF. Nonspecific binding sites were blocked with EBB for 5 min. Membranes were then incubated overnight at 4 °C with anti-ubiquitin for NFκB eluate, 4-HNE for IKBα eluate or rabbit IgG. Bands were detected using UVITEC and analyzed using ImageJ software. Values were given as RU.
## 2.18. PApp
Papp is the apparent permeability of a compound across a membrane. For drug transport studies, cells were seeded at a density of 3 × 105 cells/cm2 on permeable support with 0.4 µm Transparent PET Membrane in complete medium, cultured and treated as described above.
Drug transport across Caco-2 monolayers was studied in the Ap–Bl direction, fresh drug and provided to the apical side (donor compartment), and the fresh vehicle was provided to the basolateral side (receiver compartment). For each solution, aliquots of 500 μL were collected immediately after the addition of compounds to the cells (the “time zero”), and after appropriate dilution, an HPLC analysis was performed. At the end of the experiment, after 120 min of incubation, 500 μL of the donor chamber solution was diluted and an HPLC analysis was performed. The apparent permeability coefficient (Papp, cm/s) of each model drug was calculated according to the following equation: Papp$\frac{1}{4}$dQ/dt/1 AC0/ð1Þ, where (dQ/dt) is the steady-state rate of the appearance of drugs in receiver side (mol/s), A is the surface area of the monolayer (cm2), and C0 is the initial compound concentration in the donor compartment (mM) (Lemieux et al., 2011).
## 2.19. Sirius T3
The major physicochemical properties of the drugs were determined using the SiriusT3 apparatus (Sirius Analytical Instruments Ltd., East Sussex, UK) equipped with an Ag/AgCl double junction reference pH electrode, a Sirius D-PAS spectrometer, and a turbidity sensing device. The pH electrode was calibrated titrimetrically in the pH range 1.8–12.2 as previously described [21]. The pKas were defined by the potentiometric method by pH-metric titration. The powder (around 0.5 mg) was diluted in 1.5 mL of ISA water and the titration was analyzed in triplicate in the pH range 2.0–12.0. Each Log P was assayed in triplicate by dissolving the powder (around 1 mg) in 1 mL of ISA water followed by pH metric titration in three different percentages of Octanol (generally $50\%$, $60\%$, $70\%$) [21].
## 2.20. Statistical Analyses
All data were reported as mean ± standard deviation (SD). Data analyses were performed using GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). Multiple comparisons, one-way analysis of variance (ANOVA), followed by Tukey post-hoc tests were used. The level of significance was set at $p \leq 0.05.$
## 3.1. Physicochemical Properties and Stability of Ketoprofen and Ibuprofen
In a preliminary set of experiments, the physicochemical properties pKa, logP, LogD7.4, and the thermodynamic solubility in water of K and IBU were measured using a Sirius T3 instrument. The results, together with other relevant literature data on the two NSAIDs, are reported in Table 1.
Interestingly, whereas K and IBU exhibited highly similar pKa and logP values (pKa 4.18 and 4.42 and logP 3.05 ± 0.01 and 3.91 ± 0.01, respectively), the logD7.4 (0.12) for K was lower than for IBU (logD7.4 (0.96)). Additionally, K had a significantly higher water solubility as compared to IBU (0.118 mg/mL versus 0.049 mg/mL, respectively) (Table 1). The polar surface areas of K and IBU were also compared. The higher constant surface area exposed to the GSF medium of K and KLS explains the higher intrinsic dissolution rate (IDR) in comparison to IBU, where IDR markedly influences drug absorption, distribution, metabolism, and excretion (ADME) [22]. ( Table 1).
**Table 1**
| Unnamed: 0 | pKa | LogP | LogD7.4 | Solubility | PSA | IC50 COX-1 (Human Blood) | IC50 COX-2(Human Blood) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Ketoprofen | 4.18 | 3.05 ± 0.01 | 0.12 | 118 µg/mL | 75.7 Å2 | 0.047 µM [23]0.11 µM [24] | 2.9 µM [23]0.88 µM [24] |
| Ibuprofen | 4.42 | 3.91 ± 0.01 | 0.96 | 49 µg/mL | 49.1 Å2 | 7.6 µM [23]5.9 µM [24] | 7.2 µM [23]9.9 µM [24] |
Our results combined with previous work suggest that KLS exhibits greater solvation kinetics, thus showing a better permeability profile than other agents tested [25].
## 3.2. Cell Viability and Cell Index
Caco-2 cells treated with EtOH were used as a model of leaky gut. To fix the model, Caco-2 cells were cultured for 48 h and then exposed to different concentrations of EtOH ($2\%$, $4\%$, $6\%$, and $8\%$) to find a sub-toxic condition (40–$50\%$ of cell death), such to avoid high cellular mortality (Supplementary Figure S1A). Based on the MTS assay results, $6\%$ EtOH was selected for the subsequent experiments. In parallel, cells were cultured for 20 DIV to create a condition that closely resembles the in vivo barrier, treated with $6\%$ EtOH and then exposed to three different concentrations of IBU, IBU-Lys, IBU-Arg, K, KLS, Arg, and Lys (0.25 mM, 0.5 mM, and 1 mM) for 72 h. All the compounds tested significantly affected cell viability with the exception of KLS, showing a significant increase of cell viability with respect to ETOH treatment.
The health of the Caco-2 epithelial cells was evaluated by CI (Figure 1A) and TEER assays (Figure 1B). Both analyses showed a protective effect exerted by KLS in the epithelial recovery phase following EtOH damage with a full recovery of injury in the TEER assay. Interestingly, in the TEER assay, IBU showed a partial but significant protective activity on EtOH-induced damage that was not potentiated in the Lys salt form (Figure 1B). Based on cell viability results, a concentration of 1 mM was selected for the subsequent experiments (Supplementary Figure S1B,C).
## 3.3. Morphological Analysis of the Intestinal Barrier
The intestinal barrier integrity was evaluated by the immunostaining for ZO-1 protein (Figure 2A) and phalloidin staining of actin microfilaments (Figure 2B). Upon EtOH challenge, both ZO-1 and phalloidin expression are strongly reduced, indicating an evident disruption of the barrier layer. Among the items tested, only KLS treatment protected the monolayer morphology, partially preserving the barrier integrity (Figure 2A,B). Seeing that K and Lys alone did not show the ability to protect Caco-2 cells from the EtOH-induced insult, we investigated the mechanistic basis of the synergic-like effect observed in cells exposed to KLS.
## 3.4. Proinflammatory Signals
Increased intestinal barrier permeability leads to inappropriate proinflammatory signaling that then further contributes to gut barrier dysfunction [26,27]. To characterize these effects in our model, we first evaluated the influence of EtOH on the stimulation of proinflammatory cytokines, such as IL-8 and TNF-α. Secreted IL-8 and TNF-α levels (Figure 3A,B) were significantly increased by EtOH exposure compared to control (283.9 ± 4.1 pg/mL vs. 82.4 ± 0.44 pg/mL and 20.9 ± 0.7 pg/mL vs. 10.8 ± 1.3 pg/mL, respectively), while all K or IBU and their corresponding Lys and Arg salts were able to counteract this effect, thus supporting the hypothesis that COX activation is a key event in EtOH-induced amplification of inflammatory signals.
The effect of the EtOH challenge on NFκB, a key regulator of inflammatory processes [28], was investigated. In non-stimulated cells, NFκB complexes are sequestered in the cytoplasm in an inactive form by interaction with the monomeric form of the inhibitory IKB protein [29]. The NFkB/IKB system is finely regulated by a complex network of events in response to different stimuli, such as LPS, oxidative stress, or proinflammatory cytokines. Under stimulation, the IKB cytoplasmatic inhibitor (IKK) complex phosphorylates IKBα leading to its ubiquitination and proteasomal degradation, triggering nuclear translocation of NFkB and gene expression induction [30]. EtOH treatment induced the nuclear translocation of NFκB, accompanied by a marked reduction of IKBα protein levels and a concomitant increase in the protein levels of pIKK, the active form of IKK (Figure 3C and Figure 4A,B). Interestingly, only K and KLS treatments significantly counteracted all these effects, modulating the stress induced by NFkB/IKB pathway activation. Consistent with the observed effect on NFκB translocation, IKBα protein levels, measured by western blotting analysis, were significantly reduced upon the EtOH challenge as compared to the control, and only K and KLS counteracted the EtOH-induced IKBα decrease (Figure 4A).
We also tested the effect of EtOH on the regulation of PPARγ, a ligand-dependent nuclear receptor whose activation results in the inhibition of NFκB signaling and inflammatory cytokine production [31,32]. EtOH treatment reduced the level of PPARγ in the nucleus, thus triggering NFκB activation. Notably, K and KLS treatments restored the nuclear translocation of PPARγ almost completely (Figure 4C).
## 3.5. Oxidative Stress and Proteasome Activity
In previous studies on the gastric mucosa damage model [17], we demonstrated the effect of KLS in decreasing oxidative stress by downregulating HNE protein adducts, and hypothesized that the ε-amino group of Lys may directly react with oxidant aldehydes including 4-HNE—a terminal product of lipid peroxidation—and other oxidant aldehydes. Thus, we tested the hypothesis that a direct antioxidant action of Lys could account for the observed synergism. The obtained results refuted our hypothesis by showing that only KLS, and not Lys, was able to reduce the elevation of 4-HNE levels caused by EtOH (Figure 5A). IKBα/4-HNE adduct formation was also evaluated by immunoprecipitation for IKBα (Figure 5B). EtOH increased protein levels for IKBα/4-HNE adducts and, in agreement with the above results, only K, and KLS with higher efficacy—but not Lys—reduced the formation of the adducts.
Having excluded a direct aldehyde scavenging effect of Lys, we evaluated its action on key cellular detoxification mechanisms. Glutathione-S-transferase (GST) is a family of enzymes that exert a key role in the detoxification of xenobiotics [33]. In Figure 5C, the quantitative analysis of GST activity is shown. As expected, GST activity was significantly decreased by the EtOH challenge, while only the treatment with KLS counteracted this effect that was not observed with both K and LYS alone. It is known that glutathione S-transferase A4 (GSTA4) reduces intracellular levels of 4-HNE [34]. In agreement, in Figure 5D, EtOH treatment strongly reduced GSTA-4 protein levels, while only K and KLS treatments counteracted the HNE-adducts formation with a more pronounced effect of KLS. Due to the evidence of oxidative stress occurring upon the EtOH challenge, an OxyBlot assay was used to determine the levels of oxidized protein. High levels of oxidized proteins, due to non-physiological oxidative stress levels, are related to increased intracellular proteins damaged by oxygen free radicals. Our results confirmed that Lys did not exhibit direct antioxidant effects in this cellular model but significantly enhanced the antioxidant effect of K. This effect may be due to the ability of Lys to interfere with transport through the intestinal epithelium as reported previously [35,36], thus favouring K permeability.
Starting from the observation (Figure 4) that K exhibits a specific modulatory effect on the NFkB pathway-specific and independent-from-COX inhibitory activity, we designed additional studies to further investigate biological significance and mechanisms. The levels of oxidized proteins were increased by EtOH damage, while K, and more significantly KLS, counteracted this effect (Figure 5E). IBU and related salts did not show a protective effect, suggesting a COX-independent mechanism underlying the observed reduction of oxidized proteins levels. On the other hand, the DCFDA assay (an indicator of cellular ROS) showed a significant reduction in fluorescence intensity in all the tested compounds, which was more substantial upon K and KLS treatment, compared to the increased intensity due to the EtOH challenge alone (Figure 5F).
Oxidative stress and oxidized protein overload affect proteasome activity. Polyubiquitin chains linked via lysine 48 (K48) are the most abundant and represent the canonical signal for protein degradation by the proteasome. EtOH-induced damage increased levels of ubiquitin K48 and ubiquitin (Figure 6A,B), an effect markedly counteracted by KLS treatment. Immunoprecipitation for ubiquitinated NFκB complex (IKB-NFκB) was evaluated by WB. The EtOH challenge increased the levels of ubiquitinated NFκB/IKB complex, inducing the degradation of IKB and the nuclear translocation of NFκB. Consistent with the above results, both K and KLS were able to counteract this effect (Figure 6C). Finally, EtOH treatment strongly reduced all the proteasome activities tested, but this effect was attenuated upon KLS treatment only (Figure 6D–F).
## 3.6. Effects of R and S Ketoprofen Enantiomers on Activated Macrophages
Having found that K has a specific activity in the inhibition of the inflammatory NFkB pathway that cannot be explained by its known inhibitory activity on COX enzymes since it is not exhibited by IBU, we hypothesized that this outcome may be a previously unreported effect of the R-enantiomer of K. The R-enantiomer of K—several logs less potent than the S enantiomer as a COX inhibitor [37,38]—was previously reported to contribute to the in vivo efficacy of the racemic drug by exerting analgesic and anti-inflammatory effects through a COX-independent mechanism [39]. We tested the effects of the two enantiomers of K, R, and S, on NFkB activation. Interestingly, only R and not the S enantiomer inhibited NFkB translocation to the same extent as racemic K and KLS (Figure 7A,B).
To further corroborate our hypothesis, we tested R and S enantiomers on activated macrophages. We found that only R and not the S enantiomer inhibited NFkB translocation to the same extent as KLS, suggesting that the R enantiomer contributes to the in vivo anti-inflammatory effect of K and KLS (Figure 7C,D).
## 3.7. Permeability of R and S ketoprofen in Caco2 Cells
Furthermore, to assess the human intestinal permeability of R and S enantiomers through the epithelial barrier, we performed a permeability assay in Caco-2 cells. The results showed low permeability from the apical to basolateral direction. Interestingly, we observed that KLS showed a higher permeability compared to K (Figure 7E). We further evaluated the permeability of the single enantiomer and we found no differences in terms of their Papp (K: Papp = 0.579 ± 0.052 10−6 cm/s for R enantiomer and 0.576 ± 0.031 10−6 cm/s for S enantiomer; KLS: Papp = 1.185 ± 0.044 10−6 cm/s for R enantiomer and 1.186 ± 0.026 10−6 cm/s for S enantiomer) (Figure 7E).
## 4. Discussion
NSAIDs injure both the gastric and intestinal mucosa and are related to numerous gastrointestinal (GI) problems, including relatively mild, nuisance-type symptoms such as heartburn, nausea, dyspepsia, and abdominal discomfort [40,41]. However, in as many as $80\%$ of NSAIDs users, acute haemorrhages and mucosal erosions are identified in the gastroduodenal mucosa upon endoscopic examination [42,43], which can induce severe consequences including bleeding and perforation, erosions, ulcers, strictures, and bowel obstruction. Elderly and chronic NSAID users are especially affected by NSAID-associated side effects. NSAID management is expected to increase to relieve aging-related degenerative and inflammatory disorders; at the same time, the occurrence of NSAID-associated side effects is also likely to rise. As long-term users suffer from diminished absorption capacity and increased intestinal permeability, they are at risk for developing colitis and exacerbating pre-existing conditions such as IBD and irritable bowel syndrome (IBS) [43].
The mechanism of NSAID-induced gastric injury is still unclear. TJs participate in cell-cell adhesion and are present in epithelial and endothelial cell membranes, establishing a component of intercellular junctions and exerting crucial functions in barrier integrity, cell polarity, and cell signalling pathways [44]. TJs generate a paracellular barrier that can be altered when NSAIDs harm the gastric epithelium, leading to enhanced permeability [45]. The loss of barrier integrity is a characteristic feature of different conditions, including microbiota dysbiosis, obesity, and IBD [46]. A leaky gut, or dysfunction of the intestinal barrier increasing permeability [47], may be an early occurrence in the pathogenesis of many GI disorders, permitting bacteria-derived molecules to enter into the mucosa and leading to uncontrollable inflammatory signal cascades [47].
Among other reagents, EtOH can induce leaky gut and intestinal epithelial barrier (IEB) dysfunction [48] that the use of NSAIDs may exacerbate. Here, we used the EtOH challenge as a model of intestinal barrier injury. We found that EtOH exposure induced a complete loss of IEB function, as shown by the results of CI and TEER assays and morphological analysis. Testing the effects of K, IBU, and their corresponding Lys and Arg salts on cells stressed by EtOH allowed us to observe that only KLS rescued IEB function following EtOH injury, thus showing a clear KLS advantage versus both K and IBU or IBU-Lys and IBU-Arg.
We compared the effect of IBU, K, and their salts on the stress-induced oxidative pathways in the IEB. In line with our previous observations on the gastric mucosa, KLS decreased ROS and HNE formation, supporting a model by which K, and more potently KLS, reduces the formation of HNE protein adducts by triggering proteasome activity that leads to their degradation via GST increase. IBU and IBU-Lys did not show any modulation of these parameters suggesting COX-independent effects. In the EtOH-induced IEB damage model, a general decrease of oxidized proteins and oxidative stress was observed upon KLS treatment, suggesting that proteasome activity was rescued. We previously reported that the accumulation of oxidized proteins and protein adducts triggers proteasomal overload resulting in proteasome dysfunction [20]. The results here reported the marked protective effects of KLS already reported in the stomach mucosa [20] extending to the IEB, increasing knowledge on the involved signalling pathways.
Starting from these preliminary observations, in this work, we tried to investigate the mechanisms by which: (a) K, differently from IBU, stimulates the IEB protective maintenance in response to stress, and (b) Lys potentiates the protective effect of K treatment.
In our previous work, we formulated the hypothesis of a direct scavenging activity by Lys on cytotoxic reactive aldehydes such as HNE. The results reported here confirm the ability of Lys to favour the preservation of proteasome activity by decreasing oxidative stress response, decreasing oxidized protein accumulation, preserving the functionality of key enzymes involved in oxidative stress control, and preserving the regulation of anti-inflammatory and protective pathways, like PPARγ or ZO-1. Our results, not showing any inhibitory effect exerted by Lys alone, did not support the direct scavenging effect hypothesis, undoubtedly confirming an evident synergic contribution of the Lys counterion on the specific properties of KLS.
An alternative possible interpretation of these observations is related to the ability of Lys to alter the permeability and intracellular distribution of K, as apparently suggested by the results of the studies on Caco-2 [35,36] in which KLS showed a higher permeability compared to K. A Lys-mediated permeability increase was highly specific since IBU-Lys did not exhibit different permeability vs. IBU, and this could be explained by the ability of Lys to interfere with the cationic amino acid channels expressed in the basolateral membrane of intestinal epithelial cells [35,36] that regulate the Na + Lys trafficking and may indirectly influence the transport system involved in K absorption and excretion.
The results so far generated in both gastric mucosa and IEB damage models do not provide a full clarification of the mechanisms by which Lys potentiates the positive effect of K on IEB injury repair and warrants future studies aimed at elucidating molecular mechanisms underlying the observed modulation of inflammatory and oxidative response.
On the other hand, the results of this study nicely contribute to outlining the mechanism responsible for the specific COX-independent mechanism observed for K in the EtOH-induced stress IEB model. In fact, whereas K, IBU, and their salts showed similar behaviour on stress-induced cytokine production, a specific effect of K was observed in the modulation of the NFkB/PPARγ pathways.
EtOH treatment significantly increased the nuclear translocation of NFkB and decreased the nuclear translocation of PPARγ. These two transcription factors are known to act in opposite directions, the first inducing inflammatory signals and oxidative stress, and the latter counteracting inflammation and ROS formation [49,50,51]. In our in vitro model, K was able to increase nuclear PPARγ and decrease nuclear NFkB in a COX-independent manner. The K effect was potentiated by the presence of Lys in KLS. In this context, it is worth noting that K is as racemic formulation, and to gain more insight into K-specific effects in the NFkB pathway, the R and S enantiomers were checked. This assay allowed us to demonstrate for the first time that the R enantiomer, which is the less potent COX inhibitor [52], is fully responsible for the observed effects on NFkB translocation and activity. This finding not only offers a potential mechanistic explanation about the specific effect of K in the protection and repair of the damaged intestinal epithelial barrier, but also sheds new light on the mechanisms by which the R-enantiomer may contribute to the anti-inflammatory and analgesic effect of KLS. The specific effect of R-Ketoprofen on the NFkB pathway may also explain its reported COX-independent effect in pain management, as NFkB upregulation was recently reported in spinal and glial cells [38] as a key mediator of hyperalgesia in several animal models. Additionally, the observation that R-enantiomer inhibits NFkB nuclear localization and activation in human macrophages supports this hypothesis and prompts additional in vitro and in vivo studies. Our results suggest that the activity of R-enantiomers should be considered for molecules administered as racemic mixtures. This result agrees with a previous Phase I randomized study comparing the gastroduodenal side effects of similar doses of racemic ketoprofen and (R) and (S)-ketoprofen on normal subjects, showing that the number of gastric lesions induced by (S)-ketoprofen and racemic ketoprofen was found to be double the number of lesions observed after(R)-ketoprofen [53].
Since COX1 and COX2 inhibitory characteristics fail to fully explain the observed differences between various NSAIDs, many resources have been dedicated to identifying novel targets that may account for the specific properties. Aspirin was the first of the NSAIDs that was shown to suppress NF-kB [54], and others were subsequently characterized for the same effect [55,56,57,58]. That has been proposed as crucial for the comprehension of NSAID’s specific pharmacological characteristics, in particular regarding pain management. In this sense, NFkB upregulation has been recently suggested in spinal and glial cells as a key mediator of hyperalgesia in several experimental models [59,60,61].
The results reported here show for the first time a specific effect of R-Ketoprofen on the NFkB pathway that sheds new light on the previously reported [38] COX-independent effect in pain management, and that may account for the observed unexpected protective effect of K on stress-induced damage on the IEB. The cooperative effect of R-Ketoprofen and Lys counterion accounts for the unique efficacy and tolerability characteristics of KSL within the class of commercially available NSAIDs. The possible synergic activity of R-ketoprofen and lysine, and the effect of the lysine salt of the R-stereoisomer versus the lysine salt of the S-stereoisomer will be investigated further in future in vitro and in vivo studies, to provide the rationale for introducing the R-KLS into clinical practice.
## References
1. Vancamelbeke M., Vermeire S.. **The Intestinal Barrier: A Fundamental Role in Health and Disease**. *Expert Rev. Gastroenterol. Hepatol.* (2017) **11** 821-834. DOI: 10.1080/17474124.2017.1343143
2. Vanuytsel T., Tack J., Farre R.. **The Role of Intestinal Permeability in Gastrointestinal Disorders and Current Methods of Evaluation**. *Front. Nutr.* (2021) **8** 717925. DOI: 10.3389/fnut.2021.717925
3. Clayburgh D.R., Shen L., Turner J.R.. **A Porous Defense: The Leaky Epithelial Barrier in Intestinal Disease**. *Lab. Investig.* (2004) **84** 282-291. DOI: 10.1038/labinvest.3700050
4. Maes M., Leunis J.-C.. **Normalization of Leaky Gut in Chronic Fatigue Syndrome (CFS) Is Accompanied by a Clinical Improvement: Effects of Age, Duration of Illness and the Translocation of LPS from Gram-Negative Bacteria**. *Neuroendocrinol. Lett.* (2008) **29** 902-910. PMID: 19112401
5. Ray K.. **NAFLD. Leaky Guts: Intestinal Permeability and NASH**. *Nat. Rev. Gastroenterol. Hepatol.* (2015) **12** 123. DOI: 10.1038/nrgastro.2015.15
6. Peters A., Wekerle H.. **Autoimmune Diabetes Mellitus and the Leaky Gut**. *Proc. Natl. Acad. Sci. USA* (2019) **116** 14788-14790. DOI: 10.1073/pnas.1909224116
7. Kirby T.O., Ochoa-Repáraz J.. **The Gut Microbiome in Multiple Sclerosis: A Potential Therapeutic Avenue**. *Med. Sci.* (2018) **6**. DOI: 10.3390/medsci6030069
8. Camilleri M.. **Leaky Gut: Mechanisms, Measurement and Clinical Implications in Humans**. *Gut* (2019) **68** 1516-1526. DOI: 10.1136/gutjnl-2019-318427
9. Forsyth C.B., Tang Y., Shaikh M., Zhang L., Keshavarzian A.. **Role of Snail Activation in Alcohol-Induced INOS-Mediated Disruption of Intestinal Epithelial Cell Permeability**. *Alcohol Clin. Exp. Res.* (2011) **35** 1635-1643. DOI: 10.1111/j.1530-0277.2011.01510.x
10. Peterson V.L., Jury N.J., Cabrera-Rubio R., Draper L.A., Crispie F., Cotter P.D., Dinan T.G., Holmes A., Cryan J.F.. **Drunk Bugs: Chronic Vapour Alcohol Exposure Induces Marked Changes in the Gut Microbiome in Mice**. *Behav. Brain Res.* (2017) **323** 172-176. DOI: 10.1016/j.bbr.2017.01.049
11. Cangiano L.R., Villot C., Renaud J., Ipharraguerre I.R., McNeil B., DeVries T.J., Steele M.A.. **Induction of Leaky Gut by Repeated Intramuscular Injections of Indomethacin to Preweaning Holstein Calves**. *J. Dairy Sci.* (2022) **105** 7125-7139. DOI: 10.3168/jds.2021-21768
12. Zhang M., Xia F., Xia S., Zhou W., Zhang Y., Han X., Zhao K., Feng L., Dong R., Tian D.. **NSAID-Associated Small Intestinal Injury: An Overview From Animal Model Development to Pathogenesis, Treatment, and Prevention**. *Front. Pharmacol.* (2022) **13** 818877. DOI: 10.3389/fphar.2022.818877
13. Groschwitz K.R., Hogan S.P.. **Intestinal Barrier Function: Molecular Regulation and Disease Pathogenesis**. *J. Allergy Clin. Immunol.* (2009) **124** 3-20. DOI: 10.1016/j.jaci.2009.05.038
14. Bjarnason I., Williams P., Smethurst P., Peters T.J., Levi A.J.. **Effect of Non-Steroidal Anti-Inflammatory Drugs and Prostaglandins on the Permeability of the Human Small Intestine**. *Gut* (1986) **27** 1292-1297. DOI: 10.1136/gut.27.11.1292
15. Baltoyiannis G., Christodoulos N., Mitsis M., Stephanou D., Ioannou H., Nousias V., Kappas A.M.. **A Comparative Experimental Study of the Effects of Diclofenac and Ketoprofen on the Small-Bowel Mucosa of Canines**. *Res. Exp. Med.* (2001) **200** 125-135. DOI: 10.1007/BF03220020
16. Harirforoosh S., Asghar W., Jamali F.. **Adverse Effects of Nonsteroidal Antiinflammatory Drugs: An Update of Gastrointestinal, Cardiovascular and Renal Complications**. *J. Pharm. Pharm. Sci.* (2014) **16** 821. DOI: 10.18433/J3VW2F
17. Cimini A., Brandolini L., Gentile R., Cristiano L., Menghini P., Fidoamore A., Antonosante A., Benedetti E., Giordano A., Allegretti M.. **Gastroprotective Effects of L-Lysine Salification of Ketoprofen in Ethanol-Injured Gastric Mucosa**. *J. Cell. Physiol.* (2015) **230** 813-820. DOI: 10.1002/jcp.24809
18. Castelli V., Catanesi M., Alfonsetti M., Laezza C., Lombardi F., Cinque B., Cifone M.G., Ippoliti R., Benedetti E., Cimini A.. **PPARα-Selective Antagonist GW6471 Inhibits Cell Growth in Breast Cancer Stem Cells Inducing Energy Imbalance and Metabolic Stress**. *Biomedicines* (2021) **9**. DOI: 10.3390/biomedicines9020127
19. Catanesi M., Brandolini L., d’Angelo M., Tupone M.G., Benedetti E., Alfonsetti M., Quintiliani M., Fratelli M., Iaconis D., Cimini A.. **S-Carboxymethyl Cysteine Protects against Oxidative Stress and Mitochondrial Impairment in a Parkinson’s Disease In Vitro Model**. *Biomedicines* (2021) **9**. DOI: 10.3390/biomedicines9101467
20. Brandolini L., Antonosante A., Giorgio C., Bagnasco M., d’Angelo M., Castelli V., Benedetti E., Cimini A., Allegretti M.. **NSAIDs-Dependent Adaption of the Mitochondria-Proteasome System in Immortalized Human Cardiomyocytes**. *Sci. Rep.* (2020) **10** 18337. DOI: 10.1038/s41598-020-75394-x
21. Bianchini G., Tomassetti M., Lillini S., Sirico A., Bovolenta S., Za L., Liberati C., Novelli R., Aramini A.. **Discovery of Novel TRPM8 Blockers Suitable for the Treatment of Somatic and Ocular Painful Conditions: A Journey through p**. *J. Med. Chem.* (2021) **64** 16820-16837. DOI: 10.1021/acs.jmedchem.1c01647
22. Aramini A., Bianchini G., Lillini S., Bordignon S., Tomassetti M., Novelli R., Mattioli S., Lvova L., Paolesse R., Chierotti M.R.. **Unexpected Salt/Cocrystal Polymorphism of the Ketoprofen–Lysine System: Discovery of a New Ketoprofen–l-Lysine Salt Polymorph with Different Physicochemical and Pharmacokinetic Properties**. *Pharmaceuticals* (2021) **14**. DOI: 10.3390/ph14060555
23. Warner T.D., Giuliano F., Vojnovic I., Bukasa A., Mitchell J.A., Vane J.R.. **Nonsteroid Drug Selectivities for Cyclo-Oxygenase-1 Rather than Cyclo-Oxygenase-2 Are Associated with Human Gastrointestinal Toxicity: A Full**. *Proc. Natl. Acad. Sci. USA* (1999) **96** 7563-7568. DOI: 10.1073/pnas.96.13.7563
24. Cryer B., Feldman M.. **Cyclooxygenase-1 and Cyclooxygenase-2 Selectivity of Widely Used Nonsteroidal Anti-Inflammatory Drugs**. *Am. J. Med.* (1998) **104** 413-421. DOI: 10.1016/S0002-9343(98)00091-6
25. Sarzi- Puttini P., Atzeni F., Lanata L., Bagnasco M., Colombo M., Fischer F., D’Imporzano M.. **Pain and Ketoprofen: What Is Its Role in Clinical Practice?**. *Reumatismo* (2011) **62** 172-188. DOI: 10.4081/reumatismo.2010.172
26. Li X., Schwacha M.G., Chaudry I.H., Choudhry M.A.. **Acute alcohol intoxication potentiates neutrophil-mediated intestinal tissue damage after burn injury**. *Shock* (2008) **29** 377-383. DOI: 10.1097/SHK.0b013e31815abe80
27. Li X., Akhtar S., Kovacs E.J., Gamelli R.L., Choudhry M.A.. **Inflammatory Response in Multiple Organs in a Mouse Model of Acute Alcohol Intoxication and Burn Injury**. *J. Burn Care Res.* (2011) **32** 489-497. DOI: 10.1097/BCR.0b013e3182223c9e
28. Franceschetti L., Bonomini F., Rodella L.F., Rezzani R.. **Critical Role of NFκB in the Pathogenesis of Non-Alcoholic Fatty Liver Disease: A Widespread Key Regulator**. *CMM* (2021) **21** 495-505. DOI: 10.2174/1566524020666201026162343
29. Mulero M.C., Huxford T., Ghosh G.. **NF-ΚB, IκB, and IKK: Integral Components of Immune System Signaling**. *Adv. Exp. Med. Biol.* (2019) **1172** 207-226. DOI: 10.1007/978-981-13-9367-9_10
30. Mathes E., O’Dea E.L., Hoffmann A., Ghosh G.. **NF-ΚB Dictates the Degradation Pathway of IκBα**. *EMBO J.* (2008) **27** 1357-1367. DOI: 10.1038/emboj.2008.73
31. Su C.G., Wen X., Bailey S.T., Jiang W., Rangwala S.M., Keilbaugh S.A., Flanigan A., Murthy S., Lazar M.A., Wu G.D.. **A Novel Therapy for Colitis Utilizing PPAR-γ Ligands to Inhibit the Epithelial Inflammatory Response**. *J. Clin. Investig.* (1999) **104** 383-389. DOI: 10.1172/JCI7145
32. Gervois P., Fruchart J.-C., Delerive P., Staels B.. **Induction of IκBα Expression as a Mechanism Contributing to the Anti-Inflammatory Activities of Peroxisome Proliferator-Activated Receptor-α Activators**. *J. Biol. Chem.* (2000) **275** 36703-36707. DOI: 10.1074/jbc.M004045200
33. Perperopoulou F., Pouliou F., Labrou N.E.. **Recent Advances in Protein Engineering and Biotechnological Applications of Glutathione Transferases**. *Crit. Rev. Biotechnol.* (2018) **38** 511-528. DOI: 10.1080/07388551.2017.1375890
34. Balogh L.M., Atkins W.M.. **Interactions of Glutathione Transferases with 4-Hydroxynonenal**. *Drug Metab. Rev.* (2011) **43** 165-178. DOI: 10.3109/03602532.2011.558092
35. Thwaites D.T., Markovich D., Murer H., Simmons N.L.. **Na**. *J. Membr. Biol.* (1996) **151** 215-224. DOI: 10.1007/s002329900072
36. Ferruzza S., Ranaldi G., Di Girolamo M., Sambuy Y.. **The Efflux of Lysine from the Basolateral Membrane of Human Cultured Intestinal Cells (Caco-2) Occurs by Different Mechanisms Depending on the Extracellular Availability of Amino Acids**. *J. Nutr.* (1997) **127** 1183-1190. DOI: 10.1093/jn/127.6.1183
37. Brune K., Geisslinger G., Menzel-Soglowek S.. **Pure Enantiomers of 2-Arylpropionic Acids: Tools in Pain Research and Improved Drugs in Rheumatology**. *J. Clin. Pharmacol.* (1992) **32** 944-952. DOI: 10.1002/j.1552-4604.1992.tb04643.x
38. Ghezzi P., Melillo G., Meazza C., Sacco S., Pellegrini L., Asti C., Porzio S., Marullo A., Sabbatini V., Caselli G.. **Differential Contribution of R and S Isomers in Ketoprofen Anti-Inflammatory Activity: Role of Cytokine Modulation**. *J. Pharmacol. Exp. Ther.* (1998) **287** 969-974. PMID: 9864281
39. Bertini R., Caselli G.. **Analgesic Effect of Ketoprofen Is Mainly Associated to Its**. *Analgesia* (1999) **4** 181-186. DOI: 10.3727/107156999819565829
40. Faucheron J.-L., Parc R.. **Non-Steroidal Anti-Inflammatory Drug-Induced Colitis**. *Int. J. Colorectal Dis.* (1996) **11** 99-101. DOI: 10.1007/BF00342469
41. Yap P., Goh K.-L.. **Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Induced Dyspepsia**. *CPD* (2015) **21** 5073-5081. DOI: 10.2174/1381612821666150915105738
42. Soylu A., Dolapcioglu C., Dolay K., Ciltas A., Yasar N., Kalayci M., Alis H., Sever N.. **Endoscopic and Histopathological Evaluation of Acute Gastric Injury in High-Dose Acetaminophen and Nonsteroidal Anti-Inflammatory Drug Ingestion with Suicidal Intent**. *WJG* (2008) **14** 6704. DOI: 10.3748/wjg.14.6704
43. Tai F.W.D., McAlindon M.E.. **Non-Steroidal Anti-Inflammatory Drugs and the Gastrointestinal Tract**. *Clin. Med.* (2021) **21** 131-134. DOI: 10.7861/clinmed.2021-0039
44. Zihni C., Mills C., Matter K., Balda M.S.. **Tight Junctions: From Simple Barriers to Multifunctional Molecular Gates**. *Nat. Rev. Mol. Cell Biol.* (2016) **17** 564-580. DOI: 10.1038/nrm.2016.80
45. Thakre-Nighot M., Blikslager A.T.. **Indomethacin Induces Increase in Gastric Epithelial Tight Junction Permeability via Redistribution of Occludin and Activation of P38 MAPK in MKN-28 Cells**. *Tissue Barriers* (2016) **4** e1187325. DOI: 10.1080/21688370.2016.1187325
46. Stolfi C., Maresca C., Monteleone G., Laudisi F.. **Implication of Intestinal Barrier Dysfunction in Gut Dysbiosis and Diseases**. *Biomedicines* (2022) **10**. DOI: 10.3390/biomedicines10020289
47. Usuda H., Okamoto T., Wada K.. **Leaky Gut: Effect of Dietary Fiber and Fats on Microbiome and Intestinal Barrier**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22147613
48. Elamin E., Masclee A., Troost F., Pieters H.-J., Keszthelyi D., Aleksa K., Dekker J., Jonkers D.. **Ethanol Impairs Intestinal Barrier Function in Humans through Mitogen Activated Protein Kinase Signaling: A Combined In Vivo and In Vitro Approach**. *PLoS ONE* (2014) **9**. DOI: 10.1371/journal.pone.0107421
49. Martin H.. **Role of PPAR-Gamma in Inflammation. Prospects for Therapeutic Intervention by Food Components**. *Mutat. Res. Fundam. Mol. Mech. Mutagen.* (2010) **690** 57-63. DOI: 10.1016/j.mrfmmm.2009.09.009
50. Polvani S., Tarocchi M., Galli A.. **PPARγ and Oxidative Stress: Con(**. *PPAR Res.* (2012) **2012** 641087. DOI: 10.1155/2012/641087
51. Liu T., Zhang L., Joo D., Sun S.-C.. **NF-ΚB Signaling in Inflammation**. *Signal Transduct. Target. Ther.* (2017) **2** 17023. DOI: 10.1038/sigtrans.2017.23
52. Carabaza A., Cabré F., Rotllan E., Gómez M., Gutiérrez M., García M.L., Mauleón D.. **Stereoselective Inhibition of Inducible Cyclooxygenase by Chiral Nonsteroidal Antiinflammatory Drugs**. *J. Clin. Pharmacol.* (1996) **36** 505-512. DOI: 10.1002/j.1552-4604.1996.tb05040.x
53. Jerussi T.P., Caubet J.-F., McCray J.E., Handley D.A.. **Clinical Endoscopic Evaluation of the Gastroduodenal Tolerance to (R)-Ketoprofen, (R)-Flurbiprofen, Racemic Ketoprofen, and Paracetamol: A Randomized, Single-Blind, Placebo-Controlled Trial**. *J. Clin. Pharmacol.* (1998) **38** 19S-24S. DOI: 10.1002/j.1552-4604.1998.tb04413.x
54. Kopp E., Ghosh S.. **Inhibition of NF-ΚB by Sodium Salicylate and Aspirin**. *Science* (1994) **265** 956-959. DOI: 10.1126/science.8052854
55. Kazmi S.M.I., Plante R.K., Visconti V., Taylor G.R., Zhou L., Lau C.Y.. **Suppression of NFκB Activation and NFκB-Dependent Gene Expression by Tepoxalin, a Dual Inhibitor of Cyclooxygenase and 5-Lipoxygenase**. *J. Cell. Biochem.* (1995) **57** 299-310. DOI: 10.1002/jcb.240570214
56. Scheuren N., Bang H., Münster T., Brune K., Pahl A.. **Modulation of Transcription Factor NF-ΚB by Enantiomers of the Nonsteroidal Drug Ibuprofen: Inhibition of NF-ΚB by R-Ibuprofen**. *Br. J. Pharmacol.* (1998) **123** 645-652. DOI: 10.1038/sj.bjp.0701652
57. Chen C., Fan J., Chuang N.. **Effects of Prenyl Pyrophosphates on the Binding of S-Ras Proteins with KSR**. *J. Exp. Zool.* (2002) **293** 551-560. DOI: 10.1002/jez.10165
58. Takada Y., Bhardwaj A., Potdar P., Aggarwal B.B.. **Nonsteroidal Anti-Inflammatory Agents Differ in Their Ability to Suppress NF-ΚB Activation, Inhibition of Expression of Cyclooxygenase-2 and Cyclin D1, and Abrogation of Tumor Cell Proliferation**. *Oncogene* (2004) **23** 9247-9258. DOI: 10.1038/sj.onc.1208169
59. Luo J.-G., Zhao X.-L., Xu W.-C., Zhao X.-J., Wang J.-N., Lin X.-W., Sun T., Fu Z.-J.. **Activation of Spinal NF-ΚB/P65 Contributes to Peripheral Inflammation and Hyperalgesia in Rat Adjuvant-Induced Arthritis: Spinal NF-ΚB/P65 in Rat Arthritis**. *Arthritis Rheumatol.* (2014) **66** 896-906. DOI: 10.1002/art.38328
60. Li Y., Yang Y., Guo J., Guo X., Feng Z., Zhao X.. **Spinal NF-KB Upregulation Contributes to Hyperalgesia in a Rat Model of Advanced Osteoarthritis**. *Mol. Pain* (2020) **16** 174480692090569. DOI: 10.1177/1744806920905691
61. Palazzo I., Todd L.J., Hoang T.V., Reh T.A., Blackshaw S., Fischer A.J.. **NFκB-signaling Promotes Glial Reactivity and Suppresses Müller Glia-mediated Neuron Regeneration in the Mammalian Retina**. *Glia* (2022) **70** 1380-1401. DOI: 10.1002/glia.24181
|
---
title: 'Effect of Spatiotemporal Parameters on the Gait of Children Aged from 6 to
12 Years in Podiatric Tests: A Cross Sectional Study'
authors:
- Magdalena Martinez-Rico
- Ana Belen Ortega-Avila
- Consolacion Pineda-Galan
- Gabriel Gijon-Nogueron
- Manuel Pardo Rios
- Raquel Alabua-Dasi
- Ana Marchena-Rodriguez
journal: Healthcare
year: 2023
pmcid: PMC10001326
doi: 10.3390/healthcare11050708
license: CC BY 4.0
---
# Effect of Spatiotemporal Parameters on the Gait of Children Aged from 6 to 12 Years in Podiatric Tests: A Cross Sectional Study
## Abstract
The use of lower limb tests in the paediatric population is of great importance for diagnostic evaluations. The aim of this study is to understand the relationship between the tests performed on the feet and ankles, covering all of its planes, and the spatiotemporal parameters of children’s gait. Methods: *It is* a cross-sectional observational study. Children aged between 6 and 12 years participated. Measurements were carried out in 2022. An analysis of three tests used to assess the feet and ankles (FPI, the ankle lunge test, and the lunge test), as well as a kinematic analysis of gait using OptoGait as a measurement tool, was performed. Results: The spatiotemporal parameters show how Jack’s *Test is* significant in the propulsion phase in its % parameter, with a p-value of 0.05 and a mean difference of $0.67\%$. Additionally, in the lunge test, we studied the % of midstance in the left foot, with a mean difference between the positive test and the 10 cm test of 10.76 (p value of 0.04). Conclusions: The diagnostic analysis of the functional limitation of the first toe (Jack’s test) is correlated with the spaciotemporal parameter of propulsion, as well as the lunge test, which is also correlated with the midstance phase of gait.
## 1. Introduction
The use of lower limb tests in the paediatric population is of great importance for diagnostic evaluation and treatment. The use of these tests to assess foot and ankle functions is a controversial topic, showing a lack of consensus on how foot and ankle functions should be measured, defined, or assessed [1].
Therefore, high-reliability morpho-functional tests of the foot and ankle should be used [2,3] in order to establish an association between the morpho-functional variants [4] and other variables, such as weight [5], laxity [6], and physical activity [7] or gait.
A child’s gait can be influenced by many intrinsic factors: limb length, joint range, muscle tone [8], neuromuscular diseases [9], and also, by extrinsic factors: footwear, clothing, or carrying loads, which may change their walking pattern [10]. Measures of spatio-temporal gait parameters are used to identify and diagnose walking difficulties and may also determine the prognosis [11].
Although in many cases we are not able to interrelate the different existing diagnostic tests of the foot, this is of vital importance to facilitate subsequent treatment, so determining this interrelation should be a priority issue in lower extremity examinations.
Despite this, fundamental information on joint and foot mechanics, as written by K, Deschamps et al., on typically developing children and children with pathological condition has not yet been provided. Indeed, the joint kinetic profiles that have been reported in the past focus on the major joints of the lower extremity (e.g., the hip, the knee, and the ankle) [12] Therefore, joints distal to the ankle play a key role in energy absorption and generation during gait [13,14]. Technical limitation is the main reason why this biomechanical perspective remains understudied [15,16].
The OptoGait system is more readily accessed and can be used in primary care consultation. It is based on a photoelectric cell and is validated for the assessment of the phases of gait in clinical and research settings [17,18]. The coefficient of variation in method error values was low, ranging from $1.66\%$ to $4.06\%$, and all the parameters presented standard errors of measurement between 2.17 and $5.96\%$, indicating a strong reliability.
OptoGait has demonstrated excellent reliability for the variables of step rate, step length, and contact time during treadmill and ground walking, as well as good test–retest reliability in healthy and injured adults [19]. However, very few studies have evaluated the spatiotemporal parameters of gait with this system in children [20,21].
As described above, there is a lack of information on the relationship between foot tests and gait parameters in the pediatric population to guide our clinical practice. The hypothesis was that foot tests could be related to gait analysis. Therefore, the aim of this study is to relate diagnostic tests on the foot, covering all of its planes, with the spatiotemporal parameters of children’s gait.
## 2.1. Ethical Approval
The parent and/or legal guardian were provided with information about the study, and a statement attesting to informed consent was signed. The children were fully informed of the procedures involved and gave their consent. All the procedures were in accordance with the ethical standards of the institution, and the experimental protocol was approved by a named institutional of the University of Malaga (CEUMA $\frac{91}{2016}$H) and with the 1964 Helsinki declaration.
## 2.2. Study Design
It is a cross-sectional observational study, in which the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) criteria were followed.
## Sample Size Calculation
To calculate the sample size, we used Epidat 4.2 software (Epidemiology Service of the General Directorate of Public Health of the Consellería de Sanidade (Xunta de Galicia)), and we used the paper by Montes-Alguacil et al. [ 22] to obtain mean and standard deviations for the main outcome’s variables: the heel contact phase, the flat foot phase, and the propulsion phase. The study was designed to detect changes exceeding 0.8 (high effect size) with a type I error of 0.05 and a type II error of 0.2. This calculation used a necessary sample size of 48 subjects, although, in fact, 50 were recruited to cover any potential missing data.
## 2.3. Participants
Children aged between 6 and 12 years participated. Measurements were carried out in 2022. The participants were recruited at the Faculty of Health Science from the University of Malaga (Spain).
The inclusion criteria were participants aged between 6 and 12 years old and those not experiencing any foot pain at the time of the assessment. Participants who had any of the following conditions were excluded from the study: recent damage of the lower limbs, congenital structural alterations that affect distal areas of the ankle joint, as well as those cases with pathological flat feet caused by cerebral palsy, surgical treatments in the foot or lower extremities, or affectations of a genetic, neurological, or muscular nature.
## 2.4. Procedure
The test was performed by two different clinicians. Both were blinded to themselves and to each other’s results. One performed the 3 tests used to evaluate the foot and ankle (FPI, the ankle lunge test, and Jack’s test), and the other one performed the gait analysis (MMR) using the OptoGait gait cycle measurement tool.
## 2.4.1. Foot Posture Index (FPI)
The assessment of the foot posture was carried out by measuring the FPI with barefoot subjects in a relaxed standing position to facilitate the visual and physical inspection. The inter-examiner reliability for the FPI in the paediatric population reached a consistent weighted Kappa value (Kw = 0.86) in a sample of children aged between 5 and 16 years, and the categorization was performed using the criteria by Gijon-Nogueron et al. [ 23,24,25]
## 2.4.2. Ankle Lunge Test
The range of ankle dorsiflexion was determined by the lunge test, which is a weight-bearing test of the range of ankle dorsiflexion when the knee is flexed. The participant was stood in a relaxed standing position on a solid, horizontal surface facing a vertical wall. The test foot was parallel with a tape measure secured to the floor with the second toe, centre of the heel, and knee perpendicular to a wall. To promote upright balance during the test, the opposite limb was positioned approximately 1 foot behind the test foot in a comfortable tandem stance, and the subjects placed their hands on the wall. The test involved the participant pushing their knee as far forward over the foot as possible, while keeping the heel on the ground. The maximum angle of advancement of the tibia relative to the vertical was recorded as a measure of ankle dorsiflexion using a digital inclinometer (Smart Tool™) applied to the anterior surface of the tibia. The intra-examiner intraclass correlation coefficients were 0.98, and the inter-examiner reliability reached an excellent value of weighted Kappa (Kw = 0.97) [26].
## 2.4.3. Jack´s Test
In 1953, Jack described one of the first methods to evaluate the first MPJ in a weight-bearing position, and this method is still commonly used today. It is also known as the Hubscher manoeuvre [27].
The utility of the test assumes that restriction during the static manoeuvre is predictive of the functional limitation of this joint during gait. The test involves the examiner manually dorsiflexing the hallux, while the patient stands in a relaxed double-limb stance position.
A normal response is for these structures to ‘tighten’ and the medial longitudinal arch to rise. This response is commonly referred to as the ‘windlass mechanism’. A failed test was recorded when the examiner was unable to dorsiflex the hallux from the weightbearing surface without the application of excessive force.
The utility of the test assumes that restriction during the static manoeuvre is predictive of functional limitation of this joint during gait. The intra-examiner intraclass correlation coefficients were 0.89.
## 2.4.4. Spaciotemporal Gait by OptoGait System
The gait parameters were assessed using the OptoGait® portable photocell system [17,18]. This system provides real-time numerical parameters related to stepping, running, and jumping.
Previously, the participants were instructed to walk barefoot, as per there normal walking behavior, for a distance of five meters between two parallel bars. Six to eight strides are sufficient to obtain representative data for unimpaired adults [17], and in our case, ten strides were measured. After three trials, the data were acquired. A highly experienced podiatrist (MMR) (with more than 1000 Optogait® tests examinations performed) controlled the measurement process at all times.
The software used was OptoGait v.1.11.1.0. The Optogait system was calibrated and checked for accuracy at all times to provide an exhaustive and reliable measurement of the spatiotemporal phases of the gait cycle. Considering the heel contact phase (Phase 1), this is the time from the initial ground contact (1 LED activated is needed to be considered) to the foot flattening (the number of LEDs activated stays steady ±2 LEDs). Footflat phase (Phase 2) is the time from foot flattening to the initial take-off, and the propulsive phase (Phase 3) is the initial take-off until the end of the motion.
## 2.5. Statistical Analysis
The descriptive statistics obtained included measures of central tendency and dispersion and the distribution of percentages.
An exploratory analysis including the Kolmogorov–Smirnov test and by examining symmetry and kurtosis was performed to confirm the normality of the distributions. Subsequently, a bivariate analysis of the differences of the means using Student’s t test was applied to evaluate the differences in the gait parameters according to the results of Jack’s test.
The differences in the gait parameters were identified by ANOVA according to the four FPI and the three lunge test groups established. The homoscedasticity of the distributions was determined by the Levene test. In addition, the Browne–Forsythe test of robustness was applied, and a post hoc analysis performed using the Bonferroni test. The level of statistical significance was $95\%$ in all the cases, and all the analyses were conducted using SPSS v.23 statistical software (SPSS Inc., Chicago, IL, USA).
## 3. Results
The sample in this study was composed of 50 healthy school children, with 29 ($58\%$) girls and 21 ($42\%$) boys aged 6–12 years (mean age: 8.96 years, SD: 1.83). The mean BMIs were 18.45 kg/m2 (SD:3.30) for the girls and 18.74 kg/m2 (SD:3.52) for the boys. The difference between the genders was not statistically significant (t = −0.23; $$p \leq 0.814$$).
According to the results observed the Jack´s test showed, for the left foot, 42 negative results for 8 positive results, and for the right foot, 44 negative and 6 positive results. Regarding the lunge test, for the left foot, 12 results were positive (unable to performed the test) and 38 results were negative (20 negative, 5 cm, and 18 negative, 10 cm), and for the right foot, 10 results were positive and 40 results were negative (22 negative, 5 cm, and 18 negative, 10 cm) (Table 1).
The spaciotemporal parameters show how the results of Jack’s test are significant in the propulsion phase in its % parameter, with a p-value of 0.05 and a mean difference of 0,$67\%$ (Table 2).
If we divide the groups according to age, significant differences are only observed for the same measures as those in the global analysis ($$p \leq 0.038$$ left propulsive phase (%)), with the rest having p-values greater than 0.05 (Figure 1).
Regarding the lunge’s tests (Table 3), according to age, significant differences were only observed in the 10-year-old sample in the flat foot phase on the right foot ($$p \leq 0.022$$, with the rest of them having p-values greater than 0.05 (Figure 2).
Finally, a significance is observed in the relationship between the gait parameters and the foot posture, following the foot posture index. We found a significance in the step length between the pronated posture and normal posture in the right foot (with a mean difference of 5.51 cm and a p-value of 0.05), as well as in the contact phase (with a mean difference of 0.63 sec and a p-value of 0.05) (Table 4).
Regarding the FPI test, according to age, significant differences were only observed in the 8-year-old sample in the flat foot phase on the right foot ($$p \leq 0.009$$, the rest of them having p-values greater than 0.05 (Figure 3).
## 4. Discussion
The tests used for the evaluation of the foot can be used to identify a relationship between them and the different phases of the gait cycle, verifying which of them they influence. The objective of this study was to relate the tests performed on the foot and covering all its planes with the spatiotemporal parameters of children’s gait.
The gait cycle begins when the foot makes contact with the ground, and it ends when the same foot makes contact with the ground again. The first ray is fundamental both in the full stance phase, in which it will serve as a mobile adapter on the irregularities of the ground, forming an internal longitudinal arch, and in the propulsive phase, whose function will be to become a rigid segment capable of transferring the weight of the body forward [28].
The windlass mechanism [13] is closely related to the propulsion phase, since the correct movement of the first metatarsophalangeal joint, together with the locking of the midtarsal joint makes this gait phase functional [29].
This windlass mechanism creates tension in the plantar aponeurosis, with tensile forces approaching $100\%$ of the body weight.
Although it is highly variable, the arch rises rapidly during the late stance phase, and the navicular demonstrates an average rise of 6 mm during late push-off. Depending on the foot model used for the gait analysis, dorsiflexion of the first metatarsophalangeal joint averages around 30–50° during this same period [30].
Jack’s test is a test that has been devised to clinically evaluate the function of this first ray, therefore, a positive test would reflect an inability to dorsiflex this first metatarsophalangeal joint (causing what is known as functional hallux limitus), and therefore, an alteration of this windlass mechanism mentioned above [31].
This is observed in our results, which show an increase in the percentage of the propulsion phase in patients with a positive Jack´s test (difference 0.67 and a p-value of 0.05). Those patients who were unable to perform Jack’s test properly, and in whom, therefore, their windlass mechanism does not work properly, presented a higher percentage in the propulsion phase. Although this is only in the left foot, which leads us to believe that in many cases there are also differences between the stance phases of both feet, which would suggest a more exhaustive, unilateral analysis of gait [14].
However, this contrasts with the opinion of some authors who defend that the presence of limitation of dorsal flexion in this test is not indicative of the limitation of this movement in gait, but that there is a relationship between the pronation of the foot and the limitation of the first metatarsophalangeal joint, which could explain another part of our results [31,32]. However, the study population were patients older than 18 years, and they focused only on the movement of the first metatarsophalangeal joint and not on the entire windlass mechanism. These may be variables that have influenced the conclusion.
Another very important structure in the human gait cycle is the Achilles-calcaneal-plantar system. A weight-bearing motion in the first MP joint depends on structures that are not located at the joint itself, but more proximal ones. Among these structures, the Achilles-calcaneal-plantar system and the medial column of the foot are mainly responsible for optimally setting the first MPJ to provide for anteromedial support to the foot during the third rocker or propulsive phase of gait; this requires adequate passive dorsiflexion of the joint, while the hallux is purchasing the ground, and the verticalized first metatarsal is axially loading the hallux sesamoid complex [33].
At this point, the function of the first ray is very important. The first 20° of dorsiflexion are performed thanks to the triceps surae, which raises the heel and makes the first metatarsal initiate this plantar flexion movement. Limited passive dorsiflexion of the first MP joint limits the motion in the sagittal plane, which is necessary for the forward progression of the body during gait [34].
During the second rocker, the tibia must glide forward on the ankle to allow the body’s center of mass to progress from an initial position posterior to the supporting foot to a final position that is anterior to it. A restriction of ankle passive dorsiflexion during the second rocker will increase dorsiflexion moments at the forefoot. Under normal conditions, during the second rocker, the position of the foot must change from pronation to supination; from a relaxed and cushioning conformation to a rigid and propulsive one. If the ankle is unable to provide the necessary passive dorsiflexion for the centre of mass to be placed in front of its vertical plane, one of the ways to achieve these degrees of dorsiflexion is the pronation of the foot [33].
Therefore, our results reinforce all this, showing a relationship between the lunge test (which, as mentioned above, is predictive of dorsiflexion limitation) and an alteration in the full foot contact phases and in the propulsion phase.
Regarding these two phases of gait, we observed a decrease in the time of the total contact phase, decreasing the time that the foot is on the floor in those patients tested at 5 cm, as well as an increase in the time of the propulsion phase in those patients tested at 10 cm.
The results of the lunge test and Jack’s test could be a starting point to obtain information from the existing literature, which describe that: an increase in the dorsiflexion of the ankle during the end of stance phase produces stress on the Achilles tendon and a decrease in plantarflexion during the propulsion period [35,36].
Finally, another of the tests analysed, which also had an impact on gait, was the analysis of foot posture during pronation or supination according to the FPI, where it is observed that the mid-contact phase influences the result, producing a longer contact time in pronated feet than it does in neutral feet by 0.06 s. These data, although not obtained using the same measuring tool, are similar to those proposed by Ryan Mahaffey et al. [ 37], who proposed the increase in the pronation phase of the midfoot. Caravaggi et al. [ 38] showed significant postural and kinematic alterations in the midtarsal and tarso-metarsal joints of adolescents with planus valgus feet. The objective identification and quantification of planus valgus foot alterations via non-invasive gait-analysis is relevant to improving the diagnosis of this condition and to evaluating the effect of conservative treatments and of surgical corrections by different techniques.
Therefore, both our results and those of other authors show that a pronated foot (measured using the FPI test), as well as a limitation of the dorsal flexion of the ankle (measured using the lunge test) and a limitation of the movement of the first metatarsophalangeal joint (measured using Jack’s test) are factors that influence different moments in the gait cycle. In our case, in addition, it is demonstrated that the tests used in the foot are predictive of these presentations of gait and that, therefore, a relationship can be established between these tests and the spatiotemporal parameters of gait, and it should be studied in a different population such as the child population.
The clinical implication of this study is related to the exploration of a new option for assessing feet and the gait because clinicians will not always have the tools to evaluate the gait parameters. Therefore, these tests could serve as a proxy of this measure, but they will never be a replacement for it.
All of these results should be approached with caution, since they have limitations. The main one is the size of the sample, since it is a convenience sample. This sample was obtained in an exploratory manner, and we had to classify the participants in different subgroups of age, sex, and parameters such as physical activity and weight. In addition, it has the limitation of being a cross-sectional study that always provides punctual data, and not an evolution of the data over time, which is appropriate. Even so, to our knowledge, it is the first study that has begun to relate spatiotemporal parameters with foot tests in children in order to answer one of the great questions of clinicians, which is the interrelation between gait and diagnostic tests.
## 5. Conclusions
The diagnostic analysis tests of the functional limitation of the first toe (Jack’s test) is correlated with the spatiotemporal parameter of propulsion, as well as the lunge test, which also correlates with the midstance phase of the gait, which in turn correlates with the posture of the foot, where an increase is observed in the contact time of the pronated feet.
## References
1. Banwell H.A., Paris M.E., Mackintosh S., Williams C.M.. **Paediatric flexible flat foot: How are we measuring it and are we getting it right? A systematic review**. *Journal of Foot and Ankle Research* (2018) **Volume 11**
2. Redmond A.C., Crosbie J., Ouvrier R.A.. **Development and validation of a novel rating system for scoring standing foot posture: The Foot Posture Index**. *Clin. Biomech.* (2006) **21** 89-98. DOI: 10.1016/j.clinbiomech.2005.08.002
3. Macfarlane T.S., Larson C.A., Stiller C.. **Lower extremity muscle strength in 6- to 8-year-old children using hand-held dynamometry**. *Pediatr Phys Ther.* (2008) **20** 128-136. DOI: 10.1097/PEP.0b013e318172432d
4. Hawke F., Rome K., Evans A.M.. **The relationship between foot posture, body mass, age and ankle, lower-limb and whole-body flexibility in healthy children aged 7 to 15 years**. *J. Foot Ankle Res.* (2016) **9** 14. DOI: 10.1186/s13047-016-0144-7
5. Evans A.M., Karimi L.. **The relationship between paediatric foot posture and body mass index: Do heavier children really have flatter feet?**. *J. Foot Ankle Res.* (2015) **8** 46. DOI: 10.1186/s13047-015-0101-x
6. van der Giessen L.J., Liekens D., Rutgers K.J., Hartman A., Mulder P.G., Oranje A.P.. **Validation of beighton score and prevalence of connective tissue signs in 773 Dutch children**. *J. Rheumatol.* (2001) **28** 2726. PMID: 11764224
7. Pereira A.C., Ribeiro M.G., Araújo A.P.D.Q.C.. **Timed motor function tests capacity in healthy children**. *Arch. Dis. Child.* (2016) **101** 147. DOI: 10.1136/archdischild-2014-307396
8. Lythgo N., Wilson C., Galea M.. **Basic gait and symmetry measures for primary school-aged children and young adults. II: Walking at slow, free and fast speed**. *Gait Posture* (2011) **33** 29-35. DOI: 10.1016/j.gaitpost.2010.09.017
9. Whittle M.W.. **Generation and attenuation of transient impulsive forces beneath the foot: A review**. *Gait Posture* (1999) **10** 264-275. DOI: 10.1016/S0966-6362(99)00041-7
10. Qu X., Yeo J.C.. **Effects of load carriage and fatigue on gait characteristics**. *J. Biomech.* (2011) **44** 1259-1263. DOI: 10.1016/j.jbiomech.2011.02.016
11. Mudge S., Stott N.S.. **Outcome measures to assess walking ability following stroke: A systematic review of the literature**. *Physiotherapy* (2007) **93** 189-200. DOI: 10.1016/j.physio.2006.12.010
12. Sutherland D.. **The development of mature gait**. *Gait Posture* (1997) **6** 163-170. DOI: 10.1016/S0966-6362(97)00029-5
13. Bruening D.A., Pohl M.B., Takahashi K.Z., Barrios J.A.. **Midtarsal locking, the windlass mechanism, and running strike pattern: A kinematic and kinetic assessment**. *J. Biomech.* (2018) **73** 185-191. DOI: 10.1016/j.jbiomech.2018.04.010
14. Kevin D., Maarten E., Dirk D., Matricali G.A., Wuite S., Filip S.. **Estimation of foot joint kinetics in three and four segment foot models using an existing proportionality scheme: Application in paediatric barefoot walking**. *J. Biomech.* (2017) **61** 168-175. PMID: 28784464
15. Bruening D.A., Cooney K.M., Buczek F.L.. **Analysis of a kinetic multi-segment foot model part II: Kinetics and clinical implications**. *Gait Posture* (2012) **35** 535-540. DOI: 10.1016/j.gaitpost.2011.11.012
16. Saraswat P., MacWilliams B.A., Davis R.B., D’Astous J.L.. **A multi-segment foot model based on anatomically registered technical coordinate systems: Method repeatability and sensitivity in pediatric planovalgus feet**. *Gait Posture* (2013) **37** 121-125. DOI: 10.1016/j.gaitpost.2012.06.023
17. Mo Lee M., ho Song C., Jin Lee K., Woo Jung S., ChuLShin D., ho Shin S.. **Concurrent Validity and Test-retest Reliability of the OPTOGait Photoelectric Cell System for the Assessment of Spatio-temporal Parameters of the Gait of Young Adults**. *J. Phys. Ther. Sci.* (2014) **26** 81-85. DOI: 10.1589/jpts.26.81
18. Gomez Bernal A., Becerro-de-Bengoa-Vallejo R., Losa-Iglesias M.E.. **Reliability of the OptoGait portable photoelectric cell system for the quantification of spatial-temporal parameters of gait in young adults**. *Gait Posture* (2016) **50** 196-200. DOI: 10.1016/j.gaitpost.2016.08.035
19. Weart A.N., Miller E.M., Freisinger G.M., Johnson M.R., Goss D.L.. **Agreement Between the OptoGait and Instrumented Treadmill System for the Quantification of Spatiotemporal Treadmill Running Parameters**. *Front. Sports Act. Living* (2020) **2** 571385. DOI: 10.3389/fspor.2020.571385
20. Beulertz J., Bloch W., Prokop A., Rustler V., Fitzen C., Herich L., Streckmann F., Baumann F.T.. **Limitations in Ankle Dorsiflexion Range of Motion, Gait, and Walking Efficiency in Childhood Cancer Survivors**. *Cancer Nurs.* (2016) **39** 117-124. PMID: 25881810
21. Pathare N., Haskvitz E.M., Selleck M.. **Comparison of Measures of Physical Performance Among Young Children Who Are Healthy Weight, Overweight, or Obese**. *Pediat. Phys. Ther.* (2013) **25** 291-296. DOI: 10.1097/PEP.0b013e31829846bd
22. Montes-Alguacil J., Páez-Moguer J., Jiménez Cebrián A.M., Muñoz B.Á., Gijón-Noguerón G., Morales-Asencio J.M.. **The influence of childhood obesity on spatio-temporal gait parameters**. *Gait Posture* (2019) **71** 69-73. DOI: 10.1016/j.gaitpost.2019.03.031
23. Morrison S.C., Ferrari J.. **Inter-rater reliability of the Foot Posture Index (FPI-6) in the assessment of the paediatric foot**. *J. Foot Ankle Res.* (2009) **2** 26. DOI: 10.1186/1757-1146-2-26
24. Gijon-Nogueron G., Montes-Alguacil J., Alfageme-Garcia P., Cervera-Marin J.A., Morales-Asencio J.M., Martinez-Nova A.. **Establishing normative foot posture index values for the paediatric population: A cross-sectional study**. *J. Foot Ankle Res.* (2016) **9** 24. DOI: 10.1186/s13047-016-0156-3
25. Gijon-Nogueron G., Martinez-Nova A., Alfageme-Garcia P., Montes-Alguacil J., Evans A.M.. **International normative data for paediatric foot posture assessment: A cross-sectional investigation**. *BMJ Open* (2019) **9** e023341. DOI: 10.1136/bmjopen-2018-023341
26. Bennell K., Talbot R., Wajswelner H., Techovanich W., Kelly D.. **Intra-rater and inter-rater reliability of a weight-bearing lunge measure of ankle dorsiflexion**. *Aust. J. Physiother.* (1998) **44** 175-180. DOI: 10.1016/S0004-9514(14)60377-9
27. Enklaar J.E.. **Hübscher’s maneuver in the prognosis of flatfoot**. *Maandschr. Kindergeneeskd.* (1956) **24** 189-194. PMID: 13377760
28. Michael O.. **Seibel**. *Función Del Pie Texto Programado* (1994)
29. Durrant B., Chockalingam N.. **Functional hallux limitus**. *J. Am. Podiatr. Med. Assoc.* (2009) **99** 236-243. PMID: 19448175
30. Moisan G., McBride S., Isabelle P.L., Chicoine D., Walha R.. **Intrarater and interrater reliability of the first metatarsophalangeal joint dorsiflexion resistance test**. *Musculoskel. Care* (2022) 1-6. DOI: 10.1002/msc.1675
31. Gatt A., Mifsud T., Chockalingam N.. **Severity of pronation and classification of first metatarsophalangeal joint dorsiflexion increases the validity of the Hubscher Manoeuvre for the diagnosis of functional hallux limitus**. *Foot* (2014) **24** 62-65. DOI: 10.1016/j.foot.2014.03.001
32. Halstead J., Redmond A.C.. **Weight-bearing passive dorsiflexion of the hallux in standing is not related to hallux dorsiflexion during walking**. *J. Orthop. Sport. Phys. Ther.* (2006) **36** 550-556. DOI: 10.2519/jospt.2006.2136
33. Maceira E., Monteagudo M.. **Functional hallux rigidus and the achilles-calcaneus-plantar system**. *Foot and Ankle Clinic* (2014) 669-699
34. Vallotton J., Echeverri S., Dobbelaere-Nicolas V.. **Functional hallux limitus or rigidus caused by a tenodesis effect at the retrotalar pulley: Description of the functional stretch test and the simple hoover cord maneuver that releases this tenodesis**. *J. Am. Podiatr. Med. Assoc.* (2010) **100** 220-229. PMID: 20479455
35. Távara Vidalón P., Lafuente Sotillos G., Munuera-Martínez P.V.. **Movimiento del primer dedo en sujetos con hallux limitus vs. sujetos con pies normales**. *Rev. Esp. Pod.* (2021) **32** 116-122. DOI: 10.20986/revesppod.2021.1621/2021
36. Munuera-Martínez P.V., Távara-Vidalón P., Monge-Vera M.A., Sáez-Díaz A., Lafuente-Sotillos G.. **The validity and reliability of a new simple instrument for the measurement of first ray mobility**. *Sensors* (2020) **20**. DOI: 10.3390/s20082207
37. Mahaffey R., le Warne M., Blandford L., Morrison S.C.. **Age-related changes in three-dimensional foot motion during barefoot walking in children aged between 7 and 11 years old**. *Gait Posture* (2022) **95** 38-43. DOI: 10.1016/j.gaitpost.2022.04.001
38. Caravaggi P., Sforza C., Leardini A., Portinaro N., Panou A.. **Effect of plano-valgus foot posture on midfoot kinematics during barefoot walking in an adolescent population**. *J Foot Ankle Res.* (2018) **11** 55. DOI: 10.1186/s13047-018-0297-7
|
---
title: Impact of Sertraline, Fluoxetine, and Escitalopram on Psychological Distress
among United States Adult Outpatients with a Major Depressive Disorder
authors:
- Kwame Adjei
- Georges Adunlin
- Askal Ayalew Ali
journal: Healthcare
year: 2023
pmcid: PMC10001334
doi: 10.3390/healthcare11050740
license: CC BY 4.0
---
# Impact of Sertraline, Fluoxetine, and Escitalopram on Psychological Distress among United States Adult Outpatients with a Major Depressive Disorder
## Abstract
How impactful is the use of Sertraline, Fluoxetine, and Escitalopram monotherapy on psychological distress among adults with depression in the real world? Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. Medical Expenditure Panel Survey (MEPS) longitudinal data files from 1 January 2012 to 31 December 2019 (panel 17–23) were used to assess the effects of Sertraline, Fluoxetine and Escitalopram on psychological distress among adult outpatients diagnosed with a major depressive disorder. Participants aged 20–80 years without comorbidities, who initiated antidepressants only at rounds 2 and 3 of each panel, were included. The impact of the medicines on psychological distress was assessed using changes in Kessler Index (K6) scores, which were measured only in rounds 2 and 4 of each panel. Multinomial logistic regression was conducted using the changes in the K6 scores as a dependent variable. A total of 589 participants were included in the study. Overall, $90.79\%$ of the study participants on monotherapy antidepressants reported improved levels of psychological distress. Fluoxetine had the highest improvement rate of $91.87\%$, followed by Escitalopram ($90.38\%$) and Sertraline ($90.27\%$). The findings on the comparative effectiveness of the three medications were statistically insignificant. Sertraline, Fluoxetine, and Escitalopram were shown to be effective among adult patients suffering from major depressive disorders without comorbid conditions.
## 1. Introduction
Approximately 15 million physician office visits with depressive disorders as the primary diagnosis were recorded in 2019 [1]. An estimated 21 million adults and 4.1 million adolescents aged 12 to 17 in the USA in 2017 had at least one major depressive episode, representing $8.4\%$ and $17\%$ of the USA population, respectively [2]. According to the World Health Organization, depression is ranked as the most significant cause of disability worldwide and contributes heavily to the global disease burden [3]. Depression is the major contributing factor to suicide and ischemic heart disease [4].
“According to the Global Burden of Disease study, major depressive disorder was recorded as the mental health disorder with the highest economic burden accounting for 2.7 million disability-adjusted life years in 2016” [5]. In 2018, the economic burden of depression was estimated at USD 326 billion, representing an increase of $37.9\%$ between 2010 and 2018 [6]. “ The Center for Disease Control emphasizes that over the past two decades, the use of antidepressants has experienced tremendous growth, making them one of the most expensive and third most prescribed drugs in the USA” [7].
First-generation antidepressants, such as tricyclic antidepressants and monoamine oxidase inhibitors, used to be the main treatment for depression, but they are no longer preferred in many clinics due to their serious side effects, such as orthostatic hypotension and insomnia [8,9,10]. Second-generation antidepressants, including selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), and dopamine reuptake inhibitors, have fewer side effects than first-generation antidepressants [11]. Fluoxetine and Sertraline were among the first SSRIs approved for depression treatment in the 1990s, and Escitalopram was introduced in 2003 [12]. Although the different classes of second-generation antidepressants have similar effectiveness on quality of life, they differ in their pharmacokinetics, pharmacodynamics, and side effects, which may impact treatment selection [13]. Fluoxetine has a lower specificity of serotonin transporter (SERT) than other SSRIs, but a better binding specificity than tricyclic antidepressants and monoamine oxidase inhibitors [14,15]. Fluoxetine can lead to weight loss, agitation, and anxiety; *Sertraline is* associated with a higher incidence of diarrhea; and Escitalopram has a higher likelihood than other SSRIs of causing QT prolongation [16,17,18].
In clinical practice, second-generation antidepressants are prescribed for many conditions other than depression, such as anxiety, sleeping disorders, psychosis, and neuropathic pain [19]. “ *Sertraline is* currently approved for major depressive disorder, obsessive-compulsive disorder, panic disorder, post-traumatic stress disorder, seasonal affective disorder, and premenstrual dysphoric disorder” [14]. Escitalopram is also used in the management of generalized anxiety disorder, while *Fluoxetine is* used in the treatment of premenstrual dysphoric disorder [14]. The choice of antidepressants is influenced by drug profiles, physician characteristics, patient characteristics, and other factors such as comorbidities [20,21].
“Psychological distress refers to non-specific symptoms of stress, anxiety, and depression. High levels of psychological distress are indicative of impaired mental health and may reflect common mental disorders, like depressive and anxiety disorders” [22]. Research has shown that individuals with depression often experience high levels of psychological distress in various areas of life, which leads to a decline in physical, emotional, and social functioning [23]. Physical symptoms of depression, such as fatigue and changes in appetite and sleep patterns, can negatively impact an individual’s ability to engage in physical activity and maintain good physical health [23,24]. Emotional symptoms, such as feelings of sadness and hopelessness, can lead to difficulty in maintaining personal relationships and a lack of interest in activities. Social functioning may also be affected, as individuals with depression may withdraw from social interactions and have difficulty in forming and maintaining relationships [23].
In addition to the negative impact of psychological distress, depression also increases the risk of various physical health problems, such as cardiovascular disease, diabetes, and obesity which can be attributed to unhealthy coping mechanisms such as overeating, lack of physical activity, and substance abuse [24,25]. It is important for individuals with depression to receive appropriate treatment and support to improve their overall well-being and functioning.
There are widely used survey instruments for measuring psychological distress in people with depression, such as the Patient Health Questionnaire-9 (PHQ-9), the Beck Depression Inventory (BDI), and the Kessler Psychological Distress Scale (K6). “ The PHQ-9 is a self-administered questionnaire that assesses the presence and severity of depressive symptoms over the past two weeks, consisting of nine items rated on a four-point Likert scale” [26]. The BDI is a 21-item self-report inventory that measures the presence and severity of depression symptoms over the past two weeks, assessing symptoms such as sadness, hopelessness, and self-esteem, each rated on a four-point Likert scale [27]. The K6 is a brief, self-administered questionnaire that assesses symptoms of non-specific psychological distress over the past 30 days, consisting of six items rated on a five-point Likert scale. A score of 13 or higher on the K6 is considered to indicate clinically significant psychological distress [28].
The K6 is a reliable and valid measure of psychological distress among patients with depression. It has good test–retest reliability, with a correlation coefficient of 0.8, and strong concurrent validity, as it correlates well with other measures of depression and anxiety and is able to discriminate between patients with depression and those without depression [28,29].
Over $40\%$ of depression patients fail to improve with conventional treatment, which involves using a single antidepressant agent at a prescribed dose and duration [30,31,32,33]. In spite of the considerable amount of data available on the clinical efficacy of second-generation antidepressants, there remains insufficient evidence on the real-world impact of the most widely prescribed second-generation antidepressants on patient-reported outcomes.
This study evaluated the effectiveness of the most commonly prescribed antidepressants, Sertraline, Fluoxetine and Escitalopram, on psychological distress among various subgroup populations based on age, race, and sex using a nationally representative sample in the United States.
## 2.1. Data Source
The current retrospective longitudinal study was conducted to examine the effectiveness of Sertraline, Fluoxetine, and Escitalopram monotherapy on psychological distress as a patient-reported outcome among the non-institutionalized US population using the Medical Expenditure Panel Survey (MEPS). The MEPS data used in this study spanned the period 1 January 2012 to 31 December 2019 (panel 17 to panel 23) [34].
The MEPS is a nationally representative estimate of health care use, expenditure, sources of payment, health insurance coverage, and demographic characteristics, additionally providing data on respondents’ health status, employment, access to care, and satisfaction with healthcare [34]. “ The National Health Interview Survey (NHIS) uses a stratified, multistage probability cluster sampling design which provides a nationally representative sample of the U.S. civilian, non-institutionalized population” [34]. “ A computer assisted personal interviewing (CAPI) technology is used to collect information about each household member and the information collected for a sampled household is reported by a single household respondent. Verification of patient’s reports are conducted through a survey response from their healthcare providers and contacting the pharmacies where the participants reported of filling their prescribed medicines” [34,35]. The panel design of the survey comprises five rounds of interviews covering two full calendar years (Figure 1).
Depression was defined as a major depressive episode that affects mood, behavior, and overall health, causing prolonged feelings of sadness, emptiness, or hopelessness and loss of interest in activities that were once enjoyed [35]. Antidepressant monotherapy was defined as patients taking a single antidepressant agent to treat a major depressive disorder. All respondents who were identified as having depression in the 2012–2019 MEPS database, were aged over 19 years, and taking a single agent of Sertraline, Fluoxetine or Escitalopram, were included in the study. To appreciate the effects of the medicine on changes in depressive symptoms during the study, only participants who started taking antidepressants at round 2 and round 3 of the panel were included in the study. The “purchrd” variable was used to select participants from various rounds of the panel. The rationale was to compare the baseline depressive symptoms of the participants from the time they started taking the medications with their symptoms after they had been taking them for roughly a year (in round 4). This will enable us gain insights into the effects of the medicine on the change in depressive symptoms during the study. Patients who purchased medicine before or at the beginning of rounds 1, 4, and 5 of the panel were excluded from the study. Patients who were taking combination therapy were excluded from the study. Patients who had comorbid conditions were also excluded from the study. Respondents with missing responses on the dependent variable (K6 scores) were excluded from the analysis.
## 2.2. Study Design
The MEPS HC medical condition file was used to identify individuals with depression. The MEPS medical condition file contains information on the observation of each self-reported medical condition that a respondent experienced during the data collection year. Medical conditions reported by participants were recorded by interviews and coded to fully specified ICD-10-CM and ICD-9-CM codes. Depression was identified using ICD-9-code 296, 311, and ICD-10-code F32 [34].
Patients taking antidepressants were identified using the prescribed medicines file (Figure 2). The most commonly used antidepressants, Fluoxetine, Escitalopram, and Sertraline, were identified using “rxname” and “rxdrgnam” variables from the prescribed medicines file [34].
The patients’ demographic characteristics were identified from the patient characteristic file. In this study, we included age, race, and gender.
## 2.3. Outcome Measures
The effect of the medicines on psychological distress was assessed using the Kessler Index (K6) scores. The Kessler Index (K6) scores measure individuals’ non-specific psychological distress in the past 30 days [28]. The scale consists of six items, each rated on a five-point Likert scale (from “none of the time” to “all of the time”) [28]. Supplementary S1.
The longitudinal data files in the MEPS contain K6 scores. These scores are measured in rounds 2 and 4 of a panel and are roughly a year apart [36]. Previously reported cut off-points in the literature were used to stratify K6 scores into no/low psychological distress (0–6), mild–moderate psychological distress (7–12), and severe distress (13–24) [28].
In this study, regarding changes in the baseline K6 score (that is round 2–round 4), 1–24 was identified as improved, whereas a change in the K6 score of 0 was classified as unchanged, and when a change in the baseline K6 score ranged from −1 to −24, it was classified as having declined.
## 2.4. Statistical Analysis
Descriptive statistics were used to describe the population according to their socio-demographic characteristics. All statistical values were considered significant at a level of significance of p ≤ 0.05. The dependent variable, namely the difference in K6 scores, was categorized using 1–24 as “improved”, −1–−24 as “declined” and 0 as “unchanged”. A multinomial logistic regression model was built to determine the effect of the independent variables on the above-mentioned dependent variable. Demographic variables such as race, gender, and age were controlled in the regression analysis. Statistical analysis was conducted using STATA software (version 15.1).
## Demographic Characteristics of Study Population
Table 1 shows the demographic characteristics of the study population for each antidepressant. Among the three antidepressants used in the analysis, Sertraline was the most utilized medication among the study population ($$n = 251$$, $42.61\%$) followed by Fluoxetine ($$n = 185$$, $31.41\%$). Most of the study population were females ($$n = 417$$), representing $70.5\%$ of the total study sample. Among different races, non-Hispanic whites were the highest users ($$n = 489$$, $83.02\%$) of the three SSRIs, with American Indians being the lowest users ($$n = 9$$, $1.53\%$) of the three SSRIs. Most of the study population was within the 40–59 age group ($$n = 244$$, $38.54\%$).
Table 2 shows the percentage of patients on Sertraline, Fluoxetine, and Escitalopram who showed improvement, no change, or decline in Kessler 6 scores. The majority of the patients ($$n = 467$$, $92.48\%$) were in the improved group, regardless of which of the three medications they were taking. Fluoxetine had the highest improvement rate of $94.27\%$, compared with Sertraline, which had an improvement rate of $91.96\%$, and Escitalopram, which had an improvement rate of $91.13\%$.
Table 3 shows the multinomial logistic regression results for changes in the Kessler Index scores among patients taking Sertraline, Fluoxetine, or Escitalopram monotherapy. A total of 84 participants with missing responses on the Kessler Index score were excluded, resulting in 505 participants being included in the regression analysis. Participants in the unchanged K6 category were used as references to predict improvement in psychological distress for users on the three SSRIs. Moreover, participants taking Fluoxetine were treated as the reference group among the three medications. Among the various age groups, participants aged between 20 and 39 years were used as the reference group, while non-Hispanic whites were used as the reference for race. In comparison with the participants taking Fluoxetine, the results did not show any statistical difference between participants taking Escitalopram (OR = 0.2823, $95\%$ CI, 0.0209–3.812; $$p \leq 0.34$$) and those taking Sertraline (OR = 0.45, $95\%$ CI, 0.06–3.3249; $$p \leq 0.43$$).
## 4. Discussion
Patients with a major depressive disorder usually have deteriorating mental health that affects the physical and social aspects of their lives. The primary aim of this study was to assess the effects of Sertraline, Fluoxetine, and Escitalopram on psychological distress using changes in Kessler Index 6 scores among adult outpatients diagnosed with one major depressive disorder.
The study sample was characterized by over $70\%$ women, which corresponds with other studies that show that women are more likely than men to experience more depression. Females are also more likely than men to report to a mental health facility or seek medical attention [37,38]. In addition, the increased prevalence of depression correlates with hormonal changes in women, particularly during puberty, before menstruation, following pregnancy, and at perimenopause, suggesting that female hormonal fluctuations may trigger depression [39,40].
The majority of the study population were non-Hispanic whites. Similar racial/ethnic differences in antidepressant use are observed in the treatment of depression [41]. It has also been reported that factors such as racial/ethnic variation in mental health services and availability, treatment acceptability, and educational factors play a role in the prevalence of depression and antidepressant use among races [42]. The 40–59 age group was the highest population taking antidepressant monotherapy, representing over $38\%$ of the study sample. On the contrary, recent studies have shown that young adults aged 18–29 have a higher prevalence of depression than older adults [43,44]. In part, the COVID-19 pandemic has been identified to have played a major role in the increase in the prevalence of depression among young adults [30,31,32]. Young adults have suffered from higher levels of depression and anxiety than older adults throughout the pandemic [45]. According to the Centers for Disease Control and Prevention’s (CDC) Household Pulse Survey, $36\%$ of 18–29-year-olds had symptoms of depression in early May 2021, compared to $22\%$ of those aged 40–49 and $15\%$ of those aged 50–59 [45].
The descriptive statistics showed that $94.27\%$ of the study participants taking Fluoxetine had experienced an improvement in their psychological distress after one year on the medication, followed by Sertraline ($91.96\%$) and Escitalopram ($91.13\%$). The overall improvement rate of $92.48\%$ among the study sample indicates only that selective serotonin reuptake inhibitor medication effectively improves patient-reported outcomes, specifically psychological distress, over one year of taking the medication. In a similar study, the majority of the participants taking either first- or second-generation antidepressant monotherapy remained in the unchanged category after round 4 [36]. The authors explained that the medications might have elicited desirable responses resulting in patients having controlled depressive symptoms even at the time of the initial measure (round 2 of the panel) of psychological distress [37].
The current study compared the impact of Fluoxetine, Sertraline, and Escitalopram on patient-reported outcomes and psychological distress using changes in the Kessler 6 score. In our comparison with Fluoxetine as a reference drug, there was no statistical difference observed between the effect of Sertraline (OR = 0.45, $95\%$ CI, 0.06–3.3249; $$p \leq 0.43$$), and Escitalopram (OR = 0.2823, $95\%$ CI, 0.0209–3.812; $$p \leq 0.34$$) on psychological distress. Currently, there is insufficient data on evaluating the effectiveness of these commonly prescribed antidepressants using changes in the Kessler 6 score as a patient-reported outcome. A similar study on changes in the Kessler Index 6 score showed no significant difference between patients using monotherapy and those using add-on/switch therapy [36]. However, comparing our results to a meta-analysis involving 24,595 participants in 111 studies on the efficacy and acceptability of 12 antidepressants, Escitalopram, Sertraline, and Fluoxetine were found to have superior efficacy than the SNRIs in the meta-analysis [46]. With Fluoxetine as a reference compound, both Escitalopram and Sertraline had a significantly higher efficacy rate than Fluoxetine. However, they concluded that Sertraline may be preferable because of the balance between its efficacy and its tolerability [46,47]. In these studies, the treatment effect was measured using another instrument variable, changes in the baseline Montgomery–Asberg Depression Rating Scale (MADRS) total score.
The strength of this study was that a retrospective longitudinal database was used with a nationally representative sample. Due to the structure of the Medical Expenditure Panel Survey (MEPS), we were able to assess the outcome of the medications on psychological distress over time points approximately one year apart (from round 2 to round 4). This gives adequate time to elicit rich data on the long-term effect of the medications on the participants, which is essential for a chronic disease with a high relapse rate, such as depression. However, there were limitations to the study. This study focused on patients with a major depressive disorder without any comorbidities. This limits the generalizability of the results. The study is susceptible to response bias, as the information is self-reported by respondents and cannot therefore always be considered reliable. Moreover, this study could not adjust for the type and severity of depression, illness duration, side effects, and medication adherence, due to the structure of the MEPS. Additionally, this study could not account for the specific dose and titration of the medication, due to the nature of the MEPS, which does not provide dose-related information on the medications. We assumed that patients were prescribed the standard dose of the medications: Escitalopram 10–20 mg once a day [48], Sertraline 150–200 mg daily [49], and Fluoxetine 20–60 mg per day [50]. A future study could focus on examining the real-world impacts of these most widely prescribed antidepressants together with newly approved antidepressants, taking into account medication adherence, the tolerability of the medications, and the type and severity of depression. Due to insufficient evidence on the real-world impacts of selective serotonin reuptake inhibitors among depressed patients, this study adds to the evidence available to inform clinicians on the effect of the long-term use of selective serotonin reuptake inhibitors on patient-reported outcomes among patients with chronic depression. This study can also serve as a guide for researchers in this area, who can focus on the use of second-generation antidepressant monotherapy and dual-therapy antidepressants among patients with severe depression using real-world data.
## 5. Conclusions
Based on the descriptive statistics, all the medications effectively improve the rate of psychological distress among adult patients suffering from major depressive disorders without comorbid conditions. Moreover, no significant difference in the improvement rate of psychological distress for the participants was observed in our comparison of the three selective serotonin reuptake inhibitors. In addition to taking the effectiveness of the medications into account, it is imperative that clinicians consider patients’ preferences and tolerability toward specific antidepressant medications in their prescribing decisions.
## References
1. Santo L., Kang K.. **National Hospital Ambulatory Medical Care Survey: 2019 National Summary Tables**. (2023.0)
2. **NIMH » Major Depression. The National Institute of Mental Health Information Resource Center**. (2020.0)
3. **Depression**. (2021.0)
4. Shah A.J.. **Depression and History of Attempted Suicide as Risk Factors for Heart Disease Mortality in Young Individuals**. *Arch. Gen. Psychiatry* (2011.0) **68** 1135-1142. DOI: 10.1001/archgenpsychiatry.2011.125
5. Greenberg P.E., Fournier A.-A., Sisitsky T., Pike C.T., Kessler R.C.. **The Economic Burden of Adults with Major Depressive Disorder in the United States (2005 and 2010)**. *J. Clin. Psychiatry* (2015.0) **76** 155-162. DOI: 10.4088/JCP.14m09298
6. Greenberg P.E., Fournier A.-A., Sisitsky T., Simes M., Berman R., Koenigsberg S.H., Kessler R.C.. **The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018)**. *Pharmacoeconomics* (2021.0) **39** 653-665. DOI: 10.1007/s40273-021-01019-4
7. **Antidepressant Use among Persons Aged 12 and Over: United States, 2011–2014. Centers for Disease Control and Prevention**. (2017.0)
8. Thaler K., Gartlehner G., Hansen R., Morgan L., Lux L., Van Noord M., Mager U., Gaynes B., Thieda P., Strobelberger M.. **The comparative efficacy of second-generation antidepressants for the accompanying symptoms of depression: A systematic review**. *Eur. Psychiatry* (2011.0) **26** 697. DOI: 10.1016/S0924-9338(11)72402-6
9. Song F., Freemantle N., Sheldon T.A., House A., Watson P., Long A., Mason J.. **Selective serotonin reuptake inhibitors: Meta-analysis of efficacy and acceptability**. *BMJ* (1993.0) **306** 683-687. DOI: 10.1136/bmj.306.6879.683
10. Montgomery S.A., Henry J., McDonald G., Dinan T., Lader M., Hindmarch I., Clare A., Nutt D.. **Selective serotonin reuptake inhibitors**. *Int. Clin. Psychopharmacol.* (1994.0) **9** 47-54. DOI: 10.1097/00004850-199400910-00008
11. Ferguson J.M.. **SSRI Antidepressant Medications**. *Prim. Care Companion J. Clin. Psychiatry* (2001.0) **3** 22-27. DOI: 10.4088/PCC.v03n0105
12. Sanchez C., Reines E.H., Montgomery S.A.. **A comparative review of escitalopram, paroxetine, and sertraline**. *Int. Clin. Psychopharmacol.* (2014.0) **29** 185-196. DOI: 10.1097/YIC.0000000000000023
13. Sheridan S.D.A.. **Second-Generation Antidepressants for Depression in Adults. Implementing AHRQ Effective Health Care Reviews-American Family Physician. 15 November 2013**
14. Edinoff A., Akuly H., Hanna T., Ochoa C., Patti S., Ghaffar Y., Kaye A., Viswanath O., Urits I., Boyer A.. **Selective Serotonin Reuptake Inhibitors and Adverse Effects: A Narrative Review**. *Neurol. Int.* (2021.0) **13** 387-401. DOI: 10.3390/neurolint13030038
15. Bymaster F.P., Zhang W., Carter P.A., Shaw J., Chernet E., Phebus L., Wong D.T., Perry K.W.. **Fluoxetine, but not other selective serotonin uptake inhibitors, increases norepinephrine and dopamine extracellular levels in prefrontal cortex**. *Psychopharmacology* (2002.0) **160** 353-361. DOI: 10.1007/s00213-001-0986-x
16. Marken P.A., Munro J.S.. **Selecting a Selective Serotonin Reuptake Inhibitor**. *Prim. Care Companion J. Clin. Psychiatry* (2000.0) **2** 205-210. DOI: 10.4088/PCC.v02n0602
17. Lam R.. **Antidepressants and QTc prolongation**. *J. Psychiatry Neurosci.* (2013.0) **38** E5-E6. DOI: 10.1503/jpn.120256
18. Hashimoto K.. **Sigma-1 Receptors and Selective Serotonin Reuptake Inhibitors: Clinical Implications of their Relationship**. *Central Nerv. Syst. Agents Med. Chem.* (2009.0) **9** 197-204. DOI: 10.2174/1871524910909030197
19. Noordam R., Aarts N., Verhamme K.M., Sturkenboom M.C.M., Stricker B.H., Visser L.E.. **Prescription and indication trends of antidepressant drugs in the Netherlands between 1996 and 2012: A dynamic population-based study**. *Eur. J. Clin. Pharmacol.* (2015.0) **71** 369-375. DOI: 10.1007/s00228-014-1803-x
20. Forns J., Pottegård A., Reinders T., Poblador-Plou B., Morros R., Brandt L., Cainzos-Achirica M., Hellfritzsch M., Schink T., Prados-Torres A.. **Antidepressant use in Denmark, Germany, Spain, and Sweden between 2009 and 2014: Incidence and comorbidities of antidepressant initiators**. *J. Affect. Disord.* (2019.0) **249** 242-252. DOI: 10.1016/j.jad.2019.02.010
21. Haro J.M., Lamy F.-X., Jönsson B., Knapp M., Brignone M., Caillou H., Chalem Y., Hammer-Helmich L., Rive B., Saragoussi D.. **Characteristics of patients with depression initiating or switching antidepressant treatment: Baseline analyses of the PERFORM cohort study**. *BMC Psychiatry* (2018.0) **18**. DOI: 10.1186/s12888-018-1657-3
22. Cuijpers P., Smits N., Donker T., Have M.T., de Graaf R.. **Screening for mood and anxiety disorders with the five-item, the three-item, and the two-item Mental Health Inventory**. *Psychiatry Res.* (2009.0) **168** 250-255. DOI: 10.1016/j.psychres.2008.05.012
23. Viertiö S., Kiviruusu O., Piirtola M., Kaprio J., Korhonen T., Marttunen M., Suvisaari J.. **Factors contributing to psychological distress in the working population, with a special reference to gender difference**. *BMC Public Health* (2021.0) **21**. DOI: 10.1186/s12889-021-10560-y
24. Mauramo E., Lahti J., Lallukka T., Lahelma E., Pietiläinen O., Rahkonen O.. **Changes in common mental disorders and diagnosis-specific sickness absence: A register-linkage follow-up study among Finnish municipal employees**. *Occup. Environ. Med.* (2019.0) **76** 230-235. DOI: 10.1136/oemed-2018-105423
25. Jena B.N., Kalra S., Yeravdekar R.. **Emotional and psychological needs of people with diabetes**. *Indian J. Endocrinol. Metab.* (2018.0) **22** 696-704. DOI: 10.4103/ijem.IJEM_579_17
26. Kroenke K., Spitzer R.L., Williams J.B.W., Löwe B.. **The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: A systematic review**. *Gen. Hosp. Psychiatry* (2010.0) **32** 345-359. DOI: 10.1016/j.genhosppsych.2010.03.006
27. Beck A.T., Steer R.A., Ball R., Ranieri W.F.. **Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients**. *J. Pers. Assess.* (1996.0) **67** 588-597. DOI: 10.1207/s15327752jpa6703_13
28. Kang Y.-K., Guo W.-J., Xu H., Chen Y.-H., Li X.-J., Tan Z.-P., Li N., Gesang Z.-R., Wang Y.-M., Liu C.-B.. **The 6-item Kessler psychological distress scale to survey serious mental illness among Chinese undergraduates: Psychometric properties and prevalence estimate**. *Compr. Psychiatry* (2015.0) **63** 105-112. DOI: 10.1016/j.comppsych.2015.08.011
29. Andersen L.S., Grimsrud A., Myer L., Williams D.R., Stein D.J., Seedat S.. **The psychometric properties of the K10 and K6 scales in screening for mood and anxiety disorders in the South African Stress and Health study**. *Int. J. Methods Psychiatr. Res.* (2011.0) **20** 215-223. DOI: 10.1002/mpr.351
30. Shelton R.C., Tollefson G.D., Tohen M., Stahl S., Gannon K.S., Jacobs T.G., Buras W.R., Bymaster F.P., Zhang W., Spencer K.A.. **A Novel Augmentation Strategy for Treating Resistant Major Depression**. *Am. J. Psychiatry* (2001.0) **158** 131-134. DOI: 10.1176/appi.ajp.158.1.131
31. Rush A.J., Kraemer H.C., Sackeim H.A., Fava M., Trivedi M.H., Frank E., Ninan P.T., Thase M.E., Gelenberg A.J., Kupfer D.J.. **Report by the ACNP Task Force on Response and Remission in Major Depressive Disorder**. *Neuropsychopharmacology* (2006.0) **31** 1841-1853. DOI: 10.1038/sj.npp.1301131
32. Fava M., Davidson K.G.. **Definition and epidemiology of treatment-resistant depression**. *Psychiatr. Clin. N. Am.* (1996.0) **19** 179-200. DOI: 10.1016/S0193-953X(05)70283-5
33. Papakostas G.I., Shelton R.C., Smith J., Fava M.. **Augmentation of Antidepressants with Atypical Antipsychotic Medications for Treatment-Resistant Major Depressive Disorder: A meta-analysis**. *J. Clin. Psychiatry* (2007.0) **68** 826-831. DOI: 10.4088/JCP.v68n0602
34. **Methodology Report #24: Estimation Procedures for the 2007 Medical Expenditure Panel Survey Household Component. (n.d.)**
35. **What Is Depression? (n.d.). American Psychiatry Association**
36. Shah D., Vaidya V., Patel A., Borovicka M., Goodman M.-H.. **Assessment of health-related quality of life, mental health status and psychological distress based on the type of pharmacotherapy used among patients with depression**. *Qual. Life Res.* (2016.0) **26** 969-980. DOI: 10.1007/s11136-016-1417-0
37. Ford D.E., Erlinger T.P.. **Depression and C-Reactive Protein in US Adults**. *Arch. Intern. Med.* (2004.0) **164** 1010-1014. DOI: 10.1001/archinte.164.9.1010
38. Salk R.H., Hyde J.S., Abramson L.Y.. **Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms**. *Psychol. Bull.* (2017.0) **143** 783-822. DOI: 10.1037/bul0000102
39. Albert P.R.. **Why is depression more prevalent in women?**. *J. Psychiatry Neurosci.* (2015.0) **40** 219-221. DOI: 10.1503/jpn.150205
40. Bartels M., Cacioppo J.T., van Beijsterveldt T.C.E.M., Boomsma D.I.. **Exploring the Association between Well-Being and Psychopathology in Adolescents**. *Behav. Genet.* (2013.0) **43** 177-190. DOI: 10.1007/s10519-013-9589-7
41. Mojtabai R., Olfson M.. **National Trends in Long-Term Use of Antidepressant Medications**. *J. Clin. Psychiatry* (2013.0) **75** 169-177. DOI: 10.4088/JCP.13m08443
42. Vahratian A.. **Symptoms of Anxiety or Depressive Disorder and Use of Mental Health**. (2021.0)
43. Lee J.. **Mental health effects of school closures during COVID-19**. *Lancet Child Adolesc. Health* (2020.0) **4** 421. DOI: 10.1016/S2352-4642(20)30109-7
44. Loades M.E., Chatburn E., Higson-Sweeney N., Reynolds S., Shafran R., Brigden A., Linney C., McManus M.N., Borwick C., Crawley E.. **Rapid Systematic Review: The Impact of Social Isolation and Loneliness on the Mental Health of Children and Adolescents in the Context of COVID-19**. *J. Am. Acad. Child Adolesc. Psychiatry* (2020.0) **59** 1218-1239.e3. DOI: 10.1016/j.jaac.2020.05.009
45. Racine N., McArthur B.A., Cooke J.E., Eirich R., Zhu J., Madigan S.. **Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19**. *JAMA Pediatr.* (2021.0) **175** 1142. DOI: 10.1001/jamapediatrics.2021.2482
46. Cipriani A., Furukawa T.A., Salanti G., Chaimani A., Atkinson L.Z., Ogawa Y., Leucht S., Ruhe H.G., Turner E.H., Higgins J.P.T.. **Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis**. *Lancet* (2018.0) **391** 1357-1366. DOI: 10.1016/S0140-6736(17)32802-7
47. Kennedy S.H., Andersen H.F., Thase M.E.. **Escitalopram in the treatment of major depressive disorder: A meta-analysis**. *Curr. Med. Res. Opin.* (2008.0) **25** 161-175. DOI: 10.1185/03007990802622726
48. Wade A.G., Crawford G.M., Yellowlees A.. **Efficacy, safety and tolerability of escitalopram in doses up to 50 mg in Major Depressive Disorder (MDD): An open-label, pilot study**. *BMC Psychiatry* (2011.0) **11**. DOI: 10.1186/1471-244X-11-42
49. Simon G., Post T.W.. *Unipolar Major Depression in Adults: Choosing Initial Treatment* (2023.0)
50. Kupka R., Post T.W.. *Rapid Cycling Bipolar Disorder in Adults: Treatment of Major Depression* (2023.0)
|
---
title: Efficiency and Safety of CyberKnife Robotic Radiosurgery in the Multimodal
Management of Patients with Acromegaly
authors:
- Carlos Alfonso Romero-Gameros
- Baldomero González-Virla
- Guadalupe Vargas-Ortega
- Ernesto Sosa-Eroza
- Mario Enrique Rendón-Macías
- Lourdes Josefina Balcázar-Hernández
- Moises Mercado
- Novelthys Velasco-Cortes
- Carlos Aaron Rodea-Ávila
- Luis Flores-Robles
- José Armando Lorenzana-Hernández
- José Vázquez-Rojas
- Margarita López-Palma
journal: Cancers
year: 2023
pmcid: PMC10001340
doi: 10.3390/cancers15051438
license: CC BY 4.0
---
# Efficiency and Safety of CyberKnife Robotic Radiosurgery in the Multimodal Management of Patients with Acromegaly
## Abstract
### Simple Summary
Radiosurgery as an adjuvant treatment for acromegaly has shown efficacy in endocrine and tumor biochemical control, with an acceptable safety profile; however, the reported endocrine and tumor control rates and safety profile are heterogeneous. Therefore, the aim of the study was to evaluate the results of the efficiency and safety of radiosurgery in a well-characterized cohort of acromegalic patients, in addition to analyzing the prognostic factors associated with disease remission. We found a statistically significant reduction in IGF-1, IFG-1 x ULN, and GH concentrations at one year, and at the end of follow-up; in addition, it was observed that high basal IGF-1 concentrations were predictors of the biochemical absence of remission. We did not observe cases of optic neuritis associated with radiation toxicity or stroke.
### Abstract
Objective: To analyze, in a cohort of acromegalic patients, the results of the efficiency and safety of radiosurgery (CyberKnife), as well as the prognostic factors associated with disease remission. Material and methods: Observational, retrospective, longitudinal, and analytical study that included acromegalic patients with persistent biochemical activity after initial medical–surgical treatment, who received treatment with CyberKnife radiosurgery. GH and IGF-1 levels at baseline after one year and at the end of follow-up were evaluated. Results: 57 patients were included, with a median follow-up of four years (IQR, 2–7.2 years). The biochemical remission rate was $45.6\%$, $33.33\%$ achieved biochemical control, and $12.28\%$ attained biochemical cure at the end of follow-up. A progressive and statistically significant decrease was observed in the comparison of the concentrations of IGF-1, IFG-1 x ULN, and baseline GH at one year and at the end of follow-up. Both cavernous sinus invasion and elevated baseline IGF-1 x ULN concentrations were associated with an increased risk of biochemical non-remission. Conclusion: Radiosurgery (CyberKnife) is a safe and effective technique in the adjuvant treatment of GH-producing tumors. Elevated levels of IGF x ULN before radiosurgery and invasion of the cavernous sinus by the tumor could be predictors of biochemical non-remission of acromegaly.
## 1. Introduction
Pituitary tumors account for $20\%$ of all intracranial tumors [1]. Functioning adenomas account for more than $50\%$ of pituitary tumors [2] and are associated with clinical syndromes with significant morbidity and mortality and mechanical compression effects on vital structures [3]. Acromegaly is a chronic, deforming disease resulting from an excess production of growth hormone (GH), in most cases caused by a pituitary macroadenoma, which is presented clinically with a generalized acro-growth of soft tissue and bone. Untreated acromegaly has been associated with a greater number of metabolic, cardiovascular, osteoarticular, pulmonary, and neoplastic comorbidities. Patients with active acromegaly have excess mortality compared to the general population, which is associated with neoplastic and cardiovascular causes, and ultimately a progressive reduction in life quality [4,5,6].
Due to the heterogeneity and complexity in the presentation of the clinical picture, and low biochemical control/cure rates with the different medical–surgical strategies, most patients benefit from a multimodal treatment [7]. The initial treatment of acromegaly is surgical, through a transsphenoidal or transcranial approach, aimed in most cases at tumor demassification. In the absence of biochemical control after surgery and structural tumor remnant, adjuvant medical treatment with first- (Octreotide LAR and Lanreotide autogel) and second-generation (Pasireotide) somatostatin analogues is indicated [8]; the observed biochemical control rates of this treatment vary in the different published studies, ranging from 35–$76\%$ for the first-generation analogues, and from $26.9\%$ to $93.3\%$ for Pasireotide [9]. The biochemical control rates reported with dompaminergic agonists and Pegvisomant are of $50\%$ [10] and 58–$97\%$ [9], respectively; however, the use of Pegvisomant implies high costs for health systems, something that could be unsustainable in developing countries.
The use of second- and third-line treatments, such as fractionated stereotactic radiotherapy and radiosurgery, have shown efficacy in endocrine and tumor biochemical control, with an acceptable safety profile. However, the rates of endocrine and tumor control and safety profile in their different modalities (LINAC, CyberKnife and GammaKnife) are heterogeneous [11]. Therefore, the aim of the study was to analyze, in a well-characterized cohort of acromegalics, the efficacy and safety of radiosurgery, as well as to see which prognostic factors were associated with disease remission.
## 2. Materials and Methods
An observational, retrospective, longitudinal, and analytical study was conducted in which acromegaly patients from the clinic of the Hospital de Especialidades, Centro Médico Nacional Siglo XXI, in Mexico City, Mexico, who received medical care during the period between 2010–2020, were included. The present study was approved by the local ethics and research committee (Registry identifier: R-2018-3601-149) and was consistent with the ethical guidelines of the 1975 Helsinki Declaration and the Mexican General Health Law on Research for Health Studies.
The acromegaly clinic was established in 2000 and currently has more than 600 patients who receive a uniform follow-up, according to established protocols with neuroendocrinological, neuro-ophthalmological and neurosurgical care. All patients, except known diabetics, underwent an oral glucose tolerance test, during which both GH and glucose were measured at baseline and at 30, 60, 90, and 120 min after intake of a 75 g glucose load. Additionally, according to our standardized protocol, insulin-like growth factor 1 (IGF-1), as well as morning cortisol, thyroid-stimulating hormone (TSH), free T4, prolactin (PRL), luteinizing hormone (LH), follicle stimulating hormone (FSH), and testosterone or estradiol were measured in the initial blood sample [12]. Patients received multimodal treatment including transsphenoidal or transcranial surgery, medical treatment with first generation somatostatin analogues (SSA) (Lanreotide autogel 120 mg deep subcutaneous application every 28 days and Octreotide LAR 20 mg intramuscular application every 28 days) and dopaminergic agonists (DA) (Cabergoline 1.5 to 3 mg orally weekly), fractionated stereotactic radiotherapy, and/or radiosurgery. The sample was obtained by non-probabilistic sampling of consecutive cases.
The collection of sociodemographic data, medical history, and laboratory and imaging data was carried out through a review of the patients’ electronic records. The inclusion criteria were patients with biochemical activity of acromegaly, of either sex, older than 17 years, and if they were candidates for radiosurgery treatment according to the current guidelines at the time of patient assessment [13,14]. The selection criteria for referral to radiosurgery were persistent biochemical activity (GH > 1 ng/mL and/or an IGF x ULN > 1.2) after surgical and medical treatment, tumor remnant < 30 mm, and distance from the tumor remnant to the optic chiasm > 3 mm [14,15]. The exclusion criteria were patients without complete data in their electronic files regarding biochemical and imaging outcomes related to the disease and patients who received other types of pituitary/cranial radiation therapy.
The primary outcome to be assessed was a biochemical remission of acromegaly at the end of the follow-up as a dichotomous nominal variable. Biochemical activity was defined as the presence of GH > 1 ng/mL and/or IGF x ULN >1.2, biochemical remission after radiosurgery as GH ≤ 1 and IGF x ULN 1.2 without medical treatment, and post-surgery biochemical control that required medical treatment with first-generation somatostatin analogues and that achieved the control goals. The IGF-1 X Upper Limit Normal (IGF-1 X ULN) value was obtained through the quotient of the IGF-1 obtained from the patient at the time of evaluation and the IGF-1 standardized for age and gender [16]. Tumor volume was calculated using the Di Chiro–Nelson method [17]. The invasion of the cavernous sinus was evaluated according to the Knosp classification. Local tumor control (LC) was defined as the containment and/or non-growth of the tumor remnant. Baseline variables were considered as those measured immediately before radiosurgery. Delay in radiosurgery time was defined as the time between the last surgery and the application of radiosurgery. Delay in diagnosis was defined as the time between the appearance of the first symptoms and the biochemical and imaging diagnosis of acromegaly. Panhypopituitarism was defined as the presence of three or more affected hypothalamic–pituitary axes. Diabetes and prediabetes were defined according to the American Diabetes *Association criteria* [18]. Hypertension was considered if the systolic blood pressure reading exceeded 140 mmHg or the diastolic was above 90 mmHg. Hypercholesterolemia and hypertriglyceridemia were defined as when values exceeded 200 and 150 mg/dL, respectively. Central hypocortisolism was defined by a cortisol concentration < 5 μg/dL at 7:00 h. Central hypothyroidism was diagnosed when free T4 was below < 0.6 ng/dL, along with low or inappropriately normal TSH. Central hypogonadism was defined by total testosterone < 250 ng/dL or estradiol < 20 pg/mL accompanied oligo- or amenorrhea, along with low or inappropriately normal serum LH and FSH.
## 2.1. Treatment Parameters
Radiosurgery was administered using a CyberKnife M6 platform, with the Multi-Plan system, to develop the planning treatments (Accuray Incorporated Sunnyvale, Sunnyvale, CA, USA) for all treatments in Mexico City, the Oncology Hospital at the National Medical Center. After a patient was accepted to be treated, the IMR and CT for planning were obtained. The median radiation dose was of 23.5 Gy (range 22–25 Gy), delivered in a single day, or a maximum of five days, in 5 Gy to 22 Gy per fraction. The individual Radiosurgery radiation protocol was decided by the radiation oncologist and neurosurgeon, based on the availability of appointments and the specific circumstances of each patient. For instance, in patients living out of town, the Institution provided accommodation for the duration of their treatments. Different organs at risk were carefully protected and all passed the Normal Tissue Constraints, according to the R.D. Timmerman charts. Medical treatments with SSA or DA were suspended at least one month before and during radiotherapy [19,20].
## 2.2. Hormonal Measurements
Assays for the measurement of GH and IGF-1 have changed throughout the follow-up of the cohort. Since 2007 to date, hormonal measurements were carried out using the same commercially available immunoassays. GH was measured by means of the Immulite, 2-site chemiluminescent assay (DiaSorin–Liaison, Saluggia, Italy), which has a detection limit of 0.009 ng/mL and an intra-and-interassay coefficient of variations of $2.5\%$ and $5.8\%$, respectively. The International Reference Preparation (IRP) used in this GH assay was that of the World Health Organization (WHO), second $\frac{95}{574.}$ IGF-1 was measured by means of a 2-site chemiluminescent assay (DiaSorin–Liaison). The IRP in these IGF-1 assays was the WHO second $\frac{02}{254.}$ We established our own age-adjusted normative IGF-1 data, analyzing serum samples from 400 healthy adults, with an age range of 18 to 80 years, as previously described [4]. The hormonal assays used in prior years were: before 1999: RIA (LD 0.7) for GH and IRMA for IGF-1; 2000–2007: IMMULITE (LD 0.01) for GH and DIAGNOSTIC SYSTEM LAB for IGF-1.
## 2.3. Statistical Analysis
Descriptive and inferential statistics were used for data analysis, taking into account measures of central tendency and dispersion, according to the distribution of the variables. The Shapiro–Wilk test was used to determine the normality of the quantitative variables’ distribution. For the comparison of variables in independent groups, frequencies and proportions, Pearson’s Xi2 test, or Fisher’s exact test were used according to the expected value; and for quantitative variables, Student’s t test or Mann–Whitney U test were used, according to the type of distribution. For the comparison of variables between three dependent groups, Cochran’s Q test was used for qualitative variables, while for the comparison of quantitative variables, the Friedman’s test was used.
A comparative analysis of the baseline characteristics (before radiosurgery) between the groups of patients, with remission and without biochemical remission (at the last follow-up), was performed. Subsequently, through a crude and adjusted Cox Proportional Hazards Regression Analysis, we estimated the magnitude of association of the following variables before radiosurgery: age, gender, IGF-1 x ULN, GH, tumor size, and invasion of the cavernous sinus (grade 1–4 of Knosp’ classification), taking no biochemical remission of the disease at the last of follow-up as the outcome. A two-sided p value was used for the in-between group difference with respect to the primary outcome. A p value of $p \leq 0.05$ was considered statistically significant. The statistical software used was the Stata SE software version 16 (StataCorp, College Station, TX, USA).
## 3.1. Baseline Characteristics
Of 265 patients with biochemical activity of acromegaly, 57 patients met the criteria for radiosurgery. Of the 57 patients analyzed, the mean age at diagnosis and at the time of radiosurgery was 47.1 ± 13.4 years and 54.5 ± 12.3 years, respectively, with a female predominance of $56.1\%$; $76.3\%$ of patients had macroadenoma. The median follow-up from radiosurgery treatment was four years (IQR, 2–7.2 years). The delay in diagnosis of acromegaly in the studied cut-off was of five years (IQR, 4–8 years); all patients underwent transsphenoidal resection for tumor demassification and subsequently received radiosurgery as adjuvant treatment. The median delay time to radiosurgery was 38 months (IQR, 21–61 months). Median IGF-1 was 595.3 ng/mL ± 274.9 ng/mL and median GH was 5.66 ng/mL (IQR 2.5–18.3) before radiosurgery. The proportion of patients with tumoral invasion to the cavernous sinus was $78.95\%$. Pituitary hormone deficiencies and other patient characteristics are shown in Table 1.
Of the patients referred for radiosurgery treatment, three patients ($5.26\%$) were treated with the hypofractionated modality and 54 patients ($94.74\%$) with a single dose; no statistically significant differences were observed in the biochemical remission rate after radiosurgery when comparing single dose vs. hypofractionated. Regarding the characteristics of radiosurgery treatment, of the total number of patients included in the study [57], it was only possible to obtain information on 37 patients; these results are presented in Table S1 of the Supplementary Materials.
## 3.2. Endocrine Outcomes
The biochemical remission rate was $45.6\%$ (26 patients); of which $33.33\%$ (19 patients) achieved biochemical control and $12.28\%$ (seven patients) achieved biochemical cure. A progressive and statistically significant decrease was found in the comparison of IGF-1, IFG-1 x ULN, and basal GH concentrations at one year and at the end of follow-up. Likewise, statistically significant increases were observed in the percentage of patients who reached the control goals at the different evaluation times (Table 2). After radiosurgery, a significant reduction was observed in the percentage of patients using pharmacological therapy (Table 2). The LC tumor rate obtained in the group of patients evaluated was $100\%$ (57 patients).
Medical treatment after radiosurgery was characterized by an increase in the number of patients with no treatment at the end of follow-up ($1.75\%$ at baseline vs. 15.79 at the end of follow-up); $56.14\%$ of patients were treated with somatostatin analogues (Octreotide LAR or Lanreotide autogel) before radiosurgery, a proportion that decreased to $49.12\%$ at the end of follow-up. $36.84\%$ of patients received treatment with somatostatin analogues (Octreotide LAR or Lanreotide autogel) plus cabergoline, a proportion that decreased to $28.07\%$ at the end of follow-up. Finally, $5.26\%$ received treatment with carbegoline alone, a proportion that increased to $7\%$ at the end of follow-up.
## 3.3. Radiosurgery Safety Profile
Regarding hypothalamic-pituitary hormonal deficiencies after radiosurgery, it was observed that the percentage of hypocortisolism, hypothyroidism, and hypogonadism increased significantly throughout follow-up (Table 2). The reported rate of panhypopituitarism was $1.75\%$ (1 patient) at baseline and $24.56\%$ (14 patients) at the end of follow-up ($p \leq 0.001$). During follow-up after radiosurgery, two patients ($3.5\%$) presented central nervous system tumors of the meningioma type. No cases of optic neuritis associated with radiation toxicity were observed. There were no cases of stroke.
A sub-analysis was carried out in which the 37 patients who had the radiosurgery parameters were included; in this analysis, the radiosurgery parameters were contrasted against the presence or absence of new endocrine deficiencies of the hypothalamic-pituitary axis at the end of follow-up, in which no statistically significant differences were observed. These results are presented in Table S2 of the Supplementary Materials.
## 3.4. Bivariate Analysis between Active and Biochemical Control Groups
In the bivariate analysis between the groups of patients without and with biochemical remission of acromegaly, no significant differences were observed between the baseline characteristics of the patients (Table 3).
## 3.5. Multivariate Analysis
In a Cox Proportional Hazards Regression Analysis, only the baseline IGF-1 x ULN concentration above range was shown to be a risk factor (HR of 1.33; $95\%$ CI 1.01–1.88) for no biochemical remission. Invasion of the cavernous sinus by the growth hormone-producing tumor (grade 1–4 of Knosp’ classification) had a 2.53 ($95\%$ CI 0.92–6.97) for no biochemical remission. The rest of the variables had no statistically significant association with this lack of biochemical control (see Table 4).
## 4. Discussion
Currently, the treatment of acromegaly contemplates a multimodal approach, which initially includes surgical and medical treatment (with SSA and DA) [13]. However, despite the existence of these therapeutic tools, high persistence rates of biochemical activity have been reported, so that in such scenarios the use of high-cost drugs, such as Pasireotide and Pegvisomant, are suggested [21,22]. Unfortunately, in low-income countries, these resources are not available in public health institutions, which has led to the use of third-line therapeutic alternatives such as fractionated stereotactic radiotherapy and radiosurgery, whose results have been variable in relation to local tumor control, safety profile, and methodology in the measurement of outcomes [23]. Therefore, the aim of this study was to establish the efficiency and safety of radiosurgery (CyberKnife) as an adjuvant treatment in the multimodal approach of patients diagnosed with acromegaly, as well as to determine which factors were associated with the biochemical persistence of the disease.
In the cohort evaluated, a biochemical remission rate of $12.2\%$ was observed, $33.3\%$ of patients were in biochemical control with medical treatment, and $54.4\%$ persisted with biochemical activity; these data are similar to those reported by Iwata H. et al., with endocrine remission in $17.3\%$ ($95\%$ CI 7.02–27.58) of their cases, according to the curtain criteria [24]. Similarly, Ehret et al. found a biochemical remission rate in $18\%$ of their patients, biochemical control with medical treatment in $48\%$, and $34\%$ remaining active [25]. In our study, we obtained percentage reduction rates of both total IGF-1 and IGF-1 x ULN of $70.15\%$ and $65.5\%$, respectively, similar to those found by Ehret et al., who observed a reduction of total IGF-1 and IGF-1 x ULN at the end of their follow-up of $48.5\%$ and $48.5\%$, respectively [25]. Our LC rate was $100\%$ at the last visit, equal to that reported by Ehret et al. [ 25] and similar to Iwata with $82.5\%$ ($95\%$ CI 72.17–$92.83\%$) [26].
Roberts, BK. et al. analyzed a retrospective series of nine patients where they found that $44.4\%$ achieved biochemical remission, which was defined as a normalization of IGF-1 concentrations, at the end of follow-up, and $44.4\%$ had persistence of biochemical activity; the mean follow-up was 17.8 months [27]. Sala E. et al. conducted a study of 22 patients where an IGF-1 normalization rate of $31.5\%$ was reported after CyberKnife radiosurgery treatment at six months follow-up, while $54.5\%$ of patients remained with active disease; the study remission rate at 50 months follow-up was $50\%$ [28]. Such results cited above are consistent with the effectiveness reported in our study, where $33.3\%$ achieved an IGF-1 < 1.2 × ULN and $45.6\%$ achieved endocrine remission at the last visit (GH < 1 ng/mL AND IGF-1 < 1.2 × ULN). Singh, R. et al. performed a meta-analysis involving a total of 1533 patients with acromegaly treated with radiosurgery in the modalities (LINAC, CyberKnife, and GammaKnife), in which they found endocrine remission and endocrine control rates of $43.2\%$ ($95\%$ CI 31.7–$54.6\%$) and $55\%$ ($95\%$ CI 27.6–$82.4\%$), respectively, at five years of follow-up. The estimated local control rate at 10 years was $92.8\%$ ($95\%$ CI 83–$100\%$) [23].
In relation to the safety of radiosurgery found in our study at the end of follow-up, an increase in the rates of hypothyroidism of $28\%$ and hypocortisolism of $26\%$ was obtained; panhypopituitarism presented an increase of $22.8\%$. These data differ from those obtained by Eheret et al., who found an increase in the rates of hormone deficiencies in relation to hypogonadism of $4\%$, of hypocortisolism of $4\%$, and of hypothyroidism of $6\%$ after radiosurgery at the end of follow-up, rates of hypothalamic-pituitary deficiencies [25]. Salas et al. reported an increased rate of hypothalamic-pituitary hormone deficiencies of $22.8\%$ at the end of follow-up (5 years) that affected at least one hormonal axis [28]. The above were lower than those found in our study. The results of the meta-analysis by Singh R. et al., which included different radiosurgery modalities for the treatment of acromegaly, estimated an overall rate of new hypothalamo–hypophysiary deficiencies of $26.8\%$ ($95\%$ CI 16.8–$36.7\%$) [23]. In relation to hypocortisolism, in our clinical practice, we used a cut-off point of <5 mcg/dL accompanied by symptoms and signs of adrenal insufficiency. Patients in whom there was doubt about the diagnosis were referred for induced hypoglycemia testing. However, most patients had cortisol levels between 3 and 15 mcg/dL and the diagnosis can only be made with stimulation tests such as the Synacthen test, which we did not have in our center. Therefore, it is likely that the prevalence of hypocortisolism in our population has been underestimated.
In relation to optical radiation toxicity and cerebral vascular events, our results are in agreement with other studies such as that of Roberts B. et al. [ 27] and Iwata H. et al., [ 26] who reported absences of this complication in their cohorts. However, the rates of radiosurgery-associated visual toxicity reported in its different modalities (LINAC, Gamma, CyberKnife) are variable, ranging from $0\%$ to $5\%$, with a pooled rate $2.7\%$ ($95\%$ CI 1.3–$4.2\%$) [23].
Regarding potential predictors for the absence of biochemical remission following radiosurgery, we found a higher probability of no biochemical remission when the baseline IGF1-1 x ULN value was elevated, with an adjusted HR of 1.33 ($95\%$ CI 1.01–1.88). Similarly, Ehret F. et al. found that elevated pretreatment IGF-1 x ULN values were associated with a lower likelihood of biochemical remission of acromegaly [25]. These data are also congruent with those obtained in several studies that evaluated the association between elevated IGF-1 x ULN and total IGF-1 levels before surgical treatment and/or fractionated stereotactic radiotherapy, finding that elevated baseline concentrations were predictive of a lack of biochemical remission [25,29,30,31]. On the other hand, the presence of tumoral invasion into the cavernous sinus (grade 1–4 of Knosp’ classification) showed a tendency to statistical significance with HR of 2.53 (0.92–6.97) as a risk factor for no biochemical remission, a finding previously reported in invasive adenomas that have a low probability of cure and/or remission [30]. All of the above is a consequence of the difficulty in the surgical dissection of somatotroph cells in the cavernous sinus.
CyberKnife is a relatively new technology for frameless stereotactic radiosurgery, in which a mobile linear accelerator is mounted on a robotic arm with an image-guided robotic system. Patients are immobilized in a thermoplastic mask and radiation doses can be delivered in single or multiple fractions with a target accuracy of 0.5 to 1 mm, similar to that achieved with frame-based stereotactic radiosurgery [32]. Radiosurgery in its CyberKnife modality is an adjuvant strategy to surgery and a medical treatment in acromegaly with an acceptable effectiveness profile where, according to the series studied, biochemical control ranges from 17–$65.4\%$, with optimal tumor control rates corresponding to ranges of 96–$100\%$ [32]. Radiosurgery shows a variable safety profile, in relation to hypothalamic-pituitary deficiencies ranging from 7.8–$57\%$ of hypopituitarism and visual deficit rates from 0–$11.1\%$ [32]; the variability in its safety makes it necessary to carry out studies to establish it as a treatment tool that can be widely used in the management of the disease.
The weaknesses of our study are firstly related to the sample size, although the study population is representative of the patients under follow-up in our clinic; there was also lack of information regarding treatment parameters with radiosurgery, which would be important for the evaluation of new hormonal deficits, especially the dose administered to the pituitary stalk; and the median follow-up after radiotherapy was short, so that the efficacy and side effects could be underestimated, indicating that long-term studies are required to evaluate the efficacy and safety outcomes of CyberKnife radiosurgery. Its strengths are that data are presented from a cohort of a well-characterized rare disease, with a pre-established diagnostic and therapeutic protocol from the beginning of the acromegaly clinic, reducing the probability of some biases.
## 5. Conclusions
The results of our study suggest that radiosurgery (CyberKnife modality) is a safe and effective technique in the adjuvant treatment of GH-producing tumors. Additionally, elevated pre-radiosurgery IGF x ULN levels could be a predictor of a lack of biochemical remission of acromegaly.
## References
1. Wang A.R., Gill J.R.. **The Pituitary Gland: An Infrequent but Multifaceted Contributor to Death**. *Acad. Forensic Pathol.* (2016) **6** 206-216. DOI: 10.23907/2016.023
2. Daly A.F., Beckers A.. **The Epidemiology of Pituitary Adenomas**. *Endocrinol. Metab. Clin. North Am.* (2020) **49** 347-355. DOI: 10.1016/j.ecl.2020.04.002
3. Melmed S.. **Pituitary-Tumor Endocrinopathies**. *N. Engl. J. Med.* (2020) **382** 937-950. DOI: 10.1056/NEJMra1810772
4. Mercado M., Gonzalez B., Vargas-Ortega G., Ramirez-Renteria C., Monteros A.L.E.D.L., Sosa E., Jervis P., Roldan P., Mendoza V., López-Félix B.. **Successful mortality reduction and control of comorbidities in patients with acromegaly followed at a highly specialized multidisciplinary clinic**. *J. Clin. Endocrinol. Metab.* (2014) **99** 4438-4446. DOI: 10.1210/jc.2014-2670
5. Ayuk J., Sheppard M.C.. **Does acromegaly enhance mortality?**. *Rev. Endocr. Metab. Disord.* (2008) **9** 33-39. DOI: 10.1007/s11154-007-9067-8
6. Esposito D., Ragnarsson O., Granfeldt D., Marlow T., Johannsson G., Olsson D.S.. **Decreasing mortality and changes in treatment patterns in patients with acromegaly from a nationwide study**. *Eur. J. Endocrinol.* (2018) **178** 459-469. DOI: 10.1530/EJE-18-0015
7. Donegan D.M., Iñiguez-Ariza N., Sharma A., Nippoldt T., Young W., Van Gompel J., Atkinson J., Meyer F., Pollock B., Natt N.. **Necessity of multimodal treatment of acromegaly and outcomes**. *Endocr. Pr.* (2018) **24** 668-676. DOI: 10.4158/EP-2018-0040
8. Giustina A., Barkhoudarian G., Beckers A., Ben-Shlomo A., Biermasz N., Biller B., Boguszewski C., Bolanowski M., Bollerslev J., Bonert V.. **Multidisciplinary management of acromegaly: A consensus**. *Rev. Endocr. Metab. Disord.* (2020) **21** 667-678. DOI: 10.1007/s11154-020-09588-z
9. Chiloiro S., Bianchi A., Giampietro A., Pontecorvi A., Raverot G., De Marinis L.. **Second line treatment of acromegaly: Pasireotide or Pegvisomant?**. *Best Pr. Res. Clin. Endocrinol. Metab.* (2022) **36** 101684. DOI: 10.1016/j.beem.2022.101684
10. Sandret L., Maison P., Chanson P.. **Place of cabergoline in acromegaly: A meta-analysis**. *J. Clin. Endocrinol. Metab.* (2011) **96** 1327-1335. DOI: 10.1210/jc.2010-2443
11. Gheorghiu M.L., Fleseriu M.. **Stereotactic radiation therapy in pituitary adenomas, is it better than conventional radiation therapy?**. *Acta Endocrinol.* (2017) **13** 476-490. DOI: 10.4183/aeb.2017.476
12. Espinosa-de-los-Monteros A.L., González B., Vargas G., Sosa E., Mercado M.. **Clinical and biochemical characteristics of acromegalic patients with different abnormalities in glucose metabolism**. *Pituitary* (2011) **14** 231-235. DOI: 10.1007/s11102-010-0284-x
13. Giustina A., Barkan A., Beckers A., Biermasz N., Biller B.M.K., Boguszewski C., Bolanowski M., Bonert V., Bronstein M.D., Casanueva F.F.. **A Consensus on the Diagnosis and Treatment of Acromegaly Comorbidities: An Update**. *J. Clin. Endocrinol. Metab.* (2020) **105** e937-e946. DOI: 10.1210/clinem/dgz096
14. Landolt A.M., Haller D., Lomax N., Scheib S., Schubiger O., Siegfried J., Wellis G.. **Stereotactic radiosurgery for recurrent surgically treated acromegaly: Comparison with fractionated radiotherapy**. *J. Neurosurg.* (1998) **88** 1002-1008. DOI: 10.3171/jns.1998.88.6.1002
15. Loeffler J.S., Shih H.A.. **Radiation Therapy in the Management of Pituitary Adenomas**. *J. Clin. Endocrinol. Metab.* (2011) **96** 1992-2003. DOI: 10.1210/jc.2011-0251
16. Mercado M., Espinosa de los Monteros A.L., Sosa E., Cheng S., Mendoza V., Hernández I., Sandoval C., Guinto G., Molina M.. **Clinical-biochemical correlations in acromegaly at diagnosis and the real prevalence of biochemically discordant disease**. *Horm. Res.* (2004) **62** 293-299. DOI: 10.1159/000082032
17. Di Chiro G., Nelson K.B.. **The volume of the sella turcica**. *Am. J. Roentgenol. Radium Ther. Nucl. Med.* (1962) **87** 989-1008
18. **Standards of Medical Care for Patients With Diabetes Mellitus**. *Diabetes Care* (2003) **1** S33-S50. DOI: 10.2337/diacare.26.2007.s33
19. Pollock B.E., Jacob J.T., Brown P.D., Nippoldt T.B.. **Radiosurgery of growth hormone-producing pituitary adenomas: Factors associated with biochemical remission**. *J. Neurosurg.* (2007) **106** 833-838. DOI: 10.3171/jns.2007.106.5.833
20. Landolt A.M., Haller D., Lomax N., Scheib S., Schubiger O., Siegfried J., Wellis G.. **Octreotide may act as a radioprotective agent in acromegaly**. *J. Clin. Endocrinol. Metab.* (2000) **85** 1287-1289. DOI: 10.1210/jcem.85.3.6464
21. Coopmans E.C., van der Lely A.J., Neggers S.J.C.M.M.. **Approach to the Patient With Treatment-resistant Acromegaly**. *J. Clin. Endocrinol. Metab.* (2022) **107** 1759-1766. DOI: 10.1210/clinem/dgac037
22. Mercado M., Abreu C., Vergara-López A., González-Virla B., Espinosa-De-Los-Monteros A.-L., Sosa-Eroza E., Cadena-Obando D., Cuevas-Ramos D., A Portocarrero-Ortiz L., Pérez-Reyes S.-P.. **Surgical and Pharmacological Outcomes in Acromegaly: Real-Life Data From the Mexican Acromegaly Registry**. *J. Clin. Endocrinol. Metab.* (2020) **105** e4567-e4576. DOI: 10.1210/clinem/dgaa664
23. Singh R., Didwania P., Lehrer E.J., Sheehan D., Sheehan K., Trifiletti D.M., Sheehan J.P.. **Stereotactic radiosurgery for acromegaly: An international systematic review and meta-analysis of clinical outcomes**. *J. Neurooncol.* (2020) **148** 401-418. DOI: 10.1007/s11060-020-03552-2
24. Giustina A., Chanson P., Bronstein M.D., Klibanski A., Lamberts S., Casanueva F.F., Trainer P., Ghigo E., Ho K., Melmed S.. **A Consensus on Criteria for Cure of Acromegaly**. *J. Clin. Endocrinol. Metab.* (2010) **95** 3141-3148. DOI: 10.1210/jc.2009-2670
25. Ehret F., Kufeld M., Fürweger C., Haidenberger A., Windisch P., Fichte S., Lehrke R., Senger C., Kaul D., Rueß D.. **Robotic Radiosurgery for Persistent Postoperative Acromegaly in Patients with Cavernous Sinus-Invading Pituitary Adenomas-A Multicenter Experience**. *Cancers* (2021) **13**. DOI: 10.3390/cancers13030537
26. Iwata H., Sato K., Nomura R., Tabei Y., Suzuki I., Yokota N., Inoue M., Ohta S., Yamada S., Shibamoto Y.. **Long-term results of hypofractionated stereotactic radiotherapy with CyberKnife for growth hormone-secreting pituitary adenoma: Evaluation by the Cortina consensus**. *J. Neurooncol.* (2016) **128** 267-275. DOI: 10.1007/s11060-016-2105-1
27. Roberts B.K., Ouyang D.L., Lad S.P., Chang S.D., Harsh G.R., Adler J.R., Soltys S.G., Gibbs I.C., Katznelson L.. **Efficacy and safety of CyberKnife radiosurgery for acromegaly**. *Pituitary* (2007) **10** 19-25. DOI: 10.1007/s11102-007-0004-3
28. Sala E., Moore J.M., Amorin A., Martinez H., Bhowmik A.C., Lamsam L., Chang S., Soltys S.G., Katznelson L., Harsh G.R.. **CyberKnife robotic radiosurgery in the multimodal management of acromegaly patients with invasive macroadenoma: A single center’s experience**. *J. Neurooncol.* (2018) **138** 291-298. DOI: 10.1007/s11060-018-2793-9
29. Tomasik A., Stelmachowska-Banaś M., Maksymowicz M., Czajka-Oraniec I., Raczkiewicz D., Zieliński G., Kunicki J., Zgliczyński W.. **Clinical, hormonal and pathomorphological markers of somatotroph pituitary neuroendocrine tumors predicting the treatment outcome in acromegaly**. *Front. Endocrinol.* (2022) **13** 957301. DOI: 10.3389/fendo.2022.957301
30. Agrawal N., Ioachimescu A.G.. **Prognostic factors of biochemical remission after transsphenoidal surgery for acromegaly: A structured review**. *Pituitary* (2020) **23** 582-594. DOI: 10.1007/s11102-020-01063-x
31. Graffeo C.S., Donegan D., Erickson D., Brown P.D., Perry A., Link M.J., Young W.F., Pollock B.E.. **The Impact of Insulin-Like Growth Factor Index and Biologically Effective Dose on Outcomes After Stereotactic Radiosurgery for Acromegaly: Cohort Study**. *Neurosurgery* (2020) **87** 538-546. DOI: 10.1093/neuros/nyaa054
32. Minniti G., Flickinger J.. **The risk/benefit ratio of radiotherapy in pituitary tumors**. *Best Pr. Res. Clin. Endocrinol. Metab.* (2019) **33** 101269. DOI: 10.1016/j.beem.2019.04.003
|
---
title: Genetic and Probiotic Characteristics of Urolithin A Producing Enterococcus
faecium FUA027
authors:
- Mengjie Xia
- Shuting Mu
- Yaowei Fang
- Xiaomeng Zhang
- Guang Yang
- Xiaoyue Hou
- Fuxiang He
- Yaling Zhao
- Yichen Huang
- Wei Zhang
- Juan Shen
- Shu Liu
journal: Foods
year: 2023
pmcid: PMC10001356
doi: 10.3390/foods12051021
license: CC BY 4.0
---
# Genetic and Probiotic Characteristics of Urolithin A Producing Enterococcus faecium FUA027
## Abstract
Enterococcus faecium FUA027 transforms ellagic acid (EA) to urolithin A (UA), which makes it a potential application in the preparation of UA by industrial fermentation. Here, the genetic and probiotic characteristics of E. faecium FUA027 were evaluated through whole-genome sequence analysis and phenotypic assays. The chromosome size of this strain was 2,718,096 bp, with a GC content of $38.27\%$. The whole-genome analysis revealed that the genome contained 18 antibiotic resistance genes and seven putative virulence factor genes. E. faecium FUA027 does not contain plasmids and mobile genetic elements (MGEs), and so the transmissibility of antibiotic resistance genes or putative virulence factors should not occur. Phenotypic testing further indicated that E. faecium FUA027 is sensitive to clinically relevant antibiotics. In addition, this bacterium exhibited no hemolytic activity, no biogenic amine production, and could significantly inhibit the growth of the quality control strain. In vitro viability was >$60\%$ in all simulated gastrointestinal environments, with good antioxidant activity. The study results suggest that E. faecium FUA027 has the potential to be used in industrial fermentation for the production of urolithin A.
## 1. Introduction
Ellagitannins (ETs), the metabolic precursor of urolithins, can be hydrolyzed to ellagic acid (EA), which is subsequently metabolized by gut microorganisms to urolithins [1]. Among all types of those urolithins, urolithin A (UA) exhibited several potentially positive bioactivities, such as restoring muscle function [2], and antiobesity [3], antioxidant [4], anti-inflammation, and anticancer activities [5]. An increasing amount of the literature has recently focused on the impact of the natural compound UA on health, disease, and aging [6]. Numerous studies have shown that different urolithin metabotypes (UMs) produce significantly different amounts and types of urolithins [7]. The gut microflora in more than $40\%$ of middle-aged and elderly people cannot metabolize EA to UA [8]. Cortés et al. found that the percentage of the UM-A population declines when the intestinal flora changes with age [9]. Given the influence of intestinal flora on UA formation [10], screening strains responsible for metabolizing EA to produce UA is of interest.
Currently, little is known about the species of gut bacteria involved in EA conversion to UA. Strains found to metabolize EA to produce UA include *Bifidobacterium pseudocatenulatum* INIA P815 [11], *Streptococcus thermophilus* FUA329 [12], *Lactococcus garvieae* FUA009 [13], and *Enterococcus faecium* FUA027 [14]. S. thermophilus FUA329 was isolated from human milk. L. garvieae FUA009 and E. faecium FUA027 were screened from fecal samples. These bacteria have the potential to be developed as probiotics for the in vitro biotransformation of EA to produce UA, or for industrial fermentation to produce UA [15].
Our previous studies have proven that E. faecium FUA027, which was isolated from human fecal samples, metabolizes EA to UA by detecting UA from the fermentation broth of the strain through high-performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS). The highest yield of UA produced by E. faecium FUA027 was 10.80 μM, thereby making this strain a promising candidate for development as a probiotic [14].
The safety and probiotic properties of the strain to be used as probiotics must be evaluated [16]. In this study, whole-genome sequence information analysis and phenotypic assays were used in combination to assess antibiotic resistance, metabolite toxicity, and survival under simulated gastrointestinal conditions. The safety of E. faecium FUA027 and its potential for use in the preparation of UA by industrial fermentation were confirmed.
## 2.1. Bacterial Strain and Growth Conditions
E. faecium FUA027 was preserved in the China General Microbiological Culture Collection Center (CGMCC) under the accession number CGMCC No. 24964. All FUA027 strains, unless otherwise noted, were cultivated in Anaerobe Basal Broth (ABB) medium and incubated under anaerobic conditions consisting of N2/H2/CO2 (80:10:10, v:v:v) at 37 °C for 24 h. Staphylococcus aureus ATCC 12600, *Escherichia coli* ATCC 25922, Yeast ATCC 24060, Aspergillus niger ATCC 6273, and *Lactobacillus plantarum* ATCC 4008 strains were used partly for inhibition experiments and partly as control strains in the experiments. S. aureus and E. coli were cultured at 37 °C in Luria–Bertani broth for 24 h. Yeast and A. niger were cultured on potato dextrose agar medium at 37 °C for 48 h. L. plantarum and S. thermophilus were cultivated in Man Rogosa Sharpe broth at 37 °C for 48 h.
## 2.2. Whole-Genome Sequencing
The genomic DNA was extracted from the E. faecium FUA027 culture grown in ABB by using a bacterial DNA extraction kit from Sangon, Shanghai, Co. Ltd. (Shanghai, China). For the DNA sample preparations, 1 µg DNA per sample was used as the input material. Sequencing libraries were created using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. In brief, the DNA sample was sonicated to obtain 350-bp fragments. The DNA fragments were end-polished, A-tailed, and ligated with the full-length adaptor for Illumina sequencing with further PCR amplification. Finally, the AMPure XP system purified the PCR products, and the size distribution of the libraries was analyzed using the Agilent 2100 Bioanalyzer and quantified using real-time PCR. The whole genome of FUA027 was sequenced using the Nanopore PromethION platform and Illumina NovaSeq PE150 at the Beijing Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).
## 2.3. Genome Assembly and Annotation
The trimmed data for the FUA027 genome were combined with PE150 and *Nanopore data* and assembled using SMRT Link v5.0.1 software (https://www.pacb.com/support/software-downloads/, accessed on 15 October 2022). The quality of the genome assembly was validated using QUAST ver. 5.0.2. The final assembly was annotated using the NCBI Prokaryotic Genome Annotation Pipeline (http://www.ncbi.nlm.nih.gov/genome/annotation_prok/, accessed on 15 October 2022) [17]. We used Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), the Non-Redundant Protein Database, the Transporter Classification Database, and Swiss-Prot to predict gene function.
## 2.4.1. Identifying Safety-Related Genes from the FUA027 Genome
Bacterial virulence factors were identified by referring to the virulence factor database updated in 2019 (VFDB, http://www.mgc.ac.cn/VFs/, accessed on 11 October 2022) [18]. Protein sequences with >$50\%$ similarity in the extraction comparison results were identified as virulence genes. Antimicrobial resistance determinant identification was performed using the ABRicate program (https://github.com/tseemann/abricate, accessed on 11 October 2022) based on the ResFinder database (http://genomicepidemiology.org/, accessed on 11 October 2022) [19]. Antibiotic resistance genes of E. faecium FUA027 were identified using the comprehensive antibiotic resistance database (CARD, https://card.mcmaster.ca, accessed on 11 October 2022) [20].
## 2.4.2. Antibiotic Susceptibility Testing
Susceptibility testing was performed through disk diffusion according to EUCAST recommendations [21]. The strain FUA027 was purified, inoculated into 20 mL of ABB liquid medium, and incubated anaerobically at 37 °C for 24 h. Bacterial colonies were counted, and the concentration of the bacterial solution was adjusted to 1.0 × 108 CFU/mL. The bacterial solution was then added dropwise to a 20 mm agar plate. The FUA329 bacterial solution was evenly coated on the plate. Under aseptic conditions, antibiotic susceptibility papers were gently pressed onto the agar plates using forceps. While doing so, the spacing of each drug-sensitive tablet could not be <20 mm and the distance from the edge of the plate could not be <17 mm. The plates were sealed and continuously incubated at 37 °C for 14 h. The size of the inhibition circle was noted to determine the sensitivity of antibiotics.
## 2.4.3. Hemolytic Activity Evaluation
The hemolytic activity was studied using the method described by Buxton. In short, E. faecium FUA027 was inoculated onto Columbia Blood Agar and incubated at 37 °C for 24 h [22]. S. aureus ATCC 12600 was used as a control strain.
## 2.4.4. Nitrate Reductase and Amino Acid Decarboxylase Activity
The nitrate broth assay kit and amino acid decarboxylase assay kit obtained from Beijing Land Bridge Technology Co., Ltd. (Beijing, China). were used in the metabolic toxicity test. The test was performed following the manufacturer’s instructions.
Detection of nitrate reductase activity: Under aseptic conditions, single colonies of the test strain and the quality control strain E. coli ATCC 25922 isolated from the plate were inoculated in a nitrate broth assay ampoule by using an inoculating needle. The plate was incubated at 37 °C for 24 h. After incubation, nitrate reduction reagents A and B were added dropwise at 5:2 (v:v), and the results were observed immediately. Three parallel experiments were conducted for each sample [23].
Detection of amino acid decarboxylase activity: Under aseptic conditions, a single colony of the test strain was picked from the plate by using an inoculating needle and inoculated into the amino acid decarboxylase series ampoule as well as the amino acid decarboxylase control tube. Sterile liquid paraffin was added to cover the surface of the medium, and lysine, ornithine, and arginine ampoules were incubated at 37 °C for 24 h. After the phenylalanine ampoule was incubated for 24 h, 4–5 drops of $10\%$ FeCl3 aqueous solution were added to the ampoules, and the results were observed within 2 min. Following the incubation of the tryptophan ampoules for 24 h, 2–3 drops of the Kovacs reagent were added to the ampoules and the results were observed immediately. Three parallel experiments were conducted for each sample.
## 2.5.1. Probiotic-Associated Genes in the E. faecium FUA027 Genome
The Hidden Markov model (HMM) was used to find probiotic-associated genes in the genome as well as environmental tolerance-related genes [24]. Additionally, we searched for genes related to adhesion factors in the annotation results.
*Putative* genes involved in antimicrobial compound synthesis and secondary metabolism gene clusters in the E. faecium FUA027 genome were identified using AntiSMASH 6.0 (https://antismash.secondarymetabolites.org, accessed on 11 December 2022) [25] and BAGEL 4.0 (http://bagel4.molgenrug.nl/index.php, accessed on 11 December 2022) [26].
## 2.5.2. Evaluation of Acid and Bile Salt Tolerance In Vitro
Referring to Pieniz et al. ’s study, the survival of strains in a simulated gastrointestinal environment was measured using the viable plate count method [27]. The strain FUA027 was grown in ABB liquid medium at 37 °C for 24 h. Then, the culture was adjusted to an optical density (OD600) of 1.0 ± 0.05.
Separate preparation of ABB liquid medium of different pH values and containing different bile salt concentrations: test tubes containing 9 mL of ABB liquid medium were adjusted with HCl to attain different pH values (i.e., 2.0, 2.5, 3.0, 3.5, and 4.0). The ABB liquid medium was supplemented with bovine bile salt, thereby achieving final concentrations of $0.1\%$, $0.2\%$, $0.3\%$, $0.4\%$, and $0.5\%$ (w/v), respectively. Then, 1 mL of inoculum was added to each tube, and the normal ABB liquid medium was used as a control. Sampling was performed at 0, 1, 2, and 3 h. The samples were diluted with ABB medium and then coated and incubated on the plates for 24 h, and viable colonies on a plate were counted. The survival rate was calculated using the following formula:Survival rate (%)=(Nt/N0)×$100\%$ where Nt (log CFU/mL) represents the number of viable bacteria after t hours of treatment, and N0 (log CFU/mL) refers to the number of viable bacteria of E. faecium FUA027 before treatment.
## 2.5.3. Evaluation of the Antioxidant Activity In Vitro
The FUA027 strain was cultured in ABB liquid medium at 37 °C for 18 h. The E. faecium FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min), then discard supernatant intact cells of the strain were harvested. The cell pellet was washed twice with and suspended in 1 mL sterile distilled water [28]. The concentration of this suspension was adjusted to approximately 1.0 × 108 CFU/mL. This was considered as a sample in the antioxidant test. Using antioxidant kits from Jiancheng Bioengineering Institute (Nanjing, China), in vitro antioxidant activities were measured including the measurement of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, hydroxyl radical, and superoxide anion scavenging activities [29].
## 2.5.4. Hydrophobicity and Auto-Aggregation Tests
Hydrophobicity: The E. faecium FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min), and the pellet was washed with and suspended in distilled water. The culture suspension was adjusted to an OD600 value of 0.5 ± 0.02 (A0). Then, an equal volume of xylene solution was added to the bacterial suspension and vortexed for 20 s at 37 °C for 1 h. The absorbance of the supernatant at 600 nm (A2) was determined. Three parallel tests were conducted [30]. The hydrophobic rate was calculated using the following formula:Hydrophobic rate (%)=(A0/A2)/A0×$100\%$ Auto-aggregation: The FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min) and washed with distilled water. Its OD600 value was adjusted to 0.5 ± 0.02 (A0). The bacterial suspension was allowed to stand at 37 °C for 4 h, and the absorbance of the supernatant at 600 nm (A2) was determined. Three parallel tests were conducted. The auto-aggregation rate was calculated using the following formula:Auto−aggregation rate (%)=(A0/A2)/A0×$100\%$
## 2.5.5. Evaluation of Antibacterial Activity
A single colony of E. faecium FUA027 was picked, inoculated into ABB liquid medium, and cultured anaerobically at 37 °C for 24 h. Then, 10 mL of bacterial solution was mixed thoroughly with an equal volume of ethyl acetate extract, vortex shaken for 30 s, and transferred to a separatory funnel. This mixture was allowed to stand at room temperature for 5 min. After the solution was stratified, the upper organic phase was collected and evaporated in a rotary evaporation flask. The rotary evaporator was used to rotary evaporate the organic phase at 60 °C for 10–15 min to ensure the absence of a smell of ethyl acetate. Then, 2 mL ethyl acetate was added to dissolve the residue in the rotary steaming bottle, fully mixed, and filtered through a nylon syringe filter (pore size: 20 μm). The liquid was collected as the antibacterial solution [31]. The experimental group was the upper organic phase of E. faecium FUA027 after extraction with ethyl acetate (concentrated five times) and the lower aqueous phase of E. faecium FUA027 after extraction with ethyl acetate. ABB medium extracts and ethyl acetate were used as blank controls.
The Kirby–Bauer test for antibacterial effects: 100 μL of bacterial solution of *Staphylococcus aureus* ATCC 12600, *Escherichia coli* ATCC 25922, Yeast ATCC 24060, and Aspergillus niger ATCC 6273 were evenly applied to the plate, respectively. Then, four sterile filter papers of diameter 5 ± 0.5 mm were placed in each plate. A total of 10 μL of sample was added dropwise to each filter paper sheet and incubated for 12 h at 37 °C. Then, a vernier caliper was used to measure and record the diameter of the suppression ring. The inhibitory effect was evaluated on the basis of the inhibition circle diameter. Three independent tests were repeated [32].
## 3.1. Genome Properties
The whole genome sequence of E. faecium FUA027 contained a single, circular 2,718,096-bp-long chromosome with an average GC content of $38.27\%$ (Figure 1). The Glimmer program identified 2700 genes with an estimated coding ratio of $87.1\%$. Of them, 2617 were protein-coding genes and 83 were RNA genes. Among the 83 RNA genes, 17 genes coded for 5S, 16S, and 23S rRNAs; two genes coded for sRNAs; and 64 genes coded for tRNAs. ( Table 1). The Plasmid Finder 2.0 tool did not find any plasmid sequences. The FUA027 genome sequence was submitted to NCBI under the accession number OM670243.
## 3.2.1. Identification of Antibiotic Resistance Gene
In the clinical setting, probiotic strains resistant to a particular antibiotic are typically associated with infection. Antibiotic resistance genes in the probiotic genome are not in themselves a safety issue, if the genes are not likely transferred to other strains. Instead, probiotics containing these genes could theoretically act as a source of antibiotic resistance genes for potentially pathogenic bacteria. Probiotics must also be tested for the presence of antibiotic resistance genes because studies have confirmed that these genes may be transferred in food and in the intestinal environment.
Enterococcus exhibits stronger natural resistance than other Gram-positive bacteria and acquires resistance genes through various mechanisms to produce multiple high-level drug-resistant strains [33]. Amino acid sequences of E. faecium FUA027 were compared with the drug resistance gene database CARD (https://card.mcmaster.ca/, accessed on 11 December 2022), and protein sequences with >$50\%$ similarity in the comparison results were extracted as antibiotic resistance genes. Eighteen antibiotic resistance genes were identified. A predictive analysis of drug resistance genes identified 10 types of aminoglycoside antibiotics, fluoroquinolones, lincosamides, and vancomycin. Probiotic E. faecium strain T-110 and non-pathogenic strain E. faecium NRRL B-2354 both contain a plasmid, according to Natarajan et al. [ 34]. Importantly, we used the MobileElementFinder tool to search for MGEs. As expected, the absence of MGEs was confirmed. Consequently, because E. faecium FUA027 has no plasmid and none of the antibiotic resistance genes associated with it are located on MGEs, these drug resistance properties cannot be transferred to other pathogenic bacteria through mobile elements, implying no occurrence of drug resistance transmission. Thus, this study from the genetic level confirms that E. faecium FUA027 is safe for the horizontal transfer of drug resistance. To corroborate the results of antibiotic resistance gene analyses, the antibiotic sensitivity test was conducted. Nevertheless, the presence of resistance genes did not exactly match the experimental results observed. According to the results, E. faecium FUA027 was resistant to nine antibiotic types (Table 2). In total, 27 antibiotics were detected. As shown in Table A1, E. faecium FUA027 was resistant to nine types of antibiotics. Combined with the results of the antibiotic susceptibility test in vitro, the antibiotic resistance genes in the genome were analyzed. E. faecium FUA027 was safe in terms of antibiotic resistance.
## 3.2.2. Evaluation of Virulence Factor Genes and Toxin-Encoding Genes
According to the gene function classification, virulence genes carried by enterococci mainly encode for proteins related to adherence, exotoxin, exoenzyme, immunomodulation, and biofilm [35]. The VFDB was used to identify virulence factor genes in E. faecium FUA027; however, most putative virulence factor genes had <$60\%$ similarity with VFDB [36]. In total, seven potential virulence factor genes were identified (Table 3). *These* genes may encode for proteins involved in adhesion, immunomodulation, exoenzyme, and biofilm. Genes encoding enterococcal hemolysin A (hlyA), cytolysin (cyl), aggregation substance (as), enterococcal surface protein, sex pheromones (cob and ccf), and serum resistance-associated gene (sra), which are well-known potential virulence factors, were missing in E. faecium FUA027. According to Deng’s study, among 110 probiotic Enterococcus spp. 35 ($31.8\%$) enterococcal strains exhibited β-hemolytic characteristics. However, in our study, FUA027 exhibited γ-hemolysis on blood plates and no genes encoding Hbl, Nhe, or cytotoxin K, which are associated with hemolysis and toxin production, were found in the genome (Figure 2). These results thus confirmed that E. faecium FUA027 would be used in the preparation of UA by industrial fermentation.
## 3.2.3. Biogenic Amine Production
The results of nitrate reductase activity revealed that E. faecium FUA027 did not contain nitrate reductase. No color change was observed in the tubes containing the test strains, and the color was red after the addition of trace zinc powder, indicating that the test group was negative. The tube containing the quality control strain E. coli ATCC 25922 was red and positive.
The amino acid decarboxylase activity of E. faecium FUA027 was preliminarily detected on the basis of the color change in the amino acid decarboxylase medium. With E. faecium FUA027, the color of the amino acid decarboxylase medium remained unchanged and yellow, indicating that no biogenic amines (BA) were produced in the medium by the strain. The experimental results revealed that FUA027 did not possess lysine, ornithine, arginine, tryptophan, or phenylalanine decarboxylase activities. The main source of BA in food is the microbial decarboxylation of amino acids. For example, the decarboxylation of tyrosine, ornithine, and lysine produces tyramine, putrescine, and cadaverine, respectively. BA accumulation in food has serious implications for food safety and human health [37]. Of the 129 enterococci strains of three different origins (food, veterinary, and human) screened by Sarantinopoulos et al., none produced histamine, cadaverine, or putrescine [38]. However, >$90\%$ of E. faecium strains isolated from cheese have been identified as tyramine producers. Some E. faecium strains from humans also produced putrescine [39]. E. faecium FUA027 was found to not produce BA, and thus we believe that this strain may be used safely in industrial fermentation.
## 3.3.1. Acid and Bile Salt Tolerance In Vitro
Normal human gastric juice pH is approximately 1–3, and normal human intestinal pH is approximately 6.8–7.0. The pH in the stomach can rise to 4–5 after food is consumed. Probiotics can only exert their probiotic role if they resist the inhibitory effects of gastric acid and pepsin on the intestine [40]. A gene encoding conjugated bile acid hydrolase (cbh) and three genes encoding bile acid sodium symporter family proteins were discovered in E. faecium FUA027; these genes may have contributed to bile salt resistance. F0F1-ATPase is considered the main pH regulator inside cells. *Eight* genes coding for the F0F1-ATP synthase subunit were identified in the FUA027 genome. Furthermore, a cation transporter gene, two (Na+/H+) antiporter genes, and a sodium ion transporter gene linked to pH regulation and ion homeostasis were discovered (Table A2). The survival rates of E. faecium FUA027 in the in vitro acid tolerance test at different pH values are shown in Figure 3A. The survival rate declined steadily as the pH value decreased. Studies have shown that strains with a survival rate of >$60\%$ are acid-resistant strains. The survival rate of E. faecium FUA027 in the in vitro acid tolerance test at pH 3.0 was >$60\%$ and that at pH 2.0 was >$50\%$. Compared to acid-tolerant strains, E. faecium FUA027 was less acid-tolerant.
Another crucial sign for assessing the qualities of possible probiotics is the tolerance of strains to high bile salt concentrations in the human gastrointestine. Studies have shown that the small intestine contains approximately $0.3\%$ of bile salts. In our study, the survival rate of the strain was higher than $67\%$ at bile salt concentrations of $0.1\%$–$0.3\%$. The strain survival rate was still >$60.00\%$ at bile salt concentrations of $0.4\%$ and $0.5\%$ (Figure 3B), which indicates that the strain has excellent bile salt resistance.
We identified a gene coding for conjugated bile acid hydrolase (cbh), two conjugated bile acid hydrolase genes (namely nhaC and napA), and ABC transporter genes potentially contributing to bile salt resistance in E. faecium FUA027. *Eight* genes coding for the F0F1-ATP synthase subunit (namely atpB, atpE, atpF, atpH, atpA, atpG, atpD, and atpC) were identified in the FUA027 genome. Therefore, we suggest that the in vitro results of acid and bile salt tolerance in E. faecium FUA027 are explained by these related genes in its genome.
## 3.3.2. Antioxidant Ability In Vitro
Some probiotic metabolites can lessen the oxidative damage that causes aging and chronic diseases [41]. The results of the in vitro antioxidant ability of E. faecium FUA027 are presented in Table 4. The DPPH scavenging activity of the fermentation supernatant was as high as $57.62\%$, the superoxide anion scavenging capacity was $36.23\%$, and the clearance rate of hydroxyl radical was $30.12\%$. Polysaccharides, phosphonic acid, and peptidase, which are fundamental cell wall building blocks, are crucial for antioxidation. The extracellular metabolite structure is closely related to the antioxidant activity of the fermentation supernatant. In addition, the antioxidant activities of L. plantarum and E. faecalis were studied. The DPPH scavenging activity of L. plantarum was $62.78\%$, which was close to that of E. faecium FUA027. By contrast, the activity of E. faecalis was lower than that of E. faecium FUA027.
*Ten* genes associated with the oxidative stress response were found in the FUA027 genome; these genes could help the strain avoid damage by O2− and H2O2−, such as peroxide-responsive repressor (perR), NADH peroxidase (npr), alkyl hydroperoxide reductase (ahpC/F), glutathione peroxidase (gpx), superoxide dismutase (sodA), thioredoxin reductase (trxB), and glutathione reductase (gor). Among them, perR regulates H2O2− induced oxidative stress. In the presence of H2O2− or with iron and manganese ion deficiencies, perR upregulates antioxidant enzymes such as catA and ahpC/F to scavenge H2O2− and alkyl hydroperoxides (Table A2). The presence of these antioxidant genes indicated that E. faecium FUA027 has high antioxidant activity. Based on the results of genomic and phenotypic experimental analyses, we speculate that this may be due to the expression of antioxidant genes in the E. faecium FUA027 genome, such as catalase, glutathione peroxidase, and superoxide dismutation, which make FUA027 possess a good antioxidant capacity.
## 3.3.3. Evaluation of Adhesion-Related Genes
Probiotics play a beneficial role by adhering to intestinal mucosa and epithelial cells. We searched for gene annotation data related to adhesion, colonization, mucin binding, flagella hook, and fibrinogen/fibronectin binding. Adhesion lipoprotein, s-ribosylhomocysteine lyasef (luxS), segregation and condensation protein B (scpB) were found in the E. faecium FUA027 genome (Table 5) [42].
Biofilms of lactic acid bacteria can colonize the intestine, thereby protecting strains in gastrointestinal transit, producing certain antimicrobial compounds, and stimulating the immune response. Auto-aggregation is a crucial property of biofilm formation, and hydrophobicity may assist in adhesion. Auto-aggregation and hydrophobicity are vital indicators of the ability of microbes to respond to bacterial gut colonization. FUA027 exhibited higher hydrophobicity and auto-aggregation than the commercial probiotic strain *Bifidobacterium longum* BB536). This demonstrates that E. faecium FUA027 can better colonize the intestinal tract, and thus exert its probiotic properties.
## 3.3.4. Antibacterial Test of E. faecium FUA027 against Quality Control Strains
In the in vitro experiment, the inhibitory ability of E. faecium FUA027 against four test strains was investigated. As shown in Figure 4, FUA027 exhibited significant inhibitory effects on E. coli ATCC 25922 and S. aureus ATCC 12600, with inhibition circle sizes of 26.24 ± 0.34 mm and 22.12 ± 0.26 mm, respectively. The inhibition circle sizes were 9.2 ± 0.52 mm and 8.74 ± 0.38 mm for Yeast ATCC 24060 and A. niger ATCC 6273, respectively. E. faecium FUA027 had a significantly better inhibitory effect on bacteria than on fungi. Antimicrobial activity is a crucial property of probiotics against gastrointestinal infections. E. faecium mainly exerts its bacteriostatic effect by secreting organic acids. Furthermore, bacteriocins, bacteriocin-like, and hydrogen peroxide secreted by E. faecium can inhibit intestinal pathogenic microorganisms to some extent. Many bacteriocin-producing E. faecalis strains have been reported. Rahmeh et al. explored how E. faecium S6 exerts its antimicrobial effect by producing enterotoxins and organic acids [43]. Valenzuela et al. isolated an E. faecium PE 2-2 strain from seafood that inhibited S. aureus and demonstrated that this strain carried the enterocin A structural gene [44]. Basanta et al. reported that E. faecium L50 isolated from a Spanish dry fermented sausage produces enterocin L50 (EntL50, EntL50A, and EntL50B), enterocin P, and enterocin Q and exhibits a broad antimicrobial spectrum [45]. Enterococins are often used as a preservative for meat and dairy products. The most widely used enterococins are enterocin A and enterocin B, belonging to class II bacteriocin. In our study, four biosynthetic gene clusters associated with T3PKS, a cyclic lactone autoinducer, were identified using AntiSMASH 5.0, and BAGEL 4.0 predicted a bacteriocin from the class sactipeptide in the E. faecium FUA027 genome. Sactipeptides (sulfur-to-alpha carbon thioether cross-linked peptides) are ribosomally synthesized and post-translationally modified peptides that exhibit antibacterial activity [46]. In conclusion, in vitro experiments supported the presence and activity of extracellularly secreted bacteriocins, as they significantly inhibit the growth of E. coli ATCC 25922 and S. aureus ATCC 12600.
## 4. Conclusions
In summary, we here described the whole-genome sequence of E. faecium FUA027. FUA027 has a 2,718,096-bp-long chromosome with an average GC content of $38.27\%$. Genomic screening revealed that FUA027 lacked key virulence factor genes and toxin-coding genes. Although 18 antibiotic resistance genes were screened from the strain, the strain has no plasmids or mobile elements and is therefore unlikely to undergo the acquisition and transfer of resistance genes. The safety of this strain was further confirmed through hemolysis tests, metabolic toxicity tests, and antibiotic resistance tests. The detection of antimicrobial gene clusters and adhesion- and stress-associated genes in the genome, along with the results of tolerance tests such as tolerance to acid and bile salt and in vitro antioxidant activity-related genes, revealed the probiotic properties of the strain. Genomic analysis combined with phenotypic studies confirmed the safety and probiotic properties of this strain as a potential probiotic candidate.
## References
1. García-Villalba R., Beltrán D., Espín J.C., Selma M.V., Tomás-Barberán F.A.. **Time Course Production of Urolithins from Ellagic Acid by Human Gut Microbiota**. *J. Agric. Food Chem.* (2013) **61** 8797-8806. DOI: 10.1021/jf402498b
2. Dao T., Green A.E., Kim Y.A., Bae S.-J., Ha K.-T., Gariani K., Lee M.-R., Menzies K.J., Ryu D.. **Sarcopenia and Muscle Aging: A Brief Overview**. *Endocrinol. Metab.* (2020) **35** 716-732. DOI: 10.3803/EnM.2020.405
3. Selma M.V., Romo-Vaquero M., García-Villalba R., González-Sarrías A., Tomás-Barberán F.A., Espín J.C.. **The human gut microbial ecology associated with overweight and obesity determines ellagic acid metabolism**. *Food Funct.* (2016) **7** 1769-1774. DOI: 10.1039/C5FO01100K
4. Alfei S., Marengo B., Zuccari G.. **Oxidative Stress, Antioxidant Capabilities, and Bioavailability: Ellagic Acid or Urolithins?**. *Antioxidants* (2020) **9**. DOI: 10.3390/antiox9080707
5. Al-Harbi S.A., Abdulrahman A.O., Zamzami M.A., Khan M.I.. **Urolithins: The Gut Based Polyphenol Metabolites of Ellagitannins in Cancer Prevention, a Review**. *Front. Nutr.* (2021) **8** 647582. DOI: 10.3389/fnut.2021.647582
6. García-Villalba R., Giménez-Bastida J.A., Cortés-Martín A., Ávila-Gálvez M., Tomás-Barberán F.A., Selma M.V., Espín J.C., González-Sarrías A.. **Urolithins: A Comprehensive Update on their Metabolism, Bioactivity, and Associated Gut Microbiota**. *Mol. Nutr. Food Res.* (2022) **66** 2101019. DOI: 10.1002/mnfr.202101019
7. Xian W., Yang S., Deng Y., Yang Y., Chen C., Li W., Yang R.. **Distribution of Urolithins Metabotypes in Healthy Chinese Youth: Difference in Gut Microbiota and Predicted Metabolic Pathways**. *J. Agric. Food Chem.* (2021) **69** 13055-13065. DOI: 10.1021/acs.jafc.1c04849
8. D’Amico D., Andreux P.A., Valdés P., Singh A., Rinsch C., Auwerx J.. **Impact of the Natural Compound Urolithin A on Health, Disease, and Aging**. *Trends Mol. Med.* (2021) **27** 687-699. DOI: 10.1016/j.molmed.2021.04.009
9. Cortés-Martín A., García-Villalba R., González-Sarrías A., Romo-Vaquero M., Loria-Kohen V., Ramírez-De-Molina A., Tomás-Barberán F.A., Selma M.V., Espín J.C.. **The gut microbiota urolithin metabotypes revisited: The human metabolism of ellagic acid is mainly determined by aging**. *Food Funct.* (2018) **9** 4100-4106. DOI: 10.1039/C8FO00956B
10. Kang I., Buckner T., Shay N.F., Gu L., Chung S.. **Improvements in Metabolic Health with Consumption of Ellagic Acid and Subsequent Conversion into Urolithins: Evidence and Mechanisms**. *Adv. Nutr.* (2016) **7** 961-972. DOI: 10.3945/an.116.012575
11. Gaya P., Peirotén Á, Medina M., Alvaréz I., Landete J.M.. *J. Funct. Foods* (2018) **45** 95-99
12. Liu Q., Liu S., Ye Q., Hou X., Yang G., Lu J., Hai Y., Shen J., Fang Y.. **A Novel**. *Foods* (2022) **11**. DOI: 10.3390/foods11203280
13. Mi H., Liu S., Hai Y., Yang G., Lu J., He F., Zhao Y., Xia M., Hou X., Fang Y.. *Foods* (2022) **11**. DOI: 10.3390/foods11172621
14. Zhang X., Fang Y., Yang G., Hou X., Hai Y., Xia M., He F., Zhao Y., Liu S.. **Isolation and characterization of a novel human intestinal Enterococcus faecium FUA027 capable of producing urolithin A from ellagic acid**. *Front. Nutr.* (2022) **9** 1039697. DOI: 10.3389/fnut.2022.1039697
15. Koirala S., Anal A.K.. **Probiotics-based foods and beverages as future foods and their overall safety and regulatory claims**. *Future Foods* (2021) **3** 100013. DOI: 10.1016/j.fufo.2021.100013
16. Zhang C., Ma K., Nie K., Deng M., Luo W., Wu X., Huang Y., Wang X.. **Assessment of the safety and probiotic properties of Roseburia intestinalis: A potential “Next Generation Probiotic”**. *Front. Microbiol.* (2022) **13** 973046. DOI: 10.3389/fmicb.2022.973046
17. Tatusova T., DiCuccio M., Badretdin A., Chetvernin V., Nawrocki E.P., Zaslavsky L., Lomsadze A., Pruitt K.D., Borodovsky M., Ostell J.. **NCBI prokaryotic genome annotation pipeline**. *Nucleic Acids Res.* (2016) **44** 6614-6624. DOI: 10.1093/nar/gkw569
18. Liu B., Zheng D., Zhou S., Chen L., Yang J.. **VFDB 2022: A general classification scheme for bacterial virulence factors**. *Nucleic Acids Res.* (2022) **50** D912-D917. DOI: 10.1093/nar/gkab1107
19. Zankari E., Hasman H., Cosentino S., Vestergaard M., Rasmussen S., Lund O., Aarestrup F.M., Larsen M.V.. **Identification of acquired antimicrobial resistance genes**. *J. Antimicrob. Chemother.* (2012) **67** 2640-2644. DOI: 10.1093/jac/dks261
20. Alcock B.P., Raphenya A.R., Lau T.T.Y., Tsang K.K., Bouchard M., Edalatmand A., Huynh W., Nguyen A.-L.V., Cheng A.A., Liu S.. **CARD 2020: Antibiotic resistome surveillance with the comprehensive antibiotic resistance database**. *Nucleic Acids Res.* (2020) **48** D517-D525. DOI: 10.1093/nar/gkz935
21. Hombach M., Maurer F., Pfiffner T., Böttger E.C., Furrer R.. **Standardization of Operator-Dependent Variables Affecting Precision and Accuracy of the Disk Diffusion Method for Antibiotic Susceptibility Testing**. *J. Clin. Microbiol.* (2015) **53** 3864-3869. DOI: 10.1128/JCM.02351-15
22. Kim Y., Choi S.I., Jeong Y., Kang C.H.. **Evaluation of Safety and Probiotic Potential of Enterococcus faecalis MG5206 and Enterococcus faecium MG5232 Isolated from Kimchi, a Korean Fermented Cabbage**. *Microorganisms* (2022) **10**. DOI: 10.3390/microorganisms10102070
23. De Aguero N.L., Frizzo L.S., Ouwehand A.C., Aleu G., Rosmini M.R.. **Technological Characterisation of Probiotic Lactic Acid Bacteria as Starter Cultures for Dry Fermented Sausages**. *Foods* (2020) **9**. DOI: 10.3390/foods9050596
24. Eddy S.R.. **Accelerated Profile HMM Searches**. *PLoS Comput. Biol.* (2011) **7**. DOI: 10.1371/journal.pcbi.1002195
25. Wu Y.-P., Liu D.-M., Zhao S., Huang Y.-Y., Yu J.-J., Zhou Q.-Y.. **Assessing the safety and probiotic characteristics of Bacillus coagulans 13002 based on complete genome and phenotype analysis**. *LWT* (2022) **155** 112847. DOI: 10.1016/j.lwt.2021.112847
26. Blin K., Shaw S., Steinke K., Villebro R., Ziemert N., Lee S.Y., Medema M.H., Weber T.. **antiSMASH 5.0: Updates to the secondary metabolite genome mining pipeline**. *Nucleic Acids Res.* (2019) **47** W81-W87. DOI: 10.1093/nar/gkz310
27. Pieniz S., Andreazza R., Anghinoni T., Camargo F., Brandelli A.. **Probiotic potential, antimicrobial and antioxidant activities of Enterococcus durans strain LAB18s**. *Food Control* (2014) **37** 251-256. DOI: 10.1016/j.foodcont.2013.09.055
28. Wu Y., Li S., Tao Y., Li D., Han Y., Show P.L., Wen G., Zhou J.. **Fermentation of blueberry and blackberry juices using Lactobacillus plantarum, Streptococcus thermophilus and Bifidobacterium bifidum: Growth of probiotics, metabolism of phenolics, antioxidant capacity in vitro and sensory evaluation**. *Food Chem.* (2021) **348** 129083. DOI: 10.1016/j.foodchem.2021.129083
29. Mu G., Gao Y., Tuo Y., Li H., Zhang Y., Qian F., Jiang S.. **Assessing and comparing antioxidant activities of lactobacilli strains by using different chemical and cellular antioxidant methods**. *J. Dairy Sci.* (2018) **101** 10792-10806. DOI: 10.3168/jds.2018-14989
30. Azat R., Liu Y., Li W., Kayir A., Lin D.-B., Zhou W.-W., Zheng X.-D.. **Probiotic properties of lactic acid bacteria isolated from traditionally fermented Xinjiang cheese**. *J. Zhejiang Univ. Sci. B* (2016) **17** 597-609. DOI: 10.1631/jzus.B1500250
31. Maragkoudakis P.A., Zoumpopoulou G., Miaris C., Kalantzopoulos G., Pot B., Tsakalidou E.. **Probiotic potential of Lactobacillus strains isolated from dairy products**. *Int. Dairy J.* (2006) **16** 189-199. DOI: 10.1016/j.idairyj.2005.02.009
32. Humphries R., Bobenchik A.M., Hindler J.A., Schuetz A.N.. **Overview of Changes to the Clinical and Laboratory Standards Institute Performance Standards for Antimicrobial Susceptibility Testing, M100, 31st Edition**. *J. Clin. Microbiol.* (2021) **59** e00213-21. DOI: 10.1128/JCM.00213-21
33. Klare I., Konstabel C., Badstubner D., Werner G., Witte W.. **Occurrence and spread of antibiotic resistances in Enterococcus faecium**. *Int. J. Food Microbiol.* (2003) **88** 269-290. DOI: 10.1016/S0168-1605(03)00190-9
34. Natarajan P., Parani M.. **First Complete Genome Sequence of a Probiotic Enterococcus faecium Strain T-110 and Its Comparative Genome Analysis with Pathogenic and Non-pathogenic Enterococcus faecium Genomes**. *J. Genet. Genom.* (2015) **42** 43-46. DOI: 10.1016/j.jgg.2014.07.002
35. Graham K., Stack H., Rea R.. **Safety, beneficial and technological properties of enterococci for use in functional food applications—A review**. *Crit. Rev. Food Sci. Nutr.* (2020) **60** 3836-3861. DOI: 10.1080/10408398.2019.1709800
36. Terai T., Kato K., Ishikawa E., Nakao M., Ito M., Miyazaki K., Kushiro A., Imai S., Nomura Y., Hanada N.. **Safety assessment of the candidate oral probiotic Lactobacillus crispatus YIT 12319: Analysis of antibiotic resistance and virulence-associated genes**. *Food Chem. Toxicol.* (2020) **140** 111278. DOI: 10.1016/j.fct.2020.111278
37. Linares D.M., Martín M., Ladero V., Alvarez M.A., Fernández M.. **Biogenic Amines in Dairy Products**. *Crit. Rev. Food Sci. Nutr.* (2011) **51** 691-703. DOI: 10.1080/10408398.2011.582813
38. Sarantinopoulos P., Andrighetto C., Georgalaki M.D., Rea M., Lombardi A., Cogan T.M., Kalantzopoulos G., Tsakalidou E.. **Biochemical properties of enterococci relevant to their technological performance**. *Int. Dairy J.* (2001) **11** 621-647. DOI: 10.1016/S0958-6946(01)00087-5
39. Ladero V., Fernandez M., Calles-Enriquez M., Sanchez-Llana E., Canedo E., Martin M.C., Alvarez M.A.. **Is the production of the biogenic amines tyramine and putrescine a species-level trait in enterococci?**. *Food Microbiol.* (2012) **30** 132-138. DOI: 10.1016/j.fm.2011.12.016
40. Wang J., Da R., Tuo X., Cheng Y., Wei J., Jiang K., Lv J., Adediji O.M., Han B.. **Probiotic and Safety Properties Screening of Enterococcus faecalis from Healthy Chinese Infants**. *Probiotics Antimicrob. Proteins* (2020) **12** 1115-1125. DOI: 10.1007/s12602-019-09625-7
41. Succi M., Tremonte P., Reale A., Sorrentino E., Grazia L., Pacifico S., Coppola R.. **Bile salt and acid tolerance of Lactobacillus rhamnosus strains isolated from Parmigiano Reggiano cheese**. *FEMS Microbiol. Lett.* (2005) **244** 129-137. DOI: 10.1016/j.femsle.2005.01.037
42. Zago M., Fornasari M.E., Carminati D., Burns P., Suàrez V., Vinderola G., Reinheimer J., Giraffa G.. **Characterization and probiotic potential of Lactobacillus plantarum strains isolated from cheeses**. *Food Microbiol.* (2011) **28** 1033-1040. DOI: 10.1016/j.fm.2011.02.009
43. Rahmeh R., Akbar A., Alonaizi T., Kishk M., Shajan A., Akbar B.. **Characterization and application of antimicrobials produced by Enterococcus faecium S6 isolated from raw camel milk**. *J. Dairy Sci.* (2020) **103** 11106-11115. DOI: 10.3168/jds.2020-18871
44. Valenzuela A.S., Benomar N., Abriouel H., Cañamero M.M., Gálvez A.. **Isolation and identification of Enterococcus faecium from seafoods: Antimicrobial resistance and production of bacteriocin-like substances**. *Food Microbiol.* (2010) **27** 955-961. DOI: 10.1016/j.fm.2010.05.033
45. Basanta A., Sánchez J., Gómez-Sala B., Herranz C., Hernández P.E., Cintas L.M.. **Antimicrobial activity of Enterococcus faecium L50, a strain producing enterocins L50 (L50A and L50B), P and Q, against beer-spoilage lactic acid bacteria in broth, wort (hopped and unhopped), and alcoholic and non-alcoholic lager beers**. *Int. J. Food Microbiol.* (2008) **125** 293-307. DOI: 10.1016/j.ijfoodmicro.2008.04.011
46. Lee H., van der Donk W.. **Macrocyclization and Backbone Modification in RiPP Biosynthesis**. *Annu. Rev. Biochem.* (2022) **91** 269-294. DOI: 10.1146/annurev-biochem-032620-104956
|
---
title: 'Diagnostic Performance of Extrahepatic Protein Induced by Vitamin K Absence
in the Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis'
authors:
- Mirela Georgiana Perne
- Adela-Viviana Sitar-Tăut
- Teodora Gabriela Alexescu
- Lorena Ciumărnean
- Mircea-Vasile Milaciu
- Sorina-Cezara Coste
- Calin-Vasile Vlad
- Angela Cozma
- Dan-Andrei Sitar-Tăut
- Olga Hilda Orăşan
- Alexandra Crăciun
journal: Diagnostics
year: 2023
pmcid: PMC10001363
doi: 10.3390/diagnostics13050816
license: CC BY 4.0
---
# Diagnostic Performance of Extrahepatic Protein Induced by Vitamin K Absence in the Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis
## Abstract
Background and Objectives: the early diagnosis of hepatocellular carcinoma (HCC) benefits from the use of alpha-fetoprotein (AFP) together with imaging diagnosis using abdominal ultrasonography, CT, and MRI, leading to improved early detection of HCC. A lot of progress has been made in the field, but some cases are missed or late diagnosed in advanced stages of the disease. Therefore, new tools (serum markers, imagistic technics) are continually being reconsidered. Serum alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist II (PIVKA II) diagnostic accuracy for HCC (global and early disease) has been investigated (in a separate or cumulative way). The purpose of the present study was to determine the performance of PIVKA II compared to AFP. Materials and Methods: systematic research was conducted in PubMed, Web of Science, Embase, Medline and the Cochrane Central Register of Controlled Trials, taking into consideration articles published between 2018 and 2022. Results: a total number of 37 studies (5037 patients with HCC vs. 8199 patients—control group) have been included in the meta-analysis. PIVKA II presented a better diagnostic accuracy in HCC diagnostic vs. alpha-fetoprotein (global PIVKA II AUROC 0.851 vs. AFP AUROC 0.808, respectively, 0.790 vs. 0.740 in early HCC cases). The conclusion from a clinical point of view, concomitant use of PIVKA II and AFP can bring useful information, added to that brought by ultrasound examination.
## 1. Introduction
Hepatocellular carcinoma (HCC) is the most widespread histological subtype of primary liver cancer (accounting for approximately $90\%$ of all cases [1]), with an increasing incidence [2]; at the same time, it is currently recognized as the third most common cause of death worldwide [3,4,5]. Unfortunately, at the time of diagnosis, a small percentage of patients are eligible for curative treatment, the most common cause being an advanced tumor stage [6].
Studies in the literature have shown that chronic viral hepatitis B and C, autoimmune hepatitis, nonalcoholic steatohepatitis, and genetic and epigenetic changes are the main risk factors for the development of hepatocellular carcinoma HCC [7,8,9,10,11]. The prognosis of patients with advanced liver disease or cirrhosis (regardless of etiology), even in those responding to antiviral treatment, is influenced by the appearance of HCC [4,5].
Hepatocarcinogenesis is a gradual process characterized by genetic and molecular changes in the hepatocytes, followed over time by the appearance of a neoplastic lesion detectable by imaging.
Over the last ten years, the focus has shifted toward early HCC detection, this being known to influence treatment, curability of the disease and long-term survival [1,12]. Today, treatment strategies have diversified, including surgical resection, drug treatment, percutaneous treatment (ablation or chemoembolization) and liver transplant. Current guidelines recommend that in at-risk patients, the screening strategy should be based on complementary examination [13] like serum alpha-fetoprotein determination and abdominal ultrasonography at 3–6 months for early detection of HCC in groups of patients at risk [11,14,15,16,17,18,19].
Abdominal ultrasonography, however, remains an investigation with important limitations (patient-dependent or operator-dependent), being unable to detect small tumor formations with undesirable accuracy [11,20,21].
One of the traditional serum tumor markers for detecting and tracking HCC commonly used is alpha-fetoprotein (AFP) [22,23,24], its role evolving over time [23]. However, despite being widely used—being a non-invasive and affordable method—according to studies in the literature, it has suboptimal performance for the early detection of hepatocarcinoma [25]. Typically, a serum AFP level of 20 ng/mL is considered a borderline value to differentiate HCC from non-tumoral pathology [13]. Therefore, AFP has a high rate of false-negative results—approximately $40\%$—in the detection of early-stage tumors [16,26,27,28,29,30]. At the same time, a high proportion of patients with liver cirrhosis or chronic viral hepatitis without associated HCC frequently show false-positive results [23]. In these conditions, the diagnostic accuracy of determining AFP serum levels is unsatisfactory, due to low sensitivity (estimated between $39\%$ and $64\%$) and specificity (in the 76–$91\%$ range).
New classes of biomarkers with promising results in the early detection of HCC, such as microRNAs (miRNAs) [1,31,32], PIVKA II, known also as des-gamma-carboxy-prothrombin (DCP), or the fucosylated fraction of the AFP fraction (AFP-L3), stanniocalcin 2, APEX1 [33,34,35,36] have now been described. However, their use in medical practice may be limited by the absence of standardized analytical determining methods.
PIVKA II (first described in 1984 [37]) as an immature form of prothrombin (synthesized in the liver) can be used to estimate hepatic vitamin K status. PIVKA II seems to be a more suitable biomarker for the detection of vitamin K deficiency [38]. PIVKA II measurement shows increased sensitivity and specificity compared to other methods conventionally used (standard coagulation tests such as prothrombin time and activated partial thromboplastin time) to assess a deficiency of vitamin K [39].
In the absence of vitamin K, when its action is antagonized, or in the presence of neoplastic cells, PIVKA II is released into the blood.
In patients with gastrointestinal malignancies, PIVKA II levels were increased in most patients, with previous data pointing out a good sensitivity, respectively, the specificity for PIVKA II in gastrointestinal neoplastic disorders diagnostic: $78.67\%$ and $90.67\%$ in pancreatic adenocarcinoma, $83.93\%$ and $91.50\%$ in HCC. For establishing the association of serum levels of PIVKA II with colorectal cancer, additional studies are needed [40]. Just one case report referring to the colorectal neoplasm with secondary dissemination to the liver and the presence of increased serum levels of PIVKA II was found [40]. At the same time, previously published studies showed that PIVKA II is an effective and specific biomarker for HCC. Some researchers have demonstrated that PIVKA II levels reflect the oncogenesis and progression of HCC [41]. However, the efficacy of PIVKA II has not been sufficiently studied.
Serum and tissue overexpression of PIVKA II may be a specific tumor marker for HCC, showing promising results (no matter the hepatocarcinoma stage—$62.5\%$ sensitivity and $85.5\%$ specificity), but also indicating a poor prognosis, such as the presence of microvascular invasion and intrahepatic metastases [39,42]. According to current studies, elevated serum level of PIVKA II are associated with tumor size, microvascular invasion, and possible recurrence of HCC [12,22,43,44,45]. What differentiates PIVKA II from AFP is that the value of the former is not affected by liver disease activity [12].
In view of the above, the difficulty of performing adequate screening for HCC (to detect early cases), new screening methods are being examined. Current studies aim at comparative and summative evaluation of different methods, with Japan and other countries [46,47] implementing simultaneous determination of PIVKA II and AFP as a screening method to monitor patients at high risk of developing hepatocellular carcinoma [48].
To date, results on the diagnostic performance of PIVKA II in comparison to or in combination with AFP are conflicting. The available data come mainly from studies involving Asian patients [46,49], with results from Western studies limited by a relatively small sample size. Published studies (with the exception of the most recently published ones) have been systematized into past published meta-analyses, evaluating the accuracy of HCC detection by serum determination of PIVKA II and AFP biomarkers, alone or in combination in patients at risk of tumor development [44,45,48,49].
In the present one, the most recently published studies for establishing the role of PIVKA-II versus AFP (globally, but also in a relationship with the HCC stage) were taken into consideration.
Knowledge of this topic is needed for better screening and diagnosis of at-risk HCC patients. The aim of this work was to extend the knowledge of comparative evaluation of PIVKA II and AFP HCC diagnostic values, especially in early HCC patients.
## 2. Materials and Methods
Search strategy: literature screening for meta-analysis. A systematic search was conducted for the interval from 1 January 2018 to 4 September 2022. Searches for relevant studies were mainly conducted in PubMed, Web of Science, Embase, Medline and the Cochrane Central Register of Controlled Trials.
All publications from the databases mentioned above were reviewed, using the terms (((‘descarboxyprothrombin’ OR des-gamma-carboxy prothrombin) AND (‘liver cell carcinoma’ OR ‘hepatocellular carcinoma’) AND ‘cancer diagnosis’) OR (‘pivka’ AND (‘liver cell carcinoma’ OR ‘hepatocellular carcinoma’) AND ‘cancer diagnosis’) OR (‘DCP’ AND (‘liver cell carcinoma’ OR ‘hepatocellular carcinoma’) AND ‘cancer diagnosis’)) AND ((‘alphafetoprotein’ OR afp OR ‘alpha fetoprotein’ OR alfa-fetoprotein) AND (‘liver cell carcinoma’ OR ‘hepatocellular carcinoma’) AND ‘cancer diagnosis’). Only human studies from the mentioned period were selected for screening.
Rigorous research of the papers was performed. Two main investigators performed independent literature research in order to identify the previously published papers. All useful papers were read by both investigators, even those with negative results.
Duplicates were removed. Only articles written in English that had abstracts were taken into consideration. Articles presented just as abstracts or conference presentations, reviews, systematic reviews, meta-analyses, editorials and in vitro studies were excluded. The quality assessment of diagnostic accuracy studies (QUADAS) was applied to evaluate the selected studies from a quality point of view.
The following data were extracted from the articles studied: title, authors, year of publication, study identification item, country, number of locations where the study was conducted, number of patients included (with HCC vs. without HCC, respectively, early HCC cases), study design, etiology of liver disease; for both PIVKA II and AFP, the AUROC (overall and for early HCC cases), sensitivity and specificity were followed.
A flow diagram of the literature search strategy and study selection process is summarized in Figure 1.
According to the literature, at this moment, two tumor staging systems are used to define the extent of HCC—BCLC (Barcelona clinic liver cancer staging) staging [50,51], respectively, the 8th edition American Joint Committee on Cancer tumor–node–metastasis (TNM) staging [52]. BCLC stage 0 is defined as the tumor being less than 2 cm, performance status = 0 and the liver working normally (Child–Pugh A). BCLC stage A is defined in patients presenting single tumors of any size or 3 nodules < 3 cm in diameter, performance status = 0 and Child–Pugh class A or B.
In this meta-analysis, early-stage HCC was defined as BCLC stage 0/A and/or 8th edition TNM stage I (depending on the data reported by the included studies).
## Statistical Analysis
MedCalc software version 20.115 (Ostend, Belgium) was used for performing the meta-analysis. Using for every study each AUC value and the corresponding standard error (SE), the weighted summary AUC (sAUC) was calculated. Most of the studies did not report the standard error for AUROC. The formula used for SE (AUC) calculation was the one proposed by Hanley and McNeil [1982]—presented in Formula [1].
The publication bias was assessed using funnel plots. Forrest plots showing the overall effect were constructed. Taking into consideration the presence or absence of heterogeneity, a fixed or random effects model was preferred. An I2 value >$25\%$ was considered indicative of heterogeneity.
Formula [1]—AUROC standard error estimation [1]SEAUC=AUC1−AUC+N1−1Q1−AUC2+N2−1Q2−AUC2N1N2 where Q1=AUC2−AUC; Q2=2AUC1+AUC; N1—positive group (with HCC); N2—negative group (without HCC).
A p value < 0.05 was considered statistically significant.
## 3. Results
A total number of 37 studies were included in the meta-analysis. Overall, 13,236 patients were included: 5037 patients with HCC (case group) vs. 8199 patients (the control group). The control group was represented by healthy patients (without previous liver diseases), chronic hepatitis B or C, liver cirrhosis or at-risk condition patients. Patients with HCC were divided depending on their HCC stage—1513 early HCC. Complete data about the included studies are presented in Table 1.
For each study included, the performances of PIVKA II and AFP were reported in Table 2 and Table 3 (global and in early HCC cases). Sensibility and specificity for PIVKA II and AFP were also reported.
The sAUC of AFP, respectively PIVKA II for the discrimination between patients with HCC and those without, were 0.808 ($95\%$ CI 0.782 to 0.834) vs. 0.851 ($95\%$ CI 0.823 to 0.878)-data were reported in Figure 2. Considering that the studies showed heterogeneity (in both cases), random effects models were applied.
Taking into consideration the capacity of discrimination in early HCC cases, the sAUC of AFP, respectively, PIVKA II were 0.740 (CI $95\%$ 0.694 to 0.787), respectively, 0.790 ($95\%$ CI 0.751 to 0.828)–data were reported in Figure 3.
Some of the studies reported at the same time for AFP and PIVKA II; also, there were some studies reporting global for HCC, but also for early HCC; AFP = alpha-fetoprotein; PIVKA II = protein induced by vitamin K absence or antagonist-II.
## 4. Discussion
In our days, the neoplastic diseases show an increasing prevalence, with HCC being more and more frequently diagnosed, even in young patients. Lifestyle changes with an increased incidence of nonalcoholic steatohepatitis, chronic viral hepatitis and autoimmune hepatitis increase the risk of HCC, being responsible for HCC appearance.
A lot of progress has been made in HCC diagnosis, but some cases are missed or late diagnosed in advanced stages of the disease.
Therefore, new tools (serum markers, performant imagistic technics) are continually being reconsidered.
For decades, AFP has been widely used as a tumor marker in the surveillance of populations at high risk of developing HCC, but some limitations are well known. The reported sensitivity and specificity of this biomarker (40–$65\%$, 76–$96\%$, respectively) differ significantly depending on the characteristics of the studied group [22,86]. AFP serum values were often elevated in patients with chronic liver disease or cirrhosis without HCC [22].
All current guidelines recommend the additional use of imaging diagnosis in order to improve the diagnostic accuracy. Ultrasonography, CT or MRI present limitations, sometimes encountering difficulties in small lesion diagnosis. In view of these data, AFP and ultrasonography have been used together to improve diagnostic sensitivity in medical practice [3,86,87,88,89,90], but accuracy for the moment remains uncertain [91]. Under these conditions, HCC screening can be improved to detect neoplastic lesions at early stages. To date, several promising serum tumor markers with the potential for early diagnosis and surveillance of HCC have been proposed [3,21,44,45,86,87,88,89,90,92,93] of which PIVKA II appears to be the most promising, with recently published data on its performance (alone or in combination with AFP or ultrasonography).
No clear PIVKA II cut-offs for HCC, respectively, for early HCC diagnosis were already established. Supplementary, different methods are used for biomarker determination—so, for clarifying these aspects, more data need to be published. To this moment, to our best knowledge, clinical and laboratory factors influencing the PIVKA II values have not been exhaustively investigated. The current meta-analysis brings to attention new data about the usefulness and ability of PIVKA II to detect HCC. Literature is scarce in revealing the role of PIVKA II versus AFP. The paper provides an overview of recently published data about the role of PIVKA II vs. AFP in HCC diagnosis. In this meta-analysis, PIVKA II presented greater accuracy for HCC diagnosis, taking into consideration all cases (0.851 vs. 0.808), but also in early HCC cases (0.790 vs. 0.740). The reported results (the better discriminatory value of PIVKA II) are in line with those reported by Caviglia [3] (11 studies, published between 2011 and 2017), Chen H [94](27 studies, 2000–2016), Fan J [7] (40 studies, up to December 31, 2018), Fang Y [95] (28 studies, 2015–2021), Xing H [87] (31 studies, up to December 20, 2017). Also, the reported calculated AUROCs were approximately similar to those reported in previously mentioned studies. There also has been published some meta-analysis evaluating just one of the two biomarkers (PIVKA II or AFP), the results regarding the AUROC values being consistent with the results of this study [89,96,97].
A novel perspective brought to attention a parallel with the standard marker used (AFP)—higher accuracy for PIVKA II being found in the early diagnosis of HCC. Similar data have been published by Xing [87]. The results of this study highlight the possible role of PIVKA II in providing new data, useful for daily medical practice. A recently published study (just a few days ago, not included in the meta-analysis) also revealed that PIVKA II had a better predictive performance vs. AFP global and in early-HCC (the reported registered values being approximately similar to these ones [22]). The results of the study are supporting others’ recommendations that PIVKA II can have usefulness in early HCC diagnosis in the incipient moments [66].
It was impossible to determine the PIVKA II and AFP performances, depending on the etiology of the liver diseases, with mixed etiology being taken into consideration. Frequently, the studies do not make a difference according to the liver disease etiology; in most of the cases, the reported results are globally calculated.
The 0 and A HCC BCLC classes were taken into consideration in a unitary way, which must be mentioned. Of course, that is a discrepancy between the two classes regarding the following treatment, but for the moment it was not possible to perform a detailed, stratified analysis.
Due to the heterogeneity of the reported studies (determined by the diversity of study populations in different countries, methodology used and sample size), these findings might not be representative of all populations—further research is needed.
Stratified analysis depending on the gender, ethnicity, age or liver disease type and stage represents an area to be explored further. More data should be published regarding the cut-off values, for a unitary approach regarding HCC diagnosis.
This study provides the backbone for a future meta-analysis in order to evaluate the accuracy of PIVKA II and AFP association in HCC diagnosis. Of the listed studies, just a few of them reported combined accuracy. In addition, future studies on the topic are recommended to determine the serum values of PIVKA II after HCC treatment (surgical or chemotherapy), theoretically bringing useful information for monitoring treatment results, for predicting diagnosis, relapse and survival.
## 5. Conclusions
These results provide a significant step toward the diagnosis of HCC by determining the serum value of vitamin-K-dependent proteins used as tumor biomarkers, along with other paraclinical examinations.
From a clinical and practical point of view, the use of PIVKA II concomitantly or instead of AFP is bringing useful information, added to those reported by ultrasound examination. Probably the emerging role of PIVKA II is in patients with previous hepatic diseases (hepatitis, cirrhosis) where AFP limitations are well-known.
This study provides the backbone for future studies on the relationship with an earlier diagnosis of hepatocarcinoma.
## References
1. Caviglia G., Ciruolo M., Abate M., Carucci P., Rolle E., Rosso C., Olivero A., Troshina G., Risso A., Nicolosi A.. **Alpha-fetoprotein, protein induced by vitamin K absence or antagonist II and glypican-3 for the detection and prediction of hepatocellular carcinoma in patients with cirrhosis of viral etiology**. *Cancers* (2020) **12**. DOI: 10.3390/cancers12113218
2. Venook A.P., Papandreou C., Furuse J., Ladrón de Guevara L.. **The Incidence and Epidemiology of Hepatocellular Carcinoma: A Global and Regional Perspective**. *Oncologist* (2010) **15** 5-13. DOI: 10.1634/theoncologist.2010-S4-05
3. Caviglia G.P., Ribaldone D.G., Abate M.L., Ciancio A., Pellicano R., Smedile A., Saracco G.M.. **Performance of protein induced by vitamin K absence or antagonist-II assessed by chemiluminescence enzyme immunoassay for hepatocellular carcinoma detection: A meta-analysis**. *Scand. J. Gastroenterol.* (2018) **53** 734-740. DOI: 10.1080/00365521.2018.1459824
4. Caviglia G.P., Abate M.L., Pellicano R., Smedile A.. **Chronic hepatitis B therapy: Available drugs and treatment guidelines**. *Minerva Gastroenterol. Dietol.* (2015) **61** 61-70. PMID: 25323305
5. Kanwal F., Kramer J., Asch S.M., Chayanupatkul M., Cao Y., El-Serag H.B.. **Risk of Hepatocellular Cancer in HCV Patients Treated with Direct-Acting Antiviral Agents**. *Gastroenterology* (2017) **153** 996-1005.e1. DOI: 10.1053/j.gastro.2017.06.012
6. Bhatti A.B.H., Naz K., Abbas G., Khan N.Y., Zia H.H., Ahmed I.N.. **Clinical Utility of Protein Induced by Vitamin K Absence-II in Patients with Hepatocellular Carcinoma**. *Asian Pacific. J. Cancer. Prev.* (2021) **22** 1731-1736. DOI: 10.31557/APJCP.2021.22.6.1731
7. Fan J., Chen Y., Zhang D., Yao J., Zhao Z., Jiang Y., Li Y., Guo Y.. **Evaluation of the diagnostic accuracy of des-gamma-carboxy prothrombin and alpha-fetoprotein alone or in combination for hepatocellular carcinoma: A systematic review and meta-analysis**. *Surg. Oncol.* (2020) **34** 245-255. DOI: 10.1016/j.suronc.2020.05.002
8. Forner A., Reig M., Bruix J.. **Hepatocellular carcinoma**. *Lancet* (2018) **391** 1301-1314. DOI: 10.1016/S0140-6736(18)30010-2
9. Michelotti A., de Scordilli M., Palmero L., Guardascione M., Masala M., Roncato R., Foltran L., Ongaro E., Puglisi F.. **NAFLD-Related Hepatocarcinoma: The Malignant Side of Metabolic Syndrome**. *Cells* (2021) **10**. DOI: 10.3390/cells10082034
10. Sumida Y., Yoneda M., Seko Y., Ishiba H., Hara T., Toyoda H., Yasuda S., Kumada T., Hayashi H., Kobayashi T.. **Surveillance of Hepatocellular Carcinoma in Nonalcoholic Fatty Liver Disease**. *Diagnostics* (2020) **10**. DOI: 10.3390/diagnostics10080579
11. Bruix J., Sherman M.. **Management of hepatocellular carcinoma: An update**. *Hepatology* (2011) **53** 1020-1022. DOI: 10.1002/hep.24199
12. Basile U., Miele L., Napodano C., Ciasca G., Gulli F., Pocino K., De Matthaeis N., Liguori A., De Magistris A., Marrone G.. **The diagnostic performance of PIVKA-II in metabolic and viral hepatocellular carcinoma: A pilot study**. *Eur. Rev. Med. Pharmacol. Sci.* (2021) **25** 12675-12685
13. Lombardi A., Grimaldi A., Zappavigna S., Misso G., Caraglia M.. **Hepatocarcinoma: Genetic and epigenetic features**. *Minerva Gastroenterol. Dietol.* (2017) **64** 14-27. DOI: 10.23736/S1121-421X.17.02408-4
14. Choi J.Y., Jung S.W., Kim H.Y., Kim M., Kim Y., Kim D.G., Oh E.-J.. **Diagnostic value of AFP-L3 and PIVKA-II in hepatocellular carcinoma according to total-AFP**. *World J. Gastroenterol.* (2013) **19** 339-346. DOI: 10.3748/wjg.v19.i3.339
15. Izumi N.. **Diagnostic and treatment algorithm of the Japanese society of hepatology: A consensus-based practice guideline**. *Oncology* (2010) **78** 78-86. DOI: 10.1159/000315234
16. Saffroy R., Pham P., Reffas M., Takka M., Lemoine A., Debuire B.. **New perspectives and strategy research biomarkers for hepatocellular carcinoma**. *Clin. Chem. Lab. Med.* (2007) **45** 1169-1179. DOI: 10.1515/CCLM.2007.262
17. **Practice guidelines for management of hepatocellular carcinoma 2009**. *Korean J. Hepatol.* (2009) **15** 391. DOI: 10.3350/kjhep.2009.15.3.391
18. Durazo F.A., Blatt L.M., Corey W.G., Lin J.H., Han S., Saab S., Busuttil R.W., Tong M.J.. **Des-γ-carboxyprothrombin, α-fetoprotein and AFP-L3 in patients with chronic hepatitis, cirrhosis and hepatocellular carcinoma**. *J. Gastroenterol. Hepatol.* (2008) **23** 1541-1548. DOI: 10.1111/j.1440-1746.2008.05395.x
19. Galle P.R., Forner A., Llovet J.M., Mazzaferro V., Piscaglia F., Raoul J.-L., Schirmacher P., Vilgrain V.. **EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma**. *J. Hepatol.* (2018) **69** 182-236. DOI: 10.1016/j.jhep.2018.03.019
20. **EASL-EORTC clinical practice guidelines: Management of hepatocellular carcinoma**. *J. Hepatol.* (2012) **56** 908-943. DOI: 10.1016/j.jhep.2011.12.001
21. Caviglia G., Armandi A., Rosso C., Gaia S., Aneli S., Rolle E., Abate M., Olivero A., Nicolosi A., Guariglia M.. **Biomarkers of oncogenesis, adipose tissue dysfunction and systemic inflammation for the detection of hepatocellular carcinoma in patients with nonalcoholic fatty liver disease**. *Cancers* (2021) **13**. DOI: 10.3390/cancers13102305
22. Liu S., Sun L., Yao L., Zhu H., Diao Y., Wang M., Xing H., Lau W.Y., Guan M., Pawlik T.M.. **Diagnostic Performance of AFP, AFP-L3, or PIVKA-II for Hepatitis C Virus-Associated Hepatocellular Carcinoma: A Multicenter Analysis**. *J. Clin. Med.* (2022) **11**. DOI: 10.3390/jcm11175075
23. Chen V.L., Sharma P.. **Role of Biomarkers and Biopsy in Hepatocellular Carcinoma**. *Clin. Liver. Dis.* (2020) **24** 577-590. DOI: 10.1016/j.cld.2020.07.001
24. Xu Y., Guo Q., Wei L.. **The emerging influences of alpha-fetoprotein in the tumorigenesis and progression of hepatocellular carcinoma**. *Cancers* (2021) **13**. DOI: 10.3390/cancers13205096
25. Li C., Zhang Z., Zhang P., Liu J.. **Diagnostic accuracy of des-gamma-carboxy prothrombin versus α-fetoprotein for hepatocellular carcinoma: A systematic review**. *Hepatol. Res.* (2014) **44** E11-E25. DOI: 10.1111/hepr.12201
26. Sherman M., Peltekian K.M., Lee C.. **Screening for hepatocellular carcinoma in chronic carriers of hepatitis B virus: Incidence and prevalence of hepatocellular carcinoma in a North American urban population**. *Hepatology* (1995) **22** 432-438. PMID: 7543434
27. Toyoda H., Kumada T., Tada T.. **Highly sensitive Lens culinaris agglutinin-reactive α-fetoprotein: A new tool for the management of hepatocellular carcinoma**. *Oncology* (2011) **81** 61-65. DOI: 10.1159/000333263
28. Matsuda M., Asakawa M., Amemiya H., Fujii H.. **Lens culinaris agglutinin-reactive fraction of AFP is a useful prognostic biomarker for survival after repeat hepatic resection for HCC**. *J. Gastroenterol. Hepatol.* (2011) **26** 731-738. DOI: 10.1111/j.1440-1746.2010.06532.x
29. Cui R., He J., Zhang F., Wang B., Ding H., Shen H., Li Y., Chen X.. **Diagnostic value of protein induced by vitamin K absence (PIVKAII) and hepatoma-specific band of serum gamma-glutamyl transferase (GGTII) as hepatocellular carcinoma markers complementary to α-fetoprotein**. *Br. J. Cancer.* (2003) **88** 1878-1882. DOI: 10.1038/sj.bjc.6601018
30. Chi X., Jiang L., Yuan Y., Huang X., Yang X., Hochwald S., Liu J., Huang H.. **A comparison of clinical pathologic characteristics between alpha-fetoprotein negative and positive hepatocellular carcinoma patients from Eastern and Southern China**. *BMC Gastroenterol.* (2022) **22**. DOI: 10.1186/s12876-022-02279-w
31. Petrini E., Caviglia G., ABate M., Fagoonee S., Smedile A., Pellicano R.. **MicroRNAs in HBV-related hepatocellular carcinoma: Functions and potential clinical applications**. *Panminerva Med.* (2011) **47** 381-390
32. Jia H., Yu H., Liu Q.. **Single nucleotide polymorphisms of MIR-149 gene rs2292832 contributes to the risk of hepatocellular carcinoma, but not overall cancer: A meta-analysis**. *Minerva Med.* (2016) **107** 259-269. PMID: 27348444
33. Taketa K., Sekiya C., Namiki M., Akamatsu K., Ohta Y., Endo Y., Kosaka K.. **Lectin-reactive profiles of alpha-fetoprotein characterizing hepatocellular carcinoma and related conditions**. *Gastroenterology* (1990) **99** 508-518. DOI: 10.1016/0016-5085(90)91034-4
34. Taketa K., Endo Y., Sekiya C., Tanikawa K., Koji T., Taga H., Satomura S., Matsuura S., Kawai T., Hirai H.. **A collaborative study for the evaluation of lectin-reactive alpha-fetoproteins in early detection of hepatocellular carcinoma**. *Cancer Res.* (1993) **53** 5419-5423. PMID: 7693340
35. Wu Z., Cheng H., Liu J., Zhang S., Zhang M., Liu F., Li Y., Huang Q., Jiang Y., Chen S.. **The Oncogenic and Diagnostic Potential of Stanniocalcin 2 in Hepatocellular Carcinoma**. *J. Hepatocell. Carcinoma* (2022) **9** 141-155. DOI: 10.2147/JHC.S351882
36. Cao L., Cheng H., Jiang Q., Li H., Wu Z.. **APEX1 is a novel diagnostic and prognostic biomarker for hepatocellular carcinoma**. *Aging* (2020) **12** 4573-4591. DOI: 10.18632/aging.102913
37. Liebman H.A., Furie B.C., Tong M.J., Blanchard R.A., Lo K.-J., Lee S.-D., Coleman M.S., Furie B.. **Des-gamma-carboxi (abnormal) prothrombin as a serum marker of primary Hepatocellular carcinoma**. *N. Engl. J. Med.* (1984) **310** 1427-1431. DOI: 10.1056/NEJM198405313102204
38. Thomas O., Rein H., Strandberg K., Schött U.. **Coagulative safety of epidural catheters after major upper gastrointestinal surgery: Advanced and routine coagulation analysis in 38 patients**. *Perioper. Med.* (2016) **5** 28. DOI: 10.1186/s13741-016-0053-0
39. Dong R., Wang N., Yang Y., Ma L., Du Q., Zhang W., Tran A., Jung H., Soh A., Zheng Y.. **Review on Vitamin K Deficiency and its Biomarkers: Focus on the Novel Application of PIVKA-II in Clinical Practice**. *Clin Lab.* (2018) **64** 413-424. DOI: 10.7754/Clin.Lab.2017.171020
40. Kato K., Iwasaki Y., Taniguchi M., Onodera K., Matsuda M., Kawakami T., Higuchi M., Kato K., Kato Y., Furukawa H.. **Primary colon cancer with a high serum PIVKA-II level**. *Int. J. Surg. Case Rep.* (2015) **6** 95-99. DOI: 10.1016/j.ijscr.2014.11.072
41. Yu R., Tan Z., Xiang X., Dan Y., Deng G.. **Effectiveness of PIVKA-II in the detection of hepatocellular carcinoma based on real-world clinical data**. *BMC Cancer* (2017) **17**. DOI: 10.1186/s12885-017-3609-6
42. Inagaki Y., Tang W., Xu H., Wang F., Nakata M., Sugawara Y., Kokudo N.. **Des-gamma-carboxyprothrombin: Clinical effectiveness and biochemical importance**. *Biosci. Trends.* (2008) **2** 53-60. PMID: 20103901
43. Baek Y.-H., Lee J.-H., Jang J.-S., Lee S.-W., Han J.-Y., Jeong J.-S., Choi J.-C., Kim H.-Y., Han S.-Y.. **Diagnostic role and correlation with staging systems of PIVKA-II compared with AFP**. *Hepatogastroenterology* (2009) **56** 763-767. PMID: 19621698
44. Shirabe K., Itoh S., Yoshizumi T., Soejima Y., Taketomi A., Aishima S.-I., Maehara Y.. **The predictors of microvascular invasion in candidates for liver transplantation with hepatocellular carcinoma—With special reference to the serum levels of des-gamma-carboxy prothrombin**. *J. Surg Oncol.* (2007) **95** 235-240. DOI: 10.1002/jso.20655
45. Kim D.Y., Paik Y.H., Ahn S.H., Youn Y.J., Choi J.W., Kim J.K., Lee K.S., Chon C.Y., Han K.H.. **PIVKA-II is a useful tumor marker for recurrent hepatocellular carcinoma after surgical resection**. *Oncology* (2007) **72** 52-57. DOI: 10.1159/000111707
46. Song P.-P., Xia J.-F., Inagaki Y., Hasegawa K., Sakamoto Y., Kokudo N., Tang W.. **Controversies regarding and perspectives on clinical utility of biomarkers in hepatocellular carcinoma**. *World J. Gastroenterol.* (2016) **22** 262-274. DOI: 10.3748/wjg.v22.i1.262
47. Choi J., Park Y., Kim J.H., Kim H.S.. **Evaluation of automated serum des-gamma-carboxyprothrombin (DCP) assays for detecting hepatocellular carcinoma**. *Clin. Biochem.* (2011) **44** 1464-1468. DOI: 10.1016/j.clinbiochem.2011.08.1144
48. Arii S., Sata M., Sakamoto M., Shimada M., Kumada T., Shiina S., Yamashita T., Kokudo N., Tanaka M., Takayama T.. **Management of hepatocellular carcinoma: Report of Consensus Meeting in the 45th Annual Meeting of the Japan Society of Hepatology (2009)**. *Hepatol. Res.* (2010) **40** 667-685. DOI: 10.1111/j.1872-034X.2010.00673.x
49. Van Hees S., Michielsen P., Vanwolleghem T.. **Circulating predictive and diagnostic biomarkers for hepatitis B virus-associated hepatocellular carcinoma**. *World J. Gastroenterol.* (2016) **22** 8271-8282. DOI: 10.3748/wjg.v22.i37.8271
50. Vitale A., Morales R.R., Zanus G., Farinati F., Burra P., Angeli P., Frigo A.C., Del Poggio P., Rapaccini G., Di Nolfo M.A.. **Barcelona Clinic Liver Cancer staging and transplant survival benefit for patients with hepatocellular carcinoma: A multicentre, cohort study**. *Lancet Oncol.* (2011) **12** 654-662. DOI: 10.1016/S1470-2045(11)70144-9
51. Forner A., Reig M.E., Rodriguez de Lope C., Bruix J.. **Current Strategy for Staging and Treatment: The BCLC Update and Future Prospects**. *Semin. Liver Dis.* (2010) **30** 61-74. DOI: 10.1055/s-0030-1247133
52. Liao X., Zhang D.. **The 8th Edition American Joint Committee on Cancer Staging for Hepato-pancreato-biliary Cancer: A Review and Update**. *Arch. Pathol. Lab. Med.* (2021) **145** 543-553. DOI: 10.5858/arpa.2020-0032-RA
53. Schotten C., Ostertag B., Sowa J.-P., Manka P., Bechmann L., Hilgard G., Marquardt C., Wichert M., Toyoda H., Lange C.. **Galad score detects early-stage hepatocellular carcinoma in a european cohort of chronic hepatitis b and c patients**. *Pharmaceuticals* (2021) **14**. DOI: 10.3390/ph14080735
54. Choi J., Kim G.A., Han S., Lee W., Chun S., Lim Y.S.. **Longitudinal Assessment of Three Serum Biomarkers to Detect Very Early-Stage Hepatocellular Carcinoma**. *Hepatology* (2019) **69** 1983-1994. DOI: 10.1002/hep.30233
55. Best J., Bechmann L.P., Sowa J.P., Sydor S., Dechêne A., Pflanz K., Bedreli S., Schotten C., Geier A., Berg T.. **GALAD Score Detects Early Hepatocellular Carcinoma in an International Cohort of Patients with Nonalcoholic Steatohepatitis**. *Clin. Gastroenterol. Hepatol.* (2020) **18** 728-735. DOI: 10.1016/j.cgh.2019.11.012
56. Wu M., Liu Z., Li X., Zhang A., Li N.. **Dynamic Changes in Serum Markers and Their Utility in the Early Diagnosis of all Stages of hepatitis B-associated Hepatocellular carcinoma**. *Onco Targets Ther.* (2020) **13** 827-840. DOI: 10.2147/OTT.S229835
57. Malov S., Malov I., Kuvshinov A., Marche P., Decaens T., Macek-Jilkova Z., Yushchuk N.. **Search for effective serum tumor markers for early diagnosis of hepatocellular carcinoma associated with hepatitis**. *Sovrem. Teh. V Med.* (2021) **13** 27-34. DOI: 10.17691/stm2021.13.1.03
58. Chan H.L.Y., Vogel A., Berg T., De Toni E.N., Kudo M., Trojan J., Eiblmaier A., Klein H., Hegel J.K., Sharma A.. **Performance evaluation of the Elecsys PIVKA-II and Elecsys AFP assays for hepatocellular carcinoma diagnosis**. *JGH Open.* (2022) **6** 292-300. DOI: 10.1002/jgh3.12720
59. Chalasani N.P., Porter K., Bhattacharya A., Book A.J., Neis B.M., Xiong K.M., Ramasubramanian T.S., Edwards D.K., Chen I., Johnson S.. **Validation of a Novel Multitarget Blood Test Shows High Sensitivity to Detect Early Stage Hepatocellular Carcinoma**. *Clin. Gastroenterol. Hepatol.* (2022) **20** 173-182.e7. DOI: 10.1016/j.cgh.2021.08.010
60. Nouso K., Furubayashi Y., Shiota S., Miyake N., Oonishi A., Wakuta A., Kariyama K., Hiraoka A., Tsuji K., Itobayashi E.. **Early detection of hepatocellular carcinoma in patients with diabetes mellitus**. *Eur. J. Gastroenterol. Hepatol.* (2020) **32** 877-881. DOI: 10.1097/MEG.0000000000001638
61. Hemken P.M., Sokoll L.J., Yang X., Dai J., Elliott D., Gawel S.H., Lucht M., Feng Z., Marrero J.A., Srivastava S.. **Validation of a novel model for the early detection of hepatocellular carcinoma**. *Clin. Proteom.* (2019) **16** 2. DOI: 10.1186/s12014-018-9222-0
62. Unić A., Derek L., Duvnjak M., Patrlj L., Rakic M., Kujundžić M., Renjić V., Štoković N., Dinjar P., Jukic A.. **Diagnostic specificity and sensitivity of PIVKAII, GP3, CSTB, SCCA1 and HGF for the diagnosis of hepatocellular carcinoma in patients with alcoholic liver cirrhosis**. *Ann. Clin. Biochem.* (2018) **55** 355-362. DOI: 10.1177/0004563217726808
63. Liu Z., Wu M., Lin D., Li N.. **Des-gamma-carboxyprothrombin is a favorable biomarker for the early diagnosis of alfa-fetoprotein-negative hepatitis B virus-related hepatocellular carcinoma**. *J. Int. Med. Res.* (2020) **48** 300060520902575. DOI: 10.1177/0300060520902575
64. Song T., Wang L., Xin R., Zhang L., Tian Y.. **Evaluation of serum AFP and DCP levels in the diagnosis of early-stage HBV-related HCC under different backgrounds**. *J. Int. Med. Res.* (2020) **48** 0300060520969087. DOI: 10.1177/0300060520969087
65. Loglio A., Iavarone M., Facchetti F., Di Paolo D., Perbellini R., Lunghi G., Ceriotti F., Galli C., Sandri M.T., Viganò M.. **The combination of PIVKA-II and AFP improves the detection accuracy for HCC in HBV caucasian cirrhotics on long-term oral therapy**. *Liver Int.* (2020) **40** 1987-1996. DOI: 10.1111/liv.14475
66. Chen H., Zhang Y., Li S., Li N., Chen Y., Zhang B., Qu C., Ding H., Huang J., Dai M.. **Direct comparison of five serum biomarkers in early diagnosis of hepatocellular carcinoma**. *Cancer Manag. Res.* (2018) **10** 1947-1958. DOI: 10.2147/CMAR.S167036
67. Piratvisuth T., Tanwandee T., Thongsawat S., Sukeepaisarnjaroen W., Esteban J.I., Bes M., Köhler B., He Y., Lange M.S., Morgenstern D.. **Multimarker Panels for Detection of Early Stage Hepatocellular Carcinoma: A Prospective, Multicenter, Case-Control Study**. *Hepatol. Commun.* (2022) **6** 679-691. DOI: 10.1002/hep4.1847
68. Song T., Wang L., Su B., Zeng W., Jiang T., Zhang T., Sun G., Wu H.. **Diagnostic value of alpha-fetoprotein, Lens culinaris agglutinin-reactive alpha-fetoprotein, and des-gamma-carboxyprothrombin in hepatitis B virus-related hepatocellular carcinoma**. *J. Int. Med. Res.* (2019) **48** 300060519889270. DOI: 10.1177/0300060519889270
69. Degasperi E., Perbellini R., D’Ambrosio R., Renteria S.C.U., Ceriotti F., Perego A., Orsini C., Borghi M., Iavarone M., Bruccoleri M.. **Prothrombin induced by vitamin K absence or antagonist-II and alpha foetoprotein to predict development of hepatocellular carcinoma in Caucasian patients with hepatitis C-related cirrhosis treated with direct-acting antiviral agents**. *Aliment. Pharm. Ther.* (2022) **55** 350-359. DOI: 10.1111/apt.16685
70. Wu J., Xiang Z., Le Bai L., He L., Tan L., Hu M., Ren Y.. **Diagnostic value of serum PIVKA-II levels for BCLC early hepatocellular carcinoma and correlation with HBV DNA**. *Cancer Biomark.* (2018) **23** 235-242. DOI: 10.3233/CBM-181402
71. Qi F., Zhou A., Yan L., Yuan X., Wang D., Chang R., Zhang Y., Shi F., Han X., Hou J.. **The diagnostic value of PIVKA-II, AFP, AFP-L3, CEA, and their combinations in primary and metastatic hepatocellular carcinoma**. *J. Clin. Lab. Anal.* (2020) **34** e23158. DOI: 10.1002/jcla.23158
72. Li Y., Chen J.. **Serum Des-Gamma-carboxi Prothrombin for diagnosis of adult primary cancer in liver**. *J. Coll. Physician Surg. Pak.* (2018) **29** 972-976. DOI: 10.29271/jcpsp.2019.10.972
73. Guan M., Ouyang W., Liu S., Sun L., Chen W.. **Alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II, lens culinaris agglutinin-reactive fraction of alpha-fetoprotein alone and in combination for early detection of hepatocellular carcinoma from nonalcoholic fatty liver disease: A multicenter analysis**. *Hepatobiliary Pancreat. Dis. Int.* (2022) **21** 559-568. PMID: 35643910
74. Si Y.-Q., Wang X.-Q., Fan G., Wang C.-Y., Zheng Y.-W., Song X., Pan C.-C., Chu F.-L., Liu Z.-F., Lu B.-R.. **Value of AFP and PIVKA-II in diagnosis of HBV-related hepatocellular carcinoma and prediction of vascular invasion and tumor differentiation**. *Infect. Agent Cancer.* (2020) **15** 70. DOI: 10.1186/s13027-020-00337-0
75. Ji J., Liu L., Jiang F., Wen X., Zhang Y., Li S., Lou J., Wang Y., Liu N., Guo Q.. **The clinical application of PIVKA-II in hepatocellular carcinoma and chronic liver diseases: A multi-center study in China**. *J. Clin. Lab. Anal.* (2021) **35** e24013. DOI: 10.1002/jcla.24013
76. Wang G., Lu X., Du Q., Zhang G., Wang D., Wang Q., Guo X.. **Diagnostic value of the γ-glutamyltransferase and alanine transaminase ratio, alpha-fetoprotein, and protein induced by vitamin K absence or antagonist II in hepatitis B virus-related hepatocellular carcinoma**. *Sci. Rep.* (2020) **10** 13519. DOI: 10.1038/s41598-020-70241-5
77. Wang Q., Chen Q., Zhang X., Lu X.-L., Du Q., Zhu T., Zhang G.-Y., Wang D.-S., Fan Q.-M.. **Diagnostic value of gamma-glutamyltransferase/aspartate aminotransferase ratio, protein induced by Vitamin K absence or antagonist II, and alpha-fetoprotein in hepatitis B virus-related hepatocellular carcinoma**. *World J. Gastroenterol.* (2019) **25** 5515-5529. DOI: 10.3748/wjg.v25.i36.5515
78. Li T., Li H., Wang A., Su X., Zhao J., Cui Y., Liu J., Hu J.. **Development and validation of a simple model for detection of early hepatocellular carcinoma in a liver cirrhosis cohort**. *Cancer Manag. Res.* (2019) **11** 9379-9386. DOI: 10.2147/CMAR.S221050
79. Feng H., Li B., Li Z., Wei Q., Ren L.. **PIVKA-II serves as a potential biomarker that complements AFP for the diagnosis of hepatocellular carcinoma**. *BMC Cancer* (2021) **21**. DOI: 10.1186/s12885-021-08138-3
80. Nguyen H.B., Le X.T.T., Nguyen H.H., Vo T.T., Le M.K., Nguyen N.T., Do-Nguyen T.M., Truong-Nguyen C.M., Nguyen B.-S.T.. **Diagnostic Value of hTERT mRNA and in Combination With AFP, AFP-L3%, Des-γ-carboxyprothrombin for Screening of Hepatocellular Carcinoma in Liver Cirrhosis Patients HBV or HCV-Related**. *Cancer Inform.* (2022) **21** 11769351221100730. DOI: 10.1177/11769351221100730
81. Lee Q., Yu X., Yu W.. **The value of PIVKA-II versus AFP for the diagnosis and detection of postoperative changes in hepatocellular carcinoma**. *J. Interv. Med.* (2021) **4** 77-81. PMID: 34805952
82. Chen J., Tang D., Xu C., Niu Z., Li H., Li Y., Zhang P.. **Evaluation of Serum GDF15, AFP, and PIVKA-II as Diagnostic Markers for HBV-Associated Hepatocellular Carcinoma**. *Lab. Med.* (2021) **52** 381-389. DOI: 10.1093/labmed/lmaa089
83. Xu F., Zhang L., He W., Song D., Ji X., Shao J.. **The diagnostic value of serum PIVKA-II alone or in combination with AFP in Chinese hepatocellular carcinoma patients**. *Dis. Markers* (2021) **2021** 8868370. DOI: 10.1155/2021/8868370
84. Peng F., Yuan H., Zhou Y.F., Wu S.X., Long Z.Y., Peng Y.M.. **Diagnostic Value of Combined Detection via Protein Induced by Vitamin K Absence or Antagonist II, Alpha-Fetoprotein, and D-Dimer in Hepatitis B Virus-Related Hepatocellular Carcinoma**. *Int. J. Gen. Med.* (2022) **15** 5763-5773. DOI: 10.2147/IJGM.S362359
85. Hadi H., Shuaib W., Ali R., Othman H.. **Utility of PIVKA-II and AFP in Differentiating Hepatocellular Carcinoma from Non-malignant High-risk patients**. *Medicina* (2022) **58**. DOI: 10.3390/medicina58081015
86. Xing H., Yan C., Cheng L., Wang N., Dai S., Yuan J., Lu W., Wang Z., Han J., Zheng Y.. **Clinical application of protein induced by vitamin K antagonist-II as a biomarker in hepatocellular carcinoma**. *Tumor. Biol.* (2016) **37** 15447-15456. DOI: 10.1007/s13277-016-5443-x
87. Xing H., Zheng Y.-J., Han J., Zhang H., Li Z.-L., Lau W.-Y., Shen F., Yang T.. **Protein induced by vitamin K absence or antagonist-II versus alpha-fetoprotein in the diagnosis of hepatocellular carcinoma: A systematic review with meta-analysis**. *Hepatobiliary Pancreat. Dis. Int.* (2018) **17** 487-495. DOI: 10.1016/j.hbpd.2018.09.009
88. Hu B., Tian X., Sun J., Meng X.. **Evaluation of individual and combined applications of serum biomarkers for diagnosis of Hepatocellular carcinoma: A meta-analysis**. *Int. J. Mol. Sci.* (2013) **14** 23559-23580. DOI: 10.3390/ijms141223559
89. Chen J., Wu G., Li Y.. **Evaluation of serum des-gamma-carboxy prothrombin for the diagnosis of hepatitis B virus-related hepatocellular carcinoma: A meta-analysis**. *Dis. Markers* (2018) **2018** 8906023. DOI: 10.1155/2018/8906023
90. Saitta C., Raffa G., Alibrandi A., Brancatelli S., Lombardo D., Tripodi G., Raimondo G., Pollicino T.. **PIVKA-II is a useful tool for diagnostic characterization of ultrasound-detected liver nodules in cirrhotic patients**. *Medicine* (2017) **96** e7266. DOI: 10.1097/MD.0000000000007266
91. Burch J., Tort S.. **For adults with chronic liver disease, how accurate are abdominal ultrasound and/or alpha—fetoprotein testing for diagnosing hepatocellular carcinoma?**. *Cochrane. Libr.* (2022) **4** CD013346. DOI: 10.1002/cca.3721
92. Ludovico A., Luigi B.. **New serum markers for detection of early hepatocellular carcinoma**. *Panminerva Med.* (2017) **59** 281-282. PMID: 28714299
93. Nomura F., Ishijima M., Kuwa K., Tanaka N., Nakai T., Ohnishi K.. **Serum des-gamma-carboxy prothrombin levels determined by a new generation of sensitive immunoassays in patients with small-sized hepatocellular carcinoma**. *Am. J. Gastroenterol.* (1999) **94** 650-654. DOI: 10.1111/j.1572-0241.1999.00930.x
94. Chen H., Chen S., Li S., Chen Z., Zhu X., Dai M., Kong L., Lv X., Huang Z., Qin X.. **Combining des-gamma-carboxyprothrombin and alpha-fetoprotein for hepatocellular carcinoma diagnosing: An update meta-analysis and validation study**. *Oncotarget* (2017) **8** 90390-90401. DOI: 10.18632/oncotarget.20153
95. Fang Y.-S., Wu Q., Zhao H.-C., Zhou Y., Ye L., Liu S.-S., Li X.-X., Du W.-D.. **Do combined assays of serum AFP, AFP-L3, DCP, GP73, and DKK-1 efficiently improve the clinical values of biomarkers in decision-making for hepatocellular carcinoma? A meta-analysis**. *Expert Rev. Gastroenterol. Hepatol.* (2021) **15** 1065-1076. DOI: 10.1080/17474124.2021.1900731
96. De J., Shen Y., Qin J., Feng L., Wang Y., Yang L.. **A Systematic Review of Des-γ-Carboxy Prothrombin for the Diagnosis of Primary Hepatocellular Carcinoma**. *Medicine* (2016) **95** e3448. DOI: 10.1097/MD.0000000000003448
97. Fu J., Li Y., Li Z., Li N.. **Clinical utility of decarboxylation prothrombin combined with α-fetoprotein for diagnosing primary hepatocellular carcinoma**. *Biosci. Rep.* (2018) **38** BSR20180044. DOI: 10.1042/BSR20180044
|
---
title: Antioxidant and In Vivo Hypoglycemic Activities of Ethanol Extract from the
Leaves of Engelhardia roxburghiana Wall, a Comparative Study of the Extract and
Astilbin
authors:
- Xiaoqiang Guo
- Ting Zhou
- Hongxia Xing
- Yucheng Zhang
- Jingmei Fang
- Tairan Kang
- Caimei Yao
- Jun Yan
- Yaxuan Huang
- Qian Yao
journal: Foods
year: 2023
pmcid: PMC10001365
doi: 10.3390/foods12050927
license: CC BY 4.0
---
# Antioxidant and In Vivo Hypoglycemic Activities of Ethanol Extract from the Leaves of Engelhardia roxburghiana Wall, a Comparative Study of the Extract and Astilbin
## Abstract
The leaves of *Engelhardia roxburghiana* Wall (LERW) has been used as sweet tea in China throughout history. In this study, the ethanol extract of LERW (E-LERW) was prepared and the compositions were identified by HPLC-MS/MS. It indicates that astilbin was the predominant component in E-LERW. In addition, E-LERW was abundant in polyphenols. Compared to astilbin, E-LERW presented much more powerful antioxidant activity. The E-LERW also had stronger affinity with α-glucosidase and exerted more vigorous inhibitory effect on the enzyme. Alloxan-induced diabetic mice had significantly elevated glucose and lipid levels. Treatment with E-LERW at the medium dose (M) of 300 mg/kg could reduce the levels of glucose, TG, TC, and LDL by $16.64\%$, $12.87\%$, $32.70\%$, and $22.99\%$, respectively. In addition, E-LERW (M) decreased food intake, water intake, and excretion by $27.29\%$, $36.15\%$, and $30.93\%$, respectively. Moreover, E-LERW (M) therapy increased the mouse weight and insulin secretion by $25.30\%$ and $494.52\%$. With respect to the astilbin control, E-LERW was more efficient in reducing the food and drink consumption and protecting pancreatic islet and body organs from alloxan-induced damage. The study demonstrates that E-LERW may be a promising functional ingredient for the adjuvant therapy of diabetes.
## 1. Introduction
Nowadays, diabetes is a high incidence disease seriously challenging human health. Diabetic patients occupy $10\%$ of the world’s population. The complications of diabetes include renal injury, retinopathy, diabetic cataract, diabetic foot, coronary disease, and so on, which not only make the patients suffer great pain but also bring heavy economic burden on families and society. How to protect and treat diabetes has become a major concern in food and medicinal fields. Natural plants and their active ingredients exhibit multi-target, multi-pathway, and multi-directional hypoglycemic characteristics. Compared to chemical drugs, herbal medicines have mild and sustained effects with low toxicity. The multi-target property not only benefits glucose modulation, but also contributes to the alleviation of diabetic complications. A natural product with known hypoglycemic activity is becoming a promising alternative to the current drugs for diabetic therapy. Engelhardtia roxburghiana Wall (ERW) is a subtropical tree grown in the Guangdong, Guangxi, and Fujian provinces of China. The leaves of ERW (LERW) have been used as sweet tea in Chinese folk medicine to treat obesity, fever, and pain for a long time. Due to the abundance in flavonoids and phenols, LERW has multiple physiological activities, including inhibition of aldose reductase, bladder protection, as well as anticoagulant, hypolipidemic, and antioxidant activities [1]. Flavonoids such as astilbin, taxifolin, and engeletin are the main active ingredients responsible for the functions of LERW [2]. Among them, astilbin is the predominant component and is regarded as an important indicator to evaluate the quality of LERW.
As the major constituent of LERW, astilbin possesses versatile biological activities. Astilbin was able to inhibit the generation of superoxide anion and the peroxidation of microsomal lipid, thereby protecting red blood cells from oxidization and hemolysis [3]. Astilbin had an inhibitory effect on recombinant human aldose reductase and hampered the formation of advanced glycation end products, showing the potential in the prevention and treatment of diabetic syndrome [4]. Astilbin also presented its effects in the treatment of diabetes and related secondary complications [5], such as diabetic nephropathy. In addition, astilbin displayed the lipid-lowering capacity in rats by increasing the activity of lipoprotein lipase and promoting the lipolysis of rat fat pads [6].
Astilbin is the chief constituent of LERW. As the hypoglycemic effect of astilbin has been reported extensively, LERW is also assumed to possess hypoglycemic function. The safety and low toxicity of LERW have been well verified by its long-term usage as sweet tea, which makes it hold more immerse prosperity to serve as a healthcare product for the protection and treatment of diabetes. Astilbin, the primary active component, may be more efficient than the extract of LERW (E-LERW) in lowering glucose level. Nevertheless, there is another possibility that owing to the synergetic effect of other polyphenols present in LERW, the extract might possess stronger strength. It is important to clarify the activity difference between the purified component and E-LERW before the designing of LERW-based diabetic care products. This study aimed to compare the antioxidant activity of E-LERW and astilbin and evaluate their hypoglycemic effect via an in vitro α-glucosidase inhibitory test and an in vivo diabetic mouse model. HPLC coupled with tandem MS was used to determine and identify the polyphenols in E-LERW to illustrate the relationship between the hypoglycemic effect and the compositions of the extract.
## 2.1. Materials
LERW was purchased from Youluhuan Ecological Agriculture Co., Ltd. (Bozhou, China). 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2’-azobis-3-ethylbenzothiazoline-6-sulfonate (ABTS), α-glucosidase, tannins, acarbose, and rutin were obtained from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). p-nitrophenyl-β-D-galactopyranoside (pNPG) was obtained from Alfa Aesar Chemical Co., Ltd. (Shanghai, China). Astilbin with $98\%$ purity was purchased from Priva Technology Development Co., Ltd. (Chengdu, China). Liposomes were prepared by our lab with the size of 131.84 ± 0.67 nm [7].
## 2.2. Preparation of E-LERW
The dried LERW was crushed, passed through a 60-mesh sieve, and extracted with $60\%$ ethanol (v/v). The extraction was conducted with a MAR-3 microwave reactor (Shanghai Yuezong Instrument Company, Shanghai, China) under 56 °C for 67 s. The material-to-liquid ratio was 1:15. After the extraction, the sample was filtered, concentrated under reduced pressure, and finally freeze-dried to obtain the E-LERW [8].
## 2.3. Identification by HPLC-MS/MS
The extract was prepared into 1 mg/mL solution with $60\%$ ethanol (v/v), filtered through a 0.22 μm microporous membrane, and separated on a Waters Acquity UHPLC BEH-C18 column (2.1 mm × 100 mm, 1.7 μm). The analysis was performed by an UHPLC system coupled with Xevo triple quadrupole electrospray tandem MS (Micromass Waters, Milford, MA, USA). The electrospray ionization source (ESI) was used for the determination of the components, and the full MS/dd-MS2 scan mode for qualitative and quantitative analysis. The sample of 10 μL was injected into the system. The mobile phase consisted of acetonitrile and $0.1\%$ acetic acid (22:78, v/v) with the flow rate of 0.7 mL/min. The column temperature was 35 °C. Identification was performed by multiple reaction monitoring (MRM). The ions were detected in both positive and negative mode with m/z 100–1000. The other parameters of MS were set as follows: spray voltage 3.0 kV, S-lens voltage 50 V, capillary temperature 350 °C, and auxiliary gas heating temperature 350 °C [9]. In addition, the on-line UV spectrums of the components were obtained through diode array detection (DAD). The wavelength with maximum absorbance was determined.
## 2.4.1. Total Flavonoids
The sample was prepared into 1 mg/mL with $60\%$ ethanol. The content of total flavonoids was determined using the sodium nitrite–aluminum nitrate colorimetric method [10,11] and was expressed as mg rutin equivalent (mg RE)/g. In this study, the absorbance of the reference rutin changed linearly with the concentration in the range from 10 to 200 μg/mL. The regression equation was $A = 11.094$C − 0.0018 (r2 = 0.9993).
## 2.4.2. Total Phenols
The total phenols in E-LERW were determined using the methods reported by Yao et al. and Dirar et al. [ 12,13] and were expressed as mg gallic acid (GA) equivalent (mg GE)/g. The absorbance of the reference GA was linear with the concentration ranging from 10 to 500 μg/mL. The regression equation was $Y = 102.2$X + 0.0616 (r2 = 0.9991).
## 2.4.3. Astilbin
The sample was analyzed by a LD-20AD HPLC system (Shimadzu, Tokyo, Japan). The separation was performed on a SinoChrom ODS-BP column (4.6 mm × 150 mm, 5 μm). The detection conditions were the same as described in Section 2.3. The detection wavelength was 291 nm with the injection volume of 20 μL. In the range of 0.02 to 1.0 mg/mL, the peak area of astilbin was linear with the concentration. The regression equation was $Y = 54756$X − 255.86 (r2 = 0.9992).
## 2.5.1. Scavenging DPPH Free Radicals
E-LERW and astilbin were prepared into a series of solutions, which contained astilbin from 0.2 to 1 mg/mL, respectively. The determination was carried out according to what Makgatho et al. reported [14]. Ascorbic acid was set as the positive control.
## 2.5.2. Scavenging ABTS+ Radicals
The measurement was conducted following the method reported by Aruwa et al. [ 15].
## 2.5.3. Ferric Reducing Activity of Power
The ferric reducing activity of power (FRAP) of E-LERW and astilbin were determined conforming to the method proposed by Hao et al. [ 16].
## 2.5.4. Inhibition of Lipid Membrane Oxidation
The lyophilized liposomes were re-dispersed in deionized water, from which 0.5 mL was drawn out and blended with 0.5 mL of E-LERW or astilbin at different concentrations. The sample was incubated at 37 °C for 1 h. Subsequently, 1 mL of $1\%$ thiobarbituric acid was added, boiled for 10 min, and cooled to room temperature. The solution was centrifuged at 1000 r/min for 10 min. The absorbance of the supernatant was measured at 532 nm (A). Meanwhile, the absorbance of blank control (A0) was determined using 0.5 mL deionized water in place of the sample. Tannic acid was set as the positive control. The inhibitory rate was calculated according to the following equation (Equation [1]) [17]:Inhibitory rate = (A0 − A)/A0 × 100[1]
## 2.6. Inhibitory Effect on α-Glucosidase
The inhibitory effect on α-glucosidase was examined according to the method described by Broholm et al. [ 18]. Briefly, the sample of 50 μL was blended with 50 μL α-glucosidase of 0.5 U/mL, and incubated under 37 °C for 30 min. Afterward, 1 mM substrate pNPG of 50 μL was added and reacted at 37 °C for another 30 min. The reaction was terminated by adding 0.2 M sodium carbonate of 50 μL. The absorbance at 405 nm was determined. In addition, using PBS to replace the enzyme, the background absorbance was measured in parallel. The inhibitory curve was constructed using the inhibitory rates versus astilbin concentrations. Acarbose was set as the positive control.
## Kinetic Analysis on the Inhibition of α-Glucosidase
The concentration of α-glucosidase was fixed at 0.5 U/mL. The inhibitory velocity of E-LERW and astilbin on α-glucosidase was determined under different concentrations of substrate pNPG [19]. The double reciprocal curves were plotted based on the following Lineweaver–Burk equation:[2]1v=Kmvmax1+IKi1S+1vmax1+IαKi and a secondary plot was constructed as Equation [3]:[3]Slope=KmVmax+KmIVmaxKi where v is the inhibitory velocity of the sample on α-glucosidase and [I] and [S] represent the concentration of inhibitor and substrate, respectively. Ki and Km are the inhibition constant and Michaelis–Menten constant, respectively. α is a constant standing for the ratio of uncompetitive inhibition to competitive inhibition.
## 2.7.1. Animal Experiment Design
The animal experiment was approved by the Ethics Committee of Chengdu University, Chengdu, China (protocol number: CDPS 2020-122), and all procedures adhered to European Community Guidelines ($\frac{86}{609}$/EEC) for the Care and Use of Laboratory Animals. Male Kunming mice, weighing 18 to 22 g, were purchased from Chengdu Dashuo Experimental Animal Company (Chengdu, China). Before the experiment, all mice were allowed to adapt to the environment for 3 days. The mice in the normal control (NC) group were fasted but had free access to water for 12 h, and fasting blood glucose (FBG) was measured via the tail vein, which was used as the basic blood glucose level of normal mice. The rest of the mice were fasted for 24 h, followed by the intraperitoneal injection of alloxan at 200 mg/kg to develop a diabetic mouse model [20]. The fasting blood glucose was measured after 3 days. The mice with the blood glucose level over 11.1 mmol/L were diagnosed as diabetic mice and were randomly divided into 6 groups with 6 mice in each group. The groups include the model control of diabetes (MC); astilbin control (AC) with the dosage of 30 mg/kg; the positive control (PC) of metformin hydrochloride at the dose of 100 mg/kg; and E-LERW groups of high (H), medium (M) and low dose (L) at 600, 300, and 150 mg/kg, which were equivalent to the dose of 56.88, 28.44, and 14.22 mg astilbin/kg, respectively. The oral gavage was performed twice a day and consecutively lasted for 28 d [21]. The scheme of the experimental design was displayed in Figure 1.
## 2.7.2. Oral Glucose Tolerance Test
At the final week of treatment, all mice were orally given a glucose solution of 1.5 g/kg after being fasted for 12 h [22]. The blood glucose level was measured every half hour. Oral glucose tolerance test was expressed as AUC in 2 h.
## 2.7.3. Blood Sample Analysis
When the experiment was completed, the mice were sacrificed by breathing carbon dioxide. The mouse blood was collected in a tube pre-coated with heparin sodium and was centrifuged at 3000 r/min for 10 min. The supernatant serum was stored at −20 °C until measurement. The levels of insulin, triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL), and low-density lipoprotein (LDL) were measured by commercial ELISA kits (Nanjing Jiancheng Bioengineering Institute, Najing, China). All the determinations were carried out according to the instructions of the reagent kits.
## 2.7.4. Organ Index
After the mice were sacrificed, the livers and kidneys were detached from the body, placed on filter paper to remove blood, and weighed, respectively. The weight ratios of organ to body (organ indexes) were calculated.
## 2.8. Data Analysis
All data are expressed as mean ± standard error. The diagrams were plotted using Origin 8.0 (OriginLab Corporation, Northampton, MA, USA). The difference between the data was evaluated by one-way analysis of variance (ANOVA) and Duncan’s test using SPSS version 10.0 software (IBM SPSS Inc., Chicago, IL, USA). The difference was considered statistically significant when $p \leq 0.05.$
## 3.1. HPLC-MS/MS Analysis
The chromatogram and MS identification results of E-LERW are shown in Figure 2 and Table 1, respectively. A total of 10 components were identified with reference to the database of the instrument. α-Lactose was determined by the molecular ions of m/z 360.1497 (M+NH4)+ and 365.1050 (M+Na)+. The ion with m/z 145.0494 was assigned to hydroxypropyl pyran, which removed one water and formed the ion of m/z 127.0390. The ion further dissociated one propylene and yielded the ion with m/z 85.0289. Malic acid had the MS2 fragments of m/z 115.0023 (M-H-H2O, A) and 71.0125 (A-CO2). Compound 3 displayed the ion of hydroxyl triazole ring with m/z 96.9682, which eliminated one water and produced the ion of m/z 78.9576. Quercetin presented the MS2 fragments of m/z 285.0385 (M+H-H2O, C), 257.0442 (C-CO, D), and 238.9389 (D-CO). In addition, the fragment of m/z 183.0285 was the reduced product from the flavone bone structure exclusive of catechol [23]. The ion 153.0181 was catechol lactone ring (C6H2(OH)2(OCOO)). Astilbin displayed the MS2 fragments of m/z 303.0607 (M-H-rhamnose, E) and 285.0400 (E-H2O). The ion of m/z 178.9975 was the oxidized flavone bone structure in the absence of catechol. This fragment removed one carbon oxide and formed the ion of m/z 151.0024. The compound engeletin and taxifolin also had the characteristic ions of 179 and 151, as astilbin presented. In addition, the peak of m/z 269.0452 in the spectrum of engeletin attributed to the detachment of one rhamnose from the parent molecule. Taxifolin presented the ions with m/z 285.0401 (M-H-H2O) [24] and 125.0231, which were assigned to pyrogallol [23]. The MS2 of citric acid included the ions of m/z 111.0074 and 87.0074, which was in accordance with what AliAbadi et al. reported [25]. Compound 6 and 7 failed to be detected in the MS2 due to the weak fragment signals.
The flavonoid-like compounds from 4 to 9 had the maximum absorbance wavelength of around 290–295 nm [26]. Quercetin and maritimetin included the maximum wavelength of over 300 nm due to longer conjugate structure.
## 3.2. Determination of Active Components
The contents of astilbin, total flavonoids, and total phenols in E-LERW were 94.79 ± 2.49 mg/g, 153.42 ± 2.74 mg RE/g, and 255.74 ± 4.16 mg GE/g, respectively. It indicates that E-LERW is enriched in polyphenols.
## 3.3. Antioxidant Activity
The results of E-LERW in scavenging DPPH free radicals, ABTS+ free radicals, FRAP, and inhibition against lipid membrane oxidation are shown in Figure 3. The activity of both E-LERW and astilbin presented a concentration-dependent mode. The activity increased with the elevation of concentration. At different concentrations, the capacity of E-LERW in scavenging free radicals was significantly higher than that of astilbin ($p \leq 0.05$, Figure 2A,B). Meanwhile, E-LERW also exhibited much stronger FRAP over astilbin ($p \leq 0.05$, Figure 2C). E-LERW presented a more potent capacity in inhibiting the oxidation of lipid membrane as well (Figure 2D). When the concentration amounted to 2 mg/mL, E-LERW prevented $75\%$ lipid membrane from oxidation while the inhibitory rate of astilbin was only less than $20\%$ at the same concentration. The inhibitory effect of astilbin kept low even as the concentration reached 10 mg/mL. The control of ascorbic acid presented much stronger antioxidant activity over both astilbin and E-LERW in the examined concentration range ($p \leq 0.01$). When the concentration was below 1.5 mg/mL, tannic acid exhibited significantly higher inhibitory capacity against lipid membrane oxidation ($p \leq 0.01$).
## 3.4.1. Inhibition on α-Glucosidase
The inhibitory effect of E-LERW and astilbin on α-glucosidase is shown in Figure 4A. The inhibitory rates of both the samples and the control acarbose presented a concentration-dependent manner. The effect increased with the elevation of concentration. The inhibitory strength of E-LERW was remarkedly higher than that of astilbin in the examined concentration range ($p \leq 0.05$). Meanwhile, the control acarbose displayed much stronger inhibitory activity than E-LERW and astilbin ($p \leq 0.05$). The concentration with $50\%$ inhibitory rate (IC50) of E-LERW, astilbin, and acarbose was 0.46 ± 0.09, 1.12 ± 0.17, and 0.19 ± 0.03 mg/mL, respectively.
## 3.4.2. Inhibitory Kinetic Analysis
The Lineweaver–Burk curves of E-LERW and astilbin are shown in Figure 4B,C, respectively. The increase of the concentration accompanied with the elevation of the vertical axis intercept (1/Vmax), as well as the decrease of the net value of horizonal axis intercept, indicate that the interaction between the samples and α-glucosidase belonged to a mixed mode [19]. The secondary plot using slope-versus-inhibitor concentration was linear (Figure 4D,E), showing that both E-LERW and astilbin had a single inhibitory site on α-glucosidase. The calculated Ki of E-LERW and astilbin was 0.145 and 0.474 mg/mL, respectively.
## 3.5.1. Body Weight, Food Intake, Water Intake and Excretion
Table 2 shows the body weight, the amounts of excretion, and food and water consumption of mice in different groups. On the first day of alloxan injection, the diabetic mice had similar food intake to normal mice, but with more than threefold the water consumption and, as a result, over three times the excretion compared to the normal mice. This demonstrated a successful establishment of a diabetic mouse model. Though the body weights of mice in all groups increased after 28 d, the weights of the mice injected with alloxan were significantly lower than those in normal control (NC) group, who received no injection ($p \leq 0.05$). Nevertheless, compared to the model control (MC) group without any therapy, the groups with the treatment of metformin (PC), astilbin (AC), and E-LERW of high (H) and medium dosage (M) had the weight increment of $49\%$, $18\%$, $38\%$, and $25\%$, respectively, affirming the remedy effectiveness of metformin, astilbin, and E-LERW on diabetes. Though the weights of diabetic mice decreased, their food intake, water intake, and excretion increased dramatically ($p \leq 0.01$). The food and drink consumed by the mice in MC group were 1.8 and 6.3 times the amount consumed by normal mice. After the treatment of metformin, astilbin, and E-LERW at high (H), medium (M), and low dosage (L), the food intake diminished to 1.13, 1.38, 1.18, 1.31, and 1.74 times the normal intake, respectively. The drinking dropped to 2.79, 4.38, 3.33, 4.02, and 6.16 times normal drinking, respectively. The excretion of MC mice was seven times that of normal mice. Through treatment with different samples, the excretion reduced to 3.21, 6.30, 3.93, 5.04, and 7.09 times the normal amount, respectively. The results show that metformin (PC) has the most powerful therapeutic effect, followed by E-LERW (H) and (M). Astilbin (AC) and E-LERW (L) have weak activity in alleviating the symptoms triggered by a high glucose level.
## 3.5.2. Fasting Blood Glucose and Insulin
Figure 5A shows the fasting blood glucose (FBG) levels of mice receiving different treatments during 28 d. As time progressed, the MC and the group fed with E-LERW (L) maintained high and invariable glucose levels. Other diabetic mice treated with different samples had a gradually declining FBG. On day 28 of the therapy, the FBG of the mice receiving metformin, astilbin, and E-LERW (H) and (M) was reduced to $35\%$, $87\%$, $65\%$, and $83\%$ level of MC group, respectively. Metformin again presented the strongest hypoglycemic activity. Astilbin and E-LERW exhibited moderate strength. E-LERW (M) included approximately $10\%$ astilbin, which was equivalent to the AC group.
Figure 5B indicates that the injection of alloxan severely damaged the function of islet. The insulin level of MC mice was only $4.7\%$ that of normal mice. Under the treatment of metformin, astilbin, and E-LERW (H, M and L), insulin secretion was restored to $72.0\%$, $15.0\%$, $59.5\%$, $28.0\%$, and $5.4\%$ normal level, implying that astilbin and E-LERW helped to restore the damaged islets.
## 3.5.3. Oral Glucose Tolerance Test
Oral glucose tolerance and the corresponding area under the curve (AUC) of each group are displayed in Figure 4C,D, respectively. The results show that the glucose peak values of all groups were reached in 30 min after the oral administration of glucose, followed by a gradual decrease. The glucose peak concentration of MC was increased to 3.74 times that of normal mice. After the treatment of metformin, astilbin, and E-LERW (H, M, and L) for 28 d, the peak level was reduced to 1.81, 3.38, 2.70, 3.41, and 3.71 times the normal level, showing the therapeutic effect of metformin, astilbin, and E-LERW in improving the oral glucose tolerance of diabetic mice.
AUC is another indicator to assess the oral glucose tolerate. The AUC of MC was 3.94 times that of normal mice, verifying the alloxan-induced impairment of glucose tolerate. The value was reduced to 1.67 and 3.43 times the normal level after the remedy of metformin and astilbin, respectively. E-LERW (H, M, and L) decreased the AUC to 2.64, 3.33, and 3.93 times the normal value. The trend was similar to the effects of various samples in diminishing glucose peak concentration. Meanwhile, the hypoglycemic activity of E-LERW (M) was consistent with that of astilbin control.
## 3.5.4. Blood Lipid Analysis
Patients with diabetes and prediabetes are always at increased risk of dyslipidemia and cardiovascular disease [27]. As shown in Table 3, the injection of alloxan also significantly increased the levels of TG, TC, and LDL, and remarkedly reduced the concentration of HDL in MC mice ($p \leq 0.01$). The administration of various samples decreased the lipid levels and boosted HDL concentration to different degrees. The lipid lowering strength was metformin > E-LERW (H) > astilbin and E-LERW (M) > E-LERW (L) ($p \leq 0.05$). E-LERW (M) presented stronger activity in reducing TC and LDL with respect to astilbin, but the difference was not significant ($p \leq 0.05$).
## 3.5.5. Effects of E-LERW on Organ Indexes of Liver and Kidney
The status of high glucose level impairs livers and kidneys as well. The organ indexes of mice in each group are shown in Table 4. Compared to the normal mice, the liver index of the MC group increased $33\%$. Other groups such as metformin, astilbin, and E-LERW (H, M, and L) elevated $8\%$, $27\%$, $12\%$, $21\%$, and $34\%$, respectively. The kidney index of the MC group increased $67\%$, while that of the treatment groups rose $8\%$, $51\%$, $27\%$, $44\%$, and $65\%$, respectively. It indicates that diabetes exerts a more detrimental impact on kidneys. E-LERW has the function of preventing liver and kidney swelling. The medium dose exhibited stronger capacity than the purified compound astilbin in protecting the organs.
## 4. Discussion
Compared to astilbin, the LERW presented much stronger antioxidant as well as α-glucosidase-inhibitory activity in vitro. Perez-Najera et al. obtained astilbin enriched extract from *Smilax aristolochiifolia* Root with astilbin at 48.76 mg/g [28]. The inhibitory rate of the extract against α-glucosidase was lower than $10\%$. The vigorous strength of E-LERW may originate from the integrative effect from both astilbin and other flavonoids present in LERW, such as quercetin and engeletin. Moreover, in the inhibitory kinetic test, the Ki of astilbin was 3.27 times that of LERW, implying that the affinity between the enzyme and LERW was much stronger than astilbin.
In the animal experiment, E-LERW significantly lowered blood glucose levels of mice triggered by alloxan. The group of E-LERW (M) had a similar content of astilbin to the group of astilbin control (AC). Though E-LERW exhibited much stronger antioxidant and glucosidase-inhibitory effects over astilbin, compared to AC, E-LERW (M) did not display more powerful effect in lowering fasting glucose level or enhancing oral glucose tolerance. The possible reason is that the hypoglycemic process involves various complex mechanisms—for example, decreasing glucose absorption from small intestine, hindering glucose production in vivo, prompting glucose uptake by tissues, enhancing glucose clearance from body, and so on [29]. Recent studies found that DNA methylation, histone modification, and non-coding RNA expressing also contribute to the pathogenesis of diabetes [30]. Inhibition on α-glucosidase only means the yield of glucose is reduced and glucose absorption is slowed down. It indicates that compound astilbin is the major component responsible for the hypoglycemic function of E-LERW.
Though the glucose level of the mice treated with E-LERW (M) was similar to those with astilbin, E-LERW (M) group had significantly higher insulin concentration than AC group, implying the protective capacity of flavonoids and polyphenols present in the extract on the islet β-cells. Flavonoids were able to increase the numbers of islets and β-cells, restore the pancreatic tissues impaired by alloxan, decrease β-cell apoptosis, and activate insulin receptors, which resulted in the increase of insulin secretion [31]. The underlying mechanisms for flavonoids and polyphenols to preserve β-cells include the blocking of NF-kappa B signaling, activation of the PI3K/Akt pathway, as well as the release decrease of nitric oxide (NO) and reactive oxygen species (ROS) [32].
Alloxan injections led to hyperglycemia accompanied with significant weight loss, while food intake, water intake, and excretion amount increased dramatically (Table 2). The phenomena were in accordance with what Leme et al. reported [33]. Administration of astilbin and E-LERW (H) and (M) significantly alleviated diabetes-induced weight loss, food intake, water intake, and excretion amount ($p \leq 0.05$). Compared to astilbin, E-LERW (M) reduced water intake and excretion more efficiently ($p \leq 0.05$). Hyperglycemia also damaged the liver and kidney and made the two organs swell. E-LERW protected the liver and kidney by remarkedly diminishing the organ indexes. The group with E-LERW (M) had lower organ indexes of liver and kidney compared to the astilbin group, exhibiting more potent protective power on organs. This function is associated with the strong antioxidant activity of E-LERW [34]. Hyperglycemia mellitus is related to high yield of ROS, which may cause DNA oxidation. High levels of genomic damage led to liver and renal failure [35,36]. Antioxidant phytochemicals such as phenolic compounds and flavonoids help to scavenge ROS and protect the organs from radical related impairment [34]. The antioxidant components could also enhance the activity of antioxidant enzymes such as glutathione peroxidase and catalase [37] and lower the elevated levels of malondialdehyde (MDA) and NO in streptozotocin (STZ)-induced diabetic rats [38]. In addition, polyphenols and flavonoids were able to hinder the activity change of hepatic enzymes, for example, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and lactate dehydrogenase (LDH), and attenuated the hepatic toxicity caused by STZ [39].
## 5. Conclusions
Astilbin was the principal component of E-LERW. Compared to astilbin, E-LERW presented significantly higher activity in scavenging radicals, FRAP, and inhibiting the oxidation of lipid membrane. E-LERW also displayed stronger affinity with α-glucosidase with more powerful inhibitory strength on the enzyme, which was evidenced by Lineweaver–Burk curves. After the alloxan injection, the plasma levels of FBG, oral glucose tolerance, TG, TC, and LDL of the mice increased to 4.18, 3.93, 2.04, 2.84, and 4.63 times the normal levels, respectively. Meanwhile, insulin secretion and HDL levels were reduced to $4.72\%$ and $38.97\%$ of normal mice. Alloxan also impaired the organs, causing the indexes of the liver and kidney to elevate $33\%$ and $67\%$, respectively. Treatment with E-LERW (M) and (H) can efficiently lower the increased glucose and lipid levels induced by alloxan and boost the levels of insulin and HDL. In addition, E-LERW alleviated hyperglycemia-induced organ damage and decreased the liver and kidney indexes. Compared to astilbin control, E-LERW did not show more potent capacity in lowering glucose level and oral glucose tolerance, but presented a more efficient ability in preventing weight loss, reducing food intake, water intake, and excretion. Moreover, E-LERW was superior to astilbin in enhancing insulin secretion and protecting organs. The study indicates that E-LERW may be a promising functional ingredient in alleviating symptoms of diabetic patients.
## References
1. Ji H., He L.S., Zhou Z.T., Yuan M.. **Antioxidant constituents from leaves of**. *Chem. Nat. Compd.* (2012) **48** 679-680. DOI: 10.1007/s10600-012-0347-5
2. Xin W.B., Huang H.Q., Yu L., Shi H.M., Sheng Y., Wang T.T.Y., Yu L.L.. **Three new flavanonol glycosides from leaves of**. *Food Chem.* (2012) **2** 788-798. DOI: 10.1016/j.foodchem.2011.11.038
3. Haraguchi H., Ohmi l., Fukuda A., Tamura Y., Mizutani K., Tanaka O., Chou W.H.. **Inhibition of aldose reductase and sorbitol accumulation by astilbin and taxifolin dihydroflavonols in**. *Biosci. Biotech. Biochem.* (1997) **61** 651-654. DOI: 10.1271/bbb.61.651
4. Wirasathien L., Pengsuparp T., Suttisri R., Ueda H., Moriyasu M., Kawanishi K.. **Inhibitors of aldose reductase and advanced glycation end-products formation from the leaves of**. *Phytomedicine* (2007) **14** 546-550. DOI: 10.1016/j.phymed.2006.09.001
5. Patel D.K.. **Therapeutic potential of astilbin on diabetes and related secondary complication ‘diabetic nephropathy’: Therapeutic potential and scientific data analysis of current research work**. *Bone Rep.* (2021) **14** 21-22. DOI: 10.1016/j.bonr.2021.100927
6. Motoyashiki T., Miyake M., Morita T., Mizutani K., Masuda H., Ueki H.. **Enhancement of the vanadate stimulated release of lipoprotein lipase activity by astilbin from the leaves of**. *Biol. Pharm. Bull.* (1998) **21** 517-519. DOI: 10.1248/bpb.21.517
7. Liang L., Gou X.J., Guo X.Q., Wang S.H., Li L., Yao Q.. **Investigation on encapsulation efficiency for oleuropein liposome**. *Chin. Hosp. Pharm. J.* (2015) **35** 1286-1289
8. Cruz R.G.D., Beney L., Gervais P., Lira S.P., Vieira T.M.F.S., Dupont S.. **Comparison of the antioxidant property of acerola extracts with synthetic antioxidants using an in vivo method with yeasts**. *Food Chem.* (2019) **277** 698-705. DOI: 10.1016/j.foodchem.2018.10.099
9. Balakrishnan G., Schneider R.G.. **Quinoa flavonoids and their bioaccessibility during in vitro gastrointestinal digestion**. *J. Cereal Sci.* (2020) **95** e103070. DOI: 10.1016/j.jcs.2020.103070
10. Yang S.J., Paudel P., Shrestha S., Seong S.H., Jung H.A., Choi J.S.. **In vitro protein tyrosine phosphatase 1B inhibition and antioxidant property of different onion peel cultivars: A comparative study**. *Food Sci. Nutr.* (2018) **1** 205-215. DOI: 10.1002/fsn3.863
11. Maseko I., Mabhaudhi T., Ncube B., Tesfay S., Araya H.T., Fessehazion M.K., Chimonyo V.G.P., Ndhlala A.R., Plooy C.P.D.. **Postharvest drying maintains phenolic, flavonoid and gallotannin content of some cultivated African leafy vegetables**. *Sci. Hortic.* (2019) **20** 70-76. DOI: 10.1016/j.scienta.2019.05.019
12. Yao Q., Shen Y., Bu L., Yang P., Xu Z., Guo X.. **Ultrasound-assisted aqueous extraction of total flavonoids and hydroxytyrosol from olive leaves optimized by response surface methodology**. *Prep. Biochem. Biotechnol.* (2019) **4** 837-845. DOI: 10.1080/10826068.2019.1630648
13. Dirar A.I., Alsaadi D.H.M., Wada M., Mohamed M.A., Watanabe T., Devkota H.P.. **Effects of extraction solvents on total phenolic and flavonoid contents and biological activities of extracts from Sudanese medicinal plants**. *S. Afr. J. Bot.* (2019) **120** 261-267. DOI: 10.1016/j.sajb.2018.07.003
14. Makgatho M.E., Nxumalo W., Raphoko L.A.. **Anti-mycobacterial, -oxidative, -proliferative and -inflammatory activities of dichloromethane leaf extracts of**. *S. Afr. J. Bot.* (2018) **114** 217-222. DOI: 10.1016/j.sajb.2017.11.002
15. Aruwa C.E., Amoo S.O., Kudanga T.. **Extractable and macromolecular antioxidants of Opuntia ficus-indica cladodes: Phytochemical profiling, antioxidant and antibacterial activities**. *S. Afr. J. Bot.* (2019) **125** 402-410. DOI: 10.1016/j.sajb.2019.08.007
16. Hao G.X., Cao W.Q., Li T., Chen J., Zhang J.L., Weng W.V., Osako K., Ren H.F.. **Effect of temperature on chemical properties and antioxidant activities of abalone viscera subcritical water extract**. *J. Supercrit. Fluids* (2019) **147** 17-23. DOI: 10.1016/j.supflu.2019.02.007
17. Vamanu E., Nita S.. **Bioactive compounds, antioxidant and anti-inflammatory activities of extracts from Cantharellus cibarius**. *Rev. Chim.* (2014) **65** 372-380
18. Broholm S.L., Gramsbergen S.M., Nyberg N.T., Jager A.K., Staerk D.. **Potential of**. *J. Ethnopharmacol.* (2019) **242** e112061. DOI: 10.1016/j.jep.2019.112061
19. Yan J.K., Zhang G.W., Pan J.H., Wang Y.J.. **alpha-Glucosidase inhibition by luteolin: Kinetics, interaction and molecular docking**. *Int. J. Biol. Macromol.* (2014) **64** 213-223. DOI: 10.1016/j.ijbiomac.2013.12.007
20. Li C., Gan H., Tan X.L., Hu Z.X., Deng B., Sullivan M.A., Gilbert R.G.. **Effects of active ingredients from traditional Chinese medicines on glycogen molecular structure in diabetic mice**. *Eur. Polym. J.* (2019) **112** 67-72. DOI: 10.1016/j.eurpolymj.2018.12.039
21. Wang T.T., Li X., Zhou B., Li H.F., Zeng J., Gao W.Y.. **Anti-diabetic activity in type 2 diabetic mice and α-glucosidase inhibitory, antioxidant and anti-inflammatory potential of chemically profiled pear peel and pulp extracts (**. *J. Funct. Foods* (2015) **13** 276-288. DOI: 10.1016/j.jff.2014.12.049
22. Abdel-Haleem S.A., Ibrahim A.Y., Ismail R.F., Shaffie N.M., Hendawy S.F., Omer E.A.. **In-vivo hypoglycemic and hypolipidemic properties of tagetes lucida ethanolic extract in streptozotocin-induced hyperglycemic Wistar albino rats**. *Ann. Agr. Sci.* (2017) **62** 169-181. DOI: 10.1016/j.aoas.2017.11.005
23. Feng Y.L., Lin J.L., He G., Liang L., Liu Q.J., Yan J., Yao Q.. **Compositions and biological activities of pomegranate peel polyphenols extracted by different solvents**. *Molecules* (2022) **27**. DOI: 10.3390/molecules27154796
24. Zhou S.X., Shao Y., Fu J.H., Xiang L., Zheng Y.N., Li W.. **Characterization and quantification of taxifolin related flavonoids in**. *Int. J. Pharmacol.* (2018) **14** 534-545. DOI: 10.3923/ijp.2018.534.545
25. AliAbadi M.H.S., Karami-Osboo R., Kobarfard F., Jahani R., Nabi M., Yazdanpanah H., Mahboubi A., Nasiri A.. **Detection of lime juice adulteration by simultaneous determination of main organic acids using liquid chromatography-tandem mass spectrometry**. *J. Food. Compos. Anal.* (2021) **105** e104223. DOI: 10.1016/j.jfca.2021.104223
26. Zhang A., Wan L., Wu C.Y., Fang Y.L., Han G.M., Li H., Zhang Z.W., Wang H.. **Simultaneous determination of 14 phenolic compounds in grape canes by HPLC-DAD-UV using wavelength switching detection**. *Molecules* (2013) **18** 14241-14257. DOI: 10.3390/molecules181114241
27. Hassan A., Tajuddin N., Shaikh A.. **Retrospective case series of patients with diabetes or prediabetes who were switched from omega-3-acid ethyl esters to icosapent ethyl**. *Cardiol Ther.* (2015) **4** 83-93. DOI: 10.1007/s40119-014-0032-9
28. Pérez-Nájera V.C., Gutiérrez-Uribe J.A., Antunes-Ricardo M., Hidalgo-Figueroa S., Del-Toro-Sánchez C.L., Salazar-Olivo L.A., Lugo-Cervantes E.. *Evid. Based Complement Alternat. Med.* (2018) **2018** e6247306. DOI: 10.1155/2018/6247306
29. Ghorbani A.. **Mechanisms of antidiabetic effects of flavonoid rutin**. *Biomed. Pharmacother.* (2017) **96** 305-312. DOI: 10.1016/j.biopha.2017.10.001
30. Hossain M.K., Dayem A.A., Han J., Yin Y., Kim K., Saha S.K., Yang G.M., Choi H.Y., Cho S.G.. **Molecular mechanisms of the anti-obesity and anti-diabetic properties of flavonoids**. *Int. J. Mole. Sci.* (2016) **17**. DOI: 10.3390/ijms17040569
31. Mia M.A., Mosaib M.G., Khalil M.I., Islam M.A., Gan S.H.. **Potentials and safety of date palm fruit against diabetes: A critical review**. *Foods* (2020) **11**. DOI: 10.3390/foods9111557
32. Ghorbani A., Rashidi R., Shafiee-Nick R.. **Flavonoids for preserving pancreatic beta cell survival and function: A mechanistic review**. *Biomed. Pharmacother.* (2019) **11** 947-957. DOI: 10.1016/j.biopha.2018.12.127
33. Leme J.A.C.A., Castellar A., Remedio R.N.. **Effects in short-term of alloxan application to diabetes induction in Wistar rats**. *Biosci. J.* (2010) **26** 451-456
34. Vahid H., Rakhshandeh H., Ghorbani A.. **Antidiabetic properties of**. *Biomed. Pharmacother.* (2017) **92** 293-302. DOI: 10.1016/j.biopha.2017.05.082
35. Oršolić N., Gajski G., Garaj-Vrhovac V.. **DNA-protective effects of quercetin or naringenin in alloxan-induced diabetic mice**. *Eur. J. Pharmacol.* (2011) **656** 110-118. DOI: 10.1016/j.ejphar.2011.01.021
36. Kim J., Shon E., Kim C.S.. **Renal podocyte injury in a rat model of type 2 diabetes is prevented by metformin**. *Exp. Diabetes Res.* (2012) **2012** e210821. DOI: 10.1155/2012/210821
37. Wei C., Wang J., Duan C.. **Aqueous extracts of se-enriched auricularia auricular exhibits antioxidant capacity and attenuate liver damage in high-fat diet/streptozotocin-induced diabetic mice**. *J. Med. Food* (2020) **23** 153-160. DOI: 10.1089/jmf.2019.4416
38. Malekinejad H., Rezabakhsh A., Rahmani F., Hobbenaghi R.. **Silyrnarin regulates the cytochrome P450 3A2 and glutathione peroxides in the liver of streptozotocin-induced diabetic rats**. *Phytomedicine* (2012) **19** 583-590. DOI: 10.1016/j.phymed.2012.02.009
39. Fernandes A.A.H., Novelli E.L.B., Okoshi K.. **Influence of rutin treatment on biochemical alterations in experimental diabetes**. *Biomed. Pharmacother.* (2010) **64** 214-219. DOI: 10.1016/j.biopha.2009.08.007
|
---
title: Insights into the Role of a Cardiomyopathy-Causing Genetic Variant in ACTN2
authors:
- Sophie Broadway-Stringer
- He Jiang
- Kirsty Wadmore
- Charlotte Hooper
- Gillian Douglas
- Violetta Steeples
- Amar J. Azad
- Evie Singer
- Jasmeet S. Reyat
- Frantisek Galatik
- Elisabeth Ehler
- Pauline Bennett
- Jacinta I. Kalisch-Smith
- Duncan B. Sparrow
- Benjamin Davies
- Kristina Djinovic-Carugo
- Mathias Gautel
- Hugh Watkins
- Katja Gehmlich
journal: Cells
year: 2023
pmcid: PMC10001372
doi: 10.3390/cells12050721
license: CC BY 4.0
---
# Insights into the Role of a Cardiomyopathy-Causing Genetic Variant in ACTN2
## Abstract
Pathogenic variants in ACTN2, coding for alpha-actinin 2, are known to be rare causes of Hypertrophic Cardiomyopathy. However, little is known about the underlying disease mechanisms. Adult heterozygous mice carrying the Actn2 p.Met228Thr variant were phenotyped by echocardiography. For homozygous mice, viable E15.5 embryonic hearts were analysed by High Resolution Episcopic Microscopy and wholemount staining, complemented by unbiased proteomics, qPCR and Western blotting. Heterozygous Actn2 p.Met228Thr mice have no overt phenotype. Only mature males show molecular parameters indicative of cardiomyopathy. By contrast, the variant is embryonically lethal in the homozygous setting and E15.5 hearts show multiple morphological abnormalities. Molecular analyses, including unbiased proteomics, identified quantitative abnormalities in sarcomeric parameters, cell-cycle defects and mitochondrial dysfunction. The mutant alpha-actinin protein is found to be destabilised, associated with increased activity of the ubiquitin-proteasomal system. This missense variant in alpha-actinin renders the protein less stable. In response, the ubiquitin-proteasomal system is activated; a mechanism that has been implicated in cardiomyopathies previously. In parallel, a lack of functional alpha-actinin is thought to cause energetic defects through mitochondrial dysfunction. This seems, together with cell-cycle defects, the likely cause of the death of the embryos. The defects also have wide-ranging morphological consequences.
## 1. Introduction
Contractility in heart and skeletal muscle is achieved through highly organised structures, the sarcomeres, in which actin-rich thin filaments and myosin-rich thick filaments slide against each other. Both filaments have specially organised anchoring sites: the Z-disk for thin filaments, and the M-band for thick filaments [1,2]. In addition, single titin molecules span from Z-disks to the M-band as a third filament [3].
The Z-disk is a highly organised and complex structure. In it, alpha-actinin cross-links antiparallel actin filaments from adjacent sarcomeres and organises them, together with other actin cross-linking proteins, e.g., filamin C, into a highly ordered ultrastructural paracrystalline structure. Beyond its mechanical role, the Z-disk is recognised as a signaling hub, which integrates mechano-signaling and stress responses. Numerous proteins with signaling roles are known to be residents of the Z-disk, sometimes in a transient fashion [4].
The highly conserved alpha-actinin is a key protein of the Z-disk. It features an amino-terminal actin-binding domain consisting of two calponin homology domains (CH1, CH2) and a carboxy-terminal calmodulin-like domain. The middle rod domain, comprised of four spectrin-like repeats, allows it to form anti-parallel dimers. Titin binding is regulated via phospho-lipids (PIP2) to the actin-binding domain, triggering a conformational change of the calmodulin-like domain, leading to an open conformation capable of binding to titin Z-repeats [5]. The interaction of alpha-actinin 2 with actin filaments was shown to require the opening of the actin binding domain, where only the CH1 domain binds to the filament, while the second is dissociated from the interacting domain and acts as a negative regulator of the interaction [6,7]. This structural mechanism is the basis of the explanation of many mutations that map to the interface between CH1 and CH2 domains.
There are four genes coding for alpha-actinin (ACTN1–ACTN4). ACTN2 is highly expressed in heart and skeletal muscle, and ACTN3 in some skeletal muscle subtypes. ACTN1 and ACTN4 are found predominantly in non-muscle cells, where they perform similar actin cross-linking functions. In line with their expression pattern, ACTN2 genetic variants have been described to cause autosomal dominant skeletal muscle and cardiac diseases [2]. Four missense variants in ACTN2 (p.Gly111Val, p.Ala119Thr, p.Met228Thr and p.Thr247Met) have been identified in the actin-binding domain in patients with Hypertrophic Cardiomyopathy (HCM) [8,9,10,11], a heart muscle disease that results in stiffer and hypertrophied hearts and can be associated with life-threating arrhythmias. Of note, both ACTN2 p.Met228Thr and p.Thr247Met map to the interface between CH1 and CH2 of the actin binding domain in the closed conformation. For ACTN2 p.Ala119Thr, p.Met228Thr and p.Thr247Met, there is genetic evidence of pathogenicity through co-segregation in a multi-generation family [8,9,10], while the pathogenicity of ACTN2 p.Gly111Val is only supported by functional biochemical studies [12]. Moreover, cellular studies using patient-derived induced pluripotent stem cell-derived cardiomyocytes support the role for the pathogenicity of ACTN2 p.Thr247Met [10,13].
No animal models have been generated to study these HCM-causing ACTN2 variants in vivo. Here, we present the generation of an Actn2 mouse model harboring the p.Met228Thr variant. In the heterozygous setting, mature male mice show molecular features in keeping with HCM. To our surprise, the homozygous Actn2 p.Met228Thr mice were found to have an embryonic lethal phenotype. A detailed analysis of the embryonic hearts at E15.5 suggests that alpha-actinin 2 not only controls heart morphology during development but also affects cell cycling and mitochondrial function. Moreover, we could identify mutant ACTN2 protein instability as a driving factor of the phenotype.
## 2.1. Ethical Statement
The animal studies have been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Experimental procedures were performed in accordance with the Directive $\frac{2010}{63}$/EU and UK Home office guidelines (project licences P572C7345 and PDCE16CB0) and approved by the respective institutional ethical review boards.
Animals were housed in specific pathogen-free conditions, with the only reported positives on health screening over the entire time course of these studies being for Tritrichomonas sp. and Entamoeba spp. All animals were housed in social groups of mixed genotypes, provided with food and water ad libitum, and maintained on a 12 h light:12 h dark cycle (150–200 lux cool white LED light, measured at the cage floor).
Phenotyping experiments and offline analysis were performed blinded. All in vivo phenotyping studies of adult mice were carried out using littermates and both sexes. Animals were sacrificed by cervical dislocation and death was confirmed by the cessation of circulation.
## 2.2. Generation of the Mice and Genotyping
The p.Met228Thr variant was introduced into the orthologous position in the mouse *Actn2* gene using CRISPR-Cas9-mediated homology-directed repair in mouse embryonic stem cells. A detailed methodology is provided in the Supplementary Materials.
Heterozygous Actn2 p.Met228Thr mice were viable and fertile. The expression of the mutated allele p.Met228Thr was confirmed at the protein level by mass spectrometry (Figure S1). Animals were backcrossed onto C57BL/6J (Envigo, London, UK) for at least six generations before generating wild-type and heterozygous littermates, or embryos; aged animals were backcrossed for at least two generations.
Animals were genotyped for Actn2 p.Met228Thr mutation and a spontaneous genetic variant in the *Nnt* gene, occurring in C57BL/6J sub-strains [14] using Transnetyx services (Cordova, TN, USA). All animals of the colony were homozygous for the genetic variant in Nnt.
## 2.3. Ultrasound Echocardiography
Ultrasound echocardiography was carried out as previously described [15]. A detailed methodology is provided in the Supplementary Materials.
## 2.4. Embryo Collection and Fixation, Theiler Staging
Mouse embryos were collected at E15.5 following timed mating. Once embryos were drained of blood, hearts were either dissected and flash frozen in liquid nitrogen or used for High Resolution Episcopic Microscopy (HREM) and wholemount staining. For Theiler staging, PFA-fixed front limbs were examined under an inverted stereoscope (Tl3000 Ergo stereoscope, Leica, Mannheim, Germany) and classified according to [16]. A detailed methodology is provided in the Supplementary Materials.
## 2.5. Proteasomal Activity Assays
Chymotrypsin-like, Caspase-like and Trypsin-like activity assays were performed as previously described using commercially available indirect enzyme-based luminescent assay kits (Promega, Madison, WI, USA) [17]. A detailed methodology is provided in the Supplementary Materials.
## 2.6. High Resolution Episcopic Microscopy
Tissue and data processing for HREM was performed as previously described [18]. A detailed methodology is provided in the Supplementary Materials.
## 2.7. Immunofluorescence Staining on Cryosections
The staining of the cryosections of skeletal muscle was performed as described [5]. Immunolabelling with phospho-histone H3 (Thermo Fisher Scientific, Waltham, MA, USA), alpha-actinin (Sigma-Aldrich, St. Louis, MO, USA) and DAPI was carried out on cryosections of E15.5 hearts. A detailed methodology is provided in the Supplementary Materials.
## 2.8. Ex Vivo Studies
Tibial length measurements, mRNA isolation from ventricular tissue, reverse transcriptase and quantitative PCR (qPCR) were performed as described [19], using the Taqman probes (Applied Biosystems, Waltham, MA, USA) listed in Table S9. Western blotting was performed as described [19] with the antibodies listed in Table S9.
Histology on paraffin-embedded samples (7 µm sections) was performed with hematoxylin and eosin, using standard protocols.
## 2.9. Mass Spectrometry for Identification
An excised gel sample was digested with trypsin (Promega, Madison, WI, USA) and analysed by nano-UPLC–MS/MS using a Dionex Ultimate 3000 coupled online to an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), as described [20]. A detailed methodology is provided in the Supplementary Materials.
## 2.10. Proteomics
Wildtype and homozygous ($$n = 6$$ per group) ventricular samples, previously collected from embryos at E15.5, were prepared and labelled using solutions provided in EasyPepTM mini MS sample prep kit (Thermo Fisher Scientific, Waltham, MA, USA) following manufacturer’s instructions. For data analysis, the MS and MS/MS scans were searched against the Uniprot database using Proteome Discoverer 2.2 (Thermo Fisher Scientific, Waltham, MA, USA) with $5\%$ false discovery rate (FDR) criteria. Multiple test correction was performed using the Benjamini and Hochberg test (Perseus software [21]). Data were further analysed through the use of Ingenuity pathway analysis (IPA, QIAGEN Inc, Germantown, MD, USA), https://digitalinsights.qiagen.com/IPA, (accessed on 22 February 2023) [22]). Detailed information is provided in the Supplementary Materials.
The proteomics data underlying this article are available in PRIDE [https://www.ebi.ac.uk/pride/archive/, (accessed on 3 January 2023)], and can be accessed with identifier PXD039226.
## 2.11. Wholemount Immunohistochemistry
The hearts were dissected from PFA-fixed embryos in PBS and treated with hyaluronidase (1 mg/mL in PBS, Sigma-Aldrich, St. Louis, MO, USA) for 45 min at RT. After three washes in PBS, permeabilization with $0.2\%$ Triton X-100 was performed for 45 min. Following another three washes in PBS and blocking for 30 min with $5\%$ pre-immune goat serum (Sigma-Aldrich, St. Louis, MO, USA) in antibody dilution buffer (10 mM Tris-HCL, pH 7.2, 155 mM NaCl, 2mM EGTA, 2 mM MgCl2, $1\%$ BSA), the hearts were incubated with the primary antibody mixture (see Table S9) overnight at 4 °C. After 5 × 20 min of washing in PBT (PBS with $0.002\%$ Triton X-100), the secondary antibodies (Cy3-goat anti mouse Igs, multilabelling quality; Cy2-goat anti rabbit Igs, multi-labelling quality, both Jackson Immunochemicals, West Grove, PA, USA via Stratech Scientific, Ely, UK, DAPI, Sigma-Aldrich, St. Louis, MO, USA and Alexa647-phalloidin, Thermo Fisher Scientific, Waltham, MA, USA) were applied overnight at 4 °C. The hearts were washed in PBT for 5 × 20 min and mounted in 0.1 M Tris–HCl (pH 9.5) and glycerol (3:7), with 50 mg/mL of N-propyl-gallate as an anti-fading agent. Microscopy was carried out using an SP5 confocal (Leica, Mannheim, Germany), equipped with 405 blue diode, argon and helium-neon lasers, using a $\frac{63}{1.4}$NA oil immersion lens.
The hearts were dissected from PFA-fixed embryos in PBS and treated with hyaluronidase (1 mg/mL in PBS, Sigma-Aldrich, St. Louis, MO, USA) for 45 min at RT. After three washes in PBS, permeabilization with $0.2\%$ Triton X-100 was performed for 45 min. Following another three washes in PBS and blocking for 30 min with $5\%$ pre-immune goat serum (Sigma-Aldrich, St. Louis, MO, USA) in antibody dilution buffer (10 mM Tris-HCL, pH 7.2, 155 mM NaCl, 2mM EGTA, 2 mM MgCl2, $1\%$ BSA), the hearts were incubated with the primary antibody mixture (see Table S9) overnight at 4 °C. After 5 × 20 min of washing in PBT (PBS with $0.002\%$ Triton X-100), the secondary antibodies (Cy3-goat anti mouse Igs, multilabelling quality; Cy2-goat anti rabbit Igs, multi-labelling quality, both Jackson Immunochemicals, West Grove, PA, USA via Stratech Scientific, Ely, UK, DAPI, Sigma-Aldrich, St. Louis, MO, USA and Alexa647-phalloidin, Thermo Fisher Scientific, Waltham, MA, USA) were applied overnight at 4 °C. The hearts were washed in PBT for 5 × 20 min and mounted in 0.1 M Tris–HCl (pH 9.5) and glycerol (3:7), with 50 mg/mL of N-propyl-gallate as an anti-fading agent. Microscopy was carried out using an SP5 confocal (Leica, Mannheim, Germany), equipped with 405 blue diode, argon and helium-neon lasers, using a $\frac{63}{1.4}$NA oil immersion lens.
## 2.12. Electron Microscopy
Embryos were generated as described above. The dissection of whole hearts was carried out in fresh ice-cold PBS with 5mM EDTA. Once cleaned of surrounding tissues, hearts were briefly flushed with ice-cold PBS via the aorta. Hearts were fixed in $4\%$ PFA for 15 min before being transferred to $2.5\%$ glutaraldehyde/$2\%$ PFA for 2 h at room temperature, then 3 h at 4 °C. Subsequently, hearts were stored in $0.05\%$ glutaraldehyde at 4 °C.
Fixed hearts were briefly washed with fresh PBS and further dissected to remove the atria and blood vessels leaving the ventricles. This portion was cut crossways to produce a small tip fragment and a figure of eight fraction with most of the ventricular walls. These were further fixed in $1\%$ osmium, dehydrated in ethanol, and embedded in Araldite. Before embedding, the larger fragment was divided into three parts: the left and right ventricular walls and the septum. Then, 70 nm sections were stained with Uranyless heavy metal stain followed by Pb Citrate (both Labtech International Ltd., Heathfield, UK). The sections were viewed in a JEOL 1400 electron microscope in the Centre for Ultrastructural Imaging, KCL.
## 2.13. Image Analysis
ImageJ version 1.53a was used for the colocalisation analysis and densitometry of Western blots. The ‘JACoP’ plugin (https://imagej.net/plugins/jacop, accessed on 22 February 2023) was used to calculate a correlation coefficient. The Image J plugin ‘Colocalisation finder’ (http://punias.free.fr/ImageJ/colocalization-finder.html, accessed on 22 February 2023) was used to generate cytofluorograms to visualise colocalisation.
Cell Profiler 4.2.1. was used for nuclear assessment [23]. Sarcomere length was analysed as described [24], using MatLab (version 2021a) and the script ‘ZLineDetection’ (https://github.com/Cardiovascular-Modeling-Laboratory/zlineDetection, accessed on 22 February 2023).
## 2.14. Statistics
All values are given as mean ± standard error of mean (SEM). To compare two unpaired sample groups, data were tested for normality using the Kolmogorov–Smirnov test. Normally distributed data were analysed by Student’s t-test, and data that were not were analysed by Mann–Whitney U-test. Deviation from the expected Mendelian ratios and Theiler stages were assessed with the Chi-square test. For the comparison of the three groups with normally distributed data, a one-way ANOVA followed by Tukey’s post-hoc test was used. For the image analysis of wholemount staining, nested ANOVA was employed, allowing us to consider the number of measurements from each heart. Fisher’s exact test was used to test for the occurrence of VSD. All statistical analyses were performed with GraphPad Prism 9.3.1.
Annotations used: * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$, **** $p \leq 0.0001$ versus WT, otherwise considered not significant ($p \leq 0.05$); n indicates number of animals in each group.
## 3.1. Generation of a Mouse Model for a Hypertrophic Cardiomyopathy-Causing Genetic Variant
In order to investigate the disease mechanisms causing HCM, the p.Met228Thr substitution was introduced into the *Actn2* gene in mice by CRISPR/Cas9 genome-editing; successful engineering was confirmed by Sanger sequencing, and backcrossing eliminated CRISPR/Cas9 off-target effects. Heterozygous mice were viable and fertile. The alpha-actinin 2 protein carrying the missense change was detected in the heart by mass spectrometry (Figure S1), indicating the successful generation of the model.
## 3.2. Cardiac Phenotyping of Adult Mice with the Actn2 p.Met228Thr Variant
Young adult mice (3 months) underwent cardiac phenotyping by echocardiography. Mice carrying the heterozygous Actn2 p.Met228Thr variant had normal cardiac function and dimensions when compared to their wildtype (WT) littermates, irrespective of their sex (Table S1). In agreement, these mice had normal heart weights when normalised to tibial length (Table S1). At the molecular level, they showed no induction of the fetal gene programme typically seen in cardiomyopathic hearts (Figure S2A). The histology on these mice was unremarkable (Figure S3).
In more mature mice (>34 weeks), cardiac function and dimensions were again normal in both sexes when compared to their WT littermates (Table S2). In support, mice had normal heart weights when normalised to tibial length (Table S2). However, male mice showed a significant induction of Nppb and Acta1, two transcripts of the fetal gene programme (Figure 1, determined at 38 weeks), while female mice did not (Figure S2B). Moreover, transcripts associated with hypertrophic signaling were induced in male hearts (Figure 1, significant for Fhl1, Ankrd1 and Ankrd2), but not in female hearts (Figure S2C, apart from a less than two-fold increase in Ankrd2). At the protein level, mature male mice displayed an increased expression of small heat shock protein HspB7 (Figure S4A,B), but not of Hsp27, and a trend of increased beta-myosin heavy chain expression.
The skeletal muscle of the mice did not show any signs of pathology, regardless of genotype (Figure S5).
In conclusion, the Actn2 p.Met228Thr variant does not produce an overt cardiomyopathy phenotype in mice, however, mature male mice display molecular features consistent with HCM.
## 3.3. Actn2 p.Met228Thr Is Embryonic Lethal in the Homozygous Setting
We next attempted to generate mice homozygous (Hom) for the Actn2 p.Met228Thr change by crossing heterozygous mice. Nine breeding pairs produced 18 litters with 83 weaned pups, comprising of 24 WT and 59 heterozygous animals, however, no Hom Actn2 p.Met228Thr offspring were found (Figure S6, Table S3). This was a clear deviation from the expected Mendelian ratios (Chi-square test, $p \leq 0.0001$). Four further litters from timed matings were collected at the time of birth and again failed to identify Hom offspring (Figure S6, Table S3), suggesting that Hom Actn2 p.Met228Thr are embryonic lethal.
As the next step, we performed timed matings to harvest embryos at defined time points; E15.5 was the latest time point that viable Hom embryos were consistently present. At this timepoint, 30 litters produced 242 embryos, among them 56 Hom (Figure S6, Table S4). This was within the expected Mendelian ratios (Chi-square test, $p \leq 0.05$).
## 3.4. Detailed Morphological Analysis of Hom Actn2 p.Met228Thr E15.5 Embryos
The gross morphology and size of E15.5 embryos appeared normal regardless of the genotype (Figure S7).
HREM was performed on E15.5 wildtype and Hom hearts. Strikingly, Hom hearts had significantly increased right ventricle luminal volume ($$p \leq 0.0006$$) and smaller left atrium volume ($$p \leq 0.0030$$) when compared to control hearts (Figure 2B and Figure S8). In addition, Hom hearts had significantly reduced left ventricular free wall thickness ($$p \leq 0.0021$$, Figure S9), with a visual trend of reduced wall thickness in two other areas of LV and RV (Figure S9), although this did not reach statistical significance. Three out of eight Hom hearts had peri-membranous ventricular septal defects (Figure 2A). Despite the occurrence only in Hom hearts, this was not statistically significant ($$p \leq 0.10$$, Fisher’s exact test). No atrial septal defects were observed. Although the aortic arch and pulmonary trunk of Hom embryos had normal gross morphology (Figure S10A), the pulmonary trunk volume was significantly reduced ($$p \leq 0.0499$$, Figure S10C), despite normal length ($$p \leq 0.52$$, Figure S10B). Furthermore, significant aortic stenosis was evident in both the ascending ($$p \leq 0.0025$$, $19\%$) and descending thoracic aorta ($$p \leq 0.0497$$, $32\%$, Figure S10B), but not in the aortic root ($$p \leq 0.0609$$, Figure S10B). Finally, dysplastic pulmonary valves with thickened leaflets were observed, particularly in the right and left leaflets (Figure S11A–C).
Hom embryos were found to have malpositioned hearts within the body cavity, with a superior tilt of the heart ($$p \leq 0.0104$$, Figure S12A–C). Affected hearts also tended to have a leftward rotation (when measured from the base of the aorta to the ventricular sulcus), but this was not statistically significant.
In summary, we identified significant morphological changes in the ventricular chambers, aorta, pulmonary valve and pulmonary trunk in the hearts of Hom Actn2 p.Met228Thr embryos, with a subset of embryos also having a peri-membranous ventricular septal defect.
To exclude developmental delay as an explanation for the observed abnormalities in the Hom embryos, forelimb morphology was used for Theiler staging, and no developmental delay was observed (Figure S13, Chi-square test, $p \leq 0.05$).
## 3.5. Sarcomeric and Nuclear Organisation in Hom Hearts
Wholemount immunofluorescence was performed to interrogate the expression and localisation of alpha-actinin 2 in E15.5 hearts. Of note, residual congealed blood was observed macroscopically in the ventricles of Hom hearts, much more than in the WT hearts (Figure S14), suggesting the less efficient contractility of Hom hearts during the fixation procedure.
Sarcomeric structures were visualised with titin Z-disk epitope (T12) and were clearly present and well organised, with alpha-actinin detectable at the Z-disks of sarcomeres in both genotypes (Figure 3A). However, quantitative colocalisation analysis revealed reduced colocalisation of titin Z-disk epitope with alpha-actinin (Figure 3B,C). Moreover, sarcomere length was found to be substantially increased in the Hom hearts (2.2 versus 1.8 µm; Figure 4A). In addition, there was a reduction in the number of nuclei in the Hom hearts, and they were rounder in shape (Figure 4B, Table S5).
Electron microscopy demonstrated regular Z-disks in both WT and Hom samples (Figure S15A), however, Z-disks appeared less uniform in the Hom hearts (Figure S15B). While Z-disk measurements revealed no difference in Z-disk width between both genotypes ($$p \leq 0.12$$), the width distribution was much wider in the Hom hearts (Figure S15C).
## 3.6. Cell-Cycle Defects in Hom Hearts
The reduced number of nuclei (Figure 4B) prompted us to investigate cell-cycle markers. In a targeted transcript analysis for cell-cycle markers, Anln, Cdkn1a, Cdkn1b, Cdkn2b, Tp53 and Wee1 were all found to be dysregulated in the ventricles of Hom hearts (Figure 4C), with the most striking upregulations being observed for Anln, Cdkn1a and Wee1, which were all predicted to block the progression of the cell cycle. In support of Tp53 transcript upregulation, a non-significant trend ($$p \leq 0.13$$) towards the upregulation of the p53 protein level was observed on Western blotting (Figure S16).
To further probe for potential defects in cell division in the Hom hearts, we performed staining for phosphorylated histone H3, a nuclear marker of active proliferation in cells (Figure 5A). In Hom E15.5 hearts, fewer cells positive for the marker were observed (Figure 5B). An analysis of dividing cells at higher magnification identified clear evidence of metaphase chromosome arrangement in dividing Hom embryonic cells, but these cells seemed to have a defect in myofibril disassembly, with residual sarcomeres observed (Figure S17).
## 3.7. Proteomics Analysis Gives Insights into Disturbances in the Hom Hearts
In order to gain insight into the disturbances leading to embryonic death, WT and Hom E15.5 heart protein samples were subjected to unbiased proteomics. Overall, 244 proteins were found to be upregulated and 133 proteins were downregulated in the Hom hearts when compared to WT (Figure 6A, Tables S6 and S7). Further, ingenuity pathway analysis (IPA) revealed dysregulation in a number of key canonical pathways, the most prominent being oxidative phosphorylation, mitochondrial dysfunction, sirtuin signalling and the citric acid (TCA) cycle (Figure 6B). Moreover, proteins belonging to the ubiquitination pathway and unfolded protein response were also enriched in the dataset (Figure 6B, Table S8). A deeper analysis of the data highlighted key mitochondrial protein complexes to be downregulated (Figure 6C and Figure S18), while proteins associated with protein processing and translation, including proteasomal activity, were upregulated (Figure 6B,C).
## 3.8. Destabilisation of Actn2 Protein in the Hom Hearts
A targeted interrogation of the proteomics dataset indicated that Actn2 protein levels were reduced by approximately $25\%$ in the Hom hearts ($p \leq 0.01$, Table S6). To validate this, we probed for alpha-actinins 1–4 at the mRNA level. The transcripts for Actn2 and Actn3 were upregulated in Hom ventricles (Figure 7A and Figure S19A), while transcripts for other alpha-actinins, Actn1 and Actn4, were not affected. Equally, the expression of cardiac actin, Actc1, was normal, while skeletal muscle actin, Acta1, was downregulated.
In contrast to the Actn2 upregulation observed at the transcript level, the protein levels for Actn2 were significantly reduced (Figure 7B). At the protein level, Actn3 expression was unchanged (Figure S19B,C).
The reduced Actn2 levels in the Hom hearts suggested that the p.Met228Thr Actn2 is subject to protein degradation. Ubiquitin-proteasomal system (UPS) and autophagy are the main protein degrading machineries in cells [25]. We probed for autophagy makers (p62 and LC3, Figure S21), but failed to observe any changes in the Hom hearts. Furthermore, proteolytic activities and total ubiquitination showed no signs of changes in the Hom hearts (Figure S20). However, the fact that the UPS was enriched in the proteomics dataset (Figure 6C) argues for the UPS being responsible for the destabilisation of the Actn2 protein.
In summary, the p.Met228Thr genetic variant renders the protein less stable. In homozygous animals, cell-cycle defects and mitochondrial dysfunction are observed; these impaired functions result in a range of morphological abnormalities of the embryonic hearts and are incompatible with life.
## 4. Discussion
The heterozygous ACTN2 p.Met228Thr variant was originally identified in a four-generation Italian family with atypical Hypertrophic Cardiomyopathy [9]. The ACTN2 variant showed co-segregation with cardiomyopathy, consistent with autosomal dominant inheritance, across 11 affected and 7 healthy family members, and was hence considered pathogenic. A detailed clinical investigation of affected family members revealed a range of cardiac features, including left ventricular hypertrophy, restrictive pathology, arrhythmias (including early-onset atrial fibrillation) and non-compaction.
In order to gain insights into the disease mechanisms of the ACTN2 p.Met228Thr variant, we generated a mouse model. Young adult mice heterozygous for the variant—reflecting the genetic situation in the patients—had no cardiac phenotype. Despite normal cardiac dimensions and function on echocardiography, mature male mice displayed molecular transcript signatures compatible with early signs of cardiomyopathy [26]. These findings mirror previously studied mouse models of cardiomyopathy: often the genetic equivalent of the human disease is not sufficient to cause detectable phenotypes in mice [15,27,28]. Ageing can unmask disease features [15,29], which is in agreement with HCM being a late-onset disease in humans, with patients often presenting only as adults. For example, the index patient of the ACTN2 p.Met228Thr family was diagnosed with HCM in his 50s [9], which corresponds to approximately 10 months of age in mice. We can only speculate whether further ageing or stressors, such as adrenergic stimulation or a high-fat diet, might provoke a phenotype, as shown for other animal models [30,31,32].
Sex differences in cardiac phenotypes of C57Bl6 mice are well documented [33,34]. Of note, male mice have higher mean arterial pressure, and this sex difference increases between 3 and 12 months [35]. This could contribute to the observed molecular signs of cardiomyopathy in male, but not female, mature mice.
The lack of an overt HCM phenotype in the heterozygous mouse model, equivalent to the human ACTN2 p.Met228Thr genetic situation, might be explained by the wildtype form dominating the alpha-actinin 2 protein. In support, we identified far more wildtype peptides in mass spectrometry than peptides carrying the p.Met228Thr variant; however, we were not able to detect the ubiquitination of alpha-actinin 2 by Western blotting (Figure S4C).
Given the lack of an overt phenotype in heterozygous mice, the embryonic lethality of the variant in the homozygous setting was unexpected. Our detailed morphological analysis using HREM identified a broad range of abnormalities: The Hom E15.5 hearts had enlarged right ventricles, smaller left atria and regional decreases in the left ventricle. Three of the eight homozygous hearts had peri-membranous ventricular septal defects (VSD). Further, we observed aortic stenosis, a reduced volume of the pulmonary trunk and a thickening of the pulmonary valve leaflets, as well as an abnormal orientation of the hearts in the body cavity. While some of these defects (e.g., thinner wall, larger right ventricular lumen and VSD) might be attributable to reduced cell division and/or cell migration, others may be consequences of altered hemodynamics in the developing heart: alpha-actinin 2 has no noticeable expression in the aorta or pulmonary trunk, so changes in these structures are likely to be secondary to altered blood flow. It has been demonstrated for chicken embryos and zebrafish aortas that blood flow, or rather the lack of it, can induce remodeling [36,37]. However, the presence of a VSD did not affect other parameters measured, and there was no statistical difference between Hom hearts with and without VSD.
Of note, the combination of morphological features resembles aspects of Noonan syndrome, in which a dysplastic pulmonary valve and Hypertrophic Cardiomyopathy are observed [38,39]. However, we observed no changes in ERK phosphorylation in the embryonic hearts. Hence, if there was a joint defect with Noonan’s syndrome, it is downstream of this point. Whether such a common disease pathway exists will be the subject of future investigations.
Despite the indirect evidence of poor contractility in the Hom hearts (residual blood found in ventricular cavities after fixation), the sarcomeres appeared to be well formed and qualitatively normal in the hearts. Quantitative image analysis, however, revealed the reduced colocalisation of the titin Z-disk epitope with mutant alpha-actinin. Moreover, sarcomere length was substantially increased in the Hom hearts.
At the developmental stage investigated (E15.5), cardiomyocytes undergo cycles of cell division [40], which requires them to disassemble and later reassemble sarcomeres [41]. Based on structural analysis, we hypothesise that ACTN2 p.Met228Thr, which maps to the interface between the CH1 and CH2 domains of the actin binding domain, might have an increased affinity to F-actin and quantitatively interfere with the breakdown of the sarcomeres required to undergo cell division. In support of this hypothesis, we identified defects in myofibril disassembly in cells undergoing division (Figure S17). As a consequence, the cells in the Hom hearts appear to have a shift towards fewer nuclei, and fewer proliferating cells were identified. In addition, the cell-cycle markers Anln, Cdkn1a and Wee1 were found to be upregulated in Hom hearts. All three control various cycle check points and their upregulation impairs the progression of cell division. This impairment of cell division in Hom hearts may also explain the reduced wall thickness observed in the LV wall and the lack of muscle growth, resulting in VSD in a proportion of Hom hearts.
A striking feature of the Actn2 p.Met228Thr variant is protein destabilisation in vivo. At the protein level, alpha-actinin 2 was found to be reduced to approximately one-third in Hom E15.5 hearts. It appears that the Hom hearts try to compensate for this lack of protein by upregulating Actn2 at the transcript level. Nevertheless, this cannot make up for the decreased stability at the protein level and subsequent degradation.
The mutation may lead to the partial mis-folding of the actin binding domain, which is supported by the enrichment of the ‘unfolded protein response’ pathway in the proteomics dataset (Figure 6B). Such mis-folded proteins are recognised and targeted for protein degradation. There are two major protein degrading pathways: the ubiquitin-proteasomal system (UPS) and autophagy [42]. We have no evidence to suggest that autophagy is involved. In contrast, unbiased proteomics revealed that UPS activity is more active in Hom hearts (Figure 6B,C and Table S8), so it is likely responsible for the increased turnover of the mutant protein. While the measurements of proteolytic activities (Figure S20A) do not support the increased activity of the UPS, future experiments using proteasomal or autophagy inhibitors on cultured cells, e.g., induced pluripotent stem cell-derived cardiomyocytes, will shed light on the specific role of the protein degrading pathways.
The UPS has been implicated in other HCM-causing genetic variants, e.g., for MYBPC3 truncating variants and for CSRP3 C58G [27,32,43]. In support of a crucial role of protein stability in the pathogenesis of human HCM due to the p.Met228Thr variant, there are clear parallels to another HCM-associated ACTN2 variant (p.Thr247Met) located in the same domain [13]: Using an induced pluripotent stem cell-derived cardiomyocyte model, the authors observed a reduced stability of the mutant ACTN2 p.Thr247Met protein, with the activation of both UPS and autophagy. However, while their mutant protein formed aggregates in their cellular model upon exogenous expression, we did not detect any alpha-actinin 2 aggregates in the p.Met228Thr Hom hearts.
It is worth noting that the tightly controlled UPS function is crucial for cell homeostasis and regulating many cellular processes, e.g., normal proteasomal activity is required for sarcomere disassembly during cell division in embryonic rat cardiomyocytes. If the proteasome is inhibited, alpha-actinin 2 fails to disassemble from sarcomeric structures [41].
Unbiased proteomics identified oxidative phosphorylation and mitochondrial dysfunction as the main consequences in the Hom hearts. During embryonic development at E11.5, glycolysis is the main source of energy while oxidative phosphorylation complexes begin to form and electron transport chain activity begins [44]. By E13.5, mitochondrial structure and function resemble those of mature mitochondria, and ATP is generated mainly through oxidative phosphorylation, with glycolysis becoming a secondary source [45,46]. Guo et al. [ 47] recently showed through the cardiomyocyte-specific mosaic expression of a hypomorphic mutation that Actn2 expression is important for cardiomyocyte maturation via serum response factor signaling. As one of the downstream pathways, mitochondrial expansion and organisation were found to be impaired. If dysfunctional mitochondria cannot facilitate maturation and metabolic shift in our model, energy deficiency might be a cause of embryonic lethality.
Moreover, alpha-actinin 2 has been reported to more directly influence mitochondrial function. It controls the localization of the RNA transcripts required for oxidative phosphorylation via its interaction with the RNA binding protein IGF2BP2 [48].
However, an alternative explanation would be that mitochondrial defects occur secondary to cell death [49], however, this is less likely as mitochondrial impairment was also observed in a cellular model of the ACTN2 p.Thr247Met variant [13] in the absence of pronounced cell death.
## 5. Conclusions
In summary, our heterozygous mouse model (in mature male mice) supports a pathological role of the ACTN2 p.Met228Thr variant but provides little insight into the disease mechanisms. Differences in physiology between humans and mice, the relatively young age of the mice and lack of stressors might explain this lack of an overt phenotype [50].
However, the detailed investigations of the Hom Actn2 p.Met228Thr embryonic hearts identified alpha-actinin protein destabilisation as a key feature of the model. This has several implications: firstly, it leads to an aberrant activity of the UPS, which has been implicated in multiple studies of HCM. Secondly, the lack of functional alpha-actinin has been suggested to interfere with serum response factor signaling [47], preventing cardiomyocyte maturation and consequently ensuing mitochondrial dysfunction. Together with the observed cell division defects, energetic deficiency is the likely cause of myocardial dysfunction. Moreover, the experiments identified a range of morphological abnormalities in the developing heart, suggesting that subtle functional abnormalities in alpha-actinin can have major consequences on the structure and function of sarcomeres as well as cell division, and consequently on the developing heart.
## References
1. Lange S., Pinotsis N., Agarkova I., Ehler E.. **The M-band: The underestimated part of the sarcomere**. *Biochim. Biophys. Acta Mol. Cell Res.* (2020) **1867** 118440. DOI: 10.1016/j.bbamcr.2019.02.003
2. Wadmore K., Azad A.J., Gehmlich K.. **The Role of Z-disc Proteins in Myopathy and Cardiomyopathy**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22063058
3. Azad A., Poloni G., Sontayananon N., Jiang H., Gehmlich K.. **The giant titin: How to evaluate its role in cardiomyopathies**. *J. Muscle Res. Cell Motil.* (2019) **40** 159-167. DOI: 10.1007/s10974-019-09518-w
4. Frank D., Frey N.. **Cardiac Z-disc signaling network**. *J. Biol. Chem.* (2011) **286** 9897-9904. DOI: 10.1074/jbc.R110.174268
5. Ribeiro Ede A., Pinotsis N., Ghisleni A., Salmazo A., Konarev P.V., Kostan J., Sjoblom B., Schreiner C., Polyansky A.A., Gkougkoulia E.A.. **The structure and regulation of human muscle alpha-actinin**. *Cell* (2014) **159** 1447-1460. DOI: 10.1016/j.cell.2014.10.056
6. Avery A.W., Fealey M.E., Wang F., Orlova A., Thompson A.R., Thomas D.D., Hays T.S., Egelman E.H.. **Structural basis for high-affinity actin binding revealed by a beta-III-spectrin SCA5 missense mutation**. *Nat. Commun.* (2017) **8** 1350. DOI: 10.1038/s41467-017-01367-w
7. Galkin V.E., Orlova A., Salmazo A., Djinovic-Carugo K., Egelman E.H.. **Opening of tandem calponin homology domains regulates their affinity for F-actin**. *Nat. Struct Mol. Biol.* (2010) **17** 614-616. DOI: 10.1038/nsmb.1789
8. Chiu C., Bagnall R.D., Ingles J., Yeates L., Kennerson M., Donald J.A., Jormakka M., Lind J.M., Semsarian C.. **Mutations in alpha-actinin-2 cause hypertrophic cardiomyopathy: A genome-wide analysis**. *J. Am. Coll. Cardiol.* (2010) **55** 1127-1135. DOI: 10.1016/j.jacc.2009.11.016
9. Girolami F., Iascone M., Tomberli B., Bardi S., Benelli M., Marseglia G., Pescucci C., Pezzoli L., Sana M.E., Basso C.. **Novel alpha-actinin 2 variant associated with familial hypertrophic cardiomyopathy and juvenile atrial arrhythmias: A massively parallel sequencing study**. *Circ. Cardiovasc. Genet.* (2014) **7** 741-750. DOI: 10.1161/CIRCGENETICS.113.000486
10. Prondzynski M., Lemoine M.D., Zech A.T., Horvath A., Di Mauro V., Koivumaki J.T., Kresin N., Busch J., Krause T., Kramer E.. **Disease modeling of a mutation in alpha-actinin 2 guides clinical therapy in hypertrophic cardiomyopathy**. *EMBO Mol. Med.* (2019) **11** e11115. DOI: 10.15252/emmm.201911115
11. Theis J.L., Bos J.M., Bartleson V.B., Will M.L., Binder J., Vatta M., Towbin J.A., Gersh B.J., Ommen S.R., Ackerman M.J.. **Echocardiographic-determined septal morphology in Z-disc hypertrophic cardiomyopathy**. *Biochem. Biophys. Res. Commun.* (2006) **351** 896-902. DOI: 10.1016/j.bbrc.2006.10.119
12. Haywood N.J., Wolny M., Rogers B., Trinh C.H., Shuping Y., Edwards T.A., Peckham M.. **Hypertrophic cardiomyopathy mutations in the calponin-homology domain of ACTN2 affect actin binding and cardiomyocyte Z-disc incorporation**. *Biochem. J.* (2016) **473** 2485-2493. DOI: 10.1042/BCJ20160421
13. Zech A.T.L., Prondzynski M., Singh S.R., Pietsch N., Orthey E., Alizoti E., Busch J., Madsen A., Behrens C.S., Meyer-Jens M.. **ACTN2 Mutant Causes Proteopathy in Human iPSC-Derived Cardiomyocytes**. *Cells* (2022) **11**. DOI: 10.3390/cells11172745
14. Freeman H.C., Hugill A., Dear N.T., Ashcroft F.M., Cox R.D.. **Deletion of nicotinamide nucleotide transhydrogenase: A new quantitive trait locus accounting for glucose intolerance in C57BL/6J mice**. *Diabetes* (2006) **55** 2153-2156. DOI: 10.2337/db06-0358
15. Jiang H., Hooper C., Kelly M., Steeples V., Simon J.N., Beglov J., Azad A.J., Leinhos L., Bennett P., Ehler E.. **Functional analysis of a gene-edited mouse model to gain insights into the disease mechanisms of a titin missense variant**. *Basic Res. Cardiol.* (2021) **116** 14. DOI: 10.1007/s00395-021-00853-z
16. Geyer S.H., Reissig L., Rose J., Wilson R., Prin F., Szumska D., Ramirez-Solis R., Tudor C., White J., Mohun T.J.. **A staging system for correct phenotype interpretation of mouse embryos harvested on embryonic day 14 (E14.5)**. *J. Anat.* (2017) **230** 710-719. DOI: 10.1111/joa.12590
17. Strucksberg K.H., Tangavelou K., Schröder R., Clemen C.S.. **Proteasomal activity in skeletal muscle: A matter of assay design, muscle type, and age**. *Anal. Biochem.* (2010) **399** 225-229. DOI: 10.1016/j.ab.2009.12.026
18. Kalisch-Smith J.I., Ved N., Szumska D., Munro J., Troup M., Harris S.E., Rodriguez-Caro H., Jacquemot A., Miller J.J., Stuart E.M.. **Maternal iron deficiency perturbs embryonic cardiovascular development in mice**. *Nat. Commun.* (2021) **12** 3447. DOI: 10.1038/s41467-021-23660-5
19. Gehmlich K., Dodd M.S., Allwood J.W., Kelly M., Bellahcene M., Lad H.V., Stockenhuber A., Hooper C., Ashrafian H., Redwood C.S.. **Changes in the cardiac metabolome caused by perhexiline treatment in a mouse model of hypertrophic cardiomyopathy**. *Mol. Biosyst.* (2015) **11** 564-573. DOI: 10.1039/C4MB00594E
20. Davis S., Scott C., Ansorge O., Fischer R.. **Development of a Sensitive, Scalable Method for Spatial, Cell-Type-Resolved Proteomics of the Human Brain**. *J. Proteome Res.* (2019) **18** 1787-1795. DOI: 10.1021/acs.jproteome.8b00981
21. Tyanova S., Temu T., Sinitcyn P., Carlson A., Hein M.Y., Geiger T., Mann M., Cox J.. **The Perseus computational platform for comprehensive analysis of (prote)omics data**. *Nat. Methods* (2016) **13** 731-740. DOI: 10.1038/nmeth.3901
22. Kramer A., Green J., Pollard J., Tugendreich S.. **Causal analysis approaches in Ingenuity Pathway Analysis**. *Bioinformatics* (2014) **30** 523-530. DOI: 10.1093/bioinformatics/btt703
23. Janssen A.F.J., Breusegem S.Y., Larrieu D.. **Current Methods and Pipelines for Image-Based Quantitation of Nuclear Shape and Nuclear Envelope Abnormalities**. *Cells* (2022) **11**. DOI: 10.3390/cells11030347
24. Morris T.A., Naik J., Fibben K.S., Kong X., Kiyono T., Yokomori K., Grosberg A.. **Striated myocyte structural integrity: Automated analysis of sarcomeric z-discs**. *PLoS Comput. Biol.* (2020) **16**. DOI: 10.1371/journal.pcbi.1007676
25. Zheng Q., Wang X.. **Autophagy and the ubiquitin-proteasome system in cardiac dysfunction**. *Panminerva Med.* (2010) **52** 9-25. PMID: 20228723
26. Rajabi M., Kassiotis C., Razeghi P., Taegtmeyer H.. **Return to the fetal gene program protects the stressed heart: A strong hypothesis**. *Heart Fail. Rev.* (2007) **12** 331-343. DOI: 10.1007/s10741-007-9034-1
27. Ehsan M., Kelly M., Hooper C., Yavari A., Beglov J., Bellahcene M., Ghataorhe K., Poloni G., Goel A., Kyriakou T.. **Mutant Muscle LIM Protein C58G causes cardiomyopathy through protein depletion**. *J. Mol. Cell Cardiol.* (2018) **121** 287-296. DOI: 10.1016/j.yjmcc.2018.07.248
28. Vignier N., Schlossarek S., Fraysse B., Mearini G., Kramer E., Pointu H., Mougenot N., Guiard J., Reimer R., Hohenberg H.. **Nonsense-mediated mRNA decay and ubiquitin-proteasome system regulate cardiac myosin-binding protein C mutant levels in cardiomyopathic mice**. *Circ. Res.* (2009) **105** 239-248. DOI: 10.1161/CIRCRESAHA.109.201251
29. Singh S.R., Zech A.T.L., Geertz B., Reischmann-Dusener S., Osinska H., Prondzynski M., Kramer E., Meng Q., Redwood C., van der Velden J.. **Activation of Autophagy Ameliorates Cardiomyopathy in Mybpc3-Targeted Knockin Mice**. *Circ. Heart Fail.* (2017) **10** e004140. DOI: 10.1161/CIRCHEARTFAILURE.117.004140
30. Heinonen I., Sorop O., van Dalen B.M., Wust R.C.I., van de Wouw J., de Beer V.J., Octavia Y., van Duin R.W.B., Hoogstrate Y., Blonden L.. **Cellular, mitochondrial and molecular alterations associate with early left ventricular diastolic dysfunction in a porcine model of diabetic metabolic derangement**. *Sci. Rep.* (2020) **10** 13173. DOI: 10.1038/s41598-020-68637-4
31. Olsson M.C., Palmer B.M., Leinwand L.A., Moore R.L.. **Gender and aging in a transgenic mouse model of hypertrophic cardiomyopathy**. *Am. J. Physiol. Heart Circ. Physiol.* (2001) **280** H1136-H1144. DOI: 10.1152/ajpheart.2001.280.3.H1136
32. Schlossarek S., Englmann D.R., Sultan K.R., Sauer M., Eschenhagen T., Carrier L.. **Defective proteolytic systems in Mybpc3-targeted mice with cardiac hypertrophy**. *Basic Res. Cardiol.* (2012) **107** 235. DOI: 10.1007/s00395-011-0235-3
33. Walker C.J., Schroeder M.E., Aguado B.A., Anseth K.S., Leinwand L.A.. **Matters of the heart: Cellular sex differences**. *J. Mol. Cell Cardiol.* (2021) **160** 42-55. DOI: 10.1016/j.yjmcc.2021.04.010
34. Aguiar A.S., Speck A.E., Amaral I.M., Canas P.M., Cunha R.A.. **The exercise sex gap and the impact of the estrous cycle on exercise performance in mice**. *Sci. Rep.* (2018) **8** 10742. DOI: 10.1038/s41598-018-29050-0
35. Barsha G., Denton K.M., Mirabito Colafella K.M.. **Sex- and age-related differences in arterial pressure and albuminuria in mice**. *Biol. Sex. Differ.* (2016) **7** 57. DOI: 10.1186/s13293-016-0110-x
36. Campinho P., Lamperti P., Boselli F., Vilfan A., Vermot J.. **Blood Flow Limits Endothelial Cell Extrusion in the Zebrafish Dorsal Aorta**. *Cell Rep.* (2020) **31** 107505. DOI: 10.1016/j.celrep.2020.03.069
37. Espinosa M.G., Taber L.A., Wagenseil J.E.. **Reduced embryonic blood flow impacts extracellular matrix deposition in the maturing aorta**. *Dev. Dyn.* (2018) **247** 914-923. DOI: 10.1002/dvdy.24635
38. Digilio M., Marino B.. **Clinical manifestations of Noonan syndrome**. *Images Paediatr. Cardiol.* (2001) **3** 19-30. PMID: 22368597
39. Burch M., Sharland M., Shinebourne E., Smith G., Patton M., McKenna W.. **Cardiologic abnormalities in Noonan syndrome: Phenotypic diagnosis and echocardiographic assessment of 118 patients**. *J. Am. Coll Cardiol.* (1993) **22** 1189-1192. DOI: 10.1016/0735-1097(93)90436-5
40. Ikenishi A., Okayama H., Iwamoto N., Yoshitome S., Tane S., Nakamura K., Obayashi T., Hayashi T., Takeuchi T.. **Cell cycle regulation in mouse heart during embryonic and postnatal stages**. *Dev. Growth Differ.* (2012) **54** 731-738. DOI: 10.1111/j.1440-169X.2012.01373.x
41. Ahuja P., Perriard E., Perriard J.C., Ehler E.. **Sequential myofibrillar breakdown accompanies mitotic division of mammalian cardiomyocytes**. *J. Cell Sci.* (2004) **117** 3295-3306. DOI: 10.1242/jcs.01159
42. Wang X., Robbins J.. **Proteasomal and lysosomal protein degradation and heart disease**. *J. Mol. Cell Cardiol* (2014) **71** 16-24. DOI: 10.1016/j.yjmcc.2013.11.006
43. Bahrudin U., Morisaki H., Morisaki T., Ninomiya H., Higaki K., Nanba E., Igawa O., Takashima S., Mizuta E., Miake J.. **Ubiquitin-proteasome system impairment caused by a missense cardiac myosin-binding protein C mutation and associated with cardiac dysfunction in hypertrophic cardiomyopathy**. *J. Mol. Biol.* (2008) **384** 896-907. DOI: 10.1016/j.jmb.2008.09.070
44. Lopaschuk G.D., Jaswal J.S.. **Energy metabolic phenotype of the cardiomyocyte during development, differentiation, and postnatal maturation**. *J. Cardiovasc. Pharmacol.* (2010) **56** 130-140. DOI: 10.1097/FJC.0b013e3181e74a14
45. Hom J.R., Quintanilla R.A., Hoffman D.L., de Mesy Bentley K.L., Molkentin J.D., Sheu S.S., Porter G.A.. **The permeability transition pore controls cardiac mitochondrial maturation and myocyte differentiation**. *Dev. Cell* (2011) **21** 469-478. DOI: 10.1016/j.devcel.2011.08.008
46. Zhao Q., Sun Q., Zhou L., Liu K., Jiao K.. **Complex Regulation of Mitochondrial Function During Cardiac Development**. *J. Am. Heart Assoc.* (2019) **8** e012731. DOI: 10.1161/JAHA.119.012731
47. Guo Y., Cao Y., Jardin B.D., Sethi I., Ma Q., Moghadaszadeh B., Troiano E.C., Mazumdar N., Trembley M.A., Small E.M.. **Sarcomeres regulate murine cardiomyocyte maturation through MRTF-SRF signaling**. *Proc. Natl. Acad. Sci. USA* (2021) **118** e2008861118. DOI: 10.1073/pnas.2008861118
48. Ladha F.A., Thakar K., Pettinato A.M., Legere N., Ghahremani S., Cohn R., Romano R., Meredith E., Chen Y.S., Hinson J.T.. **Actinin BioID reveals sarcomere crosstalk with oxidative metabolism through interactions with IGF2BP2**. *Cell Rep.* (2021) **36** 109512. DOI: 10.1016/j.celrep.2021.109512
49. Orogo A.M., Gustafsson Å B.. **Cell death in the myocardium: My heart won’t go on**. *IUBMB Life* (2013) **65** 651-656. DOI: 10.1002/iub.1180
50. van der Velden J., Asselbergs F.W., Bakkers J., Batkai S., Bertrand L., Bezzina C.R., Bot I., Brundel B., Carrier L., Chamuleau S.. **Animal models and animal-free innovations for cardiovascular research: Current status and routes to be explored. Consensus document of the ESC working group on myocardial function and the ESC Working Group on Cellular Biology of the Heart**. *Cardiovasc. Res.* (2022) **118** 3016-3051. DOI: 10.1093/cvr/cvab370
|
---
title: Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance
and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan
authors:
- Yerbolat Saruarov
- Gulnaz Nuskabayeva
- Mehmet Ziya Gencer
- Karlygash Sadykova
- Mira Zhunissova
- Ugilzhan Tatykayeva
- Elmira Iskandirova
- Gulmira Sarsenova
- Aigul Durmanova
- Abduzhappar Gaipov
- Kuralay Atageldiyeva
- Antonio Sarría-Santamera
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001384
doi: 10.3390/ijerph20053918
license: CC BY 4.0
---
# Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan
## Abstract
Cardiovascular risk factors aggregate in determined individuals. Patients with Type 2 diabetes mellitus (T2DM) have higher cardiovascular This study aimed to investigate insulinresistance (IR) and β-cell function using the homeostasis model assessment (HOMA) indexes in a general Kazakh population and determine the effect he effect that cardiovascular factors may have on those indexes. We conducted a cross-sectional study among employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) aged between 27 and 69 years. Sociodemographic variables, anthropometric measurements (body mass, height, waist circumference, hip circumference), and blood pressure were obtained. Fasting blood samples were collected to measure insulin, glucose, total cholesterol (TC), triglycerides (TG), and high- (HDL) andlow-density lipoprotein (LDL) levels. Oral glucose tolerance tests were performed. Hierarchical and K-means cluster analyses were obtained. The final sample was composed of 427 participants. Spearmen correlation analysis showed that cardiovascular parameters were statistically associated with HOMA-β ($p \leq 0.001$) and not with HOMA IR. Participants were aggregated into the three clusters where the cluster with a higher age and cardiovascular risk revealed deficient β-cell functioning, but not IR ($p \leq 0.000$ and $$p \leq 0.982$$). Common and easy to obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors have been demonstrated to be associated with significant deficiency in insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role not just for risk stratification of patients for cardiovascular prevention but also for targeted vigilant glucose monitoring.
## 1. Introduction
Cardiovascular risk factors cluster and aggregate within individuals [1]. Clustering of risk factors has been associated with a higher risk of cardiovascular disease. Those risk factors, high blood pressure, abnormal cholesterol [2], high triglycerides [3,4], obesity, lack of physical activity [5], or smoking [6] have also been identified to be associated with a higher incidence of Type 2 diabetes mellitus (T2DM) [7]. Patients with T2DM have a high prevalence of prior higher cardiovascular risk [8].
Diabetes mellitus (DM) incidence is growing globally [9] as well as in Kazakhstan [10]. Diabetes is a complex and heterogeneous disease, more complex than the classification in Type 1 and Type 2 suggest [11]. Biological and clinical implications of putative subtypes of DM require further investigation [12]. Recent novel classification based on clusters attempts to make a refined classification of adult-onset diabetes subgroups and their association with a specific risk of complications, with the aim to provide a useful tool for individualized treatment [13]. Progression differences and complication incidences that are linked with differences in DM subtypes have also been explored to determine possible subtypes of patients that are at risk of developing diabetes [14,15].
Although insulin resistance (IR) and pancreatic β-cell dysfunction are the fundamental features in the development of Type 2 diabetes (T2DM), the pathogenesis of T2DM is still unclear. Both peripheral IR and insufficient insulin release from pancreatic islet β-cells induce hyperglycemia and, therefore, increase insulin demand.
IR may be defined as a subnormal glucose response to endogenous and/or exogenous insulin. It most commonly occurs in association with obesity but may result from multiple other underlying causes, both cell-extrinsic factors. This includes circulating or paracrine molecules (such as hormones, cytokines, lipids, and metabolites) that are released from a cell or tissue other than the target cell/tissue, or absorbed by the intestine from the diet or microbiome action, and cell-intrinsic factors that are most likely due to genetic or epigenetic effects, but may or may not be in the insulin signaling pathway itself [16].
The insulin receptor is a transmembrane protein that is part of the RTK (receptors of tyrosine kinase), which exists as covalently bound receptor dimers at the surface of molecules. This receptor plays crucial roles in all the important functions of cell growth and its metabolism, as well as being related to DM, and thus has been considered a novel therapeutic target. An in-depth analysis of the insulin receptor would help develop an understanding of the regulation of cellular pathways and contribute to the development of novel drugs for T2DM [17].
However, the role and sequence of those inherently complex processes, IR and β-cell dysfunction, and their interrelation for triggering the pathogenesis of T2DM are also undefined [18]. Understanding how these multi-layered molecular networks modulate insulin action and metabolism in different tissues will open new avenues for therapy and prevention of T2DM.
The homeostasis model assessment (HOMA) is derived from a mathematical assessment of the balance between hepatic glucose output and insulin secretion from fasting levels of glucose and insulin [19]. HOMA indexes provide valid estimates of insulin resistance (HOMA-IR) and of β-cell function (HOMA-β). The HOMA index calculation requires only a single measurement of insulin and fasting glucose and is thus considered a valid alternative. Well-conducted prospective studies have determined the predictive validity of both measures to identify patients that are at risk of T2DM developing [20,21,22,23,24].
Patients’ ethnic backgrounds have been associated with differences in the incidence and progression of T2DM [25]. The significant contribution in Asian populations of β-cell dysfunction in the incidence of T2DM, compared to *Caucasians is* becoming recognized. These pathophysiological differences may have an important impact on therapeutic approaches [26]. Asians may have especially vulnerable β-cells, despite relatively good insulin sensitivity, and be unable to increase insulin secretion further if there is even a slight decrease in insulin sensitivity [27]. Kazakhstan is an ethnically diverse Central Asian country, and its genetic characteristics may hold an intermediate position between European and Eastern Asian populations [28]. In Turkistan, and quite different from other regions of the country, the second most frequent ethnic group after ethnic Kazakhs are Uzbeks [29], with whom Kazakhs possibly share more genetic similarities than ethnic Russians, who in the rest of the country are the second most frequent ethnic group. A previous study has shown that the South Kazakhstan region, where Turkistan belongs to, had the highest proportion of undiagnosed diabetes cases [11].
The objective of this study is to investigate IR and β-cell function in a general Kazakh population and determine the effect that cardiovascular factors may have on those indexes.
## 2. Materials and Methods
The study was conducted at the Clinical Diagnostic Center of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) between 2019 and 2020. The study population consisted of employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University. The inclusion criteria were age between 27 and 69 years and written informed consent to participate in the study. The exclusion criteria were the presence of already diagnosed kidney disease or diabetes or who were diagnosed with diabetes with the blood tests that were analyzed in this work.
Data on study participants were collected in a patient survey card that contained a summary of the study, a written voluntary informed consent form, passport, and demographic data, questionnaires on lifestyles, as well as anthropometric and laboratory studies.
The Fagerstrom test was used as a questionnaire to determine smoking status, and the Alcohol Use Disorders Identification Test (AUDIT) questionnaire was used to identify the alcohol consumption information. An anthropometric study was conducted for determining the height, and weight for which BMI was calculated. Height was measured by a stadiometer, in which the study participants stood straight, without outerwear and shoes, heels, buttocks, and shoulders were in contact with the vertical plane of the stadiometer. The patients’ heads were kept in the “Frankfurt plane” where the lower boundaries of the orbits were in the same horizontal plane as the external auditory space. When holding their breath on inspiration, the stadiometer plate was lowered to the head of the patient, after which the subject departed. After taking three measurements, the average growth index was determined with an accuracy of 0.1 cm. Body weight was measured on electronic scales. After turning on the scale display to check the performance, when 0.00 g appeared, the participants were asked to stand on the scale. At the same time, shoes, outerwear, and heavy items in pockets (mobile phones, wallets, etc.) were removed. Study participants stood in the center of the scales with their arms freely at their sides. At the same time, the patients looked straight and remained motionless. After three measurements, the mean body weight was recorded to the nearest 0.1 kg. Based on the results of measuring height and body weight, BMI was determined by the formula: weight (kg)/height in m2. Waist circumference (WC) was measured while standing, using a soft centimeter tape with an accuracy of 0.1 cm. WC was measured after normal expiration in the middle between the lower rib and the upper part of the iliac crest. According to the measurement of WC, the presence of abdominal obesity (AO) was determined according to the International Diabetes Federation criterion [2005]. A WC of more than 94 cm in men and 80 cm in women was taken as AO. Measurement of hip circumference (HC) was carried out with a centimeter tape, in the standing position, on the most protruding part of the gluteal region above the large trochanters, the result was determined with an accuracy of 0.1 cm.
Laboratory methods included the determination of fasting glucose levels, after a 2-h oral glucose tolerance test (OGTT), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Blood sampling was carried out from the cubital vein after a 12-h fast. OGTT was performed with 75 g glucose solution, in which the plasma glucose level was measured after 0 and 120 min. For prediabetes, fasting glucose was taken as 6.1–6.9 mmol/L, after OGTT—7.8–11.1 mmol/L (WHO). Biochemical studies were determined in a biochemical analyzer Cobas Integra-400 from Roche (Basel, Switzerland). The listed laboratory studies were carried out in the laboratory of the Clinical Diagnostic Center of Khoja Akhmet Yassawi International Kazakh-Turkish University.
HOMA-IR and HOMA-β were calculated and divided into terciles and in 2 categories, namely IR and Poor β-cell function [30]. HOMA models were calculated as HOMA-IR = fasting insulin (lU/mL) × fasting glucose (mmol/L)]/22.5, and HOMA-β = [20 × fasting insulin (lU/mL)]/[fasting glucose (mmol/L) − 3.5]. IR was defined as values HOMA_IR ≥ 2.5 and Poor β-cell function when HOMA-β ≤ 50.
Correlation analysis was conducted to analyze the possible associations between the different cardiovascular risk factors and glucose metabolism variables.
Cluster analyses were conducted to identify individuals with aggregation of cardiovascular risk factors: hierarchical and k-means. To visualize clustering of individuals, first hierarchical analysis with the Wald method was obtained to create a dendrogram to visually determine the reasonable number of clusters. Second, K-means clusters were finally developed to find the separation of cases based on cardiovascular risk factors as descriptors. SPSS 29.0 statistical software was used for the analyses.
This study was approved by the Commission on Clinical Ethics of the Faculty of Medicine of Khoja Akhmet Yassawi International Kazakh-Turkish University. Before attending the study, the participants were provided with personal explanations regarding the purpose and method of the study, as well as information regarding the processing of the results. Written consent was given by all participants.
## 3. Results
Data were initially available from 632 participants, but data to calculate HOMA-IR and HOMA-β were available only for 488 participants. Cases with fasting blood glucose or OGTT compatible with a diagnosis of diabetes were eliminated. The total sample was composed of 427 subjects. The basal characteristics of cases are depicted in Table 1. The high obesity prevalence and elevated BMI is of note.
As the variables did not show normal distributions, Spearman non-parametric correlation analysis was conducted. Table 2 shows the Spearman correlation between the different quantitative variables related either to cardiovascular risk factors or glucose metabolism. All the cardiovascular parameters were inversely statistically associated with HOMA-β and none of them with HOMA IR.
Figure 1 shows the dendrogram of hierarchical clustering demonstrating that the separation of subjects into three clusters is well depicted.
Table 3 reflects the values of cardiovascular risk factors of the three proposed clusters created using the K-means method. Table 4 reveals the glucose metabolism characteristics of participants that were aggregated into the three clusters. Significant associations were identified for beta-cell functioning, but not for IR.
Supplementary Tables S1–S4 show the distribution of cardiovascular risk factors by HOMA terciles as well as IR and Poor β-cell functioning.
## 4. Discussion
The findings of this study, combining different analytical methods to identify the possible relationship between cardiovascular risk factors and homeostasis indexes that reflect susceptibility to T2DM, suggest the existence of a significant association between cardio-metabolic alterations and β-cell function, as measured by HOMA-β, while not such an association with IR. Age also showed a strong independent effect on β-cell dysfunction. Another relevant finding of this study is the aggregation of cardiovascular risk factors in certain groups of this population as well as the association of higher cardiovascular risk with age and with β-cell deficiency. Is also relevant to mention the elevated proportion of overweight and obese participants as well as abdominal obesity in this population.
HOMA-IR and HOMA-β are widely accepted surrogate measures of IR and β-cell dysfunction in clinical and epidemiological studies [31], but the interpretation and extrapolation of the current findings for its application for clinical practice or public health decision-making should be cautious; showing more or less deteriorated glucose homeostasis indexes reflecting either IR or poor β-cell functioning should not be immediately associated with a higher risk of incidence of T2DM.
The most common understanding of the T2DM pathogenic process is that IR is the primary glucose homeostasis abnormality, with β-cell dysfunction being a later manifestation when β-cells no longer sustain sufficient insulin secretion and became ‘exhausted’. However, “primary” β-cell dysfunction as an independent abnormality in the early phases of the development of dysglycemia has also been suggested [32,33].
Different mechanisms (glucotoxicity, lipotoxicity, oxidative stress, endoplasmic reticulum stress, inflammatory stress, amyloid formation, or decreased incretins) have been suggested for β-cell death [34,35]. The coexistence of adverse cardiovascular risk profiles, including overweight and obesity, high blood pressure levels, and lipid alterations in certain individuals, may create a “hostile” metabolic environment that, when acting in concert with age, may be associated with those factors and reduce functional β-cell mass and increase the risk for T2DM [36,37].
Cluster 1 that was identified in this study included $37\%$ of the sample that were analyzed and revealed advanced age and the worst cardiovascular risk in terms of blood pressure and lipid profiles, a high prevalence of obesity, and a significantly poorer β-cell function. In contrast, the cluster with lower age and most favorable cardiovascular risk showed the best pancreatic β-cell function. These data did not show an association between cardiovascular risk factors and IR.
Obesity has been classically considered a hallmark of IR [38]. We did not find this association in this study. In obesity, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines, and other factors that are involved in the development of IR [39]. However, it is only when IR is accompanied by dysfunction and failure of pancreatic β-cells to control blood glucose levels that this results in T2DM.
Our results indicate an association between obesity and reduced β-cell function [40]. Obesity may be linked to pancreatic fat infiltration leading to impaired β-cell function, and the development of T2DM [41,42]. Excess cholesterol may have a direct pancreatic β-cell lipotoxicity, contributing as an underlying factor in the progression of T2DM [43]. Cholesterol is important for β-cell function and survival, but it can cause β-cell loss if allowed to accumulate in the cells in an unregulated manner [44]. Cholesterol excess impacts several steps of the metabolic machinery that are involved in glucose-stimulated insulin release localized at the endoplasmic reticulum, mitochondria, and the cell membrane [45,46]. This study adds to the growing body of literature that suggests that obesity and lipid alteration contribute to β-cell dysfunction [47].
Aging is one of the most important factors that is implicated in the major changes that are associated with deteriorated glucose metabolism through β-cell function [48,49], and appears to be independent of IR, BMI, and waist circumference [50]. The cause of this age-dependent functional decline is not known [51]. It is also not known whether this effect is mediated by a reduction in incretin secretion or not or may be associated with an aging-related β-cell resistance to the incretin effect, thus needing an increased release of incretin hormones, glucagon-like peptide-1 and gastric inhibitory polypeptide, to stimulate adequate insulin secretion in response to the glucose load [52,53]. A better understanding of all the factors that alter the proper regulation of glucose metabolism at advanced ages will facilitate the design of therapies that allow for better management of glycemia [54].
The study has some limitations. First, this is a selected working-age population from one company. *The* generalizability of these data may be limited. The cross-sectional nature of the study design prevents establishing causality in the direction of the associations identified. Cut-off points for HOMA-IR and HOMA-β are not standardized; other cut-off points may have rendered different results. A lack of a standardized universal insulin assays limit their use for routine assessment of insulin resistance in the clinical setting and may have affected our results. This analysis has separated the cases into three clusters but having determined another number of clusters may have rendered different results. The same limitation applies for our cut-off point of IR or poor-β-cell function. Also, the ethnic or genetic factors that may influence the glucose homeostasis indexes may be valid only for specific populations where they have been obtained. No data regarding two relevant variables, use of drugs or physical activity were available for these analyses. Lastly, this study does not aim to provide mechanistic explanations of the possible associations that may have been identified by analyzing these data.
## 5. Conclusions
T2DM is a complex and multifactorial global health problem that affects millions of people worldwide, has a significant impact on their quality of life, and results in grave consequences for healthcare systems. In T2DM, deficiency of β-cell function, primary or secondary to peripherally developed IR, is a paramount factor leading to dysregulated blood glucose and long-lasting hyperglycemia. The results from this work indicate that common and easy-to-obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors are associated with significant β-cell-deficient insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role, not just for risk stratification of patients for cardiovascular prevention, but also for targeted vigilant glucose monitoring.
## References
1. Lim S., Vos T., Flaxman A., Danaei G., Shibuya K., Adair-Rohani H., Amann M., Anderson R., Andrews K., Aryee M.. **A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010**. *Lancet* (2012.0) **380** 2224-2260. DOI: 10.1016/S0140-6736(12)61766-8
2. Rhee E.-J., Han K., Ko S.-H., Ko K.-S., Lee W.-Y.. **Increased risk for diabetes development in subjects with large variation in total cholesterol levels in 2,827,950 Koreans: A nationwide population-based study**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0176615
3. Hjellvik V., Sakshaug S., Strøm H.. **Body mass index, triglycerides, glucose, and blood pressure as predictors of type 2 diabetes in a middle-aged Norwegian cohort of men and women**. *Clin. Epidemiol.* (2012.0) **4** 213-224. DOI: 10.2147/CLEP.S31830
4. Dotevall A., Johansson S., Wilhelmsen L., Rosengren A.. **Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women. A prospective 18-year follow-up of the BEDA study**. *Diabet. Med.* (2004.0) **21** 615-622. DOI: 10.1111/j.1464-5491.2004.01189.x
5. **Reduction in the incidence of Type 2 Diabetes with lifestyle intervention or metformin**. *N. Engl. J. Med.* (2002.0) **346** 393-403. DOI: 10.1056/NEJMoa012512
6. Maddatu J., Anderson-Baucum E., Evans-Molina C.. **Smoking and the risk of type 2 diabetes**. *Transl. Res.* (2017.0) **184** 101-107. DOI: 10.1016/j.trsl.2017.02.004
7. Alemán-Vega G., Garrido-Elustondo S., Del Cura-González I., Sarria-Santamera A.. **¿Is a maintained glycemia between 110/125 mg/dl a risk factor in the development of diabetes?**. *Aten. Primaria.* (2017.0) **49** 557-558. DOI: 10.1016/j.aprim.2016.06.013
8. McGurnaghan S., Blackbourn L.A.K., Mocevic E., Panton U.H., McCrimmon R.J., Sattar N., Wild S., Colhoun H.M.. **Cardiovascular disease prevalence and risk factor prevalence in Type 2 diabetes: A contemporary analysis**. *Diabet. Med.* (2019.0) **36** 718-725. DOI: 10.1111/dme.13825
9. Liu J., Ren Z.-H., Qiang H., Wu J., Shen M., Zhang L., Lyu J.. **Trends in the incidence of diabetes mellitus: Results from the Global Burden of Disease Study 2017 and implications for diabetes mellitus prevention**. *BMC Public Health* (2020.0) **20** 1-12. DOI: 10.1186/s12889-020-09502-x
10. Galiyeva D., Gusmanov A., Sakko Y., Issanov A., Atageldiyeva K., Kadyrzhanuly K., Nurpeissova A., Rakhimzhanova M., Durmanova A., Sarria-Santamera A.. **Epidemiology of type 1 and type 2 diabetes mellitus in Kazakhstan: Data from unified National Electronic Health System 2014–2019**. *BMC Endocr. Disord.* (2022.0) **22**. DOI: 10.1186/s12902-022-01200-6
11. Sarría-Santamera A., Orazumbekova B., Maulenkul T., Gaipov A., Atageldiyeva K.. **The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review**. *Int. J. Environ Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17249523
12. Imamura F., Mukamal K.J., Meigs J.B., Luchsinger J.A., Ix J.H., Siscovick D.S., Mozaffarian D.. **Risk factors for type 2 diabetes mellitus preceded by β-cell dysfunction, insulin resistance, or both in older adults: The Cardiovascular Health Study**. *Am. J. Epidemiol.* (2013.0) **177** 1418-1429. DOI: 10.1093/aje/kws440
13. Ahlqvist E., Storm P., Käräjämäki A., Martinell M., Dorkhan M., Carlsson A., Vikman P., Prasad R.B., Aly D.M., Almgren P.. **Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables**. *Lancet Diabetes Endocrinol.* (2018.0) **6** 361-369. DOI: 10.1016/S2213-8587(18)30051-2
14. Yacamán Méndez D., Zhou M., Lagerros Y.T., Velasco D.V.G., Tynelius P., Gudjonsdottir H., de Leon A.P., Eeg-Olofsson K., Östenson C.G., Brynedal B.. **Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes**. *BMC Med.* (2022.0) **20**. DOI: 10.1186/s12916-022-02551-6
15. Wagner R., Heni M., Tabák A.G., Machann J., Schick F., Randrianarisoa E., de Angelis M.H., Birkenfeld A.L., Stefan N., Peter A.. **Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes**. *Nat. Med.* (2021.0) **27** 49-57. DOI: 10.1038/s41591-020-1116-9
16. Batista T.M., Haider N., Kahn C.R.. **Defining the underlying defect in insulin action in type 2 diabetes**. *Diabetologia* (2021.0) **64** 994-1006. DOI: 10.1007/s00125-021-05415-5
17. Zhang X., Zhu X., Bi X., Huang J., Zhou L.. **The Insulin Receptor: An Important Target for the Development of Novel Medicines and Pesticides**. *Int. J. Mol. Sci.* (2022.0) **23**. DOI: 10.3390/ijms23147793
18. Cerf M.E.. **Beta cell dysfunction and insulin resistance**. *Front. Endocrinol.* (2013.0) **4** 37. DOI: 10.3389/fendo.2013.00037
19. Matthews D.R., Hosker J.P., Rudenski A.S., Naylor B.A., Treacher D.F., Turner R.C.. **Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man**. *Diabetologia* (1985.0) **28** 412-419. DOI: 10.1007/BF00280883
20. Bonora E., Kiechl S., Willeit J., Oberhollenzer F., Egger G., Meigs J.B., Bonadonna R.C., Muggeo M.. **Population-based incidence rates and risk factors for type 2 diabetes in white individuals: The Bruneck study**. *Diabetes* (2004.0) **53** 1782-1789. DOI: 10.2337/diabetes.53.7.1782
21. Haffner S.M., Kennedy E., Gonzalez C., Stern M.P., Miettinen H.. **A prospective analysis of the HOMA model: The Mexico City Diabetes Study**. *Diabetes Care* (1996.0) **19** 1138-1141. DOI: 10.2337/diacare.19.10.1138
22. Hanley A.J., Williams K., Gonzalez C., D’Agostino R.B., Wagenknecht L.E., Stern M.P., Haffner S.M.. **Prediction of type 2 diabetes using simple measures of insulin resistance: Combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study**. *Diabetes* (2003.0) **52** 463-469. DOI: 10.2337/diabetes.52.2.463
23. Hayashi T., Boyko E.J., Leonetti D.L., McNeely M.J., Newell-Morris L., Kahn S.E., Fujimoto W.Y.. **Visceral adiposity and the risk of impaired glucose tolerance: A prospective study among Japanese Americans**. *Diabetes Care* (2003.0) **26** 650-655. DOI: 10.2337/diacare.26.3.650
24. Osei K., Rhinesmith S., Gaillard T., Schuster D.. **Impaired insulin sensitivity, insulin secretion, and glucose effectiveness predict future development of impaired glucose tolerance and type 2 diabetes in pre-diabetic African Americans: Implications for primary diabetes prevention**. *Diabetes Care* (2004.0) **27** 1439-1446. DOI: 10.2337/diacare.27.6.1439
25. Kodama K., Tojjar D., Yamada S., Toda K., Patel C.J., Butte A.J.. **Ethnic differences in the relationship between insulin sensitivity and insulin response: A systematic review and meta-analysis**. *Diabetes Care* (2013.0) **36** 1789-1796. DOI: 10.2337/dc12-1235
26. Yabe D., Seino Y., Fukushima M., Seino S.. **β cell dysfunction versus insulin resistance in the pathogenesis of type 2 diabetes in East Asians**. *Curr. Diab. Rep.* (2015.0) **15** 602. DOI: 10.1007/s11892-015-0602-9
27. Fukushima M., Suzuki H., Seino Y.. **Insulin secretion capacity in the development from normal glucose tolerance to type 2 diabetes**. *Diabetes Res. Clin. Pract.* (2004.0) **66** S37-S43. DOI: 10.1016/j.diabres.2003.11.024
28. Sikhayeva N., Talzhanov Y., Iskakova A., Dzharmukhanov J., Nugmanova R., Zholdybaeva E., Ramanculov E.. **Type 2 diabetes mellitus: Distribution of genetic markers in Kazakh population**. *Clin. Interv. Aging* (2018.0) **13** 377-388. DOI: 10.2147/CIA.S156044
29. **Turkistan (city)**
30. Saha A.. **Clinical sub-typing of newly detected type 2 diabetics on the basis of pancreatic beta cell function and degree of insulin resistance and their clinical characterization**. *Asian J. Med. Sci.* (2022.0) **13** 23-27. DOI: 10.3126/ajms.v13i5.44360
31. Wallace T.M., Levy J.C., Matthews D.R.. **Use and abuse of HOMA modeling**. *Diabetes Care* (2004.0) **27** 1487-1495. DOI: 10.2337/diacare.27.6.1487
32. Cohrs C.M., Panzer J.K., Drotar D.M., Enos S.J., Kipke N., Chen C., Bozsak R., Schöniger E., Ehehalt F., Distler M.. **Dysfunction of Persisting β Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis**. *Cell Rep.* (2020.0) **31** 107469. DOI: 10.1016/j.celrep.2020.03.033
33. Esser N., Utzschneider K.M., Kahn S.E.. **Early beta cell dysfunction vs insulin hypersecretion as the primary event in the pathogenesis of dysglycaemia**. *Diabetologia* (2020.0) **63** 2007-2021. DOI: 10.1007/s00125-020-05245-x
34. Popovic D.S., Rizzo M., Stokic E., Papanas N.. **New Sub-Phenotyping of Subjects at High Risk of Type 2 Diabetes: What Are the Potential Clinical Implications?**. *Diabetes Ther.* (2021.0) **12** 1605-1611. DOI: 10.1007/s13300-021-01065-3
35. Boughton C.K., Munro N., Whyte M.. **Targeting beta-cell preservation in the management of type 2 diabetes**. *Br. J. Diabetes* (2017.0) **17** 134-144. DOI: 10.15277/bjd.2017.148
36. Joseph J., Svartberg J., Njølstad I., Schirmer H.. **Incidence of and risk factors for type-2 diabetes in a general population: The Tromsø Study**. *Scand. J. Public Health* (2010.0) **38** 768-775. DOI: 10.1177/1403494810380299
37. Pérez C.M., Soto-Salgado M., Suárez E., Guzmán M., Ortiz A.P.. **High Prevalence of Diabetes and Prediabetes and Their Coexistence with Cardiovascular Risk Factors in a Hispanic Community**. *J. Immigr. Minor Health* (2015.0) **17** 1002-1009. DOI: 10.1007/s10903-014-0025-8
38. Reaven G.M.. **Banting lecture 1988. Role of insulin resistance in human disease**. *Diabetes* (1988.0) **37** 1595-1607. DOI: 10.2337/diab.37.12.1595
39. Kahn S.E., Hull R.L., Utzschneider K.M.. **Mechanisms linking obesity to insulin resistance and type 2 diabetes**. *Nature* (2006.0) **444** 840-846. DOI: 10.1038/nature05482
40. Rothberg A.E., Herman W.H., Wu C., IglayReger H.B., Horowitz J.F., Burant C.F., Galecki A.T., Halter J.B.. **Weight Loss Improves β-Cell Function in People With Severe Obesity and Impaired Fasting Glucose: A Window of Opportunity**. *J. Clin. Endocrinol. Metab.* (2020.0) **105** e1621-e1630. DOI: 10.1210/clinem/dgz189
41. Wen Y., Chen C., Kong X., Xia Z., Kong W., Si K., Han P., Liu W.V., Li X.. **Pancreatic fat infiltration, β-cell function and insulin resistance: A study of the young patients with obesity**. *Diabetes Res. Clin. Pract.* (2022.0) **187** 109860. DOI: 10.1016/j.diabres.2022.109860
42. Sadeghi E., Hosseini S.M., Vossoughi M., Aminorroaya A., Amini M.. **Association of Lipid Profile with Type 2 Diabetes in First-Degree Relatives: A 14-Year Follow-Up Study in Iran**. *Diabetes Metab. Syndr. Obes.* (2020.0) **13** 2743-2750. DOI: 10.2147/DMSO.S259697
43. Hao M., Head S., Gunawardana S., Hasty A., Piston D.. **Direct Effect of Cholesterol on Insulin Secretion: A Novel Mechanism for Pancreatic β-Cell Dysfunction**. *Diabetes* (2007.0) **56** 2328-2338. DOI: 10.2337/db07-0056
44. Odom G.L., Gregorevic P., Chamberlain J.S.. **Biochimica et Biophysica Acta (BBA)**. *Mol. Basis Dis.* (2019.0) **1865** 2149-2156
45. Perego C., Da Dalt L., Pirillo A., Galli A., Catapano A.L., Norata G.D.. **Cholesterol metabolism, pancreatic β-cell function and diabetes**. *Biochim. Biophys. Acta. Mol. Basis Dis.* (2019.0) **1865** 2149-2156. DOI: 10.1016/j.bbadis.2019.04.012
46. Fryirs M., Barter P.J., Rye K.A.. **Cholesterol metabolism and pancreatic beta-cell function**. *Curr. Opin. Lipidol.* (2009.0) **20** 159-164. DOI: 10.1097/MOL.0b013e32832ac180
47. Kruit J.K., Kremer P.H., Dai L., Tang R., Ruddle P., de Haan W., Brunham L.R., Verchere C.B., Hayden M.R.. **Cholesterol efflux via ATP-binding cassette transporter A1 (ABCA1) and cholesterol uptake via the LDL receptor influences cholesterol-induced impairment of beta cell function in mice**. *Diabetologia* (2010.0) **53** 1110-1119. DOI: 10.1007/s00125-010-1691-2
48. Garcia G.V.F.R., Freeman R.V., Supiano M.A., Smith M.J., Galecki A.T., Halter J.B.. **Glucose metabolism in older adults: A study including subjects more than 80 years of age**. *J. Am. Geriatr Soc.* (1997.0) **45** 813-817. DOI: 10.1111/j.1532-5415.1997.tb01507.x
49. Weir G.C.. **Islet-cell biology in 2015: Understanding secretion, ageing and death in β cells**. *Nat. Rev. Endocrinol.* (2016.0) **12** 72-74. DOI: 10.1038/nrendo.2015.236
50. Aguayo-Mazzucato C.. **Functional changes in beta cells during ageing and senescence**. *Diabetologia* (2020.0) **63** 2022-2029. DOI: 10.1007/s00125-020-05185-6
51. Oya J., Nakagami T., Yamamoto Y., Fukushima S., Takeda M., Endo Y., Uchigata Y.. **Effects of age on insulin resistance and secretion in subjects without diabetes**. *Intern. Med.* (2014.0) **53** 941-947. DOI: 10.2169/internalmedicine.53.1580
52. Garduno-Garcia J.J., Amalia Gastaldelli A., DeFronzo R.A., Lertwattanarak R., Holst J.J., Musi N.. **Older Subjects With β-Cell Dysfunction Have an Accentuated Incretin Release**. *J. Clin. Endocrinol. Metab.* (2018.0) **103** 2613-2619. DOI: 10.1210/jc.2018-00260
53. De Tata V.. **Age-related impairment of pancreatic Beta-cell function: Pathophysiological and cellular mechanisms**. *Front. Endocrinol.* (2014.0) **5** 138. DOI: 10.3389/fendo.2014.00138
54. Tudurí E., Soriano S., Almagro L., Montanya E., Alonso-Magdalena P., Nadal Á., Quesada I.. **The pancreatic β-cell in ageing: Implications in age-related diabetes**. *Ageing Res. Rev.* (2022.0) **80** 101674. DOI: 10.1016/j.arr.2022.101674
|
---
title: 'Detection of Adverse Drug Reactions in COVID-19 Hospitalized Patients in Saudi
Arabia: A Retrospective Study by ADR Prompt Indicators'
authors:
- Ebtihal Al-Shareef
- Lateef M. Khan
- Mohammed Alsieni
- Shahid Karim
- Fatemah O. Kamel
- Huda M. Alkreathy
- Duaa A. Bafail
- Ibrahim M. Ibrahim
- Abdulhadi S. Burzangi
- Mohammed A. Bazuhair
journal: Healthcare
year: 2023
pmcid: PMC10001386
doi: 10.3390/healthcare11050660
license: CC BY 4.0
---
# Detection of Adverse Drug Reactions in COVID-19 Hospitalized Patients in Saudi Arabia: A Retrospective Study by ADR Prompt Indicators
## Abstract
Seeking an alternative approach for detecting adverse drug reactions (ADRs) in coronavirus patients (COVID-19) and enhancing drug safety, a retrospective study of six months was conducted utilizing an electronic medical record (EMR) database to detect ADRs in hospitalized patients for COVID-19, using “ADR prompt indicators” (APIs). Consequently, confirmed ADRs were subjected to multifaceted analyses, such as demographic attribution, relationship with specific drugs and implication for organs and systems of the body, incidence rate, type, severity, and preventability of ADR. The incidence rate of ADRs is $37\%$, the predisposition of organs and systems to ADR is observed remarkably in the hepatobiliary and gastrointestinal systems at $41.8\%$ vs. $36.2\%$, $p \leq 0.0001$, and the classes of drugs implicated in the ADRs are lopinavir-ritonavir $16.3\%$, antibiotics $24.1\%$, and hydroxychloroquine$12.8\%$. Furthermore, the duration of hospitalization and polypharmacy are significantly higher in patients with ADRs at 14.13 ± 7.87 versus 9.55 ± 7.90, $p \leq 0.001$, and 9.74 ± 5.51 versus 6.98 ± 4.36, $p \leq 0.0001$, respectively. Comorbidities are detected in $42.5\%$ of patients and $75.2\%$, of patients with DM, and HTN, displaying significant ADRs, p-value < 0.05. This is a symbolic study providing a comprehensive acquaintance of the importance of APIs in detecting hospitalized ADRs, revealing increased detection rates and robust assertive values with insignificant costs, incorporating the hospital EMR database, and enhancing transparency and time effectiveness.
## 1. Introduction
The world has experienced a massive devastating pandemic, creating extreme distress and a huge impact on all age groups, especially older persons with chronic disorders [1,2,3,4]. Remarkably, at the time of drafting this proposition, 631,935,687 infected individuals had led to a shocking number of deaths [6,588,850] across the globe according to the WHO dashboard on 14 November 2022 [5]. This global public health crisis was also reflected in the kingdom of Saudi Arabia (KSA) and, notably, there were 824,513 confirmed cases and 9433 deaths from January 2020 to 14 November 2022 [6]. This disastrous and highly contagious disease had gripped the entire world, with an absence of effective therapy to cease the upsurge and diminish morbidity and mortality [7]. The name coronavirus is derived from its unique morphology, as it comprises a solitary strand of positive-sense RNA (ribonucleic acid) and is highly contagious in nature, being transmitted mainly by the respiratory droplets of the infected person [8,9]. The basic essence of the pathogenesis of COVID-19 has a damaging effect on the lungs, but severely infected patients may develop dysfunction of the kidney, liver, cardiovascular, neurological, and hematological systems. However, the majority of the complications are essentially due to the ‘cytokine storm’ [9]. The hallmark of this ‘cytokine storm’ leads to the development of multiorgan failure and, logically, becomes the chief cause of prolonged hospitalization [9]. Remarkably, coronavirus causes clinical manifestations when replicating, hence the impacting antiviral therapy will be more effective if used before the illness extends to the active inflammatory phase proceeding to the cytokine storm [7,10].
However, selecting antiviral drugs and managing COVID-19 patients presents a great challenge and creates a therapeutic dilemma for clinicians. Paradoxically, the antiviral agents used for optimizing the treatment of COVID-19 patients are empirical, including remdesivir, chloroquine, hydroxychloroquine, lopinavir/ritonavir, and favipiravir, etc. [ 10,11]. This markedly, required the need for immediate and effective therapies, leading researchers to use repurposing approaches, permitting multiple benefits, such as reducing risk, time, and costs. In addition, it provides easy access to data regarding the pharmacokinetics, pharmacodynamics, and ADRs of the concerned drugs [12,13]. Thus, utilizing such approaches diminishes the time required to explore an effective treatment for COVID-19, reducing morbidity, mortality, and long-term consequences [14,15,16,17].
It is vital to ensure appropriate drug safety whenever seeking enhanced efficiency [18]. However, confronting a pandemic of such a great magnitude with no definitive treatment, the incidence of ADRs in patients with COVID-19 is bound to be unpredictable. Moreover, at present, a standard method for the detection of ADRs is still imperfect and relies almost entirely on a spontaneous reporting system [19,20], suffering heavily from its inherent limitations [21,22]; hence, suitable alternative methods are required for the detection of the ADRs.
A comprehensive pharmacovigilance method has been devised to detect ADRs by utilizing APIs [20,22]. The basic essence of this approach is to resolve the dilemma of under-reporting and excessive time consumption, and apparently, it has a positive impact on authentic drug safety and rational use of drugs. The primary objective of this research is to identify and characterize the pattern of ADRs due to COVID-19 drugs, such as demographic features, the incidence, and types of ADR, causality assessment, duration of hospitalization, number of drugs taken in the hospital, history of past drug allergies, history of chronic diseases; and the secondary objective is observing the out-come of drugs used in COVID-19 patients, such as the most common drugs implicated in ADRs, the system and organs involved in ADRs, preventability, and severity of ADRs, thereby facilitating and enhancing drug safety.
## 2.1. Study Design and Population
This retrospective study plans to accomplish the detection and analysis of ADRs from the EMR database by selected APIs, using the active monitoring model of the National Coordinating Council for Medication Error Reporting and Prevention Index (NCC MERP Index) [23], illustrating medication harm and comprising: Class E: Temporary harm to the patient and required intervention; Class F: Temporary harm to the patient, requiring initial or prolonged hospitalization; Class G: Permanent patient harm; Class H: Intervention required sustaining life; and Class I: Patient death. The duration of this study was 6 months from April to October 2021. The current study was conducted by enrolling 381 patients with a confirmed diagnosis of COVID-19. The study was approved by the ethical approval committee of the institute, with permission to seek the records from the inpatients’ EMR database. Because of the retrospective nature of the study, written informed consent is not required.
## 2.2. Selection of “ADR Prompt Indicators”
A professional group, an expert team consisting of two clinical pharmacologists and one medical internist, agreed upon and approved the list of “APIs” based on the signals employed in past studies [22,24] and on an identical study on the use of trigger tools [25]. The EMR database was thoroughly checked to detect and analyze the ADRs. It must be emphasized that APIs used in the current study identify the adverse event harmful to the patient and excludes medication errors.
Inclusion criteria: Patients included in this study are of either sex, aged more than 18 years, with a confirmed diagnosis of COVID-19, and are recipients of at least one medication and/or positive laboratory indicator of the APIs (Table 1).
Exclusion criteria: patients not provided with any treatment, transferred to another department or discharged within 48 h of admission.
## 2.3. Active Data Collection and Surveillance
The basic characteristics of the selected patients with COVID-19, i.e., age, sex, nationality, admission details, length of hospital stay, drug intake, use of antiviral drugs, history of drug allergies, and chronic diseases, were extracted from the EMR database of the hospital. Subsequently, the entire drug therapy of all included patients were systematically analyzed to recognize the triggers or the “prompt indicators”, in order to identify the presence of suspected ADRs, by the team of reviewers (comprising two clinical pharmacologists and one medical internist), systematically scrutinizing the information, establishing the occurrence of ADR, and utilizing two causality assessment tools, i.e., clinical judgment and Naranjo’s algorithm tool [26].
## 2.4. Causality Assessment of ADRs
During the current study, the potential ADR detection process was exclusively based on the notion that whenever the “API” is recognized from the record of the database, it is only considered as a suggestive “positive indicator” and not an “established ADR.” Subsequently, these suspected ADRs are confirmed as ADRs by the aforesaid assessment tools [26]. Finally, the association between the recognized ADRs and the drugs is marked as definite, probable, and possible for inclusion in the study.
## 2.5. Pilot Study
To establish the suitability of the designed study to reveal the ADRs of COVID-19 drug therapy, a pilot study was initially accomplished from the information of 50 patients acquired from the EMR database.
## 2.6. Comprehensive Correlation between ADRs and Characteristics of the Patients
Consequently, the demographic characteristics of the patients extracted from the EMR database were further analyzed for specific organs and systems implicated in ADR, precise drug therapy and drug intake, suspicious drugs and their involvement in the ADR, clinical outcome, type of ADR, until eventually the incidence rate of ADRs is determined. Ultimately, the clinical consequences of ADRs, comprising cure (after the symptoms of ADR have disappeared) or recovery of the abnormal indicators, is recorded appropriately.
## 2.7. Determination of Preventable ADRs
Moreover, the rationality of any drug therapy with eventual augmentation of drug safety is only made possible by the determination of preventable ADRs; our study has achieved this precisely by utilizing the Hartwig scale [27].
## 2.8. Identification of the Severity of ADRs
Finally, suitable intervention to stimulate the pharmacovigilance, and assessment of the severity of ADRs is considered to be crucial in any study for reinforcing the rationality of drug therapy; this vital facet was fulfilled in the current study by the method of Shumock and Thornton [28].
## 2.9. Statistical Analysis
The descriptive and analytical data was analyzed by using SPSS 20 software package, performing univariate, and multivariate analysis to determine the relations of prospective risk factors with hazard regarding ADRs, expressing the result suitably in percentage and absolute numbers and testing statistical significance, either by $95\%$ confidence intervals or p-value < 0.05, interpreted as statistically significant.
## 3.1. Demographic Characteristics of Patients
The number of patients in current study comprises 381 patients, of whom 78 are women and 303 are men. The mean age of the patients is 48.93 ± 14.63. Patients with a history of chronic diseases are $42.5\%$. Remarkably, API’s initial finding for the suspected ADRs is positive in 176 patients. However, on further analysis, 35 ADRs are observed to be disease-related symptoms and marked as “false positive”, hence discarded. Finally, 141 patients are categorized with confirmed ADRs, whereas patients without ADRs are 240, thereby demonstrating the incidence rate of ADRs as $37\%$.
Especially, on comparing the demographic characteristics of patients, between those with and without ADRs (Table 2 and Figure 1A), there are insignificant changes in patients without and with ADRs regarding age (48.46 ± 14.74 versus 49.74 ± 14.45, $$p \leq 0.409$$) and gender (female 54 ($22.2\%$) versus 24 ($17.0\%$) and males (186 ($77.5\%$) versus 117 ($83.0\%$), $$p \leq 0.125$$), whereas non- Saudi patients are more often without ADRs than with ADRs ($55.4\%$ versus $55.3\%$, $$p \leq 0.024$$). Age, gender, and nationality were not risk factors for ADRs due to infection with COVID–19 ($$p \leq 0.408$$, $$p \leq 0.202$$, and $$p \leq 0.985$$, respectively).
## 3.2. Clinical Characteristics of Patients of Those with and without ADRs
It needs to be highlighted that the duration of hospitalization and the number of drugs taken by the patients are observed to be significantly higher in patients with ADRs in comparison to those without ADRs (14.13 ± 7.87 versus 9.55 ± 7.90, $p \leq 0.001$ and 9.74 ± 5.51 versus 6.98 ± 4.36, $p \leq 0.0001$) (Table 3 and Figure 1D). Nevertheless, combined antiviral drug intake is higher in patients without ADRs than in those with ADRs ($73.3\%$ versus $53.2\%$, $p \leq 0.0001$). The risk factors for patients with ADRs are prolonged hospital stay (RR: 1.038; $95\%$CI: 1.014–1.063; $p \leq 0.002$) and intake of several drugs in the hospital (RR: 1.127; $95\%$CI: 1.075–1.182; $p \leq 0.0001$), in addition to the usage of a single antiviral agent (RR: 2.420; $95\%$CI: 1.563–3.748; $p \leq 0.0001$). Besides this, there are no significant changes when comparing patients with and without ADRs regarding the history of past drug allergies (Table 3).
## 3.3. Influence of Comorbidities over the Development of ADRs among the COVID-19 Patients
The current study describes the essential emphasis while observing the influence of different comorbidities on patients with COVID-19 (Table 4 and Figure 1) Markedly, $\frac{162}{381}$ ($42.5\%$) patients have different comorbidities and, remarkably, 106 ($75.2\%$) patients have developed different ADRs, while additional evaluation has revealed both controlled/uncontrolled DM, HTN and CHF displaying significant ADRs (p-value < 0.05).
## 3.4. Identification of ADRs Using Medications and Laboratory Prompt Indicators
It needs to be emphasized that, predominantly, ADRs are gastrointestinal tract (GIT) disorders ($36.2\%$), detected by medication prompt indicators (MPIs); concomitantly $2.8\%$ of skin ADRs are also recognized by MPIs. On the contrary, merely $3.5\%$ of electrolyte ADRs are revealed with MPIs (Table 5), $41.8\%$ of the hepatobiliary ADRs are detected by laboratory prompt indicators (LPIs), while $10.68\%$ of hyper-lipidemic disorder ADRs are detected by LPIs (Table 5).
## 3.5. Causality, Incidence, and Type of Adverse Drug Reactions
The incidence of ADRs in the current study (Table 6 and Figure 1C) is revealed as $37\%$, with a higher rate of ADRs in males in comparison to females. Besides, in causality assessment, males show a higher possibility of ADRs compared to females ($69.2\%$ versus $54\%$, $$p \leq 0.042$$). In contrast, type A ADRs are detected more in males than females. Surprisingly, ADR Type B is found exclusively in females ($$n = 4$$; $100\%$) (Table 6 and Figure 1C). However, the causality of ADRs displayed the pattern of possible > probable > definite.
## 3.6. Adverse Drug Reaction (ADR) with Regards to Age in the Current Study
The propensity of ADRs is observed to be more prevalent in males and noticeable in the age groups 49–59 and >60 years. Furthermore, this predominance of ADRs is generalized more in males among entirely all age groups. Besides, their duration of hospitalization is also found to increase proportionately according to the increase in age group (Table 7). There is a significant and coherent relationship between age, increase in hospitalization and ADRs ($$p \leq 0.0001$$ vs. $$p \leq 0.001$$), respectively.
## 3.7. The Organs and Systems Involved in Patients with ADRs
Emphatically, the most frequent ADRs are observed to be associated with hepatobiliary, gastrointestinal (GIT), and hyperlipidemic disorders ($41.8\%$ vs. $36.2\%$ and $10.6\%$, respectively). Moreover, GIT-related ADRs $36.2\%$ are more frequently attributed to antiviral agents ($14.9\%$), followed by antimalarial drugs ($9.2\%$), then antibiotics ($8.5\%$). On the other hand, hepatobiliary ADRs are also attributed principally to antiviral agents ($23.4\%$), then antibiotics ($10.6\%$), and thereafter monoclonal antibodies ($3.6\%$). As regards the hyperlipidemic disorders, corticosteroids are exclusively responsible for all the ADRs labeled as hypercholesteremia while hypertriglyceridemia results from monoclonal antibodies ($2.13\%$), followed by antiviral agents at $1.42\%$, and corticosteroids at $0.71\%$ (Table 8).
## 3.8. Potentiality of the Most Common Drugs Involved in ADRs of COVID-19 Patients
It is worth mentioning that the interesting fact revealed by the result of our study is that most of the ADRs are detected with antibiotics 34 ($24.1\%$) and antiviral drugs 58 ($41.1\%$). However, lopinavir/ritonavir produces $16.3\%$ of ADRs, which in turn culminates in four ($2.8\%$) serious adverse drug reactions (SADRs); additionally, two ($1.4\%$) SADRs are observed with favipiravir and oseltamivir, and one with Tocilizumab ($0.7\%$). Regarding causality assessment, probable ADRs are 43 ($30.4\%$), possibly 94 ($66.6\%$) and definite 4 ($2.8\%$) out of the 141 ADRs observed. Nevertheless, we have observed that all the encountered ADRs are tackled effectively by symptomatic management, after which $70.2\%$ fully recovered and $29.8\%$ of the affected patients were cured, Given the establishment of the correlation between the SADRs and the drugs /organs implicated, our results illustrated that $5.7\%$ are induced by antiviral and immunosuppressant agents (Lopinavir/ritonavir, and Favipiravir and Tocilizumab), which manifested as an acute liver injury with conclusively full recovery (Table 9).
## 3.9. Association of ADRs and Medications Used for COVID-19
In the current study, antiviral medications are found to be the most common drugs responsible for ADRs and account for 58 /141 ($41.1\%$) (Table 10). From these ADRs, the maximum number of corresponding to hepatobiliary disorders was $23.4\%$, and to GIT disorders $14.9\%$. This is followed by antibiotics, contributing to $\frac{34}{141}$ ($24.1\%$) ADRs, relating to hepatobiliary disorders ($10.6\%$) and GIT disorders ($8.51\%$).
## 3.10. Preventability and Severity of ADRs
In the current study, certainly preventable ADRs are identified and documented in different age groups as $9.9\%$, and probably preventable ADRs in all ages as $15.6\%$. It needs to be emphasized that unpreventable ADRs are more frequently observed in the age groups of 49–60 and >60 years, where the likelihood of associated chronic health disorders is more pronounced. However, the figure for non-preventable ADRs is exhibited as $74.5\%$ (Table 11 and Figure 2). Nevertheless, the elementary core and precise tagging of certainly preventable ADRs with specific attribution correlates to hepatobiliary disorders at $8.51\%$; surprisingly, all the GIT disorders related to ADRs account for $48.6\%$, revealed as non-preventable (Table 11 and Figure 2).
The current study highlights and demarcates the severity of ADRs and, on the scale of severity, we observed 64 ($45.4\%$), 69 ($48.9\%$) and 8 ($5.7\%$) as mild, moderate and severe, respectively (Table 11). Furthermore, the severe category of ADRs is detected in the age groups of 49–59 and <60 years, designated as a high-risk factor for chronic health care disorders, eventually augmenting susceptibility to the incidence of ADRs. Moreover, looking closely at severe ADRs, it is observed that $3.5\%$ of severe ADRs attribute to hematological disorders and $2.1\%$ to hepatobiliary disorders (Table 11 and Figure 2). Conversely, substantial numbers of mild ADRs are observed with GIT disorders at $36.2\%$, and the majority with moderate ADRs at $41.8\%$, accredited to hepatobiliary disorders (Table 11).
## 4. Discussion
The precipitous and devastating global outbreak of COVID-19 raising a dilemma internationally, spreading at a rapid velocity. This substantially reinforced the prospect of a huge impact on community health and therefore necessitated decisive efforts to explore the trends of epidemiology, which in turn helped in becoming more alert in encountering the high prevalence of COVID-19. Our current study incorporates the utilization of APIs to recognize ADRs, this monitoring system enhancing the frequency of ADR reporting and significant diminution of ADR under-reporting with regard to drug safety concerns of patients with COVID-19 [18,29].
This retrospective study describes the demographic characteristics of hospitalized COVID-19 patients in Yanbu, KSA. Fundamentally, the hallmark of any pharmaco-epidemiological study is to ascertain the incidence rate of ADRs; this vital component plays a crucial and unequivocal task in regulating the quality of health care of patients by recognizing those methods diminishing morbidity and mortality [30,31] In the current scenario of the COVID-19 pandemic, rapid infectivity and lack of definitive antimicrobial agents greatly worsened this quality indicator, shaking public trust in the present healthcare system [32,33]. The current results display a total of 141 ($37\%$) ADRs among 381 patients, and several identical recent studies conducted in Saudi Arabia, Malaysia, and China for the detection of hospitalized ADR using ADR trigger tools have also illustrated an escalated incidence rate of ADRs, which varies from $22.8\%$ to $74.2\%$, [34,35,36]. This strikingly alarming healthcare parameter is a serious concern for the improvement of both safety and the overall quality of care provided to hospitalized patients. It needs to be emphasized that, whenever the incidence rate of a disease process is disturbingly increased, there is the likelihood of an enormous decline in public trust in the governing healthcare system, therefore requiring adequate safety measures [30].
## 4.1. Characteristics of the Patients
The present study reported $20.5\%$ women and $79.5\%$ men among the 381 patients included, indicating a higher incidence of susceptibility in the male; correspondingly, ADRs are more common in males at $83.0\%$ than in females at $17\%$; however, identical observations are also illustrated in recent studies [37,38,39]. The high preponderance of male gender susceptibility to COVID-19 in comparison to females is attributed to several factors, such as differentials in inherent immunity and sex hormones. Identifying compelling evidence for the vital role executed by the androgen in the coupling of transmembrane serine pprotease2 (TMPRSS2) in alliance with the angiotensin-converting enzyme 2(ACE2) gene on the X-chromosome triggering augmented ACE2 levels may perhaps lead to a favorable effect in females infected with COVID-19 [39]. Interestingly, identical phenomena regarding the demographic dominance of male gender susceptibility over females to COVID-19 hospitalization are reported in almost all the provincial regions of Saudi Arabia [36]. In contrast, less likelihood of female infectivity with COVID-19 has been attributed to the disparity in innate immunity, steroid hormones, and additional influences, coupled with the sex chromosomes [40]. Nevertheless, lifestyle factors, such as less smoking and less drinking of alcohol, along with the attitude of females, more sensitive towards this pandemic, this might have a positive effect by leading to more precautions, such as wearing a face mask, frequent hand washing, and staying at home [41].
## 4.2. Relationship of Age to the Development of ADRs
The present study portrays the ADRs in the age group of 39–49 years as $18.4\%$ while vulnerability in the elderly age >60 contributed to $34\%$ of the ADRs (Table 7). In addition, the severity of ADRs in the current study is reported as $5.67\%$. ( Table 11) Thus, the contribution of adults seems to affect the highest proportion of ADRs in COVID-19 patients. This conforms with earlier studies [42,43]. The similarity of the current study’s results in this regard is reflected in a large database study from 17 million patients in Britain. In contrast, more than $90\%$ of infected children are either asymptomatic or have mild to moderate disease [44]. The reasons accountable for this important contributory factor to the severity of ADRs in COVID-19 in adult patients, as well as increased mortality due to COVID-19 infections due to age, include alterations in the production of T and B lymphocytes, worsening in lung capacity, and atrophic changes in bronchial smooth muscles contributing to a decline in lung reserves and the clearance of airways. Furthermore, enhanced coagulopathy and acute myocardial and liver injury are the most frequent complication noted after COVID-19 infections in the geriatric age group [45,46].
## 4.3. Drug-Induced ADRs in Hospitalized Patients of COVID-19 Are Directly Proportional to the Total Amount of Drug Intake and Simultaneously Prolonged Hospitalization
A remarkable, obvious, and inevitable core feature of hospitalized ADRs in patients of COVID-19 in this study is the effect of ADRs on the duration of hospital stay and concurrently the effect of the intake of several drugs on ADRs (Table 3). Both these dual components are significantly ($p \leq 0.001$) higher in patients with ADRs than in those without ADRs. Many similar phenomena are reported in other studies [47,48,49,50]. The variety of predisposing factors escalating the risk of developing hospitalized ADRs includes prolonged hospitalization, polypharmacy, comorbidities, inappropriate medication use and cardiovascular disorders. [ 50]. Furthermore, a systematic review illustrated that the presence of multiple chronic disorders and polypharmacy are among the top ten hazardous factors for ADRs [50,51]. Thus, the period of a patient’s hospitalization is ultimately detrimental to health care costs [18].
## 4.4. The Impact of Comorbidities on the Development of ADRs in COVID-19 Patients
It is clear that patients with intrinsic uncontrolled chronic healthcare disorders, such as diabetes mellitus, hypertension, chronic vital organ disorders and cancer, are highly susceptible to acquiring COVID-19 infection, more complications and ADRs [52,53]. Furthermore, recent studies have revealed that the outcome seems to be more severe in individuals with comorbidities when infected with COVID-19 in comparison with patients with no comorbidities [53,54]. The results of the current study reciprocate and highlight the statistically identical phenomena revealed in the aforesaid latest studies [52,53,54]. Hence, it is important to raise a global public campaign to develop awareness in order to lessen the liability of morbidity and mortality in this situation [54,55,56].
## 4.5. Relationship of Medication and Laboratory Prompt Indicators in Hospitalized ADRs Due to COVID-19
The prime objective of the utilization of medications and laboratory prompt indicators in the current study for the fundamental detection of ADRs is effectively accomplished, by utilizing the well-recognized ADR trigger tools that have a discrete and intrinsic significance of a higher detection rate and lesser cost compared to the conventional methods [20]. In addition, they also have the distinct benefit of recognizing preventable ADRs in comparison to the traditional methods, while a spontaneous reporting system is often accompanied by significant under-reporting [36]. In the current study, ADRs related to the GIT in hospitalized patients were significantly detected collectively by the MPI: Metoclopramide, Ondansetron and Loperamide at $36.2\%$ ($p \leq 0.0001$), in contrast to $43.5\%$ and $20.5\%$ in recent similar types of studies [34,36]. However, LPI for detecting liver injury accounted for $41.8\%$ of ADRs ($p \leq 0.0001$) in the current study, in comparison to $36.5\%$ and $36.2\%$ in the aforesaid recent studies [34,36]. At this juncture, focusing on this issue, the current data from the literature revealed that major medications responsible for provoking acute liver injury in COVID-19 patients are antimalarial and antimicrobial [57], and perhaps ACE2 and hypoxic damage to the liver are the most conceivable mechanism for the development of these ADRs [58,59,60]. Surprisingly, apart from the present drugs used, other possible mechanisms attributed to acute liver injury in this scenario could be concomitant drug therapy, underlying disease of the liver, and the viral effect itself [61].
## 4.6. Causality Assessment of ADRs in the Current Study
The basic hallmark and the likelihood of the causality assessment are to identify and distinguish an ADR as “definite” or “suspected”, with the prime objective to transmute skepticism to irrevocability [62].This process is essential, yet it is a complicated process in pharmacovigilance to establish the correlation between the suspected ADR and the usage of a specific drug. However, it is self-evident that the detection of ADRs makes a significant contribution towards early recognition, preventing relapse in sufferers of ADRs, thus optimizing drug therapy with the ultimate aim of enhancing the quality of care of the patients [63]. In the current study, ADRs are assessed by using the Naranjo scale [23] and $30.5\%$ of these reactions were classified as probable, $66.6\%$ as possible ($p \leq 0.042$) and only $2.8\%$ as “definite,” due to the complexity of evaluating a causal relationship between a drug and an adverse reaction. It has been highlighted that, currently, several similar studies are being conducted around the globe with the identical objective of the current study, but demonstrating distinct observations regarding their results relating to causality assessment [35,64,65,66]. This can be explained based on the adopting of numerous methodologies and different settings leading to ambiguities regarding the causal link between the ADRs and the underlying disease associated with the confounding factors, contending with the drug as possible cause of the ADRs [57].
## 4.7. Organs and Systems Involved in Hospitalized ADR in COVID-19 Patients
There seems to be a reciprocal and inadvertent relationship between the drugs used in hospitalized patients with COVID-19 and the increased susceptibility of their organs and systems to ADRs, conceivably due to weaker and suppressed body defense mechanisms, this scenario in turn perpetuating the augmented use of multiple medications and anticipation of potential drug–drug interaction [61]. According to the current study, hepatobiliary, digestive system and hyperlipidemic disorders are most affected by ADRs, according to the results of Sun et al. [ 35]. Analogous characteristics are observed in several recent studies of COVID-19 [33,35,36,67,68,69,70]. As a repercussion of the aforesaid observations, it appears to be coherent that hepatobiliary ADRs seem to be multifactorial and heterogenous in nature, which could be due to direct viral binding to ACE2-positive cholangiocytes, and this can cause hepatic injury, in addition to activation of the immune system and “Cytokine Strom”, promoting immune-mediated hepatic injury. It is pertinent to point out that altered liver function, disease severity and older age contribute to half of the incidence of ADRs in COVID-19 infections [71,72,73,74].
## 4.8. Potentiality of the Most Common Drugs Involved in ADRs of Patients Treated for COVID-19
By far, ADRs are seemingly the exclusive and crucial affiliate for weak adherence to treatment, hence it is always essential to initially optimize the choice of the antiviral agent before commencing therapy in hospitalized COVID-19 patients; regrettably, this could not be accomplished due to the unique and sudden onset of this pandemic. However, the stark reality observed in the current study revealed that the most reported class of drugs suspected of causing ADRs in COVID-19 hospitalized patients are antiviral ($41.13\%$), antibiotics ($24.1\%$) and antimalarial drugs ($12.8\%$), and the most frequently related drugs for the reported ADRs are lopinavir/ritonavir ($16.3\%$), antibiotics ($24.1\%$) and hydroxychloroquine ($12.8\%$). The current study’s results are consistent with numerous recent analogous studies [35,36,57,64,67,68,75]. Sun et al. 2020 [35] have illustrated in their study that the proportionality of the ADRs produced by lopinavir/ritonavir ($63.8\%$) seems to be the maximum. In contrast, in the study of Yang et al., 2020 [75], ADRs associated with lopinavir/ritonavir were $33.5\%$, but this disparity could be due to the modest sample size of his study. Furthermore, another recent study that was conducted by Ramírez et al. 2020 [1] in a tertiary care hospital in Spain testified in a remarkable observation that in the COVID-19 patients the incidence rate of serious ADRs is observed to be 4.75 times greater than that perceived in non-COVID-19 patients [75]. Strikingly, an enthusiastic and comprehensible element of information is revealed by Chouchana et al. [ 2021] [76], comparing the therapeutic drug monitoring of lopinavir-ritonavir levels in the serum of 24 COVID-19 patients with patients infected with human immunodeficiency virus, in which it is observed that lopinavir plasma concentrations rise by 4.6–8-fold in COVID-19 patients and this exorbitant rise of serum levels specifically necessitates care in circumventing the ADRs [77]. It needs to be highlighted that almost all the observed ADRs, including the grievous category, are confronted efficiently by symptomatic management, and no mortality was revealed in the current study.
## 4.9. Relation between ADRs and Medications Used for Treatment of COVID-19
The fundamental hallmark of this contemporary study is that the therapeutic remedies accountable for the ADR give a candid reflection of most similar studies in Saudi Arabia and worldwide. Illustrating our current finding of ADRs ($65.2\%$ with p-value < 0.0001) due to antimicrobial involvement, it is noticeable that several public sector tertiary care hospitals in Lahore, Pakistan, conducted prospective cross-sectional observational studies with an identical ADR incidence rate of approximately $38.9\%$ attributed to antimicrobial agents [77], and strikingly this also corresponds to studies conducted in Saudi Arabia [21,78]. The most realistic and noteworthy triggering factor and justification for this multiplicity of ADRs related to antimicrobial agents is the high prevalence of their use. Indeed, antimicrobials are the most consistently prescribed medications in Saudi Arabia [79].
## 4.10. Preventability and Severity of ADRs
The current study is highly determined to account for the evaluation of preventable and severe ADRs linked to hospitalized COVID-19 patients. The purpose of detecting preventable ADRs in any epidemiological study is to reinforce the rationality of drug therapy, which in turn augments drug safety [18,22]. A consistent focus in any epidemiological study of ADRs is the strong possibility of its preventability, which is outwardly decisive for strengthening the rationality of drug therapy in order to enhance drug safety [20]. ADRs determined by the Schumock and Thornton Scale [28] are observed to be preventable in $25.5\%$ of all age groups and remarkably associated with liver and biliary system disorders (Table 11 and Figure 2). However, non-preventable ADRs are detected at $74.5\%$, (Table 11 and Figure 2), mostly in the age group of >50 years, and most were ascribed as GIT disorders (Table 11). It must be reiterated that ADRs can be prevented by staying away from medications that have previously caused an ADR, avoiding medications that are inappropriate for the patient, optimizing the dosage regimen, performing regular monitoring tests, and checking for significant drug interactions. These are the most important interventions and the findings of recent studies in Saudi Arabia and our current study [36,77].
In addition, the current study reveals that the severity of ADRs (mild = $45.4\%$ and moderate = $48.9\%$) is demonstrated chiefly in the age group of >50 years (Figure 2). In addition, current analogous studies illustrated a comparable proportion for the severity of ADRs [36,64]. The crucial feature of drug safety is moreover reinforced by evaluating the severity of ADRs to assist in the identification of the vital facets of intervention in order to strengthen drug safety. Seemingly, this assertion is satisfactorily accomplished in the current study, enlightening the focus of interest in the perusal of the severity of ADRs in this age group and highlighting and substantiating a high-risk factor for chronic health care disorders, which in turn augments the susceptibility to the incidence of ADRs. As a ramification of the aforesaid observations, it is explicit that evaluation of these fundamental aspects of ADRs is a prerequisite of pharmacovigilance and would take up a pivotal position in reducing the burden of ADRs, reducing healthcare costs, and ultimately boosting quality care of the patient [18].
The results of our current study demonstrated an escalated incidence rate of ADRs at $37\%$, which distinctly matches several identical recent studies of hospitalized patients of COVID-19, conducted in Saudi Arabia and across the globe, with identical methods using trigger tools. Moreover, it is noteworthy that in this study the propensity of males over females to be susceptible to ADRs also closely resembles the finding of studies throughout the world. Furthermore, the current results in terms of susceptibility based on the vulnerability of age and length of hospital stay also correspond with several current studies. Ultimately, this study reiterates and reinforces the perception that APIs illustrate the most robust method to evaluate the EMR database for the detection of ADRs.
## 5. Limitation of the Study
Our study has utilized APIs for the detection of ADRs, often criticized for their inherent limitation of being biased and providing false positives, but this was effectively surmounted by the robust expert team of two clinical pharmacologists and one medical internist, who thoroughly scrutinized the information from the EMR database to confirm the ADRs, utilizing two causality assessment tools. Additionally, the current study detected ADRs exclusively in hospitalized patients, hence was unable to predict the comprehensive ADRs of all COVID-19 patients, focusing only on one center for Yanbu residents, hence its outcomes cannot be applied to the entire Saudi population.
## 6. Conclusions
Certainly, this study contributes to illustrating the fundamental parameters of ADRs related to the drugs utilized against COVID-19 and appears to be symbolic in providing a comprehensive acquaintance of the importance of APIs as the state of the art in recognizing in detecting hospitalized ADRs in patients with COVID-19. Moreover, it revealed escalated detection rates and robust assertive values with concomitant lesser cost, their incorporation into the hospital EMR database leading to further augmentation of transparency and time effectiveness.
## References
1. Ramírez E., Urroz M., Rodríguez A., González-Muñoz M., Martín-Vega A., Villán Y., Seco E., Monserrat J., Frías J., Carcas A.J.. **Incidence of suspected serious adverse drug reactions in coronavirus disease-19 patients detected by a pharmacovigilance program by laboratory signals in a tertiary hospital in Spain: Cautionary Data**. *Front. Pharmacol.* (2020.0) **11** 602841. DOI: 10.3389/fphar.2020.602841
2. Trindade G.G., Caxito S.M., Xavier A.R.E., Xavier M.A., Brandão F.. **COVID-19: Therapeutic approaches description and discussion**. *An. Acad. Bras. Ciênc.* (2020.0) **92** e20200466. DOI: 10.1590/0001-3765202020200466
3. **Coronavirus Disease (COVID-19) Pandemic**
4. Robba C., Battaglini D., Pelosi P., Rocco P.R.M.. **Multiple organ dysfunction in SARS-CoV-2: MODS-CoV-2**. *Expert Rev. Respir. Med.* (2020.0) **14** 865-868. DOI: 10.1080/17476348.2020.1778470
5. **WHO Coronavirus (COVID-19) Dashboard**
6. **WHO Coronavirus (COVID-19) Dashboard in Saudi Arabia**
7. Yang W., Cao Q., Qin L., Wang X., Cheng Z., Pan A., Dai J., Sun Q., Zhao F., Qu J.. **Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China**. *J. Infect.* (2020.0) **80** 388-393. DOI: 10.1016/j.jinf.2020.02.016
8. **“COVID-19”. Encyclopedia Britannica**
9. Majmundar N., Ducruet A., Prakash T., Nanda A., Khandelwal P.. **Incidence, Pathophysiology, and Impact of Coronavirus Disease 2019 (COVID-19) on Acute Ischemic Stroke**. *World Neurosurg.* (2020.0) **142** 523-525. DOI: 10.1016/j.wneu.2020.07.158
10. Mongia A., Saha S.K., Chouzenoux E., Majumdar A.. **A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials**. *Sci. Rep.* (2021.0) **11** 9047. DOI: 10.1038/s41598-021-88153-3
11. Zhang J., Xie B., Hashimoto K.. **Current status of potential therapeutic candidates for the COVID-19 crisis**. *Brain Behav. Immun.* (2020.0) **87** 59-73. DOI: 10.1016/j.bbi.2020.04.046
12. Rodrigues L., Cunha R.B., Vassilevskaia T., Viveiros M., Cunha C.. **Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates**. *Molecules* (2022.0) **27**. DOI: 10.3390/molecules27092723
13. Serafin M.B., Bottega A., Foletto V.S., da Rosa T.F., Hörner A., Hörner R.. **Drug repositioning is an alternative for the treatment of coronavirus COVID-19**. *Int. J. Antimicrob. Agents* (2020.0) **55** 105969. DOI: 10.1016/j.ijantimicag.2020.105969
14. Zheng W., Sun W., Simeonov A.. **Drug repurposing screens and synergistic drug-combinations for infectious diseases**. *Br. J. Pharmacol.* (2018.0) **175** 181-191. DOI: 10.1111/bph.13895
15. Wang R., Xu Q., Li L.. **Pharmacological care strategy for antivirals in patients with COVID-19 complicated by underlying disorders**. *Chin. J. Hosp. Pharm.* (2020.0) **40** 612-616
16. Osorio T.L., Rivera C.M., Pino-Marín D., Giraldo N., Amariles P.. **Relevancia clínica de las interacciones medicamentosas en pacientes infectados con el virus de la inmunodeficiencia humana: Actualización 2015–2017. (The clinical relevance of drug interactions in patients with human immunodeficiency virus infection: Update 2015–2017)**. *Organo Of. Soc. Chil. Infectol.* (2019.0) **36** 475-489. DOI: 10.4067/S0716-10182019000400475
17. Poutanen S.M., Low D.E., Henry B., Finkelstein S., Rose D., Green K., Tellier R., Draker R., Adachi D., Ayers M.. **Identification of severe acute respiratory syndrome in Canada**. *N. Engl. J. Med.* (2003.0) **348** 1995-2005. DOI: 10.1056/NEJMoa030634
18. Khan L.M.. **Comparative epidemiology of hospital-acquired adverse drug reactions in adults and children and their impact on cost and hospital stay—A systematic review**. *Eur. J. Clin. Pharmacol.* (2013.0) **69** 1985-1996. DOI: 10.1007/s00228-013-1563-z
19. Alj L., Touzani M., Benkirane R., Soulaymani R.. **Detecting Medication Errors in Pharmacovigilance Database: Capacities and Limits**. *Int. J. Risk Saf. Med.* (2007.0) **30** 919-990. DOI: 10.2165/00002018-200730100-00110
20. Khan L.M., Al-Harthi S., Alkreathy H., Osman A.-M.M., Ali A.S.. **Detection of adverse drug reactions by medication antidote signals and comparison of their sensitivity with common methods of ADR detection**. *Saudi Pharm. J.* (2014.0) **23** 515-522. DOI: 10.1016/j.jsps.2014.10.003
21. Khan L.M., Al-Harthi S., Saadah O., Al-Amoudi A., Sulaiman M., Ibrahim I.. **Impact of pharmacovigilance on adverse drug reactions reporting in hospitalized internal medicine patients at Saudi Arabian teaching hospital**. *Saudi Med. J.* (2012.0) **33** 863-868. PMID: 22886119
22. Khan L.M., Kamel F.O., Alkreathy H., Al-Harthi S., Saadah O.I., Osman A.-M.M., Allibaih M.. **Benefits of Medication Antidote Signals for the Detection of Potential Adverse Drug Reactions over Contemporary Methods of Pharmacovigilance in Hospitalized Children**. *Int. J. Pharmacol.* (2016.0) **13** 64-73. DOI: 10.3923/ijp.2017.64.73
23. Rozich J.D., Haraden C.R., Resar R.K.. **Adverse drug event trigger tool: A practical methodology for measuring medication related harm**. *BMJ Qual. Saf.* (2003.0) **12** 194-200. DOI: 10.1136/qhc.12.3.194
24. Morimoto T., Gandhi T.K., Seger A.C., Hsieh T.C., Bates D.W.. **Adverse drug events and medication errors: Detection and classification methods**. *BMJ Qual. Saf.* (2004.0) **13** 306-314. DOI: 10.1136/qshc.2004.010611
25. Griffin F.A., Resar R.K.. *IHI Global Trigger Tool for Measuring Adverse Events* (2009.0)
26. Naranjo C.A., Busto U., Sellers E.M., Sandor P., Ruiz I., Roberts E.A., Janecek E., Domecq C., Greenblatt D.J.. **A method for estimating the probability of adverse drug reactions**. *Clin. Pharmacol. Ther.* (1981.0) **30** 239-245. DOI: 10.1038/clpt.1981.154
27. Hartwig S.C., Siegel J., Schneider P.J.. **Preventability and severity assessment in reporting adverse drug reactions**. *Am. J. Health Pharm.* (1992.0) **49** 2229-2232. DOI: 10.1093/ajhp/49.9.2229
28. Schumock G.T., Thornton J.P.. **Focusing on the preventability of adverse drug reactions**. *Hosp. Pharm.* (1992.0) **27** 538. PMID: 10118597
29. Li X., Li H., Deng J., Zhu F., Liu Y., Chen W., Yue Z., Ren X., Xia J.. **Active pharmacovigilance in China: Recent development and future perspectives**. *Eur. J. Clin. Pharmacol.* (2018.0) **74** 863-871. DOI: 10.1007/s00228-018-2455-z
30. Allen-Duck A., Robinson J.C., Stewart M.W.. **Healthcare Quality: A Concept Analysis**. *Nurs. Forum.* (2017.0) **52** 377-386. DOI: 10.1111/nuf.12207
31. Grøndahl V.A., Kirchhoff J.W., Andersen K.L., Sørby L.A., Andreassen H.M., Skaug E.-A., Roos A.K., Tvete L.S., Helgesen A.K.. **Health care quality from the patients’ perspective: A comparative study between an old and a new, high-tech hospital**. *J. Multidiscip. Healthc.* (2018.0) **11** 591-600. DOI: 10.2147/JMDH.S176630
32. Alyami M.H., Naser A.Y., Orabi M.A.A., Alwafi H., Alyami H.S.. **Epidemiology of COVID-19 in the Kingdom of Saudi Arabia: An Ecological Study**. *Front. Public Health* (2020.0) **8** 506. DOI: 10.3389/fpubh.2020.00506
33. Alharbi A.A., Alqassim A.Y., Muaddi M.A., Alghamdi S.S.. **Regional Differences in COVID-19 Mortality Rates in the Kingdom of Saudi Arabia: A Simulation of the New Model of Care**. *Cureus* (2021.0) **13** e20797. DOI: 10.7759/cureus.20797
34. Lee J.Y., Ang A.S.Y., Ali N.M., Ang L.M., Omar A.. **Incidence of adverse reaction of drugs used in COVID-19 management: A retrospective, observational study**. *J. Pharm. Policy Pract.* (2021.0) **14** 84. DOI: 10.1186/s40545-021-00370-3
35. Sun J., Deng X., Chen X., Huang J., Huang S., Li Y., Feng J., Liu J., He G.. **Incidence of Adverse Drug Reactions in COVID-19 Patients in China: An Active Monitoring Study by Hospital Pharmacovigilance System**. *Clin. Pharmacol. Ther.* (2020.0) **108** 791-797. DOI: 10.1002/cpt.1866
36. Al-Shehail B., Al Jamea Z., Chacko R., Alotaibi F., Ismail N., Alshayban D.. **Incidence and risk factors of adverse drug reactions in patients with coronavirus disease 2019: A pharmacovigilance experience utilizing an ADR trigger tool**. *Saudi Pharm. J.* (2022.0) **30** 407-413. DOI: 10.1016/j.jsps.2022.01.021
37. Omar S.M., Musa I., Salah S., Elnur M., Al-Wutayd O., Adam I.. **High Mortality Rate in Adult COVID-19 Inpatients in Eastern Sudan: A Retrospective Study**. *J. Multidiscip. Healthc.* (2020.0) **13** 1887-1893. DOI: 10.2147/JMDH.S283900
38. Argenziano M.G., Bruce S.L., Slater C.L., Tiao J.R., Baldwin M.R., Barr R.G., Chang B.P., Chau K.H., Choi J.J., Gavin N.. **Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: Retrospective case series**. *BMJ* (2020.0) **369** m1996. DOI: 10.1136/bmj.m1996
39. Ortolan A., Lorenzin M., Felicetti M., Doria A., Ramonda R.. **Does gender influence clinical expression and disease outcomes in COVID-19? A systematic review and meta-analysis**. *Int. J. Infect. Dis.* (2020.0) **99** 496-504. DOI: 10.1016/j.ijid.2020.07.076
40. Conti P., Younes A.. **Coronavirus COV-19/SARS-CoV-2 affects women less than men: Clinical response to viral infection**. *J. Biol. Regul. Homeost. Agents.* (2020.0) **34** 339-343. DOI: 10.23812/EDITORIAL-CONTI-3
41. Bwire G.M.. **Coronavirus: Why Men are More Vulnerable to COVID-19 Than Women?**. *SN Compr. Clin. Med.* (2020.0) **2** 874-876. DOI: 10.1007/s42399-020-00341-w
42. Acter T., Uddin N., Das J., Akhter A., Choudhury T.R., Kim S.. **Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency**. *Sci. Total Environ.* (2020.0) **730** 138996. DOI: 10.1016/j.scitotenv.2020.138996
43. Xia W., Shao J., Guo Y., Peng X., Li Z., Hu D.. **Clinical and CT features in pediatric patients with COVID-19 infection: Different points from adults**. *Pediatr. Pulmonol.* (2020.0) **55** 1169-1174. DOI: 10.1002/ppul.24718
44. Ortiz-Prado E., Simbaña-Rivera K., Barreno L.G., Rubio-Neira M., Guaman L.P., Kyriakidis N.C., Muslin C., Jaramillo A.M.G., Barba-Ostria C., Cevallos-Robalino D.. **Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review**. *Diagn. Microbiol. Infect. Dis.* (2020.0) **98** 115094. DOI: 10.1016/j.diagmicrobio.2020.115094
45. Zhang H., Wu Y., He Y., Liu X., Liu M., Tang Y., Li X., Yang G., Liang G., Xu S.. **Age-Related Risk Factors and Complications of Patients With COVID-19: A Population-Based Retrospective Study**. *Front. Med.* (2022.0) **8** 757459. DOI: 10.3389/fmed.2021.757459
46. Nikolich-Zugich J., Knox K.S., Rios C.T., Natt B., Bhattacharya D., Fain M.J.. **SARS-CoV-2 and COVID-19 in older adults: What we may expect regarding pathogenesis, immune responses, and outcomes**. *Geroscience* (2020.0) **42** 505-514. DOI: 10.1007/s11357-020-00186-0
47. Amelung S., Meid A.D., Nafe M., Thalheimer M., Hoppe-Tichy T., Haefeli W.E., Seidling H.M.. **Association of preventable adverse drug events with inpatients’ length of stay-A propensity-matched cohort study**. *Int. J. Clin. Pract.* (2017.0) **71** e12990. DOI: 10.1111/ijcp.12990
48. Sahilu T., Getachew M., Melaku T., Sheleme T.. **Adverse Drug Events and Contributing Factors Among Hospitalized Adult Patients at Jimma Medical Center, Southwest Ethiopia: A Prospective Observational Study**. *Curr. Ther. Res.* (2020.0) **93** 100611. DOI: 10.1016/j.curtheres.2020.100611
49. Stevenson J., Williams J.L., Burnham T.G., Prevost T., Schiff R., Erskine S.D., Davies J.G.. **Predicting adverse drug reactions in older adults; a systematic review of the risk prediction models**. *Clin. Interv. Aging* (2014.0) **9** 1581-1593. DOI: 10.2147/CIA.S65475
50. Molokhia M., Majeed A.. **Current and future perspectives on the management of polypharmacy**. *BMC Fam. Pract.* (2017.0) **18** 1-9. DOI: 10.1186/s12875-017-0642-0
51. Suggett E., Marriott J.. **Risk Factors Associated with the Requirement for Pharmaceutical Intervention in the Hospital Setting: A Systematic Review of the Literature**. *Drugs Real World Outcomes* (2016.0) **3** 241-263. DOI: 10.1007/s40801-016-0083-4
52. **Symptoms of COVID-19**
53. Wang B., Li R., Lu Z., Huang Y.. **Does comorbidity increase the risk of patients with COVID-19: Evidence from meta-analysis**. *Aging* (2020.0) **12** 6049-6057. DOI: 10.18632/aging.103000
54. Sanyaolu A., Okorie C., Marinkovic A., Patidar R., Younis K., Desai P., Hosein Z., Padda I., Mangat J., Altaf M.. **Comorbidity and its Impact on Patients with COVID-19**. *SN Compr. Clin. Med.* (2020.0) **2** 1069-1076. DOI: 10.1007/s42399-020-00363-4
55. Singh A.K., Gupta R., Ghosh A., Misra A.. **Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations**. *Diabetes Metab. Syndr. Clin. Res. Rev.* (2020.0) **14** 303-310. DOI: 10.1016/j.dsx.2020.04.004
56. Zhao Q., Meng M., Kumar R., Wu Y., Huang J., Lian N., Deng Y., Lin S.. **The impact of COPD and smoking history on the severity of COVID-19: A systemic review and meta-analysis**. *J. Med. Virol.* (2020.0) **92** 1915-1921. DOI: 10.1002/jmv.25889
57. Melo J.R.R., Duarte E.C., de Moraes M.V., Fleck K., e Silva A.S.D.N., Arrais P.S.D.. **Adverse drug reactions in patients with COVID-19 in Brazil: Analysis of spontaneous notifications of the Brazilian pharmacovigilance system**. *Cad. Saúde Pública* (2021.0) **37** e00245820. DOI: 10.1590/0102-311x00245820
58. Clark R., Waters B., Stanfill A.G.. **Elevated liver function tests in COVID-19: Causes, clinical evidence, and potential treatments**. *Nurse Pract.* (2021.0) **46** 21-26. DOI: 10.1097/01.NPR.0000722316.63824.f9
59. Su Y.-J., Chang C.-W., Chen M.-J., Lai Y.-C.. **Impact of COVID-19 on liver**. *World J. Clin. Cases* (2021.0) **9** 7998-8007. DOI: 10.12998/wjcc.v9.i27.7998
60. Balzano T., El Hiba O., del Rey N.L.-G., El Amine S., Smimih K., El Hiba O.. **Liver Injury in COVID-19 Patients: An Overview of the Current Evidence**. *Handbook of Research on Pathophysiology and Strategies for the Management of COVID-19* (2022.0) 141-158. DOI: 10.4018/978-1-7998-8225-1.ch009
61. Alqahtani S., Schattenberg J.M.. **Liver injury in COVID-19: The current evidence**. *United Eur. Gastroenterol. J.* (2020.0) **8** 509-519. DOI: 10.1177/2050640620924157
62. Khan L.M., Al-Harthi S.E., Osman A.-M.M., Sattar M.A.A.A., Ali A.S.. **Dilemmas of the causality assessment tools in the diagnosis of adverse drug reactions**. *Saudi Pharm. J.* (2015.0) **24** 485-493. DOI: 10.1016/j.jsps.2015.01.010
63. Varallo F.R., Planeta C.D.S., Herdeiro M.T., Mastroianni P.D.C.. **Imputation of adverse drug reactions: Causality assessment in hospitals**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0171470
64. Nazir N., Chopra D., Sidhu J., Bhandari B.. **Adverse Drug Reactions in COVID-19 Patients Admitted to Intensive care Unit: Analysis of Individual Case Study Reports**. *Res. Sq.* (2021.0). DOI: 10.21203/rs.3.rs-596922/v1
65. Alshammari T.M., Al-Kathiri W.H., Le Louet H., Aljadhey H.S.. **Completeness of adverse drug reactions reports of the Saudi adverse event reporting system**. *Saudi Med. J.* (2015.0) **36** 821-828. DOI: 10.15537/smj.2015.7.11751
66. Crescioli G., Brilli V., Lanzi C., Burgalassi A., Ieri A., Bonaiuti R., Romano E., Innocenti R., Mannaioni G., Vannacci A.. **Adverse drug reactions in SARS-CoV-2 hospitalised patients: A case-series with a focus on drug–drug interactions**. *Intern. Emerg. Med.* (2020.0) **16** 697-710. DOI: 10.1007/s11739-020-02586-8
67. Rhodes N.J., Dairem A., Moore W.J., Shah A., Postelnick M.J., Badowski M.E., Michienzi S.M., Borkowski J.L., Polisetty R.S., Fong K.. **Multicenter point prevalence evaluation of the utilization and safety of drug therapies for COVID-19 at the onset of the pandemic timeline in the United States**. *Am. J. Health Pharm.* (2021.0) **78** 568-577. DOI: 10.1093/ajhp/zxaa426
68. Almazrou D., Egunsola O., Ali S., Bagalb A.. **Medication Misadventures Among COVID-19 Patients in Saudi Arabia**. *Cureus* (2021.0) **13** e15513. DOI: 10.7759/cureus.15513
69. Olry A., Meunier L., Délire B., Larrey D., Horsmans Y., Le Louët H.. **Drug-Induced Liver Injury and COVID-19 Infection: The Rules Remain the Same**. *Drug Saf.* (2020.0) **43** 615-617. DOI: 10.1007/s40264-020-00954-z
70. Cabral F.F., Pereira M., Borges K., de Brito Passos A., Francelino E., Monteiro M., Arrais P.. **Eventos Adversos A Medicamentos No Tratamento Da COVID-19 No Ceará: Adverse Events to Medicines in the Treatment of COVID-19 in Ceará**. *Cad. ESP-Rev. Científica Esc. Saúde Pública Ceará* (2020.0) **14** 30-37
71. Zhang C., Shi L., Wang F.-S.. **Liver injury in COVID-19: Management and challenges**. *Lancet Gastroenterol. Hepatol.* (2020.0) **5** 428-430. DOI: 10.1016/S2468-1253(20)30057-1
72. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X.. **Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China**. *Lancet* (2020.0) **395** 497-506. DOI: 10.1016/S0140-6736(20)30183-5
73. Mehta P., McAuley D.F., Brown M., Sanchez E., Tattersall R.S., Manson J.J.. **COVID-19: Consider cytokine storm syndromes and immunosuppression**. *Lancet* (2020.0) **395** 1033-1034. DOI: 10.1016/S0140-6736(20)30628-0
74. Hoffmann M., Kleine-Weber H., Schroeder S., Krüger N., Herrler T., Erichsen S., Schiergens T.S., Herrler G., Wu N.-H., Nitsche A.. **SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor**. *Cell* (2020.0) **181** 271-280.e278. DOI: 10.1016/j.cell.2020.02.052
75. Yang S., Liang J., Liu T.. **Analysis of diarrhea associated with lopinavir-ritonavir for COVID-19 and its prevention**. *Med. J. West China* (2020.0) **32** 485-488. DOI: 10.3748/wjg.v27.i23.3208
76. Chouchana L., Boujaafar S., Gana I., Preta L.-H., Regard L., Legendre P., Azoulay C., Canouï E., Zerbit J., Carlier N.. **Plasma Concentrations and Safety of Lopinavir/Ritonavir in COVID-19 Patients**. *Ther. Drug Monit.* (2021.0) **43** 131-135. DOI: 10.1097/FTD.0000000000000838
77. Iftikhar S., Sarwar M.R., Saqib A., Sarfraz M.. **Causality and preventability assessment of adverse drug reactions and adverse drug events of antibiotics among hospitalized patients: A multicenter, cross-sectional study in Lahore, Pakistan**. *PLoS ONE* (2018.0) **13**. DOI: 10.1371/journal.pone.0199456
78. Alenzi K.A., Alanazi N.S., Almalki M., Alomrani H., Alatawi F.O.. **The evaluation of adverse drug reactions in Saudi Arabia: A retrospective observational study**. *Saudi Pharm. J.* (2022.0) **30** 735-741. DOI: 10.1016/j.jsps.2022.03.018
79. AlKhamees O.A., AlNemer K.A., Maneea M.W.B., AlSugair F.A., AlEnizi B.H., Alharf A.A.. **Top 10 most used drugs in the Kingdom of Saudi Arabia 2010–2015**. *Saudi Pharm. J.* (2018.0) **26** 211-216. DOI: 10.1016/j.jsps.2017.12.009
|
---
title: Analysis of PTPN22 −1123 G>C, +788 G>A and +1858 C>T Polymorphisms in Patients
with Primary Sjögren’s Syndrome
authors:
- Paula Annahi Menchaca-Tapia
- Miguel Marín-Rosales
- Diana Celeste Salazar-Camarena
- Alvaro Cruz
- Edith Oregon-Romero
- Raziel Tapia-Llanos
- José Francisco Muñoz-Valle
- Claudia Azucena Palafox-Sánchez
journal: Diagnostics
year: 2023
pmcid: PMC10001387
doi: 10.3390/diagnostics13050899
license: CC BY 4.0
---
# Analysis of PTPN22 −1123 G>C, +788 G>A and +1858 C>T Polymorphisms in Patients with Primary Sjögren’s Syndrome
## Abstract
Background: Primary Sjögren’s syndrome (pSS) is an autoimmune exocrinopathy characterized by lymphocytic infiltration, glandular dysfunction and systemic manifestations. Lyp protein is a negative regulator of the T cell receptor encoded by the tyrosine phosphatase nonreceptor-type 22 (PTPN22) gene. Multiple single-nucleotide polymorphisms (SNPs) in the PTPN22 gene have been associated with susceptibility to autoimmune diseases. This study aimed to investigate the association of PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A), rs2476601 (+1858 C>T) with pSS susceptibility in Mexican mestizo subjects. Methods: One hundred fifty pSS patients and 180 healthy controls (HCs) were included. Genotypes of PTPN22 SNPs were identified by PCR-RFLP. PTPN22 expression was evaluated through RT–PCR analysis. Serum anti-SSA/Ro and anti-SSB/La levels were measured using an ELISA kit. Results: Allele and genotype frequencies for all SNPs studied were similar in both groups ($p \leq 0.05$). pSS patients showed 17-fold higher expression of PTNP22 than HCs, and mRNA levels correlated with SSDAI score (r2 = 0.499, $$p \leq 0.008$$) and levels of anti-SSA/Ro and anti-SSB/La autoantibodies (r2 = 0.200, $$p \leq 0.03$$ and r2 = 0.175, $$p \leq 0.04$$, respectively). Positive anti-SSA/Ro pSS patients expressed higher PTPN22 mRNA levels ($$p \leq 0.008$$), with high focus scores by histopathology ($$p \leq 0.02$$). Moreover, PTPN22 expression had high diagnostic accuracy in pSS patients, with an AUC = 0.985. Conclusions: Our findings demonstrate that the PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) are not associated with the disease susceptibility in the western Mexican population. Additionally, PTPN22 expression may be helpful as a diagnostic biomarker in pSS.
## 1. Introduction
Primary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by lymphocyte infiltration to lachrymal and salivary glands and impaired secretory activity, leading to the most important manifestations of the disease, keratoconjunctivitis sicca and xerostomia [1]. The etiology of this disease is incompletely understood; however, a key element in the pathogenesis is T and B lymphocyte hyperactivity, leading to autoantibody production mainly against ribonucleoproteins (SSA/Ro and SSB/La) and consequent presence of hypergammaglobulinemia [2,3]. It has been suggested that pSS is a complex and multifactorial disease, with genetic, environmental and hormonal factors involved in the disease pathogenesis. The protein tyrosine phosphatase nonreceptor type 22 (PTPN22) gene encodes the cytoplasmic protein lymphoid tyrosine phosphatase protein (Lyp), a potent downregulator of T cells, by inhibiting signaling through dephosphorylation of several substrates [4]. PTPN22 is involved in calibrating the T cell activation threshold and terminating TCR signaling [5].
Diverse case-control studies have examined the potential contribution of PTPN22 SNPs and their haplotypes to susceptibility to different autoimmune diseases (AIDs); however, results are inconsistent, in part because of ethnic and racial differences [6,7,8,9]. For example, rs2488457 (−1123 C) has been associated with type 1 diabetes mellitus in the Korean population [10]. In the Chinese population, rs2488457 is associated with rheumatoid arthritis (RA) [11], latent autoimmune diabetes in adults [12] and ulcerative colitis (UC) [13], whereas it is reported to be associated with less risk of systemic lupus erythematosus (SLE) in the Mexican population [14]. In addition, Muñoz-Valle et al. found an association between rs2488457 and lower levels of anti-citrullinated antibodies in RA patients [15].
The SNP rs33996649 (+788 G>A) is located in region encoding the catalytic domain of Lyp and represents a change in arginine (R) to glutamine (Q) (R263Q). This amino acid alteration leads to loss of function through reduced phosphatase activity [7]. rs33996649GA has also been related to protection against autoimmune diseases in European and American populations [16,17].
Another functional SNP is rs2476601 (+1858 C>T), involving substitution of arginine for tryptophan at codon 620 (R620 W) in the first proline-rich domain (P1) of Lyp. This variation alters the Lyp/C-Src tyrosine kinase interaction domain and results in a gain of function Lyp (increased phosphatase activity) that inhibits TCR signaling [16]. This polymorphism has been related to SLE in North America [18], RA in Mexico [19], and pSS in Colombia [20]. In the present case-control study, we investigated whether there is an association between PTPN22 polymorphisms, their haplotypes and PTPN22 mRNA expression and susceptibility to pSS in a Mexican population.
## 2.1. Patients and Healthy Controls
One hundred eighty healthy controls and one hundred fifty pSS patients were included in the present study. The pSS patients were classified according to the 2016 American College of Rheumatology (ACR) and European League Against Rheumatism (EURLAR) classification criteria for pSS [21]. The sample size was calculated according to the formula n=[Zα2p^q^+Zβp1q1+p0q0]2(p1−p0)2, and the minimum number of alleles was $$n = 283$$, based on the frequencies for PTN22 +1858C>T gene polymorphism previously published in Latin-American pSS patients [20]. This study was conducted in the Hospital General de Occidente, México, and Instituto de Investigación en Ciencias Biomédicas, Universidad de Guadalajara, México. All participants were born in western Mexico with a minimum of third-generation ancestry and a Spanish-derived last name [22]. We excluded HCs with a family history of autoimmune diseases. At the time of inclusion, the pSS patients were evaluated with Sjogrën’s Syndrome Disease Activity Index (SSDAI) and Sjogrën’s Syndrome Disease Index (SSDDI) [23]. All study subjects signed informed consent. The institutional ethics and research committees approved the study under approval number: $\frac{449}{16.}$
## 2.2. Genotyping of rs2488457 −1123 G>C, rs33996649 +788 G>A and rs2476601 +1858 C>T Polymorphisms
Peripheral blood was collected from pSS patients and HCs. Genomic DNA (gDNA) extraction was performed using Miller’s technique [24]. We used polymerase chain reaction (PCR) to identify rs2488457 (−1123 G>C), rs33996649, (+788 G>A), and rs2476601 (+1858 C>T) genotypes. The primers, enzymes, and digestive products to evaluate the SNP genotypes in our study are provided in Table 1. The forward primer for rs2488457 (−1123 G>C) contains a recognition site for the endonuclease Sac1 (GAGCTxC) with an A>G substitution (underlined) [14,25]. PCR was carried out in a final volume of 10 µL including 1× of 10× supplied buffer enzyme, 4 mM MgCl2, 2.5 mM of each dNTP, 3 mM of each primer, 0.04 units of Taq DNA polymerase (Invitrogen Life Technologies, Carlsbad, CA, USA) and 100 ng/μL of gDNA. The amplification protocol was as follows: initial denaturalization at 95 °C for 3 min, followed by 29 cycles of 94 °C for 30 s, 67 °C for 30 s and 72 °C for 30 s with a final extension of 72 °C for 3 min (Thermal cycler TechNet TC-5000, Cole-Palmer, Beacon Rode, ST, UK). The PCR products were digested with 3 U of SacI (New England Biolabs, Ipswich, MA, USA) at 37 °C for 3 h. The restriction fragments were assessed by $6\%$ polyacrylamide electrophoresis and stained with $2\%$ AgNO3. The products after digestion with SacI are shown in Table 1.
For rs33996649 (+788 G>A), PCR was carried out in a final volume of 10 µL containing 1× of supplied 10× buffer enzyme, 2.5 mM of each dNTP, 3 mM of each primer, 0.2 units of Taq DNA polymerase (DONGCHEN Biotech, Guangdong, China) and 100 ng/μL of gDNA. The amplification protocol was as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of 95 °C for 40 s, 53 °C for 40 s, and 72 °C for 40 s, with a final extension of 72 °C for 5 min (Thermal cycler TechNet TC-5000, Cole-Palmer, Beacon Rode, ST, UK). The PCR product was digested with 3 U of MspI (New England Biolabs, Ipswich MA, USA) at 37 °C for 3 h, and the restriction fragments were observed on a $6\%$ acrylamide gel and stained with $2\%$ AgNO3. Table 1 show digestion products with MspI.
The PCR mixture for rs2476601 (+1858 C>T) was the same as for rs2488457 (−1123 G>C). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 3 min, 33 cycles of denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s and extension at 72°. The products were digested with 3 U of XcmI (New England Biolabs, Ipswich, MA, USA) at 37 °C for 3 h. The restriction fragments were separated by $6\%$ gel polyacrylamide electrophoresis and stained with $2\%$ AgNO3. The products after digestion with XcmI are shown in Table 1.
## 2.3. RNA Extraction and Reverse Transcription
Total cellular RNA was extracted from peripheral blood mononuclear cells (PMBCs) using TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol. Repeated phenol–chloroform extraction was performed for the RNA samples, which were subjected to isolation using the Chomiczyki and Sacchi method [26]. The $\frac{260}{280}$ ratio was used to provide an estimate of purity. Low-quality and degraded RNA samples were excluded. According to the reverse transcriptase protocol (Promega, Madison WI, USA), Oligo-Dt primers and reverse transcriptase (MMLV) were used to synthesize complementary DNA (cDNA) from 1 μg of total RNA. PTPN22 mRNA expression was determined in twenty-eight pSS patients and twenty-eight HCs of different genotypes.
## 2.4. Quantitative PCR (qPCR)
Quantitative real-time polymerase chain reaction (qPCR) was carried out to quantify the expression of the gene of interest. The RT–qPCR protocol followed the guidelines of Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) [27] using a Nano Light Cycler 2.0 (Roche Applied Science, Branford, CT, USA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene to determine relative quantification after it was shown to be stably expressed in the sample [28]. The primers and hydrolysis probes were designed with Roche Universal Probe Library (PTPN22: cat. no. 04689011001, GAPDH: probe cat. no. 05190541001). All samples were run as duplicates. After validation of PCR efficiency for both genes, the data obtained were analyzed. A comparative threshold cycle (Cq) method with a cutoff of 40 cycles was used to determine the PTPN22 mRNA copy number relative to GAPDH, and data are shown based on the 2−ΔΔCq method [29] and 2−ΔCq method [30].
## 2.5. Anti-SSA/Ro and Anti-SSB/La Serum Level Determination
Anti-SSA/Ro and anti-SSB/*La serum* levels were determined from serum samples stored at −80 °C until measurement using a commercially available ELISA kit (cat. no. ORG. 506 and ORG. 508, respectively, ORGENTEC Diagnostika GmbH Carl-Zeiss-Straße 49, 55129 Mainz, Germany) with a sensitivity of 1 U/mL and 0–200 U/mL standard range. A Multiskan GO spectrophotometer (Thermo Fisher Scientific Oy, Ratastie, PO, Finland) was employed to obtain the optical density of all samples. The concentration was calculated based on a standard curve, and the results are reported as U/mL. According to the ORGENTEC ELISA kit protocol, samples with values of >25 U/mL were considered positive.
## 2.6. Statistical Analysis
Concerning the evaluation of PTPN22 gene polymorphisms, Hardy–*Weinberg equilibrium* (HWE) was tested using the χ2 test or Fisher’s exact test. Genotypic and allelic frequencies were compared by a 2 × 2 contingency table, and a χ2 test was performed. The Lewontin normalized coefficient D0 was used for assessing linkage disequilibrium (LD) between pairs or markers. SHEsis software was applied for haplotype analysis [31], and haplotypes with a low frequency (<$1\%$) were not included. Student’s t test, the Mann–Whitney U test, one-way ANOVA, the Kruskal–Wallis test and Dunn’s post hoc test were applied according to the data distribution. SPSS25 (IBM Corporation; Armonk, NY, USA) and GraphPad Prism 8.0 (GraphPad Software, Incorporation; La Jolla, CA, USA) software were used for all statistical analyses. Differences were considered significant at a p value < 0.05 and were corrected with Bonferroni’s method according to the case. Statistical analysis to determine the fold change in PTPN22 mRNA expression between pSS patients and HCs was performed by using the 2−ΔΔCq method, and statistically significant differences were determined through the 2−ΔCq method. Values were obtained using the following formulas: ΔCq = (CqPTPN22 average − CqGAPDH average) and ΔΔCq = (ΔCqpSS − ΔCqHC). Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to assess the performance of PTPN22 mRNA expression level as a diagnostic tool for pSS diagnosis.
## 3.1. Demographic and Clinical Characteristics
One hundred fifty pSS patients were included in this study. The average age was 55 (±10) years, and all patients were female. The disease duration was 2.3 years [interquartile range (IQR) 1–5.5], and the average lymphocytic infiltration obtained from biopsies of the minor saliva gland was 2.3 (±1.7) foci in 4 mm2. Anti-SSA/Ro autoantibodies were positive in $23.3\%$ of the pSS patients and anti-SSA/La autoantibodies in $13\%$. SSDAI and SSDDI means were 3 (±1) and 1 (±1), respectively. The main clinical manifestations and treatments are shown in Table 2.
## 3.2. Genotype Distribution of PTPN22 rs2488457 (−1123 G>C), rs33996649 (+788 G>A), and rs2476601 (+1858 C>T) Polymorphisms
The genotypic and allelic frequencies of the rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) PTPN22 polymorphisms in pSS patients and HCs and their comparison are shown in Table 3. All PTPN22 gene polymorphisms were in Hardy-Weinberg equilibrium. Overall, genotypic and allelic frequencies for rs2488457 (−1123 G>C) in the pSS patients were similar to those in HCs (GG $52\%$, GC $40.7\%$ and CC $7.3\%$ vs. GG $52.2\%$, GC $40\%$ and CC $7.8\%$, respectively), with no significant differences ($p \leq 0.05$). Similarly, for rs33996649 (+788 G>A), there were no statistically significant differences in allele and genotype frequencies between the groups (GG $96.6\%$, GA $2.7\%$ and AA $0.7\%$ vs. GG $98.3\%$ and GA $1.7\%$). Regarding rs2476601 (+1858 C>T), allele and genotype frequencies were similar in pSS patients and HCs (CC $98\%$ CT $1.3\%$ and TT $0.7\%$ vs. CC $98.9\%$, CT $1.1\%$ and TT $0\%$), with no significant differences between genotypic and allelic frequencies in pSS patients compared to HCs and a very low frequency of the T allele.
## 3.3. PTPN22 rs2488457 (−1123 G>C), rs33996649 (+788 G>A), and rs2476601 (+1858 C>T) Haplotypes
rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T) were found to be in medium linkage disequilibrium (LD) (D’ = 0.70). On the other hand, the loci rs33996649 (+788 G>A) did not found in linkage disequilibrium with rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T). The most frequent haplotype in pSS patients and HCs was GGC ($70.7\%$ vs. $71\%$, respectively), which included the three wildtype alleles of the SNPs. CGC frequencies were similar in pSS ($26.3\%$) and HC ($27.73\%$) ($p \leq 0.05$) (Table 3).
## 3.4. PTPN22 mRNA Expression and Clinical Association
PTPN22 expression was determined in 28 pSS patients and 28 HCs. The pSS patients showed 17.9-fold higher PTPN22 gene expression than the HCs (Figure 1a) ($$p \leq 0.001$$, Figure 1b). When comparing PTPN22 gene expression according to rs2488457 (−1123 G>C) genotype in the pSS group, carriers of the GC genotype showed slightly higher expression (0.51-fold more) than GG carriers; however, no significant difference was found ($p \leq 0.05$; see Figure 1c). In addition, patients with active pSS expressed 1.94-fold higher levels of PTPN22 than patients with inactive pSS (Figure 1d). Quantitative expression of PTPN22 was higher in pSS patients with active disease ($p \leq 0.05$, Figure 1e) and in those positive for anti-SSA/Ro antibodies ($$p \leq 0.006$$, Figure 1f), and a positive correlation with SSDAI was also observed (r2 = 0.499, $$p \leq 0.008$$, Figure 1g). According to damage status and SSDDI score, PTPN22 expression was similar in pSS patients (Figure 1h) but higher than that in HCs (Figure 1i, $p \leq 0.001$), with no statistical correlation (r2 = −0.096, $p \leq 0.05$, Figure 1g).
Regarding clinical manifestations and autoantibody profiles, SSDAI score had a positive correlation with anti-SSA/Ro (r2 = 0.200, $$p \leq 0.03$$, Figure 2a) and anti-SSB/La (r2 = 0.175, $$p \leq 0.046$$, Figure 2b) serum levels. Additionally, a significantly higher focus score for MSG biopsies and ANA titers was found in anti-SSA/Ro-positive patients ($p \leq 0.05$, Figure 2c and Figure 2d). Patients with high SSDAI hematological domain scores showed 2.58-fold higher expression than patients with quiescent disease (Figure 2e). Furthermore, PTPN22 expression displayed an AUC = 0.98 for accurate diagnosis of pSS (Figure 2f).
## 4. Discussion
pSS is a systemic autoimmune disorder characterized by focal lymphocytic infiltration into the exocrine glands, causing dry eyes and dry mouth [1]. It has been suggested that pSS etiology is complex; however, TCR dysregulation plays an important role in the pathogenesis of autoimmune diseases [32]. Lyp is a tyrosine phosphatase that regulates T cells through inhibitory signaling by dephosphorylating several substrates, including the Src family kinases Lck and Fyn, as well as ZAP-70, during TCR lymphocyte activation [4,33]. The Lyp protein is encoded by the PTPN22 gene on chromosome 1. rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) are functional polymorphisms of the PTPN22 gene associated with multiple inflammatory conditions, including autoimmune disorders such as pSS [7,20,33].
Our study analyzed the SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) in the PTPN22 gene and susceptibility to pSS development in a Mexican mestizo population. The minor C allele of rs2488457 was detected in $27.78\%$ of HCs, which is a lower proportion than the frequencies reported in the Asian population ($33\%$ to $41\%$). Nevertheless, we found a similar frequency of the rs2488457 GC genotype ($40\%$ vs. 37–$46.1\%$) and a lower percentage of the rs2488457 CC genotype (7.8 vs. 13.7–$18.1\%$) [10,11,12,13]. The distribution of the major rs33996649 G allele and the rs33996649 GG genotype are similar in the Mexican population [34], and the absence of the rs33996649 AA genotype is consistent with reports for European and Argentine populations [16,17,35,36]. Additionally, the minor allele frequency of rs2476601 T in the western Mexico population ($0.6\%$) is similar to that reported in Amerindian and African populations (<$1\%$) [7] but lower than that in Northern European populations ($15\%$) [9]. The rs2476601 (+1858CT) genotype frequency in our study was $2.2\%$, lower than in European and American populations [18]. However, the rs2476601 TT genotype was absent in the Occidental Mexican population, which is consistent with previous reports for the same population [14,15,19].
Previous studies have analyzed the distribution of all these SNPs in healthy unrelated Mexican Mestizo subjects, showing genotypic and allelic frequencies similar to those reported in our study [14,15,19,35]. *In* general, ancestry studies in Mexican mestizos from the west region (State of Jalisco), based on maternal ancestry (mtDNA haplogroups) underscore the predominance of the Native American contribution ($87\%$), followed by European ($9\%$), African ($3\%$) and Eurasian ($1\%$) contributions [37]. However, when the Mexican admixture are analyzed based on the paternal contribution (Y-STRs), the Native American contribution decrease ($28\%$), followed by African ($5\%$), while the European ($67\%$) raised [38].
rs2488457 (−1123 G>C), rs33996649 (+788 GA) and rs2476601 (+1858 C>T) were not found to be associated with an increased risk of developing pSS in the Mexican mestizo population from western Mexico. In contrast, rs2488457 (−1123 G>C) has been associated with UC, RA, and autoimmune diabetes mellitus in Asians [11,13]. The genotypic and allelic frequencies observed in west Mexican pSS patients and HCs for rs2488457 (−1123 G>C) were similar to those reported for European population and the total allelic frequencies reported in the Phase 3 of the 1000 Genomes Project [39]. Additionally, the rs2476601 T allele is associated with a risk for developing pSS in the Colombian population [20], and with RA in west [19] and central Mexican AR patients [40]. rs33996649 (+788 GA) has been reported to have a protective role against SLE and RA in European populations [16,36].
This is the first study to investigate three SNPs, rs2488457 (−1123 G>C), rs33996649 (+788 GA) and rs2476601 (+1858 C>T), in the PTNP22 gene. The haplotype analysis showed a medium LD between rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T) but not LD was found with the rs33996649 (+788 GA), and the haplotype frequencies were similar in both, pSS and HCs. Different studies evaluating PTPN22 haplotypes with polymorphic alleles have described an increased risk of developing RA in Norway and western Mexican populations [19,41].
In addition, PTPN22 gene polymorphisms have been associated with higher gene expression in RA and UC [13,35]. In this study, the pSS patients showed 17-fold higher mRNA expression than HCs. In another study by our group, patients with SLE showed similar PTPN22 mRNA expression levels as controls [14]. *In* general, polymorphisms might explain higher gene expression. Lyp1 is mainly present in the cytoplasm of active T lymphocytes, whereas Lyp2 is found in the nucleus, perinuclear membrane, and cytoplasm of inactive peripheral T lymphocytes [42]. The third isoform reported, named PTPN22.6, lacks the catalytic site and is reported to be predominant in RA patient carriers of the rs2476601 (+1858 C>T) R620W functional variant. PTPN22.6 leads to higher nuclear factor of activated T cells (NFAT) expression and elevated IL-2 levels, with uncontrolled autoreactive T cell clonal expansion, by exerting a dominant negative effect over Lyp 1. Additionally, expression of PTPN22.6 correlates with RA activity [43]. Similar to Chang et al., we found an association between PTPN22 mRNA expression and clinimetric indices and autoantibody profiles in RA patients, which is the most important finding of our study.
T cell receptor dysregulation is a key factor in glandular tissue damage: it is associated with a higher concentration of inflammatory cytokines [2] and promotes B cell activation, class switching, the T cell-dependent autoantibody response and germinal center (GC) expansion [44]. GC expansion has also been associated with higher production of pSS autoantibodies, such as anti-SSA/Ro, anti-SSB/La, antinuclear antibodies, and rheumatoid factor. On the other hand, murine model studies have demonstrated that PTPN22 loss of function in myeloid cells results in an augmented inflammatory effector phase of autoimmune disease and GC generation by influencing the number and activity of Th follicular cells [44,45]. The presence of anti-SSA/Ro and anti-SSB/La correlates with severe lymphocytic infiltration of the salivary glands, a higher prevalence of extraglandular manifestations and recurrent swelling of the parotid glands [46]. In our patients with pSS, we observed a clinical association between pSS activity and damage indices, autoantibodies, and MSG infiltration.
Anti-SSA/Ro and histopathological MSG focus scores are the only two diagnostic tools used to classify pSS patients. Therefore, we evaluated PTPN22 gene expression as a biomarker. The area under the curve of PTNP22 expression was 0.985 (the cutoff suggested was >60 relative expression units, with $100\%$ sensitivity, $91.67\%$ specificity, and likelihood ratio 12; data not shown), demonstrating high diagnostic performance for pSS, which is similar to the accuracy of anti-SSA/Ro autoantibody diagnosis [47]. In populations such as ours, with a low frequency of anti-SSA/Ro ($25\%$) antibody positivity, PTPN22 expression may be helpful as a molecular biomarker for pSS diagnosis.
This study has important limitations as small sample size, selective recruiting of the western Mexican population, lack of inclusion of patients with the homozygous rs2488457 (−1123 CC) genotype for analysis of PTPN22 mRNA expression, lack of inclusion of control disease for comparative analysis of PTPN22 mRNA, as well as heterogeneity in the treatment of pSS, which may reflect differences in PTPN22 mRNA expression. Moreover, the PTPN22.6 isoform was not evaluated.
## 5. Conclusions
In summary, the rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) polymorphisms of the PTNP22 gene are not associated with the risk susceptibility of pSS in the Mexican population. We propose that PTPN22 expression could be used as a molecular biomarker in pSS, as PTNP22 expression is associated with autoantibody presence, disease activity index, and extraglandular manifestations. However, further studies are required to analyze interacting epigenetic factors, as well as the relationship between Lyp and the local environment of the germinal centers on exocrine glands.
## References
1. Brito-Zerón P., Baldini C., Bootsma H., Bowman S.J., Jonsson R., Mariette X., Sivils K., Theander E., Tzioufas A., Ramos-Casals M.. **Sjögren syndrome**. *Nat. Rev. Dis. Prim.* (2016.0) **2** 16047. DOI: 10.1038/nrdp.2016.47
2. Agmon-Levin N., Lian Z., Shoenfeld Y.. **Explosion of autoimmune diseases and the mosaic of old and novel factors**. *Cell. Mol. Immunol.* (2011.0) **8** 189-192. DOI: 10.1038/cmi.2010.70
3. Mitsias D.I., Kapsogeorgou E.K., Moutsopoulos H.M.. **Sjögren’s syndrome: Why autoimmune epithelitis?**. *Oral Dis.* (2006.0) **12** 523-532. DOI: 10.1111/j.1601-0825.2006.01292.x
4. Bottini N., Musumeci L., Alonso A., Rahmouni S., Nika K., Rostamkhani M., MacMurray J., Meloni G.F., Lucarelli P., Pellecchia M.. **A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes**. *Nat. Genet.* (2004.0) **36** 337-338. DOI: 10.1038/ng1323
5. Singh K., Deshpande P., Pryshchep S., Colmegna I., Liarski V., Weyand C.M., Goronzy J.J.. **ERK-Dependent T Cell Receptor Threshold Calibration in Rheumatoid Arthritis**. *J. Immunol.* (2009.0) **183** 8258-8267. DOI: 10.4049/jimmunol.0901784
6. Lee H.-S., Korman B., Le J.M., Kastner D.L., Remmers E.F., Gregersen P.K., Bae S.-C.. **Genetic risk factors for rheumatoid arthritis differ in caucasian and Korean populations**. *Arthritis Rheum.* (2009.0) **60** 364-371. DOI: 10.1002/art.24245
7. Stanford S.M., Bottini N.. **PTPN22: The archetypal non-HLA autoimmunity gene**. *Nat. Rev. Rheumatol.* (2014.0) **10** 602-611. DOI: 10.1038/nrrheum.2014.109
8. Chung S.A., Criswell L.A.. *Autoimmunity* (2007.0) **40** 582-590. DOI: 10.1080/08916930701510848
9. Burn G.L., Svensson L., Sanchez-Blanco C., Saini M., Cope A.P.. **Why is**. *FEBS Lett.* (2011.0) **585** 3689-3698. DOI: 10.1016/j.febslet.2011.04.032
10. Kawasaki E., Awata T., Ikegami H., Kobayashi T., Maruyama T., Nakanishi K., Shimada A., Uga M., Kurihara S., Kawabata Y.. **Systematic search for single nucleotide polymorphisms in a lymphoid tyrosine phosphatase gene (PTPN22): Association between a promoter polymorphism and type 1 diabetes in Asian populations**. *Am. J. Med. Genet. Part A* (2006.0) **140** 586-593. DOI: 10.1002/ajmg.a.31124
11. Huang J.-J., Qiu Y.-R., Li H.-X., Sun D.-H., Yang J., Yang C.-L.. **A PTPN22 promoter polymorphism −1123G>C is associated with RA pathogenesis in Chinese**. *Rheumatol. Int.* (2012.0) **32** 767-771. DOI: 10.1007/s00296-010-1705-x
12. Liu F., Liu J., Zheng T.-S., Li Q., Wang C., Pan X.-P., Lu H., Zhao Y.-W.. **The −1123G>C Variant of PTPN22 Gene Promoter is Associated with Latent Autoimmune Diabetes in Adult Chinese Hans**. *Cell Biochem. Biophys.* (2012.0) **62** 273-279. DOI: 10.1007/s12013-011-9291-4
13. Chen Z., Zhang H., Xia B., Wang P., Jiang T., Song M., Wu J.. **Association of PTPN22 gene (rs2488457) polymorphism with ulcerative colitis and high levels of PTPN22 mRNA in ulcerative colitis**. *Int. J. Color. Dis.* (2013.0) **28** 1351-1358. DOI: 10.1007/s00384-013-1671-3
14. Machado-Contreras J.R., Muñoz-Valle J.F., Cruz A., Salazar-Camarena D.C., Marín-Rosales M., Palafox-Sánchez C.A.. **Distribution of PTPN22 polymorphisms in SLE from western Mexico: Correlation with mRNA expression and disease activity**. *Clin. Exp. Med.* (2016.0) **16** 399-406. DOI: 10.1007/s10238-015-0359-0
15. Muñoz-Valle J.F., Padilla-Gutiérrez J.R., Hernández-Bello J., Ruiz-Noa Y., Valle Y., Palafox-Sánchez C.A., Parra-Rojas I., Gutiérrez-Ureña S.R., Rangel-Villalobos H.. **Polimorfismo −1123G>C en el gen PTPN22 y anticuerpos antipéptido citrulinado cíclico en la artritis reumatoide**. *Med. Clin.* (2017.0) **149** 95-100. DOI: 10.1016/j.medcli.2017.01.025
16. Orrù V., Tsai S.J., Rueda B., Fiorillo E., Stanford S.M., Dasgupta J., Hartiala J., Zhao L., Ortego-Centeno N., D’Alfonso S.. **A loss-of-function variant of PTPN22 is associated with reduced risk of systemic lupus erythematosus**. *Hum. Mol. Genet.* (2009.0) **18** 569-579. DOI: 10.1093/hmg/ddn363
17. López-Cano D.J., Cadena-Sandoval D., Beltrán-Ramírez O., Barbosa-Cobos R.E., Sánchez-Muñoz F., Amezcua-Guerra L.M., Juárez-Vicuña Y., Aguilera-Cartas M.C., Moreno J., Bautista-Olvera J.. **The PTPN22 R263Q polymorphism confers protection against systemic lupus erythematosus and rheumatoid arthritis, while PTPN22 R620W confers susceptibility to Graves’ disease in a Mexican population**. *Inflamm. Res.* (2017.0) **66** 775-781. DOI: 10.1007/s00011-017-1056-0
18. Kyogoku C., Langefeld C.D., Ortmann W.A., Lee A., Selby S., Carlton V.E.H., Chang M., Ramos P., Baechler E.C., Batliwalla F.M.. **Genetic Association of the R620W Polymorphism of Protein Tyrosine Phosphatase PTPN22 with Human SLE**. *Am. J. Hum. Genet.* (2004.0) **75** 504-507. DOI: 10.1086/423790
19. Torres-Carrillo N.M., Ruiz-Noa Y., Martínez-Bonilla G.E., Leyva-Torres S.D., Torres-Carrillo N., Palafox-Sánchez C.A., Navarro-Hernández R.E., Rangel-Villalobos H., Oregón-Romero E., Muñoz-Valle J.F.. **The +1858C/T PTPN22 gene polymorphism confers genetic susceptibility to rheumatoid arthritis in Mexican population from the Western Mexico**. *Immunol. Lett.* (2012.0) **147** 41-46. DOI: 10.1016/j.imlet.2012.05.007
20. Gomez L.M., Anaya J.-M., Gonzalez C.I., Pineda-Tamayo R., Otero W., Arango A., Martín J.. **PTPN22 C1858T polymorphism in Colombian patients with autoimmune diseases**. *Genes Immun.* (2005.0) **6** 628-631. DOI: 10.1038/sj.gene.6364261
21. Shiboski C.H., Shiboski S.C., Seror R., Criswell L.A., Labetoulle M., Lietman T.M., Rasmussen A., Scofield H., Vitali C., Bowman S.J.. **2016 American College of Rheumatology/European League Against Rheumatism Classification Criteria for Primary Sjögren’s Syndrome: A Consensus and Data-Driven Methodology Involving Three International Patient Cohorts**. *Arthritis Rheum.* (2017.0) **76** 9-16. DOI: 10.1136/annrheumdis-2016-210571
22. Gorodezky C., Alaez C., Vázquez-García M.N., de la Rosa G., Infante E., Balladares S., Toribio R., Pérez-Luque E., Muñoz L.. **The Genetic structure of Mexican Mestizos of different locations: Tracking back their origins through MHC genes, blood group systems, and microsatellites**. *Hum. Immunol.* (2001.0) **62** 979-991. DOI: 10.1016/S0198-8859(01)00296-8
23. Vitali C., Palombi G., Baldini C., Benucci M., Bombardieri S., Covelli M., Del Papa N., De Vita S., Epis O., Franceschini F.. **Sjögren’s syndrome disease damage index and disease activity index: Scoring systems for the assessment of disease damage and disease activity in Sjögren’s syndrome, derived from an analysis of a cohort of Italian patients**. *Arthritis Rheum.* (2007.0) **56** 2223-2231. DOI: 10.1002/art.22658
24. Miller S.A., Dykes D.D., Polesky H.F.. **A simple salting out procedure for extracting DNA from human nucleated cells**. *Nucleic Acids Res.* (1988.0) **16** 55404. DOI: 10.1093/nar/16.3.1215
25. Padilla-Gutiérrez J.R., Valle Y., Mercado M.V.-D., Maldonado M., Muñoz-Valle J.F.. **A new PCR-RFLP assay for –1123 G>C polymorphism in the PTPN22 gene: Allele and genotype frequencies in a western Mexican population**. *Clin. Chem. Lab. Med.* (2009.0) **47** 491-493. DOI: 10.1515/CCLM.2009.103
26. Chomczynski P., Sacchi N.. **Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction**. *Anal. Biochem.* (1987.0) **162** 156-159. DOI: 10.1016/0003-2697(87)90021-2
27. Bustin S.A., Benes V., Garson J.A., Hellemans J., Huggett J., Kubista M., Mueller R., Nolan T., Pfaffl M.W., Shipley G.L.. **The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments**. *Clin. Chem.* (2009.0) **55** 611-622. DOI: 10.1373/clinchem.2008.112797
28. Huggett J.F., Dheda K., Bustin S., Zumla A.. **Real-time RT-PCR normalisation; strategies and considerations**. *Genes Immun.* (2005.0) **6** 279-284. DOI: 10.1038/sj.gene.6364190
29. Livak K.J., Schmittgen T.D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. *Methods* (2001.0) **25** 402-408. DOI: 10.1006/meth.2001.1262
30. Schmittgen T.D., Livak K.J.. **Analyzing real-time PCR data by the comparative C(T) method**. *Nat. Protoc.* (2008.0) **3** 1101-1108. DOI: 10.1038/nprot.2008.73
31. Yong Y., He L.. **SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci**. *Cell Res.* (2005.0) **15** 97-98. DOI: 10.1038/sj.cr.7290272
32. Notarangelo L.D.. **Immunodeficiency and immune dysregulation associated with proximal defects of T cell receptor signaling**. *Curr. Opin. Immunol.* (2014.0) **31** 97-101. DOI: 10.1016/j.coi.2014.10.003
33. Mustelin T., Bottini N., Stanford S.M.. **The Contribution of**. *Arthritis Rheumatol.* (2019.0) **71** 486-495. DOI: 10.1002/art.40790
34. Diaz-Gallo L.M., Espino-Paisán L., Fransen K., Gómez-García M., Van Sommeren S., Cardeña C., Rodrigo L., Mendoza J.L., Taxonera C., Martin J.. **Differential association of two PTPN22 coding variants with Crohn’s disease and ulcerative colitis**. *J. Transl. Med.* (2010.0) **8** 1-2. DOI: 10.1186/1479-5876-8-S1-P2
35. Ramírez-Pérez S., Sánchez-Zuno G.A., Chavarría-Buenrostro L.E., Montoya-Buelna M., Reyes-Pérez I.V., Ramírez-Dueñas M.G., Palafox-Sánchez C.A., Martínez-Bonilla G.E., Muñoz-Valle J.F.. **PTPN22 +788 G>A (R263Q) Polymorphism is Associated with mRNA Expression but it is not a Susceptibility Marker for Rheumatoid Arthritis Patients from Western Mexico**. *Biochem. Genet.* (2019.0) **57** 455-465. DOI: 10.1007/s10528-019-09902-8
36. Rodríguez-Rodríguez L., Taib W.R.W., Topless R., Steer S., González-Escribano M.F., Balsa A., Pascual-Salcedo D., González-Gay M.A., Raya E., Fernandez-Gutierrez B.. **The PTPN22 R263Q polymorphism is a risk factor for rheumatoid arthritis in Caucasian case-control samples**. *Arthritis Rheum.* (2011.0) **63** 365-372. DOI: 10.1002/art.30145
37. Martínez-Cortés G., Salazar-Flores J., Haro-Guerrero J., Rubi-Castellanos R., Velarde-Félix J.S., Muñoz-Valle J.F., López-Casamichana M., Carrillo-Tapia E., Canseco-Avila L.M., Bravi C.M.. **Maternal admixture and population structure in Mexican-Mestizos based on mtDNA haplogroups**. *Am. J. Phys. Anthropol.* (2013.0) **151** 526-537. DOI: 10.1002/ajpa.22293
38. Rangel-Villalobos H., Salazar-Flores J., Dondiego R., Anaya-Palafox M., Nuño-Arana I., Canseco-Ávila L., Flores-Flores G., Romero-Rentería O., Morales-Vallejo M., Muñoz-Valle J.. **“South to North increasing gradient of paternal European ancestry throughout the Mexican territory: Evidence of Y-linked short tandem repeats”**. *Forensic Sci. Int. Genet. Suppl. Ser.* (2009.0) **2** 448-450. DOI: 10.1016/j.fsigss.2009.08.003
39. **rs114764573 (SNP)—Population Genetics—Ensembl Genome Browser 107**
40. Rincón J.F.M., Cano D.L., Morales S.J., Jiménez M.L.R., Cobos R.E.B., Bello J.R.. **The functional PTPN22 C1858T polymorphism confers risk for rheumatoid arthritis in patients from Central Mexico**. *Clin. Rheumatol.* (2016.0) **35** 1457-1462. DOI: 10.1007/s10067-016-3223-z
41. Viken M.K., Olsson M., Flåm S.T., Førre Ø., Kvien T.K., Thorsby E., Lie B.A.. **The PTPN22 promoter polymorphism –1123G>C association cannot be distinguished from the 1858C>T association in a Norwegian rheumatoid arthritis material**. *Tissue Antigens* (2007.0) **70** 190-197. DOI: 10.1111/j.1399-0039.2007.00871.x
42. Cohen S., Dadi H., Shaoul E., Sharfe N., Roifman C.M.. **Cloning and Characterization of a Lymphoid-Specific, Inducible Human Protein Tyrosine Phosphatase, Lyp**. *Blood* (1999.0) **93** 2013-2024. DOI: 10.1182/blood.V93.6.2013.406k25_2013_2024
43. Chang H.-H., Tai T.-S., Lu B., Iannaccone C., Cernadas M., Weinblatt M., Shadick N., Miaw S.-C., Ho I.-C.. **PTPN22.6, a Dominant Negative Isoform of PTPN22 and Potential Biomarker of Rheumatoid Arthritis**. *PLoS ONE* (2012.0) **7**. DOI: 10.1371/journal.pone.0033067
44. Maine C.J., Marquardt K., Cheung J., Sherman L.A.. **PTPN22 Controls the Germinal Center by Influencing the Numbers and Activity of T Follicular Helper Cells**. *J. Immunol.* (2014.0) **192** 1415-1424. DOI: 10.4049/jimmunol.1302418
45. Katakura K., Lee J., Rachmilewitz D., Li G., Eckmann L., Raz E.. **Toll-like receptor 9–induced type I IFN protects mice from experimental colitis**. *J. Clin. Investig.* (2005.0) **115** 695-702. DOI: 10.1172/JCI22996
46. Baldini C., Ferro F., Elefante E., Bombardieri S.. **Biomarkers for Sjögren’s syndrome**. *Biomark. Med.* (2018.0) **12** 275-286. DOI: 10.2217/bmm-2017-0297
47. Shiboski S.C., Shiboski C.H., Criswell L.A., Baer A.N., Challacombe S., Lanfranchi H., Schiodt M., Umehara H., Vivino F., Zhao Y.. **American College of Rheumatology classification criteria for Sjögren’s syndrome: A data-driven, expert consensus approach in the Sjögren’s International Collaborative Clinical Alliance Cohort**. *Arthritis Care Res.* (2012.0) **64** 475-487. DOI: 10.1002/acr.21591
|
---
title: 'Digital Health Literacy and Person-Centred Care: Co-Creation of a Massive
Open Online Course for Women with Breast Cancer'
authors:
- Yolanda Álvarez-Pérez
- Andrea Duarte-Díaz
- Ana Toledo-Chávarri
- Analía Abt-Sacks
- Vanesa Ramos-García
- Alezandra Torres-Castaño
- Amado Rivero-Santana
- Lilisbeth Perestelo-Pérez
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001393
doi: 10.3390/ijerph20053922
license: CC BY 4.0
---
# Digital Health Literacy and Person-Centred Care: Co-Creation of a Massive Open Online Course for Women with Breast Cancer
## Abstract
The diagnosis of breast cancer (BC) can make the affected person vulnerable to suffering the possible consequences of the use of low-quality health information. Massive open online courses (MOOCs) may be a useful and efficient resource to improve digital health literacy and person-centred care in this population. The aim of this study is to co-create a MOOC for women with BC, using a modified design approach based on patients’ experience. Co-creation was divided into three sequential phases: exploratory, development and evaluation. Seventeen women in any stage of BC and two healthcare professionals participated. In the exploratory phase, a patient journey map was carried out and empowerment needs related to emotional management strategies and self-care guidelines were identified, as well as information needs related to understanding medical terminology. In the development phase, participants designed the structure and contents of the MOOC through a Moodle platform. A MOOC with five units was developed. In the evaluation phase, participants strongly agreed that their participation was useful for the MOOC’s development and participating in the co-creation process made the content more relevant to them (experience in the co-creation); most of the participants positively evaluated the content or interface of the MOOC (acceptability pilot). Educational interventions designed by women with BC is a viable strategy to generate higher-quality, useful resources for this population.
## 1. Introduction
Breast cancer (BC) represents one of the most frequently diagnosed cancers in women worldwide [1]. According to the most recent data from the European Cancer Information System (ECIS), there were approximately 355,460 new cases of BC diagnosed across Europe in 2020, with 34,088 of those cases occurring in Spain [2]. However, thanks to early diagnosis and therapeutic advances, BC survival has increased in recent years [3], with a survival rate of around $85\%$ [4]. Increasing prevention and treatment for BC have lowered mortality, but the diagnosis and treatment continue to have a significant impact in many areas of patients’ lives (physical, emotional, cognitive and social) [5]. The diagnosis of BC, which in most cases necessitates an effort to adjust and adapt to the new situation [5,6], is typically perceived as a traumatic event with a significant impact on the health-related quality of life of the women who suffer from it, making them more vulnerable to the potential consequences of using biased or low-quality health information.
Person-centred care (PCC) is defined as the provision of care that considers a patient’s clinical needs, life circumstances, and personal values and preferences [7]. A central component of PCC is to ensure quality communication between patients and healthcare professionals, with the aim of fostering the process of shared decision making (SDM) [8]. SDM-based interventions, such as patient decision aids (PtDAs), have been shown to improve patients’ knowledge about available treatments and their benefits/risks, decisional conflict and other decisional process variables [9]. There is a need to develop interventions to increase knowledge about PCC and digital health literacy (DHL) [10,11], particularly in chronic pathologies such as BC, where the impact of their diagnosis or treatment may increase the number of queries on the Internet and directly influence the understanding of health information [12]. Health literacy (HL) integrates the skills and motivation to find, understand, evaluate and use health information. As a result, HL facilitates informed decision making and improves the ability to manage and address health disparities, giving patients more autonomy and empowerment to take responsibility for their own health, as well as the health of their families and communities. In turn, low HL impacts health outcomes and health-related costs, leading to inefficient healthcare utilization and delivery [13]. DHL is an extension of HL that employs the same operational definition but in the context of information and communication technology resources. It involves both the provision of information and the degree to which information is understood. When these skills are lacking, technology solutions have the potential to either promote or hinder HL [14]. Due to the complexity of health information, it is recommended that DHL interventions be based on a design of co-creation of resources, websites and health tools through collaborative work with patients, allowing them to improve the medical care they receive [15,16,17].
Massive open online courses (MOOCs) are designed to engage a large number of participants learning remotely, offering the general population, clinical subpopulations or health professionals good quality knowledge on health issues through interactive and flexible technological resources, with little or no prior learning required [18]. To date, most MOOCs have been developed for the education of medical students and health professionals [19,20], but they have also been directed at the general population or clinical subpopulations, showing positive effects in several areas such as healthy nutrition habits [21], self-management of diabetes [22] or learning risk factors for dementia [23].
As has been observed in some projects with other populations, the development of educational interventions with a MOOC based on a co-creation design, which combines several resources in different formats and adapts to different educational, cultural levels and needs of the users, could be a strategy to face the HL, self-care and empowerment challenges for women with BC. One example is the IC-Health European project (https://cordis.europa.eu/project/id/727474/es accessed on 20 December 2022), whose results have shown good acceptance of co-created teaching resources aimed at improving the DHL of people with chronic diseases and the general population [24,25,26].
In recent years, the framework of participatory action research has been used for the development of eHealth. It is an approach that involves collaboration to develop a process through the construction of knowledge and social change in a community following a cyclical approach and involving stakeholders as co-investigators in the process [27,28]. As occurs in other participatory processes, the co-design of health interventions contributes to improving the services offered, to the extent that they are adjusted to the needs and priorities of its participants while incorporating their own skills [29,30,31].
*In* general, digital interventions, such as MOOCs, have the potential to improve the quality of life and outcomes for women with BC by providing access to information from anywhere at any time, thereby increasing accessibility and flexibility, as well as support to complement traditional medical treatments. Therefore, the aim of this study is to co-create a MOOC of PCC and DHL for and with women with BC.
## 2.1. Design
The MOOC was co-created using a modified experience-based design approach [32]. The co-creation process was divided into three sequential phases: (a) exploratory phase, (b) development phase and (c) evaluation phase (see Section 2.3).
## 2.2. Participants and Recruitment
Adult women (≥18 years) in any of their cancer stages and BC survivors (regardless of DHL level and knowledge about PCC), their families/carers and any healthcare professionals involved in the management of BC (oncologists, gynaecologists, nurses, psycho-oncologists, etc.) were invited to participate voluntarily in the MOOC co-creation process. A theoretical sample optimized the maximum variability of sociodemographic and clinical profiles (age, educational level, time since diagnosis and active treatment) of women with BC. The recruitment was carried out via snowball sampling [33] through healthcare professionals and expert patients (BC survivors) between May and June 2020. Participants signed an informed consent declaration.
## 2.3. Procedure
The co-creation process was carried out in three online sessions of 120 min each (via the Zoom platform due to the COVID-19 pandemic and delivered by members of the research team) between June 2020 and March 2021 and was supported by a Moodle platform.
The first session (exploratory phase) was held in June 2020 and consisted of (i) a brief presentation of the participants; (ii) identifying the different diagnosis, treatment and long-term follow-up paths for BC represented through a patient journey map (PJM)—a scheme that aims to reflect the care pathway followed by a person [5,32]—based on their experiences, emotions, feelings and thoughts; (iii) exploring their empowerment and information needs in each phase of the disease; (iv) and exploring patients’ information needs and experiences on patient empowerment and SDM. Health professionals did not participate in the development of the PJM; they offered advice and their experiences on the most frequent concerns found in clinical practice with these patients, according to the phase of the disease.
In the second session (development phase), held in July 2020, the participants reviewed the PJM and designed the structure and proposed the contents of the MOOC (self-care, myths related to BC, strategies to improve DHL, etc.) based on the empowerment and information needs identified in the first session and their previous experiences managing BC information online. At the end of this session, participants were encouraged to continue the process of co-creation online between July and December 2020 through a Moodle platform where the participants were registered and which they accessed with an individual username and password (assessment phase). The research team developed and shared some content proposals weekly for the different units of the MOOC, and participants were asked to provide feedback and/or new content proposals (see Section 3.3). Initially, the content of the units was presented in infographic format (see Supplemental Materials Figure S1) and was mainly related to PCC, self-care and DHL applied to BC. Once all the suggestions for improvement provided by the participants on the content were compiled, a graphic designer developed videos and edited the infographics to provide them with interactivity and visually improve their appearance. Updated contents were shared again with the participants in March 2021. Through questionnaires on the Moodle platform (see Section 2.4.2), they could give feedback on the definitive contents of the MOOC (see Section 2.4.1).
A third session (evaluation phase) was held in March 2021 to offer final feedback about the content and interface of the MOOC (acceptability pilot) and to evaluate the experience in the co-creation of the MOOC by means of specific questionnaires (see Section 2.4). Four gift cards were raffled off as a token of appreciation during this last meeting.
## 2.4.1. Experience in the Co-Creation Process
A 13-item questionnaire was specifically developed to explore patients’ and healthcare professionals’ experience in the co-creation process. The first 6 items were measured using a 5-point Likert scale (from “strongly disagree” to “strongly agree”), addressing satisfaction with communication, objective adequacy, usefulness of patient involvement in the co-creation process, importance of co-creation to design relevant content for patients, self-perception of increased knowledge and feeling of being part of the team project. The following 4 items were also assessed on a 5-point Likert scale (from “insufficient” to “excellent”) and were related to participants’ opinions on the quality and clarity of the co-creation sessions, the methodology employed, the interactions between participants and the researchers’ implication. The last 3 items were open-ended questions about what participants liked the most and the least about the MOOC co-creation process, which aspects they found most useful and which aspects could be improved in the co-creation process (see Section 3.4).
## 2.4.2. Acceptability Pilot of the MOOC
The MOOC’s acceptability was evaluated using a specific scale created in the context of the project following the technology acceptance model’s (TAM) methodology [34] and based on previous related studies [35,36]. This scale assessed factors such as ease of navigation, clarity of objectives and language, appropriateness of learning activities and quizzes, and other characteristics of the MOOC. The acceptability questionnaire, answered by both patients and healthcare professionals, included 18 items: the first 15 were rated on a 5-point Likert scale, and the last 3 items were open-ended questions about strengths and weaknesses, improvement suggestions and the main points learned throughout the MOOC (see Section 3.5).
## 2.5. Analysis
The PJM and MOOC content were progressively developed in conjunction with participants. A draft was created with the information obtained from the online co-creation sessions. The different sections of the PJM summarize the experiences of participants with BC or survivors. The research group reviewed the contributions of the participants and proposed a draft version based on a PCC framework. Subsequently, this version of PJM and MOOC content was reviewed by all participants through an iterative process until consensus was reached.
For the experience in the co-creation process and the acceptability pilot of the MOOC measures, means and standard deviations (SD) were calculated for all items assessed, and we also analysed the response distribution for each item.
## 3. Results
Twenty-eight participants from Tenerife and Gran Canaria (Canary Islands, Spain) were contacted between May and June 2020, of whom 19 participated in the co-creation process: 17 patients (Table 1) and two healthcare professionals (nurses from gynaecology and breast pathology units; mean age 40 (1.41) years and with more than 10 years of professional experience).
## 3.1. Patient Journey Map
Points of contact, experience with healthcare received, emotions, feelings and thoughts, diagnostic and therapeutic treatments, and perception about own participation in shared decision making for the three stages of the trajectory of care of BC (early detection and diagnosis, treatment and long-term follow-up) were collected on the co-designed PJM (Figure 1).
## 3.1.1. Early Detection and Diagnosis Stage
Most of the participants received their diagnosis during routine controls (specialized care) or as a result of the presence of symptoms (primary care), and the main emotions that emerged during this time were shock, anxiety, uncertainty and worry about the future. The main diagnostic techniques that the participants underwent were physical examination (palpation), imaging tests (mammography and ultrasound) and biopsy. The experiences collected about the healthcare received in this stage were related to the perception of professionalism, friendliness, a predisposition to resolve doubts and the transmission of calm and encouragement from the healthcare professionals who attended to them. However, the participants expressed that there were other drawbacks in the medical care received at this time related to the challenges of early detection and the complexity of some administrative processes (e.g., medical appointments). Some participants expressed that they would have liked more advice from medical staff. Other participants expressed that they felt involved in the decision-making process in this phase, and this helped them accept the disease and have trust in the therapeutic approach to be used.
## 3.1.2. Treatment Stage
Participants identified the involvement of other healthcare professionals (e.g., oncology, gynaecology, surgery and rehabilitation, among others). While uncertainty remained the predominant emotion in this stage, other emotions started to emerge as well, including concern for appearance and shock by the physical changes that were occurring as a result of the therapeutic techniques used in this stage (e.g., chemotherapy, radiotherapy, surgery, etc.). *In* general, the participants experienced empathetic care and a certain psychological accompaniment by the healthcare professionals who assisted them. The participants felt more involved in the decision-making process in the gynaecology units than in the oncology units. They all concurred that the experience of informed participation in their treatment process was positive.
## 3.1.3. Long-Term Follow-Up Stage
The main experience was less follow-up by healthcare professionals, giving rise to feelings of helplessness or loneliness and uncertainty about self-care. Other concerns, such as going back to work or looking for a new job more adapted to their health needs, were shared among the participants. The treatments at this stage focused on breast reconstruction surgery and medication. All the participants said they had received limited information on self-care, medical care to follow from this stage and possible new treatments required by healthcare professionals. However, they commented that at this stage they felt empowered to choose the aspects of their health in which they wanted to be involved, leading them to request personalized attention and to ask questions in order to be more involved in the decision-making process.
## 3.1.4. Recommendations of the Participants for Other Women with BC or Survivors
Additionally, and at their own initiative, the participants in the co-creation sessions provided a series of recommendations or tips for other women diagnosed with BC and suggested their inclusion in the MOOC as another resource. These tips were about family, social, work and empowerment areas and specifically for each of the stages worked (Figure 2).
## 3.2. Empowerment and Information Needs
Figure 3 shows the empowerment and information needs identified in each phase. The main empowerment needs identified were related to strategies for emotional management and guidelines for self-care throughout the process from diagnosis until long-term follow-up. The main information needs were related to the lack of understanding of the meanings of biomarkers, parameters and acronyms found in reports, as well as medical jargon, treatment options and the likelihood of cancer recurrence. The need to have guidelines for accessing information and support resources available online, including association websites and online experiences of other women with BC, was highlighted.
## 3.3. MOOC Content Development
Between July and October 2020, a weekly activity was published on the Moodle platform to carry out the process of co-design of the MOOC content. Table 2 shows the themes of these activities. Finally, the MOOC was composed of five units: (i) BC (definition, types and stages, diagnostic process, treatments, myths, etc.), ( ii) PCC (definition, implementation strategies, tips for preparing consultations with the healthcare professional, etc.); ( iii) DHL (definition, guidelines to improve each skill, etc.), ( iv) self-care (management of physical side effects, emotional management, etc.) and (v) experiences and advice from patients in different areas (healthcare, family, social and work area) and moments of the disease (diagnosis, treatment and long-term follow-up).
## 3.4. Experience in the Co-Creation Process
Data was available for seventeen participants ($89.47\%$) (Table 3). All of them strongly agreed or agreed that the general objectives of the project were adequate (item 2) and that the participation of women who have or have had BC is useful for the development of a MOOC on this content (item 3). More than $88\%$ of the participants strongly agreed or agreed that being part of the MOOC co-creation process made the content more relevant to them (item 4) and rated the quality of the activities carried out in the co-creation process (item 7) and the methodology applied (item 8) as very good or excellent. Regarding open questions, participants appreciated the way their experiences were incorporated into the MOOC and how they felt part of something meaningful, sharing experiences with other women in similar situations (item 11). In order to fully engage in the co-creation process, participants expressed that they would have liked to attend a face-to-face session. Additionally, some participants found it challenging to devote more time to the MOOC due to personal issues (item 12). See Supplementary Materials Table S1 to consult illustrative quotes from participants’ responses to open questions.
## 3.5. Acceptability Pilot of the MOOC
Data was available for seven participants ($36.84\%$) (Table 4). Combining the “totally agree” and “agree” categories, most of the participants positively evaluated the acceptability of the MOOC in terms of language, content, relevance, proposed activities and suitability of the MOOC objectives. Regarding open questions, most participants emphasized the usefulness of the MOOC’s content (especially related to SDM) and the way it is presented (through infographics and other audio-visual materials) as strengths. Nevertheless, one participant pointed out some navigation difficulties, while another emphasized the lengthy process (item 16). When it was possible, improvements suggested by participants were implemented, such as adding an initial summary of the MOOC’s content (item 17). All the contents were mentioned as important topics learned after completing the MOOC (item 18). See Supplementary Materials Table S2 to consult illustrative quotes from participants’ responses to open questions.
## 4. Discussion
This study presents the development of a MOOC aimed to improve the DHL of women with BC. We used a co-creation approach involving 17 patients and survivors and two nurses. In order to inform the content of the MOOC, we explored participants’ perceptions of the extent they were involved in the decision-making process, as well as their feelings, emotions and information needs throughout the therapeutic process. Most participants indicated that the MOOC co-creation experience was positive and made them feel involved in the project, and they positively valued the final product. Similar results were obtained by our team with other MOOCs developed for pregnant and lactating women [26] and people with type-2 diabetes [25], including larger samples than the one used in this study. In these two studies, participants’ self-perceived DHL significantly improved after completing the MOOC development compared to baseline. Future work is warranted to evaluate the effectiveness of this MOOC at improving BC patients’ actual DHL (not only self-perceived), objective knowledge of the disease and treatments, and their involvement in treatment decisions.
Women in this study pointed out information needs concerning different stages of the cancer, from diagnosis to long-term follow-up, as shown in previous studies [37,38]. Increasingly, these patients want to be involved in the decisions related to their health, and some studies have focused on involving the patient experience to improve the healthcare they receive [31,39]. As a result of an exchange of information and values between patients and healthcare providers, SDM engages patients as partners in their own care and optimizes the decision-making process [40]. To support SDM and the use of PtDA in the practice, it is important that patients also have a certain level of HL to increase patient empowerment and allow them to adopt a more participatory role in their healthcare [41]. Online interventions that provide information and support to women with BC appear to cushion the uncertainty they experience at different stages of the disease, and MOOCs can be an effective educational resource for meeting these unmet needs and promoting both DHL and SDM processes [24,39].
The PJM considered the evolving requirements for empowerment during the stages of diagnosis, treatment and long-term follow-up. Knowledge of the patients’ experiences, through a PJM, facilitates the identification of key moments in which to provide more precise information [5]. As we have seen in the results of this study, depending on the individual experiences of each woman, the care received during various BC periods could be perceived as more or less satisfactory. Based on our results, women with BC positively valued the experience of participating in the co-creation process of the MOOC, which made the content more relevant to them. This result aligns with previous evidence suggesting that a user-centred design process involves the participation of groups of users throughout the entire development cycle, during which they describe the context in which the generated resources will be used, their needs as users, and take part in user tests [42,43]. These are all contributions for designing and building health information technology through iterations [44].
This intervention represents an opportunity to reach a larger population that, due to health, availability and/or travel circumstances, may find it impossible to attend another type of face-to-face training on this subject. Technology provides great options for enhancing patient care; however, disparities in access and DHL continue to negatively impact vulnerable populations because of potential barriers in the digital sphere for those with low HL [11]. This problem can be especially aggravated as more information is provided online and healthcare professionals must be involved in the development of these skills in their patients with BC, but they also require support and a strategy at the institutional level. Therefore, healthcare organizations must prioritize achieving accessibility for all patients when designing eHealth services [11].
In this regard, the integration of educational materials designed by a representative sample of the target population to which they are addressed makes this proposal an opportunity to contribute to obtaining relevant health results for both affected patients and their healthcare professionals and, ultimately, decision-makers with financial capacity. From a managerial perspective, healthcare organizations should reframe their strategies, procedures and approaches, embracing a patient-centred perspective to become health literate [45]. From a policy perspective, it suggests that individual HL and organizational HL should be handled as two complementary tools to empower people and to engage them in self-care and health policy making [45].
The main strength of this project is having involved the intended audience in the creation of MOOCs, which enhances the significance of the material covered and how it is delivered. This is important because they have valuable insights and perspectives on the subject matter and can provide feedback on the relevance and effectiveness of the content and its delivery. This can lead to the creation of more engaging and effective MOOCs that better meet the needs and expectations of the intended audience. Nonetheless, there are several limitations to the study. Initially, it had been proposed that the co-creation process be based on face-to-face sessions with the participants followed by some online sessions through the Moodle platform. However, due to the COVID-19 pandemic, face-to-face sessions were replaced by online sessions carried out through the Zoom platform. This fact made the co-creation process last a few weeks longer than expected by adapting the work rhythms to the availability and web resources of the participants. However, the online sessions had several advantages: participants did not have to travel, the meetings were easier to organize and fewer financial resources were needed to support the development of the sessions. Another limitation is that, although all professionals related to BC were invited to participate, only two nurses did so. Perhaps the participation of other professionals involved in the process (e.g., gynaecologists and oncologists), as well as family members and/or caregivers, could have been beneficial for the generation of more useful resources. Even though women of all educational levels participated, the majority had higher education, so there was not much variability in this regard and lower educational levels may have been under-represented. In addition, there is a need for independent evaluation of acceptability to confirm the results obtained. Likewise, it is necessary to carry out an evaluation of the effectiveness of the MOOC with an independent sample that allows us to know if there really is an improvement in the levels of DHL and a change in knowledge in all the areas that are included in the different modules of the MOOC (BC, PCC, DHL, etc.).
## 5. Conclusions
The work carried out in this project is an example of how the development of educational interventions in MOOC format directed and designed by women with BC, with resources in different formats adapted to different educational/cultural levels and needs of the users, seems to be a viable strategy to generate higher-quality and useful resources for this population. The co-creation methodology and this type of resource aim to address the literacy and empowerment challenges of women with BC.
## References
1. **Las Cifras del Cáncer en España**
2. **Cancer Burden Statistics and Trends across Europe**
3. Clèries R., Rooney R.M., Vilardell M., Espinàs J.A., Dyba T., Borras J.M.. **Assessing predicted age-specific breast cancer mortality rates in 27 European countries by 2020**. *Clin. Transl. Oncol.* (2018.0) **20** 313-321. DOI: 10.1007/s12094-017-1718-y
4. Gómez-Acebo I., Dierssen-Sotos T., Palazuelos-Calderón C., Pérez-Gómez B., Amiano P., Guevara M., Molina A.J., Domingo L., Fernández-Ortiz M., Moreno V.. **Tumour characteristics and survivorship in a cohort of breast cancer: The MCC-Spain study**. *Breast Cancer Res. Treat.* (2020.0) **181** 667-678. DOI: 10.1007/s10549-020-05600-x
5. Ciria-Suarez L., Jiménez-Fonseca P., Palacín-Lois M., Antoñanzas-Basa M., Fernández-Montes A., Manzano-Fernández A., Castelo B., Asensio-Martínez E., Hernando-Polo S., Calderon C.. **Breast cancer patient experiences through a journey map: A qualitative study**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0257680
6. Campbell-Enns H.J., Woodgate R.L.. **The psychosocial experiences of women with breast cancer across the lifespan: A systematic review**. *Psychooncology* (2017.0) **26** 1711-1721. DOI: 10.1002/pon.4281
7. Doyle C., Lennox L., Bell D.. **A systematic review of evidence on the links between patient experience and clinical safety and effectiveness**. *BMJ Open* (2013.0) **3** e001570. DOI: 10.1136/bmjopen-2012-001570
8. Elwyn G., Durand M.A., Song J., Aarts J., Barr P.J., Berger Z., Cochran N., Frosch D., Galasiński D., Gulbrandsen P.. **A three-talk model for shared decision making: Multistage consultation process**. *BMJ* (2017.0) **359** j4891. DOI: 10.1136/bmj.j4891
9. Stacey D., Légaré F., Lewis K., Barry M.J., Bennett C.L., Eden K.B., Holmes-Rovner M., Llewellyn-Thomas H., Lyddiatt A., Thomson R.. **Decision aids for people facing health treatment or screening decisions**. *Cochrane Database Syst. Rev.* (2017.0) **2017** CD001431. DOI: 10.1002/14651858.CD001431.pub5
10. Liu C., Wang D., Liu C., Jiang J., Wang X., Chen H., Ju X., Zhang X.. **What is the meaning of health literacy? A systematic review and qualitative synthesis**. *Fam. Med. Community Health* (2020.0) **8** e000351. DOI: 10.1136/fmch-2020-000351
11. Smith B., Magnani J.W.. **New technologies, new disparities: The intersection of electronic health and digital health literacy**. *Int. J. Cardiol.* (2019.0) **292** 280-282. DOI: 10.1016/j.ijcard.2019.05.066
12. Basagoiti I.. *Alfabetización en Salud de la Información a la Acción* (2012.0)
13. Sørensen K., Van den Broucke S., Fullam J., Doyle G., Pelikan J., Slonska Z., Brand H.. **Health literacy and public health: A systematic review and integration of definitions and models**. *BMC Public Health* (2012.0) **12**. DOI: 10.1186/1471-2458-12-80
14. Dunn P., Hazzard E.. **Technology approaches to digital health literacy**. *Int. J. Cardiol.* (2019.0) **293** 294-296. DOI: 10.1016/j.ijcard.2019.06.039
15. Halvorsrud K., Kucharska J., Adlington K., Rüdell K., Brown Hajdukova E., Nazroo J., Haarmans M., Rhodes J., Bhui K.. **Identifying evidence of effectiveness in the co-creation of research: A systematic review and meta-analysis of the international healthcare literature**. *J. Public Health* (2021.0) **43** 197-208. DOI: 10.1093/pubmed/fdz126
16. Lee D.. **Effects of key value co-creation elements in the healthcare system: Focusing on technology applications**. *Serv. Bus.* (2019.0) **13** 389-417. DOI: 10.1007/s11628-018-00388-9
17. Kinnane N.A., Milne D.J.. **The role of the Internet in supporting and informing carers of people with cancer: A literature review**. *Support. Care Cancer* (2010.0) **18** 1123-1136. DOI: 10.1007/s00520-010-0863-4
18. Masters K.. **A Brief Guide To Understanding MOOCs**. *Internet J. Med. Educ.* (2011.0) **1** 2. DOI: 10.5580/1f21
19. Nieder J., Nayna Schwerdtle P., Sauerborn R., Barteit S.. **Massive Open Online Courses for Health Worker Education in Low- and Middle-Income Countries: A Scoping Review**. *Front. Public Health* (2022.0) **10** 891987. DOI: 10.3389/fpubh.2022.891987
20. Longhini J., Rossettini G., Palese A.. **Massive open online courses for nurses’ and healthcare professionals’ continuous education: A scoping review**. *Int. Nurs. Rev.* (2021.0) **68** 108-121. DOI: 10.1111/inr.12649
21. Adamski M., Truby H., Bennett C., Gibson S.. **Exploring Impacts of a Nutrition-Focused Massive Open Online Course**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14183680
22. Mackenzie S.C., Cumming K.M., Mehar S., Wilson L., Cunningham S.G., Bickerton A., Wake D.J.. **Education at scale: Improvements in type 1 diabetes self-management following a massive open online course**. *Diabet. Med.* (2022.0) **17** e0267205. DOI: 10.1111/dme.14842
23. Farrow M., Fair H., Klekociuk S.Z., Vickers J.C.. **Educating the masses to address a global public health priority: The Preventing Dementia Massive Open Online Course (MOOC)**. *PLoS ONE* (2022.0) **17**. DOI: 10.1371/journal.pone.0267205
24. Perestelo-Perez L., Torres-Castaño A., González-González C., Alvarez-Perez Y., Toledo-Chavarri A., Wagner A., Perello M., Van Der Broucke S., Díaz-Meneses G., Piccini B.. **IC-Health Project: Development of MOOCs to Promote Digital Health Literacy: First Results and Future Challenges**. *Sustainability* (2020.0) **12**. DOI: 10.3390/su12166642
25. Alvarez-Perez Y., Perestelo-Perez L., Rivero-Santana A., Wagner A.M., Torres-Castaño A., Toledo-Chávarri A., Duarte-Díaz A., Alvarado-Martel D., Piccini B., Van den Broucke S.. **Cocreation of Massive Open Online Courses to Improve Digital Health Literacy in Diabetes: Pilot Mixed Methods Study**. *JMIR Diabetes* (2021.0) **6** e30603. DOI: 10.2196/30603
26. Álvarez-Pérez Y., Perestelo-Pérez L., Rivero-Santanta A., Torres-Castaño A., Toledo-Chávarri A., Duarte-Díaz A., Mahtani-Chugani V., Marrero-Díaz M.D., Montanari A., Tangerini S.. **Co-Creation of Massive Open Online Courses to Improve Digital Health Literacy in Pregnant and Lactating Women**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph19020913
27. Cordeiro L., Soares C.B.. **Action research in the healthcare field**. *JBI Database Syst. Rev. Implement. Rep.* (2018.0) **16** 1003-1047. DOI: 10.11124/JBISRIR-2016-003200
28. Oberschmidt K., Grünloh C., Nijboer F., van Velsen L.. **Best Practices and Lessons Learned for Action Research in eHealth Design and Implementation: Literature Review**. *J. Med. Internet Res.* (2022.0) **24** e31795. DOI: 10.2196/31795
29. Toledo-Chávarri A., Ramos-García V., Koatz D., Torres-Castaño A., Perestelo-Pérez L., Ramírez-Puerta A.B., Tello-Bernabé M.-E., García-García J.-M., García-García J., Pacheco-Huergo V.. **Co-Design Process of a Virtual Community of Practice for the Empowerment of People with Ischemic Heart Disease**. *Int. J. Integr. Care* (2020.0) **20** 9. DOI: 10.5334/ijic.5514
30. Van Beusekom M., Cameron J., Bedi C., Banks E., Harris R., Humphris G.. **Using Co-design With Breast Cancer Patients and Radiographers to Develop “KEW” Communication Skills Training**. *Front. Psychol.* (2021.0) **12** 629122. DOI: 10.3389/fpsyg.2021.629122
31. Tsianakas V., Robert G., Maben J., Richardson A., Dale C., Wiseman T.. **Implementing patient-centred cancer care: Using experience-based co-design to improve patient experience in breast and lung cancer services**. *Support. Care Cancer* (2012.0) **20** 2639-2647. DOI: 10.1007/s00520-012-1470-3
32. Bate P., Robert G.. **Experience-based design: From redesigning the system around the patient to co-designing services with the patient**. *Qual. Saf. Health Care* (2006.0) **15** 307-310. DOI: 10.1136/qshc.2005.016527
33. Goodman L.A.. **Snowball Sampling**. *Ann. Math. Stat.* (1961.0) **32** 148-170. DOI: 10.1214/aoms/1177705148
34. Venkatesh V., Morris M., Davis G., Davis F.. **User Acceptance of Information Technology: Toward a Unified View**. *MIS Q.* (2003.0) **27** 425. DOI: 10.2307/30036540
35. García Toribio G., Polvo Saldaña Y., Hernández Mora J.J., Sánchez Hernández M.J., Nava Bautista H., Collazos Ordóñez C.A., Hurtado Alegría J.A.. **Medición de la usabilidad del diseño de interfaz de usuario con el método de evaluación heurística: Dos casos de estudio**. *Rev. Colomb. Comput.* (2019.0) **20** 23-40. DOI: 10.29375/25392115.3605
36. Yılmaz N.G., Sungur H., van Weert J.C.M., van den Muijsenbergh M.E.T.C., Schouten B.C.. **Enhancing patient participation of older migrant cancer patients: Needs, barriers, and eHealth**. *Ethn. Health* (2022.0) **27** 1123-1146. DOI: 10.1080/13557858.2020.1857338
37. Sheehy E.M., Lehane E., Quinn E., Livingstone V., Redmond H.P., Corrigan M.A.. **Information Needs of Patients With Breast Cancer at Years One, Three, and Five After Diagnosis**. *Clin. Breast Cancer* (2018.0) **18** e1269-e1275. DOI: 10.1016/j.clbc.2018.06.007
38. León-Salas B., Álvarez-Pérez Y., Ramos-García V., del Mar Trujillo-Martín M., de Pascual y Medina A.M., Esteva M., Brito-García N., González-Hernández N., Bohn-Sarmiento U., Biurrun-Martínez M.C.. **Information needs and research priorities in long-term survivorship of breast cancer: Patients and health professionals’ perspectives**. *Eur. J. Cancer Care* (2022.0) **31** e13730. DOI: 10.1111/ecc.13730
39. Kemp E., Koczwara B., Butow P., Turner J., Girgis A., Schofield P., Hulbert-Williams N., Levesque J., Spence D., Vatandoust S.. **Online information and support needs of women with advanced breast cancer: A qualitative analysis**. *Support. Care Cancer* (2018.0) **26** 3489-3496. DOI: 10.1007/s00520-018-4206-1
40. Abrams E.M., Shaker M., Oppenheimer J., Davis R.S., Bukstein D.A., Greenhawt M.. **The Challenges and Opportunities for Shared Decision Making Highlighted by COVID-19**. *J. Allergy Clin. Immunol. Pract.* (2020.0) **8** 2474-2480.e1. DOI: 10.1016/j.jaip.2020.07.003
41. Schulz P.J., Nakamoto K.. **Health literacy and patient empowerment in health communication: The importance of separating conjoined twins**. *Patient Educ. Couns.* (2013.0) **90** 4-11. DOI: 10.1016/j.pec.2012.09.006
42. 42.ISO 9241-210:2019Ergonomics of Human-System Interaction. Part 210: Human-Centred Design for Interactive SystemsInternational Organization for Standardization (ISO)Geneva, Switzerland2019Available online: https://www.iso.org/standard/77520.html(accessed on 21 December 2022). *Ergonomics of Human-System Interaction. Part 210: Human-Centred Design for Interactive Systems* (2019.0)
43. Gulliksen J., Göransson B., Boivie I., Blomkvist S., Persson J., Cajander Å.. **Key principles for user-centred systems design**. *Behav. Inf. Technol.* (2003.0) **22** 397-409. DOI: 10.1080/01449290310001624329
44. Smaradottir B.F., Bellika J.G., Fredeng A., Fagerlund A.J.. **User-Centred Design with a Remote Approach: Experiences from the Chronic Pain Project**. *Integrated Citizen Centered Digital Health and Social Care* (2020.0) 197-201
45. Palumbo R.. **Leveraging Organizational Health Literacy to Enhance Health Promotion and Risk Prevention: A Narrative and Interpretive Literature Review**. *Yale J. Biol. Med.* (2021.0) **94** 115-128. PMID: 33795988
|
---
title: Palmitic Acid Regulation of Stem Browning in Freshly Harvested Mini-Chinese
Cabbage (Brassica pekinensis (Lour.) Rupr.)
authors:
- Hongdou Gao
- Shixian Zeng
- Xiaozhen Yue
- Shuzhi Yuan
- Jinhua Zuo
- Qing Wang
journal: Foods
year: 2023
pmcid: PMC10001398
doi: 10.3390/foods12051105
license: CC BY 4.0
---
# Palmitic Acid Regulation of Stem Browning in Freshly Harvested Mini-Chinese Cabbage (Brassica pekinensis (Lour.) Rupr.)
## Abstract
The effect of palmitic acid (PA) on stem browning was investigated in freshly harvested mini-Chinese cabbage (Brassica pekinensis). Results indicated that concentrations of PA ranging from 0.03 g L−1 to 0.05 g L−1 inhibited stem browning and decreased the rate of respiration, electrolyte leakage, and weight loss, as well as the level of malondialdehyde (MDA) in freshly harvested mini-Chinese cabbage stored at 25 °C for 5 d. The PA treatment enhanced the activity of antioxidant enzymes (ascorbate peroxidase (APX), catalase (CAT), peroxidase (POD), 4-coumarate:CoA ligase (4CL) and phenylalamine ammonia lyase (PAL)), and inhibited the activity of polyphenol oxidase (PPO). The PA treatment also increased the level of several phenolics (chlorogenic acid, gallic acid, catechin, p-coumaric acid, ferulic acid, p-hydroxybenzoic acid, and cinnamic acid) and flavonoids (quercetin, luteolin, kaempferol, and isorhamnetin). In summary, results indicate that treatment of mini-Chinese cabbage with PA represents an effective method for delaying stem browning and maintaining the physiological quality of freshly harvested mini-Chinese cabbage due to the ability of PA to enhance antioxidant enzyme activity and the level of phenolics and flavonoids during 5 d.
## 1. Introduction
Mini-Chinese cabbage (*Brassica pekinensis* (Lour.) Rupr.) is a green leafy vegetable in the family Cruciferae [1]. It is a common component of Asian diets and is becoming increasingly used in Western diets [2,3]. It has great health benefits, including anticancer, anti-obesity, and antioxidant effects [2,4]. Freshly harvested mini-Chinese cabbage, however, is very susceptible to browning, vitamin loss, softening, and the production of off-flavors, which decline its economic value [5,6]. Stem browning represents a major factor affecting the quality of freshly harvested mini-Chinese cabbage, reducing its appearance and consumer acceptance [7]. Thus, stem browning reduces the marketable shelf life of mini-Chinese cabbage. In addition to appearance, stem browning also affects the flavor of mini-Chinese cabbage, rendering it inedible.
Browning is one of the most significant defects of leafy vegetables [8]. Polyphenol oxidase (PPO) induces the synthesis of phenolics when leafy tissues are injured, which are then converted to quinones [9], resulting in a rapid browning reaction in leaf tissues. Plants also produce several antioxidant enzymes, phenolics, and flavonoids to counteract the excessive production of reactive oxygen species (ROS) in response to tissue injury. Excessive ROS accumulation induces the peroxidation and breakdown of unsaturated fatty acids in membrane lipids [10]. Several different treatments have been used to protect leafy vegetables from browning and maintain their quality during storage. Application of dimethyl dicarbonate has been reported to reduce browning in Chinese cabbage and N-phenyl-N-(2-chloro-4-pyridyl) urea was also reported to regulate browning in Chinese flowering cabbage [11,12]. A combination of 0.001 g L−1 4-hexylresorcinol, 0.05 g L−1 potassium sorbate, and 0.025 g L−1 N-acetylcysteine was also reported to prevent browning of radish slices [13].
The use of palmitic acid (PA) to regulate browning has also been investigated in longan fruit [14,15]. PA (16:0) is one of the common saturated fatty acids in humans and can be obtained from ingested foods or synthesized from other carbohydrates, fatty acids, and amino acids [16]. PA is also the main component of fatty acids in cell membranes. Previous studies in longan fruit (Dimocarpus longan) found that the content of saturated fatty acids (including PA and stearic acid) increases when fruit starts to brown, which affects the structural integrity of cell membranes [14,15]. The structural changes in membranes induce the synthesis of PPO and phenolics, which accelerate browning [17]. Current studies indicate that PA content is enhanced during browning but that exogenous PA may inhibit browning. Thus, the role of PA in browning and the physiological response of plant tissues to PA requires additional investigation. While the effect of PA has been investigated in animal cell experiments, no reports have been published on the effect of PA on stem browning in freshly harvested mini-Chinese cabbage. This study’s object was to determine the effect of PA on freshly harvested mini-Chinese cabbage. We assessed the effect of PA on stem browning, respiration rate, electrolyte leakage, MDA content, antioxidant enzyme activity, and the level of phenolics and flavonoids in mini-Chinese cabbage during storage.
## 2.1. Plant Materials and Treatments
Mature mini-Chinese cabbages (*Brassica pekinensis* (Lour.) Rupr. cv ‘Xiaoqiao’, Beijing Shinong Seeds Co., Ltd., Beijing, China) were harvested from a farm in Xiaotangshan, Beijing, China. Mini-Chinese cabbages were approximately 0.25 m in height and harvested 60 d after planting. Harvested plants were transported to laboratory within 3 h. Mini-Chinese cabbages utilized in this study were 8–9 cm in diameter and free of any evidence of pests, disease, or mechanical damage. The freshly harvested mini-Chinese cabbages were divided into 4 groups; each group was 25 cabbages. Individual groups were immersed in either 0.03 g L−1, 0.04 g L−1, or 0.05 g L−1 PA dissolved in ethanol (Aladdin, AR, Shanghai, China) for 30 s with immersion in just ethanol serving as the control. The treated cabbages were placed in trays and air-dried for 10 min, after which the trays were covered with 0.03 mm polyethylene film (there is no hole on the packaging film). The cabbages were then placed in storage at 25 °C and 85–$90\%$ relative humidity in a constant temperature and humidity warehouse. Leaves and stems tissues were sampled at 0, 1, 2, 3, 4, and 5 d of storage and immediately ground to a powder in liquid nitrogen and stored at −80 °C until being further processed. Each experiment and each of the subsequent assays utilized three replicates, and the experiment was repeated three times ($$n = 9$$).
## 2.2. Weight Loss and Respiration Rate
Weight loss was measured as described by Duan et al. [ 18]. Weight loss was expressed as the percentage loss from the original weight and calculated using the formula:Weight loss (%) = $100\%$ × (Initial weight − final weight)/Initial weight[1] The respiration rate was measured using a F-940 Gas Analyzer (Felix, Washington, DC, USA). A 500 g sample of cabbage was placed in a 1 L gas-tight box for 1 h after which the concentration of CO2 was determined. Results are expressed as mg CO2 kg−1 h−1.
## 2.3. Color and Browning Index (BI)
An CR-400 automated colorimeter (Konica Minolta Holdings, Inc., Tokyo, JAPAN) was used to measure the color and browning index (BI) [19]. L*, a*, and b* of sample were determined and the BI was calculated using the formula as follows:[2]BI=100×(x−0.31)0.172+180 [3]x=a*+1.75L*5.645L*+a*−3.012b*
## 2.4. Electrolyte Leakage and MDA Content
Electrolyte leakage was measured with a DDS-11A conductivity meter (Shanghai Instrument and Electronic Scientific Instruments, Ltd., Shanghai, China) by the method of Li et al. [ 12]. A leaf disc with a diameter of 1 cm was collected from ten different leaves and each disc was placed in 25 mL of distilled water (dH2O). Conductivity of the solution was measured after 2 h of incubation at room temperature and used as the initial value (P1). The solutions containing the leaves were then boiled at 100 °C for 5 min, cooled, and conductivity (P2) was again assessed. Percentage electrolyte leakage was calculated using the method as follows: P1/P2 × 100.
MDA content was determined by the method of Xu et al. with minor modifications [20]. Samples (1 g) were homogenized in 5 mL of $10\%$ (w/v) trichloroacetic acid (TCA) (Shanghai Macklin Biochemical Co., Ltd., AR, Shanghai, China) and centrifuged at 12,000× g for 15 min at 4 °C. Then, 1 mL supernatant was mixed with 3 mL of $0.5\%$ (w/v) thiobarbituric acid (Shanghai Macklin Biochemical Co., Ltd., Analytical reagent(AR), Shanghai, China), boiled 20 min. After this, the absorbance (UV-1800 spectrophotometer, Shimadzu, Tokyo, Japan) of the solution was determined at 450, 532 and 600 nm. MDA content was calculated (μmol L−1) = {[6.45 × (OD532 − OD600) − 0.56 × OD450] × V}/(VS × m × 1000), where V is the total volume of the extracting solution, mL; *Vs is* the measured volume of the extracting solution, mL; and m is the weight of the sample, g.
## 2.5. Total Phenolics and Total Flavonoids
Frozen samples of cabbage (2 g) were powdered and mixed with 10 mL $80\%$ ethanol (AR, Aladdin, Shanghai, China) (diluted by dH2O), and extracted for 40 min at 40 °C. The mixture was then centrifuged (D-37520 centrifuge, Beijing Chengmao Industrial Science & Development Co., Ltd., Beijing, China) at 12,000× g for 25 min at 4 °C. The supernatant was used for measuring total phenolics and flavonoids. Total phenolics were determined using Folin–Ciocalteu (FC) (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China) reagent by the method of Fan et al. [ 21]. Test tubes containing 800 μL dH2O and 200 μL FC reagent were prepared and then 400 μL supernatant was added to the test tube, vortexed, and incubated about 3 min at 20 °C. Subsequently, 400 μL $20\%$ (w/v) Na2CO3 (Aladdin, AR, Shanghai, China) and 1.2 mL dH2O were added to the mixture. Then a water bath (XMTD-6000 water bath kettle, Yuyao Jindian Instrument Co., Ltd., Yuyao, China) was prepared at 20 °C for 60 min. Absorbance of the sample solutions at 760 nm were then measured in a spectrophotometer (UV-1800 spectrophotometer, Shimadzu, Tokyo, Japan) and used to determine the level of total phenolics based on the use of a standard curve constructed using different concentrations of gallic acid (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China).
Flavonoid content was measured using the method by Zhou et al. [ 22] with some modification. Amounts of 1 mL supernatant, 0.25 mL of $10\%$ (w/v) AlCl3 (Aladdin, AR, Shanghai, China) and 1 mL $5\%$ (w/v) NaNO2 (Aladdin, AR, Shanghai, China) were mixed. After 5 min, 1 mL of 1.0 mol L−1 NaOH (Aladdin, AR, Shanghai, China) was added to the mixture. Catechinic acid (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China) was used as a standard to calculate the flavonoid content. Absorbance of the resulting sample solution was measured at 510 nm and used to determine flavonoid content. The concentration of total phenolics and flavonoid content were expressed as g kg−1.
The level of specific phenolics (gallic acid, catechin, chlorogenic acid, p-Hydroxybenzoic acid, p-Coumaric acid, ferulic acid, and cinnamic acid) (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China) and specific flavonoids (quercetin, kaempferol, luteolin, and isorhamnetin) (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China) were determined by HPLC (1260, Agilent Technologies Co., Ltd., Palo Alto, CA, USA) according to the method by Xu et al. [ 20]. Briefly, 2 g sample powder were mixed with 2 mL of methanol (Aladdin, GR, Shanghai, China), which was ultrasonicated for 1 h (40 kHz), after which the mixture was centrifuged at 10,000× g for 15 min. The supernatants were utilized in the HPLC analysis. Conditions were as follows: utilization of an Eclipse Plus C18 (250 mm × 4.6 mm, 5 μm) column, with a temperature of 30 °C. The detector was a UV-detector (280 nm). The mobile phase consisted of $1\%$ of formic acid-water (A) and methanol ($100\%$) (B) and the gradient elution conditions were 0–3 min, $15\%$ to $30\%$ B; 3–35 min, $30\%$ to $45\%$ B; 35–45 min, $45\%$ to $65\%$ B; 45–50 min, 65–$15\%$ B.
## 2.6. CAT, APX, PPO, and POD Enzyme Activity
CAT and APX enzyme activity was assessed according to the method by Wang et al. [ 23]. Briefly, 2.0 g of cabbage powder was mixed with 10 mL of 0.1 mol L−1 phosphate buffer solution (PBS, pH 7.8 containing 0.05 g of polyvinylpyrrolidone (PVPP, Golden Clone (Beijing) Biotechnology Co., Ltd., Beijing, China)), and then centrifuged at 12,000× g for 10 min at 4 °C. The supernatant was utilized to determine CAT and APX activity.
Amounts of 0.1 mL of supernatant, 1 mL of $0.3\%$ (v/v) dH2O2, and 1.9 mL 0.05 mol L−1 PBS (pH 7.8) were mixed. Absorbance of the resulting solution was determined at 240 nm to calculate CAT activity. APX activity reaction mixture included 1.2 mL supernatant, 2.6 mL of 0.1 mmol L−1 EDTA (Shanghai Macklin Biochemical Co., Ltd., AR, Shanghai, China) and 0.5 mmol L−1 AsA (Aladdin, GR, Shanghai, China) (in PBS, pH 7.5), and 0.3 mL of 2 mmol L−1 dH2O2. Absorbance was measured at 290 nm to calculate APX activity.
PPO and POD activity were determined by the method of Zhou et al. with slight modification [22]. Frozen cabbage powder (2 g) was added to 10 mL of 0.1 mol L−1 of PBS (pH 6.4), containing 0.05 g PVPP. The resulting mixture was then centrifugated at 12,000× g for 30 min at 4 °C. Next, 0.1 mL of supernatant was mixed with 0.6 mL of 50 mmol L−1 catechol (Nantong Runfeng Petrochemical Co., Ltd., AR, Nantong, China) substrate to measure PPO activity, while 0.9 mL of $0.2\%$ guaiacol (Nantong Runfeng Petrochemical Co., Ltd., AR, Nantong, China) (v/v) was mixed with 1 mL of $0.3\%$ dH2O2 (v/v) to measure POD activity. Absorbance was measured at 410 nm and used to calculate PPO activity and at 470 nm to calculate POD activity.
## 2.7. PAL and 4CL Enzyme Activity
The assessment of PAL activity was conducted by Kamdee et al. with slight modification [24]. Briefly, 1 g of powdered cabbage leaf tissue was added to 4 mL of 50 mmol L−1 borate buffer (BBS, pH 8.5) including 5.0 mmol L−1 2-mercaptoethanol (Aladdin, GR, Shanghai, China) and 0.4 g PVPP. The resulting mixture was then centrifuged at 12,000× g for 20 min at 4 °C. An amount of 0.3 mL supernatant was added to 0.7 mL of 100 mmol L−1 l-phenylalanine (AR, Xiya Reagent, Linyi, China) and 3 mL of 50 mmol L−1 borate buffer (BBS, pH 8.5). The mixture was incubated at 40 °C for 1 h and then 0.1 mL of 5 mmol L−1 HCl (Beijing Institute of Chemical Reagents, AR, Beijing, China) was added to stop the reaction. Absorbance of the solution was measured at 290 nm to calculate PAL activity.
The level of 4CL enzyme activity was determined using a commercial assay kit (Comin Biotechnology Co., Ltd., Suzhou, China). Briefly, 2.0 g of sample were homogenized in 10 mL of extraction buffer, and the resulting mixture was then centrifuged at 8000× g for 10 min at 4 °C. Absorbance at 333 nm was measured to calculate 4CL enzyme activity.
## 2.8. Data Analysis
SPSS ver. 22 (SPSS Inc., Chicago, IL, USA) software was used to conduct the statistical analyses. Data were subjected to a two-way ANOVA and mean separations were performed using a Pearson’s multiple range test. In the case of single mean comparisons, data were subjected to an LSD analysis in which differences at $p \leq 0.05$ were considered significant. All results presented are means ± standard deviation (SD).
## 3.1. Stem Browning
The visual color of cabbage stems is an excellent indicator of their degree of browning. The level of stem browning in the control group greatly reduced their visual quality over the 4 d of storage (Figure 1A). Cabbage stems treated with PA, however, exhibited a slower rate of browning than the control over the 4 d of storage. This was also reflected in the BI (Figure 1B). The BI increased during storage in both PA-treated and control cabbage stems; however, the level of BI was lower in PA-treated samples than it was in the control group (Figure 1B). Notably, cabbage stems treated with 0.05 g L−1 PA had the lowest BI relative to the control and to cabbages treated with lower concentrations of PA.
The BI is calculated from the values obtained for L*, a*, and b*. Figure 1C–E illustrate the changes in L*, a*, and b* in stems of PA-treated and control groups over 5 d of storage at 25 °C. The level of L* in both control and PA-treated samples decreased during storage; however, in the control group the L* value was lower than it was in PA-treated cabbages during the entire course of storage. In contrast, a* and b* exhibited an increasing trend, with the level of increase in the different treatment groups ranging from the control > 0.03 g L−1 PA > 0.04 g L−1 PA > 0.05 g L−1 PA.
## 3.2. Respiration Rate, Weight Loss, Electrolyte Leakage, and MDA Content
The respiration rate exhibited a rapid increase after cabbages were placed in storage, reaching its highest level after 2 d, after which the respiration rate decreased and maintained a relatively stable value (Figure 2A). Peak respiration rates were 17.752 (control), 14.063 (0.03 g L−1 PA), 11.782 (0.04 g L−1 PA), and 10.625 (0.05 g L−1 PA) mg CO2 kg−1 h−1.
The percentage weight loss increased in all groups (Figure 2B); however, the weight loss in control samples was higher than it was in any of the PA-treated samples from 1 to 5 d of storage. The percentage weight loss in the control group was $0.735\%$, which was higher than the $0.517\%$ weight loss observed in the 0.05 g L−1 PA-treated sample group after 5 d.
Electrolyte leakage also showed an increasing trend in all groups (Figure 2C). The increase in the control group was higher than that in the PA-treated groups starting from 2 d. The 0.03 and 0.04 g L−1 PA-treated cabbages exhibited a similar pattern to each other and were higher than they were in the 0.05 g L−1 PA-treated samples. Electrolyte leakage had increased to $0.216\%$, $0.192\%$, and $0.181\%$ in the 0.03, 0.04, and 0.05 g L−1 PA treatment groups, respectively, and $0.290\%$ in the control group, after 5 d.
MDA levels increased before the first 3 d of storage in all treatment groups, and then declined (Figure 2D). Lower MDA levels were observed in PA-treated samples than in the control group. Peaks of MDA content in the 0.03, 0.04, and 0.05 g L−1 PA-treated samples were 1.868, 2.111, and 1.213 μmol kg−1, compared to the control group (3.080 μmol kg−1) of mini-Chinese cabbages.
## 3.3. PPO, 4CL, PAL, APX, CAT, and POD Activity
PPO activity in the control group was higher than it was in the PA-treated samples (Figure 3A), exhibiting an increasing trend during the first 3 d in all groups and then declining. Peak PPO activity was 0.363 units in the control group, 0.325 units in the 0.03 g L−1 PA-treated group, 0.108 units in the 0.04 g L−1 PA-treated group, and 0.095 units in the 0.05 g L−1 PA-treated group, all of which were lower than that in the control group.
The enzymatic activity of 4CL rapidly increased in the first 2 d and then fluctuated from 2 to 5 d. Higher activity was observed in the PA-treated group. After 2 d of storage, 4CL activity was 800.312, 837.459, and 828.569 units in the 0.03, 0.04, and 0.05 g L−1 PA-treated samples, respectively.
Changes of PAL activity in the control and PA-treated groups are shown in Figure 3C. PAL activity indicated an increasing trend in all groups during the first 2 d, with PA-treated groups generally exhibiting higher activity. The 0.05 g L−1 PA-treated samples exhibited the highest level of activity (93.600 units).
An increase in APX activity was exhibited in the control and PA-treated groups during the first 2 d and then declined (Figure 3D). APX activity in the control group was lower (0.036 units) at 2 d of storage, while APX activity was 0.042 units, 0.045 units, and 0.053 units in the 0.03, 0.04, and 0.05 g L−1 PA treatment groups, respectively.
The data indicated that CAT activity in all groups reached a maximum at 3 d and then decreased (Figure 3E). Although the pattern of CAT activity was similar in all of the treatment groups, CAT activity was higher in general in the PA-treated sample groups than it was in the control group.
POD activity reached a maximum after 2 d of storage and then remained stable (Figure 3F). POD activity after 5 d of storage was 0.185, 0.185, 0.248, and 0.288 units in the control group and the 0.03, 0.04, 0.05 g L−1 PA treatment groups, respectively.
## 3.4. Total Phenolics, Gallic Acid, Catechin, Chlorogenic Acid, p-Hydroxybenzoic Acid, p-Coumaric Acid, Ferulic Acid, and Cinnamic Acid
The level of total phenolics increased in both the control and PA-treated groups over the 5 d (Figure 4A). Although the trend was similar in all of the treatment groups, the levels of total phenolics in PA-treated groups were higher than they were in the control group. The level of total phenolics after 5 d of storage was 1.456 g kg−1, 1.863 g kg−1, 2.331 g kg−1, and 1.658 g kg−1 in the 0.03 g L−1 PA, 0.04 g L−1 PA, 0.05 g L−1 PA and control treatment groups, respectively.
Cinnamic acid content gradually increased in all groups (Figure 4B). The highest level of cinnamic acid (2.361 mg kg−1) was observed at 3 d in the 0.05 g L−1 PA-treated group.
The content of gallic acid exhibited an initial decrease at 1 d of storage and then an increasing trend in all groups from 2 to 5 d (Figure 4C). The content of gallic acid after 5 d of storage was 4.030 mg kg−1, 5.067 mg kg−1, 5.615 mg kg−1, and 6.873 mg kg−1 in the control, 0.03 g L−1 PA, 0.04 g L−1 PA, and 0.05 g L−1 PA treatment groups, respectively. The chromatogram and standard curve are in Figure S1.
Catechin content exhibited an increasing trend in the control and PA-treated groups during the first 4 d and then declined (Figure 4D). At 4 d, the 0.04 and 0.05 g L−1 PA-treated samples exhibited a higher level (20.632 and 20.293 mg kg−1) of catechin.
Chlorogenic acid content exhibited an increasing trend during storage, with PA-treated groups having a higher content than that in the control group (Figure 4E). Chlorogenic acid content at 5 d was 21.757 mg kg−1 (0.03 g L−1 PA), 20.866 mg kg−1 (0.04 g L−1 PA), 29.859 mg kg−1 (0.05 g L−1 PA), and 17.276 mg kg−1 (control).
The level of p-Hydroxybenzoic acid showed an enhanced trend during storage in PA-treated groups (Figure 4F). The level of activity in the 0.05 g L−1 PA treatment group exhibited a change at 3 d (7.640 mg kg−1), a level that was much higher than in other groups.
The level of p-Coumaric acid exhibited a slight to high increase in all groups during the first 3 d, and then declined. The level of p-Coumaric acid exhibited a peak of activity in the 0.03 g L−1, 0.04 g L−1, and 0.05 g L−1 PA treatment groups at 3 d of storage when levels increased to 3.818 mg kg−1, 3.822 mg kg−1, and 9.083 mg kg−1, while the level in the control group remained stable (Figure 4G).
The level of ferulic acid fluctuated in all groups (Figure 4H). The level of ferulic acid in the 0.05 g L−1 PA-treated group at 3 d was 6.973 mg kg−1, which was more than 1.28 times greater than it was in the other treatment groups.
## 3.5. Total Flavonoids, Quercetin, Luteolin, Kaempferol, and Isorhamnetin
Flavonoid content exhibited the same trend as total phenolics, increasing with storage time in all the treatment groups (Figure 5A). The highest content of flavonoids (1.455 g kg−1) was observed in the 0.05 g L−1 PA-treated group and was higher (1.63 times greater) than in the control group.
Quercetin content increased before the first 4 d and then decreased (Figure 5B). Quercetin content, however, was higher in the 0.05 g L−1 PA treatment group than it was in the control group during all the storage time.
Luteolin content increased during the first day and then decreased in all groups (Figure 5C). Peak content was observed at 2 d in 0.03 g L−1, 0.04 g L−1, 0.05 g L−1 PA treatment groups, at which luteolin content was 6.788 mg kg−1, 7.418 mg kg−1, and 9.109 mg kg−1, respectively.
Kaempferol levels greatly increased in PA-treated groups (Figure 5D). In contrast, kaempferol content changed only slightly in the control group. Kaempferol content in all of the PA-treated groups was higher than it was in the control group.
Isorhamnetin content exhibited a decreasing trend in all groups (Figure 5E). Isorhamnetin content was higher in the 0.04 g L−1 and 0.05 g L−1 PA-treated groups than it was in other groups from 1 to 3 d.
## 3.6. Correlation Analysis
Pearson coefficients were used to determine the correlation between the different measured parameters (Figure 6A). The analysis indicated that BI levels were significantly positively correlated with a*, b*, weight loss, electrolyte leakage, MDA content, and 4CL activity. BI levels were negatively correlated with L* (Figure 6A). Total phenol content was significantly positively correlated with the level of cinnamic acid, gallic acid, catechin, chlorogenic acid, p-Hydroxybenzoic acid, flavonoids, quercetin and kaempferol. Total phenol content was significantly negatively correlated with the content of luteolin and isorhamnetin (Figure 6B).
## 4. Discussion
Previous studies have indicated that tissue browning in plants induces an increase in the level of PA, which may explain why exogenous PA can inhibit browning [14,15]. Mini-Chinese cabbage stems are prone to browning after cabbages are harvested and browning is the basis for a loss in quality. This study indicated that mini-Chinese cabbages treated with PA for 30 s exhibit a reduction in weight loss and the rate of respiration, and reduced levels of stem browning and electrolyte leakage. In contrast, antioxidant enzyme activity and many phenolics and flavonoid compounds were enhanced. These responses collectively helped to maintain the quality of cabbage heads in storage and extend their shelf life.
The commercial value of mini-Chinese cabbages is dependent on their quality. Thus, it is essential to maintain their quality during storage. Weight loss and high respiration rates are known indicators of quality degradation in mini-Chinese cabbage and reports have indicated that strategies that suppress respiration and reduce evaporation in harvested plants help to maintain their postharvest quality [25]. In our study, weight loss and respiration rate were reduced in response to immersion of the cabbage heads in PA for 30 s. These results confirm that PA lowers the basal metabolism of mini-Chinese cabbage. The permeability of cell membranes increases when plants begin to experience senescence, which promotes the peroxidation of lipids [10]. MDA is commonly recognized as an indicator of oxidative stress and peroxidation [26], and increased electrolyte leakage and MDA levels serve as indicators of membrane degradation and peroxidation of membrane lipids, respectively [27]. Results exhibited that PA treatment reduced both the level of electrolyte leakage and MDA levels in mini-Chinese cabbages, relative to the untreated control. These results are similar to those in a previous study by Li et al. [ 12], who reported that electrolyte leakage and MDA levels in Chinese flowering cabbage were maintained during storage by treatment with N-phenyl-N-(2-chloro-4-pyridyl) urea (CPPU). Additionally, PA is the main fatty acid component of cell membranes and contributes to the resilience of cell membranes exposed to stress. Our collective results indicate that PA treatment of mini-Chinese cabbage can help to maintain their quality in storage.
Browning is a complex process that can be affected by many enzymes and chemical compounds, in which tissue color is the most recognizable visual manifestation. In our study, the PA treatment delayed the process of stem browning, as indicated by a reduction in the BI in stems of mini-Chinese cabbage, relative to the control. High levels of PPO activity are known to induce enzymatic browning of plant tissues during storage by catalyzing the oxidation of phenolics to quinones [28]. In our study, PPO activity was positively correlated with BI in mini-Chinese cabbage. PAL and 4CL are enzymes that play an essential role in the synthesis of phenolics and their activity is a component of phenylpropane metabolism. Thus, increased PAL and 4CL activity can be responsible for an increase in the level of phenolics [29,30]. Our results revealed that the PA treatment enhanced both 4CL and PAL activity. This may be attributed to the ability of PA to reduce PPO activity which would have reduced the production of quinones [9]. PAL and 4CL activity would also increase the synthesis and accumulation of phenolics, resulting in enhanced antioxidant capacity [30]. Thus, the increase in PAL and 4CL activity by PA would increase antioxidant capacity. The increased accumulation of phenolic compounds would also enhance defense capacity [31]. Considerable evidence indicates that the oxidative stress resulting from excess production of ROS may induce the browning of plant tissues and that antioxidant enzymes, such as APX, CAT, and POD, play an essential role in reducing ROS levels and inhibiting browning [20,32,33,34]. In the present study, PA increased APX, CAT, and POD activity in mini-Chinese cabbage. These results confirm that PA plays a role in reducing excessive levels of ROS through its ability to enhance antioxidant enzyme capacity.
Browning is a great stress in wounding fruit and vegetables during postharvest, a significant postharvest physiological disorder. Plant cells subjected to stress respond by activating two aspects of phenolic metabolism [35]. One involves the antioxidant properties of phenolic compounds, along with antioxidant enzymes, that work together to reduce oxidative stress. The other involves the use of monomeric and polymeric phenolic compounds, whose synthesis is catalyzed by PAL in the phenylpropanoid pathway, to seal off injured tissues. These monomeric and polymeric phenolic compounds have strong antioxidant properties and are also used to form a physical barrier against invading pathogens [31]. In our study, the level of phenolic compounds (including gallic acid, chlorogenic acid, p-Coumaric acid, catechin, p-Hydroxybenzoic acid, ferulic acid, and cinnamic acid) and flavonoids (such as luteolin, quercetin, kaempferol, and isorhamnetin) were enhanced during storage by the PA treatment, relative to the control group. These compounds represent monomeric and polymeric phenolics that play a role in inhibiting stem browning through their ability to scavenge ROS [36] and inhibit lipid oxidation [37]. Both activities would inhibit stem browning in min-Chinese cabbages. PA enhanced the level of both total phenolics and flavonoids, and the enhanced level of these compounds was associated with reduced stem browning in mini-Chinese cabbages during storage at 25 °C.
## 5. Conclusions
Treatment of mini-Chinese cabbages with PA greatly inhibited weight loss, reduced the rate of respiration, and inhibited stem browning, which collectively served to maintain the overall postharvest quality of mini-Chinese cabbage stored at 25 °C for 5 d. The mechanism underlying the inhibition of stem browning in mini-Chinese cabbage by PA was associated with decreased levels of membrane damage, as evidenced by lower levels of electrolyte leakage and MDA, and an increase in antioxidant metabolism, as evidenced by higher antioxidant enzyme activity, and elevated levels of phenolics and flavonoids. The collective results of the present study determine that the application of PA has the potential to be used as a method to maintain the quality and to extend the shelf life of mini-Chinese cabbage after harvest and during storage.
## References
1. Tao P., Zhong X., Li B., Wang W., Yue Z., Lei J., Guo W., Huang X.. **Genome-wide identification and characterization of aquaporin genes (AQPs) in Chinese cabbage (**. *Mol. Genet. Genom.* (2014) **289** 1131-1145. DOI: 10.1007/s00438-014-0874-9
2. Hong E., Kim S.J., Kim G.H.. **Identification and quantitative determination of glucosinolates in seeds and edible parts of Korean Chinese cabbage**. *Food Chem.* (2011) **128** 1115-1120. DOI: 10.1016/j.foodchem.2010.11.102
3. Kim J.J., John K., Hae-Kyung M., Jin K., Enkhtaivan G., Kim D.H.. **Morphological and biochemical variation of Chinese cabbage (**. *J. Food Compos. Anal.* (2014) **36** 12-23
4. Kim H.W., Jang J.J., Kim N.H., Lee N.Y., Cho T.J., Kim S.H., Rhee M.S.. **Factors that determine the microbiological quality of ready-to-use salted napa cabbage (**. *Food Control* (2018) **87** 1-8. DOI: 10.1016/j.foodcont.2017.12.009
5. Hu H., Zhao H., Zhang L., Zhou H., Li P.. **The application of 1-methylcyclopropene preserves the postharvest quality of cabbage by inhibiting ethylene production, delaying chlorophyll breakdown and increasing antioxidant capacity**. *Sci. Hortic-Amst.* (2021) **281** 109986. DOI: 10.1016/j.scienta.2021.109986
6. Grzegorzewska M., Badeek E., Szczech M., Kosson R., Wrzodak A., Kowalska B., Colelli G., Szwejda-Grzybowska J., Maciorowski R.. **The effect of hot water treatment on the storage ability improvement of fresh-cut Chinese cabbage**. *Sci. Hortic-Amst.* (2022) **291** 110551. DOI: 10.1016/j.scienta.2021.110551
7. Zeng S.X., Zhao X.L., Zuo J.H., Yan Z.C., Shi J.Y., Wang Q., Cui J.C., Sui Y.. **Effect of kojic acid treatment on postharvest browning of baby cabbages**. *Food Sci.* (2021) **42** 241-247. DOI: 10.7506/spkx1002-6630-20200602-034
8. Varzakas T.. **Application of Antibrowning Agents in Minimally Processed Cabbage**. *J. Food Nutr. Disord.* (2014) **3** 1-5. DOI: 10.4172/2324-9323.1000131
9. Cabezas-Serrano A.B., Amodio M.L., Colelli G.. **Effect of solution pH of cysteine-based pre-treatments to prevent browning of fresh-cut artichokes**. *Postharvest Biol. Technol.* (2013) **75** 17-23. DOI: 10.1016/j.postharvbio.2012.07.006
10. Pilarska M., Skowron E., Pietra R., Krupinska K., Niewiadomska E.. **Changes in lipid peroxidation in stay-green leaves of tobacco with senescence-induced synthesis of cytokinins**. *Plant Physiol. Biochem.* (2017) **118** 161-167. DOI: 10.1016/j.plaphy.2017.06.018
11. Chen Y., Wang H., Xu Y., Wu J., Xiao G.. **Effect of treatment with dimethyl dicarbonate on microorganisms and quality of Chinese cabbage**. *Postharvest Biol. Technol.* (2013) **76** 139-144. DOI: 10.1016/j.postharvbio.2012.10.005
12. Li F., Huang H., Ding X., Liu J., Jiang Y.. **Effect of CPPU on postharvest attributes of Chinese flowering cabbage during storage**. *Postharvest Biol. Technol.* (2021) **174** 111438. DOI: 10.1016/j.postharvbio.2020.111438
13. González-Aguilar G., Wang C.Y., Buta J.G.. **Inhibition of Browning and Decay of Fresh-cut Radishes by Natural Compounds and their Derivatives**. *LWT—Food Sci. Technol.* (2001) **34** 324-328. DOI: 10.1006/fstl.2000.0780
14. Lian C., Mengyin C., Hetong L., Yihui C., Yifen L., Shaojun C.. **Effects of 2,4-dinitrophenol on Lipoxygenase Activity and Fatty Acid Constituents of Membrane Lipids in Pericarp of Harvested Longan Fruit**. *J. Trop. Subtrop. Bot.* (2009) **17** 477-482
15. Chen Y.H., Lin H.T., Lin Y.F., Zhao Y.F., Zhang J.N.. **Effects of Phomopsis longanae Chi Infection on Lipoxygenase Activity and Fatty Acid Constituents of Membrane Lipids in Pericarp of Harvested Longan Fruits**. *J. Trop. Subtrop. Bot.* (2011) **19** 260-266
16. Gianfranca C., Elisabetta M., Sebastiano B., Claudia M.. **Palmitic Acid: Physiological Role, Metabolism and Nutritional Implications**. *Front. Physiol.* (2017) **8** 902. PMID: 29167646
17. Lin H.T., Xi Y.F., Chen S.J.. **A review of enzymatic browning in fruit during storage**. *J. Fuzhou Univ. (Nat. Sci. Ed.)* (2002) **30** 696-703
18. Duan J., Wu R., Strik B.C., Zhao Y.. **Effect of edible coatings on the quality of fresh blueberries (Duke and Elliott) under commercial storage conditions**. *Postharvest Biol. Technol.* (2011) **59** 71-79. DOI: 10.1016/j.postharvbio.2010.08.006
19. Palou E., López-Malo A., Barbosa-Cánovas G.V., Welti-Chanes J., Swanson B.G.. **Polyphenoloxidase Activity and Color of Blanched and High Hydrostatic Pressure Treated Banana Puree**. *J. Food Sci.* (1999) **64** 42-45. DOI: 10.1111/j.1365-2621.1999.tb09857.x
20. Xu D., Gu S., Zhou F., Hu W., Feng K., Chen C., Jiang A.. **Mechanism underlying sodium isoascorbate inhibition of browning of fresh-cut mushroom (**. *Postharvest Biol. Technol.* (2021) **173** 111357. DOI: 10.1016/j.postharvbio.2020.111357
21. Fan L., Shi J., Zuo J., Gao L., Lv J., Wang Q.. **Methyl jasmonate delays postharvest ripening and senescence in the non-climacteric eggplant (**. *Postharvest Biol. Technol.* (2016) **120** 76-83. DOI: 10.1016/j.postharvbio.2016.05.010
22. Fza B., Aja B., Ke F., Sga B., Dxa B., Wha B.. **Effect of methyl jasmonate on wound healing and resistance in fresh-cut potato cubes**. *Postharvest Biol. Technol.* (2019) **157** 110958
23. Wang Q., Ding T., Zuo J.H., Gao L.P., Fan L.L.. **Amelioration of postharvest chilling injury in sweet pepper by glycine betaine**. *Postharvest Biol. Technol.* (2016) **112** 114-120. DOI: 10.1016/j.postharvbio.2015.07.008
24. Kamdee C., Ketsa S., Doorn W.. **Effect of heat treatment on ripening and early peel spotting in cv. Sucrier banana**. *Postharvest Biol. Technol.* (2009) **52** 288-293. DOI: 10.1016/j.postharvbio.2008.12.003
25. Giménez M., Valverde J.M., Valero D., Zapata P.J., Castillo S., Serrano M.. **Postharvest methyl salicylate treatments delay ripening and maintain quality attributes and antioxidant compounds of ‘Early Lory’ sweet cherry**. *Postharvest Biol. Technol.* (2016) **117** 102-109. DOI: 10.1016/j.postharvbio.2016.02.006
26. Bhattacharjee S.. **Membrane lipid peroxidation and its conflict of interest: The two faces of oxidative stress**. *Curr. Sci.* (2014) **107** 1811-1823
27. Gürbüz G., Heinonen M.. **LC–MS investigations on interactions between isolated β-lactoglobulin peptides and lipid oxidation product malondialdehyde**. *Food Chem.* (2015) **175** 300-305. DOI: 10.1016/j.foodchem.2014.11.154
28. Tareen M.J., Abbasi N.A., Hafiz I.A.. **Postharvest application of salicylic acid enhanced antioxidant enzyme activity and maintained quality of peach cv. ‘Flordaking’ fruit during storage**. *Sci. Hortic-Amst.* (2012) **142** 221-228. DOI: 10.1016/j.scienta.2012.04.027
29. Sreenivas K.M., Singhal R.S., Lele S.S.. **Chemical pretreatments and partial dehydration of ash gourd (**. *LWT—Food Sci. Technol.* (2011) **44** 2281-2284. DOI: 10.1016/j.lwt.2011.06.009
30. Han C., Li J., Jin P., Li X., Wang L., Zheng Y.. **The effect of temperature on phenolic content in wounded carrots**. *Food Chem.* (2017) **215** 116-123. DOI: 10.1016/j.foodchem.2016.07.172
31. Pati S., Losito I., Palmisano F., Zambonin P.G.. **Characterization of caffeic acid enzymatic oxidation by-products by liquid chromatography coupled to electrospray ionization tandem mass spectrometry**. *J. Chromatogr. A* (2006) **1102** 184-192. DOI: 10.1016/j.chroma.2005.10.041
32. Zhou F., Gu S., Zuo J., Gao L., Wang Q., Jiang A.. **LED irradiation delays the postharvest senescence of garland chrysanthemum (**. *J. Food Meas. Charact.* (2019) **13** 3005-3014. DOI: 10.1007/s11694-019-00221-5
33. Yan J., Song Y., Li J., Jiang W.. **Forced-air precooling treatment enhanced antioxidant capacities of apricots**. *J. Food Process. Preserv.* (2018) **42** e13320. DOI: 10.1111/jfpp.13320
34. Ribeiro C.W., Korbes A.P., Garighan J.A., Jardim-Messeder D., Carvalho F.E.L., Sousa R.H.V., Caverzan A., Teixeira F.K., Silveira J.A.G., Margis-Pinheiro M.. **Rice peroxisomal ascorbate peroxidase knockdown affects ROS signaling and triggers early leaf senescence**. *Plant Sci.* (2017) **263** 55-65. DOI: 10.1016/j.plantsci.2017.07.009
35. Surjadinata B.B., Cisneros-Zevallos L.. **Biosynthesis of phenolic antioxidants in carrot tissue increases with wounding intensity**. *Food Chem.* (2012) **134** 615-624. DOI: 10.1016/j.foodchem.2012.01.097
36. Cheng J.-C., Dai F., Zhou B., Yang L., Liu Z.-L.. **Antioxidant activity of hydroxycinnamic acid derivatives in human low density lipoprotein: Mechanism and structure-activity relationship**. *Food Chem.* (2007) **104** 132-139. DOI: 10.1016/j.foodchem.2006.11.012
37. Sroka Z., Cisowski W.. **Hydrogen peroxide scavenging, antioxidant and anti-radical activity of some phenolic acids**. *Food Chem. Toxicol.* (2003) **41** 753-758. DOI: 10.1016/S0278-6915(02)00329-0
|
---
title: 'Artificial Intelligence for Evaluation of Retinal Vasculopathy in Facioscapulohumeral
Dystrophy Using OCT Angiography: A Case Series'
authors:
- Martina Maceroni
- Mauro Monforte
- Rossella Cariola
- Benedetto Falsini
- Stanislao Rizzo
- Maria Cristina Savastano
- Francesco Martelli
- Enzo Ricci
- Sara Bortolani
- Giorgio Tasca
- Angelo Maria Minnella
journal: Diagnostics
year: 2023
pmcid: PMC10001401
doi: 10.3390/diagnostics13050982
license: CC BY 4.0
---
# Artificial Intelligence for Evaluation of Retinal Vasculopathy in Facioscapulohumeral Dystrophy Using OCT Angiography: A Case Series
## Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a slowly progressive muscular dystrophy with a wide range of manifestations including retinal vasculopathy. This study aimed to analyse retinal vascular involvement in FSHD patients using fundus photographs and optical coherence tomography-angiography (OCT-A) scans, evaluated through artificial intelligence (AI). Thirty-three patients with a diagnosis of FSHD (mean age 50.4 ± 17.4 years) were retrospectively evaluated and neurological and ophthalmological data were collected. Increased tortuosity of the retinal arteries was qualitatively observed in $77\%$ of the included eyes. The tortuosity index (TI), vessel density (VD), and foveal avascular zone (FAZ) area were calculated by processing OCT-A images through AI. The TI of the superficial capillary plexus (SCP) was increased ($p \leq 0.001$), while the TI of the deep capillary plexus (DCP) was decreased in FSHD patients in comparison to controls ($$p \leq 0.05$$). VD scores for both the SCP and the DCP results increased in FSHD patients ($$p \leq 0.0001$$ and $$p \leq 0.0004$$, respectively). With increasing age, VD and the total number of vascular branches showed a decrease ($$p \leq 0.008$$ and $p \leq 0.001$, respectively) in the SCP. A moderate correlation between VD and EcoRI fragment length was identified as well ($r = 0.35$, $$p \leq 0.048$$). For the DCP, a decreased FAZ area was found in FSHD patients in comparison to controls (t [53] = −6.89, $$p \leq 0.01$$). A better understanding of retinal vasculopathy through OCT-A can support some hypotheses on the disease pathogenesis and provide quantitative parameters potentially useful as disease biomarkers. In addition, our study validated the application of a complex toolchain of AI using both ImageJ and Matlab to OCT-A angiograms.
## 1. Introduction
Facioscapulohumeral muscular dystrophy (FSHD) is a slowly progressive muscular dystrophy with a distinctive pattern of skeletal muscle weakness and a wide range of disease severity [1]. Subjects show progressive loss of muscle mass and strength, as well as replacement with fat and connective tissue in selected muscle groups [2], often with an asynchronous and asymmetrical pattern [3,4].
As first revealed by Fitzsimons et al. in 1987 [5] and then confirmed by Padberg et al. in 1995 [6], a retinal vasculopathy is considered an established component of the FSHD phenotype [7]. Traditional ophthalmologic findings in FSHD include retinal vascular tortuosity and retinal vessel abnormalities on fluorescein angiography (FA) such as teleangectasia, microaneurysms, areas of capillary closure, and fluorescein leakage due to increased permeability. The leakage of plasma constituents can occasionally lead to exudative retinal detachment [5]. A secondary glaucoma due to neovascularization can develop [8]. However, retinal vascular changes in FSHD patients are often subclinical. Current guidelines advise referral to ophthalmological specialists for FSHD patients with visual complaints or with a severe form of the disease. However, data on the frequency of assessment and the techniques to be used for accurate ophthalmological monitoring in FSHD are lacking.
Minor retinal vascular alterations are undetectable with fundus examination; thus, fluorescein angiography (FA) is considered the gold standard for evaluating retinal vasculature [9]. However, FA is an invasive and time-consuming technique that allows the visualization of the superficial vascular plexus only [10], and dye leakage as well as haemorrhage or opacities can make the underlying retinal pathology undetectable. First adapted from optical coherence tomography (OCT), OCT-angiography (OCT-A) is a recently developed imaging technique that can non-invasively image all the layers of the retinal vasculature without dye injection by processing the motion of erythrocytes [11]. More specifically, OCT-A provides depth-resolved images of blood flow in the retina and choroid with a resolution level several times higher than that obtained with older forms of imaging [12], providing quantitative parameters such as the foveal avascular zone (FAZ) area and vessel density (VD). Despite these advantages, except for one study [13], updated information about ophthalmological findings in FSHD detected using OCT-A is missing.
In medicine and healthcare, artificial intelligence (AI) has been primarily applied to the field of medical image analysis, where it has shown robust diagnostic performance. Over the past few years, AI has similarly been applied to ocular imaging, mainly fundus photographs, OCT, and OCT-A. A better understanding of retinal vasculopathy through OCT-A and AI-based analysis of angiograms appears particularly promising since it can provide quantitative parameters potentially serving as disease biomarkers.
The aim of this study was to evaluate retinal vascular involvement in FSHD using colour fundus photography and swept-source OCT-A, analysed through artificial intelligence (AI).
## 2. Materials and Methods
This retrospective study was approved by the Ethics Committee/Institutional Review Board of the Catholic University. This research adhered to the tenets of the Declaration of Helsinki and informed consent was obtained from all patients after full and detailed explanation of the goals and procedures of the study. All the clinical and imaging data reported in this study were retrospectively re-evaluated. Recruitment was performed according to a collaboration protocol with the Department of Neurology of Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli IRCCS.
## 2.1. Inclusion and Exclusion Criteria
Inclusion criteria were an established clinical diagnosis of FSHD type 1, confirmed by genetic testing (presence of a 4q35 BlnI resistant, p13-E11 EcoRI fragment whose length was <40 kb), and the possibility of obtaining good quality ocular imaging.
A pre-existing dataset of healthy controls was used for comparison.
## 2.2. Neurological Examination
Patients underwent a complete neurological examination inclusive of the clinical severity score (CSS) [14] to assess disease severity. The CSS is a 10-grade scale, which takes into account the extent of weakness in various muscular districts, considering the descending spread of symptoms from the face and shoulders to pelvic and leg muscles typical of FSHD. Higher scores were assigned to patients with involvement of pelvic and proximal lower limb muscles [14]. The CSS was then corrected for the patient’s age at examination (aCSS):Age-corrected CSS = ((CSS × 2)/age at examination) × 1000 Before dividing by the age at examination, the severity score is multiplied by two to generate whole numbers. Then, the outcome of this calculation is multiplied by 1000 to improve the interpretation of the results and visualization in graphs [15].
## 2.3. Ophthalmological Assessment
All patients underwent a full ophthalmologic evaluation including best corrected visual acuity (BCVA), anterior segment slit lamp biomicroscopy examination, tonometry, and fundus ophthalmoscopy after pupil dilation.
Colour fundus photography and 3 mm × 3 mm OCT-A scans (320 × 320 pixels, 24-bit RGB) of the superficial (layer 1) and deep capillary (layer 2) were acquired for each patient using a DRI Triton Swept-Source OCT device (Topcon, Tokyo, Japan). The level of segmentation for each capillary plexus was automatically provided by the instrument. To detect the superficial capillary plexus (SCP), the upper segmentation line was situated at 2.6 µm under the inner limiting membrane (ILM), whereas the lower segmentation line was located 15.6 µm under the junction between the inner plexiform layer (IPL) and inner nuclear layer (INL). To identify the deep capillary plexus (DCP), segmentation lines were placed 15.6 µm under the junction between the IPL and INL and 70.2 µm under the junction between the IPL and INL. In case of incorrect automatic segmentation, segmentation boundaries were manually adjusted.
Colour fundus photographs were qualitatively assessed by two independent graders, blinded for the patient characteristics, to score vessel tortuosity through a four-point grading scale (none, mild, moderate, or severe).
## 2.4. OCT-A Image Processing
Each enrolled subject had both eyes scanned. However, in order to provide statistical sample independence [16], data from only one eye, randomly selected for each subject, were included in the analysis.
OCT scans were preliminarily examined for the presence of artifacts and then processed to obtain quantitative parameters including vessel tortuosity, vessel density, FAZ area, and FAZ acircularity. Image processing was performed using a combination of Mathwork’s Matlab (MathWorks, Inc., Natick, MA, USA), Fiji [17], and other Fiji plugins as described in Figure 1. Before the image processing steps, all OCT scans were converted into grayscale. Matlab and ImageJ/Fiji integration was made possible using two other Fiji plugins [18,19]. The machine learning classification task was performed using Fiji’s Trainable Weka Segmentation plugin [20,21], a wrapper around a Java-based machine learning workbench called WEKA (Waikato environment for knowledge analysis), developed by the Machine Learning Group at the University of Waikato (Hamilton, New Zealand) [21]. We followed the procedure described by Goselink et al. [ 13] and Lee et al. [ 11]. Briefly, the training features selected were Gaussian blur, Sobel filter, Hessian, difference of Gaussian, and membrane projections (membrane thickness 1, membrane patch size 19, minimum sigma 1, maximum sigma 16). Training of the algorithm was performed on a randomly selected OCT-A image. Two distinct models were trained, one for the superficial retinal layer, and one for the deep retinal layer. Using the trained model(s), classification was performed for all the images in the dataset (patients and healthy subjects) using the FastRandomForest classifier, a multi-threaded version of random forest [22], initialized with 200 trees and 2 random features per node. The classifier’s output consists of a segmentation probability map highlighting the retinal structures detected as vessels. The probability map was converted into a binarized image in Matlab.
## 2.4.1. Tortuosity Index (TI)
The binarized image was skeletonized (each white object in the binary image was converted to a single-pixel line) in Fiji, and the skeleton features (branch length, vertices positions, branch euclidean distance) calculated in Fiji were used to compute the tortuosity index, as defined in Lee et al. 2017 [11]. In detail, the actual length of each branch and the imaginary straight length between two branch nodes—points of connections—were marked and calculated. Then, vessel tortuosity was calculated as the sum of branch lengths divided by the sum of imaginary straight lines between branch nodes [11]. Vessel tortuosity = sum of actual branch lengths/sum of straight lengths between branch nodes.
## 2.4.2. Vessel Density Score (VD Score)
From the binarized image, the VD score was calculated as a ratio of the number of pixels of the corresponding vascular tissue to the total number of pixels in the image, following the approach described in Minnella et al., 2019 [23].
## 2.4.3. FAZ Area
FAZ area calculations were performed on the binarized image, with a morphological closing on the image itself using a single-disk structuring element of fixed size (20 pixels) [24]. Conversion from measurements expressed in pixels in metric lengths and areas was performed considering a pixel transverse size of 9.37 µm [25].
## 2.5. Statistical Analysis
All statistical calculations were performed using OriginLabs’ Origin Pro 2016. A p-value < 0.05 was considered statistically significant. Values were expressed as frequencies (%), mean ± standard deviation (SD), or median (interquartile range, IQR) as appropriate.
After checking for normality, a two-sample t-test was used to assess differences in patient and control measurements.
## 3.1. Population
A total of 33 patients (15 males, 18 females, mean age 50.4 ± 17.4, ranging from 13 to 76 years) with a diagnosis of FSHD and 22 healthy subjects (8 males, 14 females, mean age 44.7 ± 11.3 years) were included in the analysis. A total of 66 eyes from the 33 patients were initially included in this study. All patients were clinically affected with a median of 3.5 points (range 1.5–5) on the 10-point CSS [14] and 148.1 points (±60.9, 46.2–333.3) on the aCSS [15]. The mean EcoRI fragment size was 22.3 kb (±6.1 SD) ranging from 10 to 35 kb. Clinical data are summarized in Table 1.
## 3.2. Ophthalmological Examination
The mean BCVA was 0.9 (decimal) ± 0.2 standard deviation (SD) and intraocular pressure (IOP) was within normal values in all the examined eyes.
## 3.2.1. Colour Fundus Photography
Tortuosity of the retinal arteries was observed in 48 ($71\%$) eyes: mild, moderate, and severe vascular tortuosity were found in 17, 25, and 6 eyes, respectively (Table 1, Figure 2)
## 3.2.2. Optical Coherence Tomography Angiography
A random sampling was performed in order to select a single (left or right), random eye OCT-A from the 132 available patient scans.
For the deep layer, 33 patients (20 right eyes and 13 left eyes) and 22 healthy subjects (11 right eyes and 11 left eyes) were selected for the following analyses, while for the superficial layer, 33 patients (16 right eyes and 17 left eyes) and 22 healthy subjects (14 right eyes and 8 left eyes) were included for a total of 110 OCT-A scans.
## 3.3. Machine Learning Results
The machine learning method correctly identified the major vessels. A representative sample of the processing results for a patient and a control is shown in Figure 3.
## 3.3.1. Tortuosity Index
The TI of the superficial layer (SCP) was increased in FSHD patients (mean 1.16 ± 0.01) in comparison to controls (mean 1.15 ± 0.01); (t [53] = 3.62, $p \leq 0.001$) (Figure 4A). The deep layer (DCP) showed a decrease (−0.07,) in the TI in FSHD (mean 1.17 ± 0.01) patients in comparison to controls (mean 1.24 ± 0.01) (t [53] = −23.5, $$p \leq 0.05$$) (Figure 4B).
However, although statistically significant, the interpretation of this last result should take into consideration that the reliability of vessel length calculations could not be visually assessed in the deep layer (Figure 5). No significant correlations were found between the TI and clinical parameters.
## 3.3.2. Vessel Density Score
The VD score in the SCP (Figure 6A) was increased in FSHD patients (mean 38.03 ± 4.32) compared to normal (mean 25.40 ± 1.58) controls (t [53] = 13.10, $$p \leq 0.0001$$).
Similarly, the VD score in the DCP (Figure 6B) was increased in FSHD patients (mean 45.52 ± 2.37) compared to normal (mean 29.07 ± 1.88) controls (t [53] = 27.31, $$p \leq 0.0004$$). In addition, a significant correlation was found between age and vascular parameters. With increasing age, VD scores and the total number of vascular branches showed a decrease (r = −0.45, $$p \leq 0.008$$ and r = −0.51, $p \leq 0.001$, respectively) in the superficial layer, realistically for a progressive age-related vascular rarefaction. A moderate correlation between VD and EcoRI fragment length was identified as well ($r = 0.35$, $$p \leq 0.048$$).
## 3.3.3. Foveal Avascular Zone
The FAZ was automatically delineated, and its area was calculated considering a pixel size of 9.375 µm. As an example, we show in Figure 7 some cases of FAZ calculations. For the SCP, no statistically significant differences were found between FSHD patients (mean 0.29 ± 0.12) and healthy controls in FAZ areas (mean 0.34 ± 0.13) (Figure 7A). FSHD patients showed a sex difference, with the FAZ area being larger in females than in males (0.33 mm2 vs. 0.22 mm2; t [31] = −3.2, $$p \leq 0.003$$), in SCP only. The FAZ area of the SCP showed a positive correlation with CSS ($r = 0.55$ $p \leq 0.001$). In the DCP, a decreased FAZ area (−0.39 mm2) was found in FSHD patients (mean 0.40 ± 0.16) in comparison to controls (mean 0.79 ± 0.26), t [53] = −6.89, $$p \leq 0.01$$) (Figure 7B). The results are summarized in Table 2.
## 4. Discussion
The present study analysed retinal vascular involvement in FSHD, using fundus photographs and swept-source OCTA, in order to refine the ophthalmological phenotype of FSHD subjects, both in qualitative and quantitative terms. In our study, fundus photographs showed a high prevalence of retinal arterial tortuosity ($77\%$), confirming evidence in the literature [5,6,13]. However, these retinal vascular changes did not cause complaints or vision loss in any patient. The use of OCTA provided a more detailed insight into FSHD ophthalmological manifestations. The quantitative analysis of the TI, VD score, and FAZ area showed statistically significant differences between FSHD patients and controls. FSHD patients showed an increase in the TI of the SCP, a decrease in the TI in the DCP, an increase in the VD score of both SCP and DCP, and a decrease in the FAZ area in the DCP in comparison to controls. Interestingly, a gender difference was found in the FAZ area (SCP), with higher values in females. Currently, there is no consensus in the literature about the effects of gender on retinal vascularity. VD and perfusion density (PD) seem to be not affected by sex [26]. The sole parameter potentially affected by gender is the FAZ area, which is larger in females compared to males [27]. Our findings on the FAZ area in FSHD patients reflect those of the general population.
In the present study, we found that, with increasing age, VD and the total number of vascular branches showed a decrease in FSHD patients. Age can have an impact on vascular results, considering that elderly subjects usually present a vascular rarefaction of the capillary plexa. However, the FSHD population (tendentially older than controls) presented an increased VD both in the SCP and DCP.
Our data on the TI support what was found by Goselink et al. [ 13]: the absence of smooth muscle in the capillary vessel wall as opposed to retinal arterioles could provide a possible explanation for the TI increase in the superficial layers. Our results are instead novel regarding VD scores. A possible molecular link between FSHD pathophysiology and neoangiogenesis, plausibly responsible for the increase in VD, is provided by the upregulation of several genes involved in neovessel formation [28], and in particular of the Wnt/Frizzled signalling pathway in the skeletal muscle of patients with active disease [29]. Further studies will be needed to confirm the relevance of this or other molecular pathways to the reported retinal vascular proliferation [30].
The FAZ area decreasing in DCP was likely related to the VD increase. The FAZ area is inversely connected to the microcirculatory condition, as demonstrated by the FAZ area enlargement in diabetic retinopathy and retinal vascular occlusive diseases for the destruction of the vascular arcades [31]. In addition, our study is noteworthy for validating the application of a complex toolchain of AI using both ImageJ and Matlab to OCTA angiograms. This pipeline could be replicated and applied in other studies.
## Considerations of AI and Study Limitations
While the FAZ area and VD calculations have shown a very good tolerance to image quality and artefacts, TI figures should be interpreted with the greatest care. We strictly followed the approach by Goselink et al. [ 13] for the TI calculations, in order to be able to compare our results to previous works. However, we have already raised some concerns [32] about the reliability of this method on the SCP. Specifically, we noticed that TI calculation reliability may be affected by the quality of segmentation in a somehow unpredictable manner. Even more, regarding the DCP, we must highlight that the presence of a complex lattice of smaller capillaries turns into the detection of an extremely fragmented short vessel network, a situation where tortuosity computation by itself may be rendered meaningless. Moreover, in the skeletonization process, the lengths of the vessels are calculated. However, this process calculates the lengths of the “branches”, which are the lines between the dots along the vessels, and therefore does not calculate the total length of the vessels, as a human evaluator would do.
The relatively small sample size is also a partial limitation of this study. In addition, we included only FSHD type 1 patients, but exploring possible similarities or differences in ophthalmological characteristics of FSHD type 2 patients could be worthwhile.
Further studies would be needed to clarify the potential correlations between the quantitative OCTA parameters and the severity of the muscular disease, and to determine which patients may have or develop more severe disease [7]. Thus, OCTA could become an important tool for the routine ophthalmological evaluation of FSHD patients.
## 5. Conclusions
The importance of assessing ophthalmological abnormalities in FSHD is not restricted to the possibility of treatable vision loss. Looking at retinal vasculopathy as an integral part of FSHD opens new horizons, helping to understand the pathogenesis of the disease. OCTA is a non-invasive tool potentially useful to assess retinal vasculopathy and to provide promising disease biomarkers. The identification of new parameters potentially associated with prognosis appears pivotal in neuromuscular disorders such as FSHD where the extent and severity of involvement can vary enormously.
## References
1. Tawil R., Kissel J.T., Heatwole C., Pandya S., Gronseth G., Benatar M.. **Evidence-based guideline summary: Evaluation, diagnosis, and management of facioscapulohumeral muscular dystrophy: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology and the Practice Issues Review Panel of the American Association of Neuromuscular & Electrodiagnostic Medicine**. *Neurology* (2015.0) **85** 357-364. PMID: 26215877
2. De Simone A.M., Pakula A., Lek A., Emerson C.P.. **Facioscapulohumeral Muscolar Dystrophy**. *Compr. Physiol.* (2017.0) **7** 1229-1279. PMID: 28915324
3. Tasca G., Monforte M., Ottaviani P., Pelliccioni M., Frusciante R., Laschena F., Ricci E.. **Magnetic resonance imaging in a large cohort of facioscapulohumeral muscular dystrophy patients: Pattern refinement and implications for clinical trials**. *Ann. Neurol.* (2016.0) **79** 854-864. DOI: 10.1002/ana.24640
4. Monforte M., Laschena F., Ottaviani P., Bagnato M.R., Pichiecchio A., Tasca G., Ricci E.. **Tracking muscle wasting and disease activity in facioscapulohumeral muscular dystrophy by qualitative longitudinal imaging**. *J. Cachexia Sarcopenia Muscle* (2019.0) **10** 1258-1265. DOI: 10.1002/jcsm.12473
5. Fitzsimons R.B., Gurwin E.B., Bird A.C.. **Retinal vascular abnormalities in facioscapulohumeral muscular dystrophy. A general association with genetic and therapeutic implications**. *Brain* (1987.0) **110** 631-648. DOI: 10.1093/brain/110.3.631
6. Padberg G.W., Brouwer O.F., De Keizer R.J., Dijkman G., Wijmenga C., Grote J.J., Frants R.R.. **On the significance of retinal vascular disease and hearing loss in facioscapulohumeral muscular dystrophy**. *Muscle Nerve* (1995.0) **2** 73-80. DOI: 10.1002/mus.880181314
7. Longmuir S.Q., Mathews K.D., Longmuir R.A., Joshi V., Olson R.J., Abràmoff M.D.. **Retinal but not venous tortuosity correlates with facioscapulohumeral muscular dystrophy severity**. *J. AAPOS* (2010.0) **14** 240-243. DOI: 10.1016/j.jaapos.2010.03.006
8. Shields C.L., Zahler J., Falk N., Furuta M., Eagle R.C., Espinosa L.E., Fischer P.R., Shields J.A.. **Neovascular glaucoma from advanced Coats disease as the initial manifestation of facioscapulohumeral dystrophy in a 2-year-old child**. *Arch. Opthalmol.* (2007.0) **125** 840-842. DOI: 10.1001/archopht.125.6.840
9. Wang L., Murphy O., Caldito N.G., Calabresi P.A., Saidha S.. **Emerging Applications of Optical Coherence Tomography Angiography (OCTA) in neurological research**. *Eye Vis.* (2018.0) **5** 11. DOI: 10.1186/s40662-018-0104-3
10. Spaide R.F., Klancnik J.M., Cooney M.J.. **Retinal Vascular Layers Imaged by Fluorescein Angiography and Optical Coherence Tomography Angiography**. *JAMA Ophthalmol.* (2015.0) **133** 45-50. DOI: 10.1001/jamaophthalmol.2014.3616
11. Lee H., Lee M., Chung H., Kim H.C.. **Quantification of retinal vessel tortuosity in diabetic retinopathy using optical coherence tomography angiography**. *Retina* (2018.0) **38** 976-985. DOI: 10.1097/IAE.0000000000001618
12. Spaide R.F., Fujimoto J.G., Waheed N.K., Sadda S.R., Staurenghi G.. **Optical coherence tomography angiography**. *Prog. Retin. Eye Res.* (2018.0) **64** 1-55. DOI: 10.1016/j.preteyeres.2017.11.003
13. Goselink R.J., Schreur V., van Kernebeek C.R., Padberg G.W., van der Maarel S.M., van Engelen B.G., Erasmus C.E., Theelen T.. **Ophthalmological findings in facioscapulohumeral dystrophy**. *Brain Commun.* (2019.0) **1** 1-9. DOI: 10.1093/braincomms/fcz023
14. Ricci E., Galluzzi G., Deidda G., Cacurri S., Colantoni L., Merico B., Piazzo N., Servidei S., Vigneti E., Pasceri V.. **Progress in the molecular diagnosis of facioscapulohumeral muscular dystrophy and correlation between the number of KpnI repeats at the 4q35 locus and clinical phenotype**. *Ann. Neurol.* (1999.0) **45** 751-757. DOI: 10.1002/1531-8249(199906)45:6<751::AID-ANA9>3.0.CO;2-M
15. Van Overveld P.G., Enthoven L., Ricci E., Rossi M., Felicetti L., Jeanpierre M., Winokur S.T., Frants R.R., Padberg G.W., Van Der Maarel S.M.. **Variable hypomethylation of D4Z4 in facioscapulohumeral muscular dystrophy**. *Ann. Neurol.* (2005.0) **58** 569-576. DOI: 10.1002/ana.20625
16. Armstrong R.A.. **Statistical guidelines for the analysis of data obtained from one or both eyes**. *Ophthalmic Physiol. Opt.* (2013.0) **33** 7-14. DOI: 10.1111/opo.12009
17. Schindelin J., Arganda-Carreras I., Frise E., Kaynig V., Longair M., Pietzsch T., Preibisch S., Rueden C., Saalfeld S., Schmid B.. **Fiji: An open-source platform for biological-image analysis**. *Nat. Methods* (2012.0) **9** 676-682. DOI: 10.1038/nmeth.2019
18. Sage D., Prodanov D., Tinevez J.Y., Schindelin J.. **MIJ: Making Interoperability Between ImageJ and Matlab Possible**. *ImageJ User & Developer Conference (IUDC’12), Mondorf-les-Bains* (2012.0)
19. Hiner M.C., Rueden C.T., Eliceiri K.W.. **ImageJ-MATLAB: A bidirectional framework for scientific image analysis interoperability**. *Bioinformatics* (2017.0) **33** 629-630. DOI: 10.1093/bioinformatics/btw681
20. Arganda-Carreras I., Kaynig V., Rueden C., Schindelin J., Cardona A., Seung H.S., Ezzat M., Hiner M., Freydiere P.. **Trainable_Segmentation: Release v3.1.2**. *Zenodo* (2016.0) 59290
21. **Software—Artificial Intelligence Institute: University of Waikato**
22. Breiman L.. **Random Forests**. *Mach. Learn.* (2001.0) **45** 5-32. DOI: 10.1023/A:1010933404324
23. Minnella A.M., Barbano L., Verrecchia E.. **Macular impairment in Fabry disease: A morpho-functional assessment by swept-source OCT angiography and Focal Electroretinography**. *Investig. Opthalmol. Vis. Sci.* (2019.0) **60** 2667-2675. DOI: 10.1167/iovs.18-26052
24. Savastano M.C., Gambini G., Cozzupoli G.M., Crincoli E., Savastano A., De Vico U., Culiersi C., Falsini B., Martelli F., Minnella A.M.. **Retinal capillary involvement in early post-COVID-19 patients: A healthy controlled study**. *Graefe’s Arch. Clin. Exp. Ophthalmol.* (2021.0) **259** 2157-2165. DOI: 10.1007/s00417-020-05070-3
25. Lu Y., Wang J.C., Zeng R., Katz R., Vavvas D.G., Miller J.W., Miller J.B.. **Quantitative Comparison Of Microvascular Metrics On Three Optical Coherence Tomography Angiography Devices In Chorioretinal Disease**. *Clin. Ophthalmol.* (2019.0) **13** 2063-2069. DOI: 10.2147/OPTH.S215322
26. Yu J., Jiang C., Wang X., Zhu L., Gu R., Xu H., Jia Y., Huang D., Sun X.. **Macular perfusion in healthy Chinese: An optical coherence tomography angiogram study**. *Investig. Ophthalmol. Vis. Sci.* (2015.0) **56** 3212-3217. DOI: 10.1167/iovs.14-16270
27. Gómez-Ulla F., Cutrin P., Santos P., Fernandez M., Abraldes M., Abalo-Lojo J.M., Gonzalez F.. **Age and gender influence on foveal avascular zone in healthy eyes**. *Exp. Eye Res.* (2019.0) **189** 107856. DOI: 10.1016/j.exer.2019.107856
28. Osborne R.J., Welle S., Venance S.L., Thornton C.A., Tawil R.. **Expression profile of FSHD supports a link between retinal vasculopathy and muscular dystrophy**. *Neurology* (2007.0) **68** 569-577. DOI: 10.1212/01.wnl.0000251269.31442.d9
29. Tasca G., Pescatori M., Monforte M., Mirabella M., Iannaccone E., Frusciante R., Cubeddu T., Laschena F., Ottaviani P., Ricci E.. **Different molecular signatures in magnetic resonance imaging-staged facioscapulohumeral muscular dystrophy muscles**. *PLoS ONE* (2012.0) **7**. DOI: 10.1371/journal.pone.0038779
30. Fitzsimons R.B.. **Retinal vascular disease and the pathogenesis of facioscapulohumeral muscular dystrophy. A signalling message from Wnt?**. *Neuromuscul. Disord.* (2011.0) **21** 263-271. DOI: 10.1016/j.nmd.2011.02.002
31. Freiberg F.J., Pfau M., Wons J., Wirth M.A., Becker M.D., Michels S.. **Optical coherence tomography angiography of the foveal avascular zone in diabetic retinopathy**. *Graefe’s Arch. Clin. Exp. Ophthalmol.* (2016.0) **254** 1051-1058. DOI: 10.1007/s00417-015-3148-2
32. Martelli F., Giacomozzi C.. **Tortuosity Index Calculations in Retinal Images: Some Criticalities Arising from Commonly Used Approaches**. *Information* (2021.0) **2**. DOI: 10.3390/info12110466
|
---
title: The Use of Complementary and Alternative Medicine among Peritoneal Dialysis
Patients at a Second-Level Hospital in Yucatán Mexico
authors:
- Carlos Gracida-Osorno
- Sandra Luz Jiménez-Martínez
- Andrés Humberto Uc-Cachón
- Gloria María Molina-Salinas
journal: Healthcare
year: 2023
pmcid: PMC10001407
doi: 10.3390/healthcare11050722
license: CC BY 4.0
---
# The Use of Complementary and Alternative Medicine among Peritoneal Dialysis Patients at a Second-Level Hospital in Yucatán Mexico
## Abstract
Background: Complementary and alternative medicine (CAM) is widely used for multiple reasons such as treatment of diseases and their symptoms, empowerment, self-care, disease prevention, dissatisfaction, adverse effects or cost of conventional medicine, perception of compatibility with beliefs, and idiosyncrasy. This study investigated CAM use in patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD). Methods: A cross-sectional survey study was conducted with 240 eligible patients with CKD in the PD program. By applying the I-CAM-Q-questionnaire, the frequency, level of satisfaction, and reasons for CAM use were explored, and the demographic and clinical data of users and non-users were analyzed. Data analysis included descriptive analysis, Student’s t-test, Mann-Whitney U, chi-square, and Fisher tests. Results: The main types of CAM used were herbal medicine, and chamomile was the most commonly used. To improve the state of well-being was the main reason for use, the attributable benefit of CAM was high, and only a low percentage of users reported side effects. Only $31.8\%$ of the users informed their physicians. Conclusion: The use of CAM is popular among renal patients, and physicians are not adequately informed; in particular, the CAM type ingested represents a risk for drug interactions and toxicity.
## 1. Introduction
Chronic kidney disease (CKD) is a condition that leads to disability, decreased quality of life, and substantial social and financial costs. It is now recognized as a global public health priority that has reached epidemic proportions worldwide, with a consequent impact on morbidity and mortality and cost to the health system. In 2017, the global prevalence of CKD was $9.1\%$, and in 2019, the Pan American Health Organization estimated that it is a leading cause of disease burden, categorized as the 8th cause of mortality and the 10th cause of years of life lost in both sexes [1,2].
CKD is more prevalent in people with obesity, hypertension, and/or diabetes mellitus as well as elderly people, women, and racial minorities and is expected to increase, including the stage with the requirement of a renal replacement therapy, namely peritoneal dialysis (PD) [3,4].
Approximately $80\%$ of the world’s population uses complementary and alternative medicine (CAM) to maintain their health [5,6]. The use of CAM by the population has experienced significant growth in the last 15 years, with consequent medical, economic, and sociological impacts; this increase is especially evident in individuals with chronic diseases. Patients mention using it for multiple reasons, such as for the treatment of diseases and their symptoms, but also for the maintenance of health, empowerment, self-care, disease prevention, improvement of quality-of-life dissatisfaction with allopathic medicine, adverse effects of the medications, and their cost. Additional reasons include the need for the patient to control the evolution of their disease and the perception of compatibility of the use of CAM with their values, beliefs, and idiosyncrasy [7,8,9,10].
Studies conducted to date suggest that adult patients with chronic diseases such as diabetes mellitus, systemic arterial hypertension, chronic kidney disease, cancer, chronic obstructive pulmonary disease, and rheumatoid arthritis, among others, are more prone to the use of CAM as part of self-care and management of their condition and are more likely to use CAM at greater comorbidity [11,12,13,14,15].
Doctors are often inadequately informed by their patients about their CAM use; for example, only $18\%$ of Polish cancer patients discussed using CAM with a doctor [16] in contrast with the $60\%$ of pediatric oncology patients from Switzerland that discussed use of CAM with their oncologist [17] and the $31.3\%$ of Colombian rheumatic patients who use CAM and informed their rheumatologists because of fear of retaliation [18].
In Mexico, only a few studies have been carried out that described the use of CAM in this population. Carmona-Sanchez reported use of CAM to treat various digestive disorders (prevalence 28–$51\%$) [19]; Herrera-Arellano et al. reported that $73.4\%$ of HIV-positive patients were users of some type of CAM [20], and the cactus—nopal—was the most common indigenous remedy used to treat diabetes mellitus ($73.1\%$). The geographic region of the Yucatán Peninsula was the region of founding of the Mayan culture, which reached an important degree of development in the field of traditional medicine, characterized using various preparations of plants, animals, and minerals with curative action by traditional doctors or healers; in addition, the exercise of Mayan medicine was entrusted to three specialists of different ranks: the h-men (priest), dza-dzac (the one who heals with herbs), and the pulyah (sorcerer) [21]. Mayan medicine continues to be used both in rural and urban-zone populations of the Yucatán Peninsula.
The use of CAM in patients with chronic diseases has become an interesting topic for academics and for medical doctors. Patients with chronic diseases generally have more than one type of ailment, so it is very important that physicians know and understand the reasons that influence patients to use any type of CAM, with the aim to be their guide in decisions to better treatments and consequently aid health care systems. The absence of adequate information from patients to physicians may also be related to social perceptions [22,23].
CAM is widely used in the general population, and its use in patients with CKD has been only slightly investigated worldwide. Patients with PD may be more likely to be users of CAM in view of the chronicity of their disease and the various comorbidities. Moreover, this group of patients could be at an increased risk of drug interactions, toxicity, or electrolyte disturbances owing to the absence of renal excretory function.
The prevalence and types of CAM used among Mexican CKD patients are unknown. Therefore, the present survey was designed to document the frequency and types of CAM usage in patients with CKD treated with PD who attended at a regional hospital in Yucatán, Mexico, using a validated questionnaire. This study also investigated the report to their doctors about the use of CAM, reason for use, and perception of the benefit of patients.
## 2.1. Study Design and Setting
This is a cross-sectional study. Data collection was conducted among patients with CKD who received PD and were invited to participate for a phone call, and in the medical appointment follow-up, the patients were interviewed face-to-face by two experienced researchers. The study was conducted at the Dialysis Clinic in the Regional General Hospital “Ignacio García Téllez” of the Instituto Mexicano del Seguro Social (IMSS), a second-level hospital located in Mérida Yucatán, Mexico, from November to December 2018.
## 2.2. Study Population
A total of 319 patients from the PD program of the Ignacio García Téllez Hospital were invited to participate in this study ($$n = 319$$). The inclusion criteria were an age older than 18 years, with at least one clinical evaluation by a nephrologist of the PD program within the last two months, and who agreed to participate with verbal answers to the questionnaire by interview. The exclusion criteria were as follows: renal transplant, age < 18 years old, refusal to participate, undergoing hemodialysis, deceased during the study period, and did not attend the appointment.
## 2.3. Sample Size
The sample size for survey was estimated using an $18\%$ anticipated prevalence of use of CAM for dialysis patients [24], and with a $95\%$ level of confidence and a $5\%$ margin of error, the estimated sample size was 227 (Epi Info v. 7.2 CDC, Atlanta, GA, USA). The final sample was comprised of 240 PD patients from our hospital (Figure 1).
## 2.4. Research Instrument
The data were collected by a questionnaire previously reported and validated and known as I-CAM-Q (the International Complementary and Alternative Medicine Questionnaire), which includes four parts: (a) examination by health provider, (b) complementary treatments, (c) use of herbal medicine and dietary supplements, and (d) self-help practices [25,26] (Supplementary Materials).
## 2.5. Clinical Characteristics and Biochemical Parameters of Patients
The clinical characteristics of patients were obtained during the medical appointment by clinical exploration.
The biochemical parameters of the patients were obtained from their clinical appointment and corresponded to previous bimonthly medical appointments. After at least 12 h fasting, venous blood samples were collected to measure the complete blood count and various biochemical components (glucose, creatinine, urea, uric acid, albumin, calcium, phosphorus, and potassium).
## 2.6. Ethics
The project was approved by the local committee of Investigation and Ethic 3201 of Regional General Hospital Ignacio García Tellez IMSS (registered number R-2018-3201-26). The participants were given information about the aim of the study and the content of the questionnaire. Informed consent was obtained from all patients before confirming their participation in this investigation.
## 2.7. Data Analysis
Continuous variables are expressed as arithmetic mean ±1 standard deviation (±1SD), and categorical variables are presented as frequencies and percentages. For comparison and analysis, the study population was divided into two groups: CAM users and non-CAM users. Continuous variables were analyzed using the Student’s t-test or Mann-Whitney’s U test depending on the normality distribution of the data, while the categorical variables of the two groups were analyzed using the chi-square test or Fisher’s exact test. Differences were considered statistically significant at p-value < 0.05.
## 3.1. Use of CAM
The sociodemographic characteristics of patients are shown in Table 1. The frequency of CAM use in the study population was $55.0\%$ ($\frac{132}{240}$), of which $50.8\%$ ($\frac{67}{132}$) were male and $49.2\%$ ($\frac{65}{132}$) women. No statistically significant differences were observed between the sociodemographic characteristics of the CAM users and non-users (Table 1).
## 3.2. Type of CAM
The most common type of CAM used by patients was herbal medicine ($50.0\%$, $\frac{66}{132}$), followed by mind-body practices such as music therapy ($24.2\%$, $\frac{32}{132}$) and relaxation techniques ($18.9\%$, $\frac{25}{132}$) (Table 2).
Most of our study population ($65.1\%$; $\frac{86}{132}$) referred to using only one type of CAM, while $34.8\%$ ($\frac{46}{132}$) of patients used more than one CAM in combination; the majority of them used two types of CAM ($67.4\%$; $\frac{31}{46}$), followed by three ($28.3\%$; $\frac{13}{46}$). The most frequent combination of CAM was herbal medicine and music therapy, followed by herbal medicine and spiritual healing. The distribution of the combinations of CAM types used by the study population is shown in Figure 2.
Regarding herbal medicine, the patients used more than two types of herbal products ($40.9\%$; $\frac{27}{66}$), such as teas, referencing a total 36 different types of herbs and natural products. The most frequently used was chamomile with $25.8\%$ ($\frac{25}{97}$), followed by moringa leaves with $16.0\%$ ($\frac{15}{97}$), chaya leaves with $8.5\%$ ($\frac{8}{97}$), and orange leaves with $6.2\%$ ($\frac{6}{97}$) (Table 3).
## 3.3. Reason to Use CAM
The most frequent reason for using CAM was to improve well-being ($71.7\%$; $\frac{132}{184}$). This reason was referred to by $100\%$ of practitioners of relaxation techniques ($\frac{25}{25}$), meditation ($\frac{9}{9}$), and Tai Ji Quan ($\frac{2}{2}$); $85.7\%$ of practitioners of music therapy ($\frac{30}{35}$); $83.6\%$ of those who attended healing ceremonies ($\frac{5}{6}$); and $83.3\%$ of spiritual healing practitioners ($\frac{10}{12}$). The second reason for using CAM was for chronic health problems, which was the most frequent answer among herbal medicine users ($40.9\%$, $\frac{27}{66}$) (Figure 3).
## 3.4. Perception of Benefit of Using CAM
Regarding the question the benefits attributed to the use of CAM, of the 184 responses, the majority indicated its use was very beneficial ($73.3\%$; $\frac{135}{184}$). This was specifically reported by users of relaxation techniques ($92.0\%$; $\frac{23}{25}$) and music therapy ($90.6\%$; $\frac{29}{32}$), those who take vitamins ($90.0\%$; $\frac{9}{10}$), and those who engage in spiritual healing $75.0\%$ ($\frac{9}{12}$). In contrast, fewer patients consuming herbal plants ($56.1\%$; $\frac{37}{66}$) indicated their use was very beneficial (Figure 4).
## 3.5. Adverse Effects and Starting Time of CAM Use
Overall, $97.0\%$ ($\frac{128}{132}$) of CAM users stated that their use did not cause side effects, while the remaining $3\%$ ($\frac{4}{132}$) reported secondary effects on gastrointestinal tract ($\frac{2}{4}$) and nervous system ($\frac{2}{4}$). Regarding the start time of their utilization of CAM, $54.5\%$ ($\frac{72}{132}$) of patients noted prior use, and $45.5\%$ ($\frac{60}{132}$) began use after starting treatment with peritoneal dialysis.
## 3.6. Recommending the Use of CAM
The main sources of recommendation for use of CAM were family members ($40.1\%$; $\frac{53}{132}$), followed by friends ($22.7\%$; $\frac{30}{132}$), allopathic doctors ($8.3\%$; $\frac{11}{132}$), other health professionals ($6\%$ $\frac{8}{132}$), and marketing ($2.2\%$; $\frac{3}{132}$).
## 3.7. Inform the Use of CAM to Medical Doctor
Only $31.8\%$ ($\frac{42}{132}$) of CAM users reported to their doctors about use of CAM. The remaining $68.2\%$ ($\frac{90}{132}$) of the users did not report it for the following reasons: (a) the doctor did not ask ($72.2\%$, $\frac{65}{90}$), (b) the patients did not consider it necessary ($20.0\%$; $\frac{18}{90}$), (c) the patients did not provide information for fear of disapproval ($6.7\%$; $\frac{6}{90}$), and (d) the patients did not have medical assistance at the time they used CAM ($1.1\%$; $\frac{1}{90}$).
## 3.8. Clinical Characteristics and Biochemical Parameters of Patients
The duration of PD therapy in the participating patients ranged from 1 to 168 months, with a mean of 27.4 months (±27.6), and no statistically significant difference was observed between the months of PD and CAM use or not ($$p \leq 0.412$$). The average volume of residual uresis in our study population ranged from 0 to 3000 mL, with a mean of 649 mL (±564), and no statistically significant difference was observed between the volume of residual uresis and the CAM users or non-users of CAM ($$p \leq 0.447$$).
Table 4 and Table 5 display the clinical characteristics and biochemical parameters of CAM users and non-users, respectively. We did not find significant differences in clinical and biochemical characteristics between the two groups; only the diastolic pressure was significant significantly higher in CAM users (Table 5). On the other hand, most of the patients were overweight ($44.2\%$; $\frac{106}{240}$) or obese ($26.3\%$; $\frac{63}{240}$). According to levels of albumin and BMI, $72.9\%$ ($\frac{175}{240}$) had adequate nutritional status. Further, $60\%$ of patients ($\frac{144}{240}$) had a Karnosfsky index higher than 80 points, which suggests that they were able to independently carry out daily activities. The main etiology of CKD reported was diabetic nephropathy ($62.5\%$; $\frac{150}{240}$), and the patients had between two and seven comorbidities.
## 4. Discussion
The use of CAM has increased in recent decades, mainly for the prevention and management of chronic diseases and the well-being needs of the older population.
In recent years, the WHO has implemented a strategy for integration, validation, and safety to harness the potential contribution of CAM to health, wellness, and people-centered health care [27]. In Mexico, native peoples have a wide tradition of CAM use. However, studies on CAM use in chronic diseases are scarce. Our study explored the prevalence of CAM use in patients with CKD treated with PD, the types and reasons for its usage, the perception of its benefit, and its adverse effects.
Fifty-five percent of our study population was identified to be using CAM therapy; this finding was similar to the reports of a study in a German population, where $57\%$ of dialysis patients reported to be regular CAM consumers [28]. Studies in a Turkish ($72.5\%$) [29] population showed high frequencies of CAM use; on the other hand, studies in American ($18.0\%$) [24] patients reported lower frequencies. The use of CAM can vary by diverse demographic factors such as age, sex, educational status, socioeconomic status, and occupational status [30]; however, in our study, none of the demographic factors analyzed had a significant influence on CAM. Women are more likely to use CAM than men [31], but we did not find a sex effect on CAM use for CKD in our study. However, studies in patients with kidney disease showed that both men and women are likely to use CAM; in studies in Egyptian [32] and Indian [33] patients, men were more likely to use CAM, whereas studies in Saudi patients showed that women are more likely [34]. Regarding the activity of our patients that identified as CAM users, $24\%$ were housewives, followed by retirees ($17.5\%$), patients with only primary education, and patients with medium-low ($21.3\%$) and medium ($22.1\%$) socioeconomic levels.
Medicinal plants are part of the therapeutic resources of traditional pre-Hispanic medicine in Mexico, and these are culturally and historically popular [35], so it is not unusual for herbal medicine ($50.0\%$) to be the most common type of CAM used by patients. Similar findings were reported in American ($67.8\%$) [24] and Turkish ($76.9\%$) [29] patients; however, it differs from that reported in German patients, whose most common type of CAM was mineral supplements [28]. Occasionally, factors such as culture, history, idiosyncrasies, and beliefs influence the use of different CAM types [36]. On the other hand, $34.8\%$ ($\frac{46}{132}$) of our patients employed more than one type of CAM-even up to six CAM; in German patients, $27.0\%$ employed more than one CAM, and patients reported using up to five CAM.
Herbal medicines can include a variety of potentially hepatotoxic compounds: (a) natural products such as volatile compounds, glycosides, terpenoids, alkaloids, anthraquinones, phenolics compounds, and other toxins; (b) contaminants or adulterants such as metals, mycotoxins, and pesticides; and herbicidal residues, and their mechanism to induce hepatotoxicity remains mostly imprecise in many cases [37]. In addition, herbal medicines can carry a variety of nephrotoxic compounds such as organic acids, alkaloids, terpenes, lactones, saponins, indeed minerals, and toxic proteins [38]. The use of herbal medicines by CKD patients is especially detrimental because of hepatotoxicity and nephrotoxicity, hemodynamic changes, electrolyte abnormalities, and effects on blood pressure, blood glucose, and coagulation parameters [29,30]. With the increasing of use of herbal medicines, there is a need to monitor and study their safety, especially in patients with CKD. In fact, the WHO recommends including the herbal medicine pharmacovigilance systems [39]. Chamomile ($25.8\%$, $\frac{25}{97}$) and moringa leaves ($16.0\%$, $\frac{15}{97}$) were the most common herbs used by patients, and various studies have shown the beneficial effects and low side effects of both plants [40,41].
Improving well-being ($71.7\%$; $\frac{132}{184}$) was the most frequent reason for using CAM in this study, unlike American patients, who use CAM to improve their energy and concentration [24], and German and Turkish patients, who use it to strengthen their immune system [28,29]. The majority of CAM ($73.3\%$; $\frac{135}{184}$) referred to by patients was considered as beneficial, which is similar to the report by Duncan et al. in American patients ($77.8\%$) [24] but less so in Turkish patients ($95.5\%$) [29]. With respect to side effects, $95.4\%$ ($\frac{126}{132}$) of CAM users did not present adverse effects; however, in a Turkish study, a smaller number of patients ($77.3\%$) did not experience side effects of CAM, probably due to the fact that Turkish patients used more herbal plants, or the plants employed by the patients had undesirable effects [29].
In our study, similar to that reported by Uzdil and Kılıç, the majority of people who recommended CAM were family members and friends. In addition, this investigation reported that $81.6\%$ of patients recommended CAM to another person [29].
A low number of patients ($31.8\%$; $\frac{42}{132}$) informed their physicians about CAM consumption compared to German ($59\%$) [28] and American ($36.8\%$) [24] patients. Physicians not asking patients about the use of CAM was the main reason for patients not informing physicians, which reflects the poor interest of medical doctors in the use of CAM. This interest needs to be improved because, as shown before, herbal plants are the most common type of CAM referred by patients, and CKD patients use many drugs for different complications at the same time, and interactions between drugs and herbs may mimic, decrease, or increase the action of prescribed drugs [30,42]. Improving patient–physician communication is essential for positive health outcomes. The lack of adequate discussion about CAM use raises the risk of adverse effects, including interactions with conventional treatments, which could be related to social perceptions [22,43].
All patients included in the study had clinical and biochemical characteristics; previous studies in the literature did not consider these parameters; therefore, we considered these as contributions. Most participants were overweight or obese, and increasing evidence suggests that obesity is a risk factor for diabetes and CKD, and high BMI has been reported to be related to diabetic nephropathy [44]. These data are consistent with the findings of our study, for which the main etiology of CKD was diabetic nephropathy ($62.5\%$; $\frac{150}{240}$).
According to levels of serum albumin and BMI, $72.9\%$ ($\frac{175}{240}$) of patients showed good nutritional status; in addition, other biochemical parameters were analyzed, such as hemoglobin, urea, creatinine, glucose, uric acid, calcium, phosphorus, and potassium, and we did not observe significant differences between users and non-users of CAM. These results indicate that CAM use does not have a negative effect on the health of patients with CKD. In addition, no significant differences were observed in either group with respect to edema grade or systolic pressure, suggesting that the use of CAM is not associated with changes in fluid status in patients with CKD on PD. In contrast, the diastolic pressure was significantly higher in CAM users; however, we believe that this is not clinically relevant.
The patients in our study had between two and seven comorbidities such as acute myocardial infarction, heart failure, peripheral vascular disease, dementia, chronic lung disease, connective tissue diseases, peptic ulcer disease, liver diseases, HIV, and diabetes mellitus, and according to the comorbidity scale, no significant differences were observed between users (score = 3) and non-users (score = 3.1) of CAM. Contrary to other studies, CAM users have a greater number of diseases [28,45].
This investigation has limitations: as a cross-sectional design, the conclusions drawn from the study cannot suggest causation and only included patients from the unique dialysis clinic of one hospital; therefore, our results may not reveal CAM use in other provinces considering the wide difference in culture, beliefs, and idiosyncrasies of Mexico. Despite these limitations, our results provide an important new understanding, and to the best of our knowledge, this is the first study on the use of CAM in CKD patients in Mexico.
## 5. Conclusions
The use of CAM is popular among renal patients on PD ($55\%$), with the main type of herbal medicine being chamomile, followed by relaxation as part of the practice of mind and body techniques. The main reason for the use of CAM in our patients was to improve their state of well-being, and only $3\%$ of users reported side effects. Just as $31.8\%$ of the users of CAM informed their doctor, we need continued research and education to identify and break down barriers to the communication of CAM-use topics between patient and doctor, as this is mandatory.
## References
1. Cockwell P., Fisher L.A.. **The global burden of chronic kidney disease**. *Lancet* (2020.0) **395** 662-664. DOI: 10.1016/S0140-6736(19)32977-0
2. **PAHO. The Burden of Kidney Diseases**
3. Kovesdy C.P.. **Epidemiology of chronic kidney disease: An update 2022**. *Kidney Int. Suppl.* (2022.0) **12** 7-11. DOI: 10.1016/j.kisu.2021.11.003
4. Vasquez-Jimenez E., Madero M.. **Global dialysis perspective: Mexico**. *Kidney360* (2020.0) **1** 534-537. DOI: 10.34067/KID.0000912020
5. **Salud Complementaria, Alternativa O Integral: ¿Qué Hay Detrás De Estos Nombres?**
6. Youn B.-Y., Moon S., Mok K., Cheon C., Ko Y., Park S., Jang B.-H., Shin Y.C., Ko S.-G.. **Use of traditional, complementary and alternative medicine in nine countries: A cross-sectional multinational survey**. *Complement Ther. Med.* (2022.0) **71** 102889. DOI: 10.1016/j.ctim.2022.102889
7. Tangkiatkumjai M., Boardman H., Walker D.-M.. **Potential factors that influence usage of complementary and alternative medicine worldwide: A systematic review**. *BMC Complement Med. Ther.* (2020.0) **20**. DOI: 10.1186/s12906-020-03157-2
8. Abheiden H., Teut M., Berghöfer A.. **Predictors of the use and approval of CAM: Results from the German General Social Survey (ALLBUS)**. *BMC Complement Med. Ther.* (2020.0) **20**. DOI: 10.1186/s12906-020-02966-9
9. Ünal Aslan K.S.. **Investigation of the effects of complementary and alternative therapy usage on physical activity and self-care in individuals diagnosed with type 2 diabetes**. *Holist. Nurs. Pract.* (2022.0) **36** 93-104. DOI: 10.1097/HNP.0000000000000499
10. Mori Y., Daikuhara H., Oshima T., Suzuki H., Okada S., Miyatake N.. **Use of complementary and alternative medicine and its relationship with health-related quality of life (HRQOL) in patients with type 2 diabetes mellitus**. *Epidemiologia* (2023.0) **4** 53-59. DOI: 10.3390/epidemiologia4010005
11. Ng J.Y., Verma K.D., Gilotra K.. **Quantity and quality of complementary and alternative medicine recommendations in clinical practice guidelines for type 2 diabetes mellitus: A systematic review**. *Nutr. Metab. Cardiovasc. Dis.* (2021.0) **31** 3004-3015. DOI: 10.1016/j.numecd.2021.07.029
12. Palileo-Villanueva L.M., Palafox B., Amit A.M.L., Pepito V.C.F., Ab-Majid F., Ariffin F., Balabanova D., Isa M.-R., Mat-Nasir N., My M.. **Prevalence, determinants and outcomes of traditional, complementary and alternative medicine use for hypertension among low-income households in Malaysia and the Philippines**. *BMC Complement Med. Ther.* (2022.0) **22**. DOI: 10.1186/s12906-022-03730-x
13. Sari N.M., Devansyah S., Modjaningrat I., Suryawan N., Susanah S., Rakhmillah L., Wahyudi K., Kaspers G.J.L.. **Type of cancer and complementary and alternative medicine are determinant factors for the patient delay experienced by children with cancer: A study in West Java, Indonesia**. *Pediatr. Blood Cancer* (2023.0) **70** e30192. DOI: 10.1002/pbc.30192
14. Kwon C.-Y., Lee B., Lee B.-J., Kim K.-I., Jung H.-J.. **Herbal medicine compared to placebo for chronic obstructive pulmonary disease: A systematic review and meta-analysis**. *Front. Pharmacol.* (2021.0) **12** 717570. DOI: 10.3389/fphar.2021.717570
15. Gözcü E., Çakmak İ., Öz B., Karataş A., Akar Z.A., Koca S.S.. **Complementary alternative medicine in rheumatic diseases: Causes, choices, and outcomes according to patients**. *Eur. J. Rheumatol.* (2022.0) **9** 36-41. DOI: 10.5152/eurjrheum.2021.20194
16. Kasprzycka K., Kurzawa M., Kucharz M., Godawska M., Oleksa M., Stawowy M., Slupinska-Borowka K., Sznek W., Gisterek I., Boratyn-Nowicka A.. **Complementary and alternative medicine use in hospitalized cancer patients-study from Silesia, Poland**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph19031600
17. Lüthi E., Diezi M., Danon N., Dubois J., Pasquier J., Burnand B., Rodondi P.-Y.. **Complementary and alternative medicine use by pediatric oncology patients before, during, and after treatment**. *BMC Complement Med. Ther.* (2021.0) **21**. DOI: 10.1186/s12906-021-03271-9
18. Padilla-Ortiz D., Contreras-Yáñez I., Cáceres-Giles C., Ballinas-Sánchez Á., Valverde-Hernández S., Merayo-Chalico F., Fernández-Ávila D., Londoño J., Pascual-Ramos V.. **Association between physician-patient relationship and the use of complementary and alternative medicine in patients with rheumatoid arthritis**. *Rev. Colomb. De Reumatol.* (2021.0) **28** 28-37. DOI: 10.1016/j.rcreue.2020.06.008
19. Carmona-Sánchez R., Tostado-Fernández F.A.. **Prevalence of use of alternative and complementary medicine in patients with irritable bowel syndrome, functional dyspepsia and gastroesophageal reflux disease**. *Rev. Gastroenterol. Mex.* (2005.0) **70** 393-398. PMID: 17058977
20. Herrera-Arellano A., Jaime-Delgado M., Herrera-Alvarez S., Oaxaca-Navarro J., Salazar-Martínez E.. **The alternative medicine used as complementary in patients positive for HIV**. *Rev. Med. Del Inst. Mex. Del Seguro Soc.* (2009.0) **47** 651-658
21. Yam Sosa B., Quiñones M., Pérez Aguilar E.. *La Medicina Tradicional Entre Los Henequeneros Y Maiceros Yucatecos* (1992.0)
22. Berretta M., Rinaldi L., Taibi R., Tralongo P., Fulvi A., Montesarchio V., Madeddu G., Magistri P., Bimonte S., Trovò M.. **Physician attitudes and perceptions of complementary and alternative medicine (CAM): A multicentre italian study**. *Front. Oncol.* (2020.0) **10** 594. DOI: 10.3389/fonc.2020.00594
23. Akeeb A.A., King S.M., Olaku O., White J.D.. **Communication between cancer patients and physicians about complementary and alternative medicine: A systematic review**. *J. Integr. Complement. Med.* (2023.0) **29** 80-98. DOI: 10.1089/jicm.2022.0516
24. Duncan H.J., Pittman S., Govil A., Sorn L., Bissler G., Schultz T., Faith J., Kant S., Roy-Chaudhury P.. **Alternative medicine use in dialysis patients: Potential for good and bad!**. *Nephron Clin. Pract.* (2007.0) **105** c108-c113. DOI: 10.1159/000097986
25. Quandt S.A., Ip E.H., Saldana S., Arcury T.A.. **Comparing two questionnaires for eliciting CAM use in a multi-ethnic US population of older adults**. *Eur. J. Integr. Med.* (2012.0) **4** e205-e211. DOI: 10.1016/j.eujim.2011.12.009
26. Esteban S., Vázquez Peña F., Terrasa S.. **Translation and cross-cultural adaptation of a standardized international questionnaire on use of alternative and complementary medicine (I-CAM-Q) for Argentina**. *BMC Complement. Altern. Med.* (2016.0) **16**. DOI: 10.1186/s12906-016-1074-4
27. **Global Report on Traditional and Complementary Medicine**
28. Nowack R., Balle C., Birnkammer F., Koch W., Sessler R., Birck R.. **Complementary and alternative medications consumed by renal patients in southern**. *J. Ren. Nutr.* (2009.0) **19** 211-219. DOI: 10.1053/j.jrn.2008.08.008
29. Uzdil N., Kılıç Z.. **Health literacy and attitudes to holistic, complementary and alternative medicine in peritoneal dialysis patients: A descriptive study**. *Eur. J. Integr. Med.* (2022.0) **55** 102185. DOI: 10.1016/j.eujim.2022.102185
30. Rao A.S.M.A., Phaneendra D., Pavani C.D., Soundararajan P., Rani N.V., Thennarasu P., Kannan G.. **Usage of complementary and alternative medicine among patients with chronic kidney disease on maintenance hemodialysis**. *J. Pharm. Bioallied Sci.* (2016.0) **8** 52-57. PMID: 26957870
31. Ndao-Brumblay S.K., Green C.R.. **Predictors of complementary and alternative medicine use in chronic pain patients**. *Pain Med.* (2010.0) **11** 16-24. DOI: 10.1111/j.1526-4637.2009.00767.x
32. Osman N.A., Hassanein S.M., Leil M.M., NasrAllah M.M.. **Complementary and alternative medicine use among patients with chronic kidney disease and kidney transplant recipients**. *J. Ren. Nutr.* (2015.0) **25** 466-471. DOI: 10.1053/j.jrn.2015.04.009
33. Castelino L.R., Nayak-Rao S., Pradeep Shenoy M.. **Prevalence of use of complementary and alternative medicine in chronic kidney disease: A cross-sectional single-center study from south**. *Saudi J. Kidney Dis. Transplant.* (2018.0) **29** 1017-1101
34. Alanizy L., Almatham K., Al Basheer A., Al Fayyad I.. **Complementary and alternative medicine practice among saudi patients with chronic kidney disease: A cross-sectional study**. *Int. J. Nephrol. Renov. Dis.* (2020.0) **13** 11-18. DOI: 10.2147/IJNRD.S240705
35. Zolla C.. **La medicina tradicional indígena en el México actual**. *Arqueol. Mex.* (2005.0) **13** 62-65
36. Dehghan M., Namjoo Z., Bahrami A., Tajedini H., Shamsaddini-lori Z., Zarei A., Dehghani M., Ranjbar M., Ra F., Nasab S.. **The use of complementary and alternative medicines, and quality of life in patients under hemodialysis: A survey in southeast Iran**. *Complement Ther. Med.* (2020.0) **51** 102431. DOI: 10.1016/j.ctim.2020.102431
37. Quan N.V., Dang Xuan T., Teschke R.. **Potential hepatotoxins found in herbal medicinal products: A systematic review**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21145011
38. Xu X., Zhu R., Ying J., Zhao M., Wu X., Cao G., Wang K.. **Nephrotoxicity of Herbal Medicine and Its Prevention**. *Front. Pharmacol.* (2020.0) **11** 569551. DOI: 10.3389/fphar.2020.569551
39. Touiti N., Houssaini T.S., Achour S.. **Overview on pharmacovigilance of nephrotoxic herbal medicines used worldwide**. *Clin. Phytoscience* (2021.0) **7** 9. DOI: 10.1186/s40816-021-00248-6
40. Akter T., Rahman M.A., Moni A., Apu M.A.I., Fariha A., Hannan M.A., Uddin M.J.. **Prospects for protective potential of**. *Plants* (2021.0) **10**. DOI: 10.3390/plants10122818
41. **Chamomile**
42. Parvez M.K., Rishi V.. **Herb-drug interactions and hepatotoxicity**. *Curr. Drug Metab.* (2019.0) **20** 275-282. DOI: 10.2174/1389200220666190325141422
43. Zaidi S.F., Alzahrani A., Alghamdy Z., Alnajar D., Alsubhi N., Khan A., Ahmed M.E.. **Use of complementary and alternative medicine in the general public of western saudi arabia: A cross-sectional survey**. *Cureus* (2022.0) **14** e32784. DOI: 10.7759/cureus.32784
44. Hsu C.-Y., McCulloch C.E., Iribarren C., Darbinian J., Go A.S.. **Body mass index and risk for end-stage renal disease**. *Ann. Intern. Med.* (2006.0) **144** 21-28. DOI: 10.7326/0003-4819-144-1-200601030-00006
45. Lee G.B.W., Charn T.C., Chew Z.H., Ng T.P.. **Complementary and alternative medicine use in patients with chronic diseases in primary care is associated with perceived quality of care and cultural beliefs**. *Fam. Pract.* (2004.0) **21** 654-660. DOI: 10.1093/fampra/cmh613
|
---
title: 'Association of Body Mass Index (BMI) with Lip Morphology Characteristics:
A Cross-Sectional Study Based on Chinese Population'
authors:
- Yiyin Chen
- Hongmei Yang
- Zhijin Zheng
- Xiaoqi Zhang
- Xinyu Yan
- Hu Long
- Wenli Lai
journal: Diagnostics
year: 2023
pmcid: PMC10001408
doi: 10.3390/diagnostics13050997
license: CC BY 4.0
---
# Association of Body Mass Index (BMI) with Lip Morphology Characteristics: A Cross-Sectional Study Based on Chinese Population
## Abstract
Background: Lip morphology is essential in diagnosis and treatment of orthodontics and orthognathic surgery to ensure facial aesthetics. Body mass index (BMI) has proved to have influence on facial soft tissue thickness, but its relationship with lip morphology is unclear. This study aimed to evaluate the association between BMI and lip morphology characteristics (LMCs) and thus provide information for personalized treatment. Methods: A cross-sectional study consisted of 1185 patients from 1 January 2010 to 31 December 2020 was conducted. Confounders of demography, dental features, skeletal parameters and LMCs were adjusted by multivariable linear regression to identify the association between BMI and LMCs. Group differences were evaluated with two-samples t-test and one-way ANOVA test. Mediation analysis was used for indirect effects assessment. Results: After adjusting for confounders, BMI is independently associated with upper lip length (0.039, [0.002–0.075]), soft pogonion thickness (0.120, [0.073–0.168]), inferior sulcus depth (0.040, [0.018–0.063]), lower lip length (0.208, [0.139–0.276]), and curve fitting revealed non-linearity to BMI in obese patients. Mediation analysis found BMI was associated with superior sulcus depth and basic upper lip thickness through upper lip length. Conclusions: BMI is positively associated with LMCs, except for nasolabial angle as negatively, while obese patients reverse or weaken these associations.
## 1. Introduction
Soft tissue aesthetics, as the major motivation of patients seeking orthodontic and orthognathic treatment, has become an important concern in orthodontic treatment planning [1,2,3]. Therefore, it is of great value to explore the potential influencing factors related to soft tissue aesthetics and to make personalized treatment plans for patients. Recent studies have paid increasing attention to lip profile, as it has been proved to be a key feature affecting facial esthetic perception [4]. However, increasing evidence has shown that, in addition to hard tissue morphology, soft tissue morphology is affected by many factors, including heredity and environment (race, age, gender, etc.). Specifically, compared with females, males were found to have more prominent and thicker lips [5]. Besides, dental features such as dental crowding, occlusal relationship, and especially the incisor position, also have an impact, with the anterior and posterior position of the upper incisor proving to be closely related to upper lip thickness [6]. Moreover, recent study has demonstrated that the upper lip morphology varies significantly between different skeletal patterns [7].
Body Mass Index (BMI), a commonly used value to measure the body shape and health status, is calculated as weight in kg divided by height in m squared [8]. Previous studies have found that BMI is associated with various systemic diseases [9]. Concerning oral health, some evidence exists that there might be an association between increased BMI and an increased risk for caries [10,11], periodontal diseases [12], root dilaceration [13] and less cooperation [14]. In addition, it has been proved that obesity can affect facial bone and soft tissue structures by affecting growth and development, bone metabolism and fat distribution [15]. For example, mandibular growth and lower facial height were found to be significantly associated with BMI [16]. Overweight subjects were found to have larger maxillary width and obese people were found to have larger maxillary length and mandibular width [17]. Besides, many studies have determined that BMI is one of the key factors affecting facial soft tissue thickness (FSTT) [18,19], which increased with increase in BMI. Specifically, overweight subjects were found to have thicker nasion soft tissue, whereas obese subjects were found to have thicker pogonion, glabella and gnathion soft tissue. In recent years, research on lip aesthetics has been carried out worldwide and standard values have been established in some populations [20,21]. Previous studies have mainly explored facial soft tissue characteristics based on age, gender, race, and skeletal patterns. Recent stereophotogrammetric analysis first reported the association between larger BMI and increased linear lip measurements, which may suggest that increases in BMI were associated with directional lip stretch [22].
Current studies on the relationship between BMI and facial soft tissue mainly focus on its thickness (FSTT). There are few studies concerning lip morphology, and the relationship between BMI and lip morphology remains unclear. In addition, many existing studies have defects such as limited sample size or imperfect statistical methods. Given the nonnegligible influence of BMI on facial soft tissue and bone structures, and the importance of lip morphology in aesthetic considerations for orthodontic treatment, it is necessary to explore the clear association between BMI and lip morphology, to provide a basis for more accurate diagnosis and to help orthodontists develop more personalized aesthetic treatment plans for patients with different BMI. Therefore, a statistically well-designed study with a larger sample size has been conducted in a Chinese population, aimed to (a) explore the average value of lip morphology characteristics and reference value for BMI in Chinese population; (b) compare lip morphology characteristics among four BMI categories; (c) to explore the lip characteristics which are independently affected by BMI by adjusting for various confounding variables; and (d) to explore the mediators between BMI and lip characteristics. The null hypothesis of this study was that lip morphology characteristics did not differ significantly between four BMI categories.
## 2.1. Study Population and Data Collection
The study was a cross-sectional study, which was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [23]. Figure 1 shows the flow chart of the analysis process and research contents. Patients who received consecutive orthodontic treatment in the Department of Orthodontics, West China Hospital of Stomatology, Chengdu, Sichuan, from 1 January 2010 to 31 December 2020, were identified retrospectively. The exclusion criteria were: (a) participants aged < 12 y; (b) participants with a history of orthodontic treatment; (c) participants who do not have permanent dentition; (d) participants without the required complete baseline information. Prior to orthodontic treatment, all participants received a series of examinations, including demographic questionnaires, intraoral and facial photographs, plaster, digital dental models and radiographic examinations. The above data were collected for subsequent analysis, and informed consent was obtained from all adult participants and the guardian of each minor.
Lateral cephalometric radiographs were taken using the same device (Veraviewepocs, Morita, Kyoto, Japan). Patients were in their natural head position and centric occlusion, and were instructed to stay relaxed and not compress their lips during exposure. Pre-treatment cephalometric radiographs were imported into Dolphin imaging software version 11.9.07.23 (Patterson Dental, Los Angeles, CA, USA), independently traced and measured by two experienced orthodontists, the mean values of which were used for subsequent analyses. A total of 7 linear parameters and one angular parameter were measured using cephalometric landmarks. Landmarks, reference lines and measurements used in this study are shown in Figure 2A. The cephalometric landmarks are defined in Table 1.
## 2.2. Lip Characteristics and BMI
Lip morphology characteristics (LMCs) were described using eight indices: nasolabial angle (NLA), superior sulcus depth (SSD), basic upper lip thickness (BULT), upper lip thickness (ULT), upper lip length (ULL), soft pogonion thickness (SPT), inferior sulcus depth (ISD) and lower lip length (LLL). Specifically, (a) NLA is the intersection angle between the line Cm-Sn and the line Sn-UL; (b) SSD is the distance from the most concave point of upper lip to the line perpendicular to the Frankfort (FH) plane (the line Or-Po); (c) BULT is the distance between point Sn and the point 3 mm below point A; (d) ULT is the distance from point UL to the labial surface of the upper central incisor; (e) ULL is the distance between two parallel lines in the FH plane through point Sn and point Stms; (f) SPT is the distance between point Pog and Pog’; (g) ISD is the vertical distance from point Si to the line LL-Pog’; (h) LLL is the distance between two parallel lines in the FH plane through point Stmi and point Me’.
BMI was calculated using objectively measured height and weight records from demographic questionnaires prior to orthodontic treatment. According to the World Health Organization standards, BMI can be divided into the following four categories: underweight (BMI < 18.5 kg/m2); normal weight (18.5 ≤ BMI < 25.0 kg/m2); overweight (25.0 ≤ BMI < 30 kg/m2) and general obesity (BMI ≥ 30 kg/m2). Figure 2B shows a representative underweight patient profile and Figure 2C shows a representative overweight patient profile.
## 2.3. Covariates
Demographic information on age and gender was obtained from the medical record system of the hospital. Dental features including crowding, molar relationship, overbite, and overjet were assessed based on intraoral photographs, dental models, and medical examination records at the first visit. Molar relationship is diagnosed as class I when the mesi-obuccal cusp of the upper first molar (U6) occludes with the buccal groove of the lower first molar (L6), as class II-1 when U6 is mesial to L6 and upper incisors proclined and as class II-2 when upper incisors retroclined (U1-SN < 100°), and as class III when U6 is distal to L6. In addition, crowding evaluated both in upper and lower dentition is defined as I (<4 mm), II (4~8 mm), and III (≥8 mm). Cephalometric indices on skeletal and incisor parameters were obtained, mainly including the development and relative position of the jaw (SNA, SNB, ANB, SN-MP, FH-MP), as well as the inclination and position of the central incisor (U1-NA, U1-SN, L1-NB, L1-MP). To control for potentially confounding effects and make the results convincing, the above variables were adjusted for covariates in our study, according to previously literature [7], which apparently have an effect on the appearance characteristics of soft tissues. Besides, the eight LMCs were also considered as confounders because they may influence each other.
## 2.4. Statistical Analysis
Categorical variables were expressed as frequencies (percentages) and continuous variables were described as means (standard deviations, SDs) or medians (interquartile ranges, IQRs). Demographic and clinical characteristics were presented according to BMI categories and compared by one-way ANOVA test and Chi-square test as appropriate. Spearman correlation analysis was used to investigate the relationships of eight LCMs to each other. Multivariable linear regression models were used to explore the association between BMI with LCMs when considering confounders. Among this process, four models were adjusted by confounders: model 1 for basic diagnosis information (“Age”, “Gender”, “Molar Relationship”, “Upper crowding”, “Lower crowding”, “Overbite” and “Overjet”), model 2 for anterior teeth and skeletal information (“SNA”, “SNB”, “ANB”, “SN-MP”, “FH-MP”, “U1-NA (mm)”, “U1-SN” “L1-NB (mm)” and “L1-MP”), model 3 for LMCs themselves (NLA, SSD, BULT, ULT, ULL, SPT, ISD and LLL), and model 4 for all of these. Adjusted LCMs value were compared among four BMI categories by two-samples t-test and one-way ANOVA test. Loess (Local Polynomial Regression Fitting) was employed to describe the variation tendency of eight adjusted LCMs with the BMI. The multi-variable linear regression results were expressed as coefficient and $95\%$ confidence interval (CI). Regression-based mediation analysis was used to distinguish the direct effect of BMI on LMCs, and the indirect effect mediated by ULL. Three estimates were obtained as follows: (a) total effect, i.e., the overall association between BMI and LMCs, including direct and indirect effects; (b) direct effect, the association between BMI and LMCs; and (c) indirect effect, the association between BMI and LMCs, mediated by ULL. All statistical analyses and results’ visualization were performed using R software (version 4.1.2). p-value < 0.05 was considered statistically significant.
## 3.1. Study Participant Characteristics
A total of 2079 patients were initially identified, with 1185 remaining as final participants after applying exclusion criteria. Among the 1185 including participants, the eight LMCs were normally distributed according to Kolmogorov-Smirnov tests ($p \leq 0.05$), which is a natural distribution of the data in the real world (Figure 3A). Table 2 shows the mean and standard deviation of each LMC and other clinical information in detail. Except for NLA-SPT and ULT-ULL, all other LMC pairs had significant correlations ($p \leq 0.05$), confirming that they are closely related and influence each other (Figure 3B). The BMI was not normally distributed according to Kolmogorov-Smirnov tests ($p \leq 0.05$), with a mean of 19.41 ± 2.93 kg/m2 and mediation of 19.03 (17.58–20.08) (Figure 3C), leading to the speculation that this is due to increasing obesity. As for BMI categories, normal weight was the most prevalent with a percentage of $55.27\%$, underweight was the second most prevalent ($40.93\%$), overweight was third ($2.87\%$), and obese was the least frequent ($0.93\%$) (Figure 3D).
## 3.2. Associations between BMI and Lip Characteristics
The univariate analysis between BMI and LMCs indicated that NLA was negatively correlated with BMI, and the other LMCs were positively correlated with BMI (Figure 3E). However, this analysis did not consider confounders including demographic information, dental features, cephalometric indices on skeletal and incisor parameters and LMCs. To explore whether BMI independently affected LMCs, multivariable linear regression models were used, and the results are reported in Table 3. The result show that NLA was negatively correlated with BMI (model 1: β= −0.260, $95\%$ CI −0.465 to −0.056; model 2: β= −0.342, $95\%$ CI −0.529 to −0.154; model 3: β= −0.177, $95\%$ CI −0.337 to −0.017), but not significantly after adjusting all considered covariates in model 4. On the contrary, SSD was positively correlated with BMI (model 1: β = 0.059, $95\%$ CI 0.016 to 0.102; model 2: β= 0.044, $95\%$ CI 0.007 to 0.080), but not significantly after adjusting LMCs or all covariates (model 3 and model 4). The same trend as for SSD can be seen in BULT and ULT, indicating that BMI did not independently influence these, but rather via other routes. The remaining four LMCs, ULL, SPT, ISD and LLL, had a significant positive correlation with BMI in all four models, suggesting that BMI is an independent factor influencing these four LMCs after adjusting for confounders.
## 3.3. Tendency of Lip Characteristics with BMI Variation
To fully understand how LMCs vary with BMI, the scatter plot of the correlation between LMCs and BMI was made after adjusting for all covariates by multivariable linear regression (Figure 4). The local polynomial regression fitting of the blue line revealed the true variation tendency of LMCs according to the BMI. The gray dashed line was fitted by linear regression, revealing the general trend. The result shows that NLA was negatively correlated with BMI, while the other LMCs were positively correlated with BMI, which is in accordance with univariable analysis. Interestingly, the linear relationship did not hold in the obese category, which seemed to weaken or reverse the association. Furthermore, sensitivity analysis of BMI categories demonstrated similar results (Figure 5). Overweight had the smallest value of NLA and the biggest value for SSD, BULT, ULT, ULL, ISD and LLL. Only the SPT was gradually increased by BMI categories. These results suggest that obese patients have a different pattern of effects on LMCs, and the linear relationship between BMI and LMCs may only remain in non-obese patients.
## 3.4. Mediation Analysis
Mediation analyses were performed to investigate why and how BMI related to NLA, SSD, BULT and ULT, while BMI is not an independent factor. The result shows that total effects (0.014, $p \leq 0.05$; 0.031, $$p \leq 0.014$$; respectively) of BMI toward SSD and BULT consisted of direct effect (0.005, 0.020 respectively, $p \leq 0.05$) and indirect effect (0.009 = 0.046 × 0.191, $$p \leq 0.018$$; 0.011 = 0.053 × 0.207, $$p \leq 0.010$$; respectively), indicating that BMI may relate to SSD and BULT through ULL (Figure 6B,C). However, total effect (−0.056, −0.007, $p \leq 0.05$, respectively) of BMI towards NLA and ULT consisted of direct effect (−0.081, 0.000, $p \leq 0.05$, respectively) and indirect effect (0.025 = 0.036 × 0.679, −0.008 = 0.041 × (−0.197), $p \leq 0.05$, respectively), indicating that BMI did not directly relate to NLA and ULT, or through ULL (Figure 6A,D). ULL is the only upper lip characteristic independently affected by BMI, thus we consider it a mediator.
## 4. Discussion
As the main part of the lower facial soft tissue, lips are vital to the perception of facial aesthetics. One of the primary concerns in orthodontic treatment is a coordinated and beautiful soft tissue profile, in which lips make a major contribution. Body Mass Index (BMI), the most widely used clinical measure of general obesity, has been recently found to affect facial bone and soft tissue structures [15]. Previous studies mainly focused on its relationship with FSTT and considered BMI as one of the key factors affecting FSTT. These findings are mainly applied to the field of forensic science for more detailed facial soft tissue reconstruction and facial recognition [24]. However, a recent study reported an association between BMI with linear lip measurements, but did not examine this in detail [22]. Lips being one of the most important factors affecting facial aesthetics, relevant studies on lip morphology characteristics and their association with BMI are few, and the relationship between the two is unclear [18,19]. Therefore, the study aimed to investigate the relationships between BMI and LMCs.
Most existing cephalometric analyses have been derived from orthodontics in Western countries, thus the reference values of cephalometric measurements were mainly standardized according to Caucasians [25,26]. However, there are significant differences in soft tissue characteristics and aesthetic preferences among different ethnic groups [1] and it is necessary to conduct a study with sufficient sample size in a Chinese population to facilitate the diagnosis and treatment planning of orthodontists and contribute to the development of facial reconstruction in the field of forensic science. In our study, the prevalence of different BMI categories and mean values of lip morphology characteristics, including NLA, SSD, BULT, ULT, ULL, SPT, ISD and LLL, were revealed in the Chinese population.
Previous studies have shown that facial soft tissue thickness (FSTT) increased with increase in BMI and that larger BMI was associated with increased linear measurements [18,22]. Similarly, we also demonstrated significant differences in lip characteristics across BMI categories and found that lip characteristics were positively correlated with BMI, except for nasolabial angle, which was negatively correlated with BMI. With increasing BMI, facial soft tissue thickness increases and, conceivably, the lip length and thickness measurements also increase, while the nasolabial angle decreases due to the protrusion of the upper lip, as confirmed by previous studies [7]. However, it is interesting that this linear relationship did not hold in obese patients. One possibility is that the sample size of the obese group was too small to detect a true trend. Another reason may be that the effect of increased BMI on soft tissue is limited. A very large BMI can cover all other anatomical factors [27]. In the obese category, changes in soft tissue size have reached their limits. This is similar to previous studies’ speculation that directional stretching of soft tissues is limited and there is mutual compensation [22,28]. Therefore, when BMI increases to a certain level, the increase in soft tissue volume may no longer influence length and thickness and will be compensated by width. Further research using three-dimensional imaging technology is needed to confirm this, as this study only involved cephalometric analysis and did not measure lip width. In addition, we suspected this, because the lip is closely related to the supporting hard tissue structures and is limited by adjacent structures [29], with the upper lip limited by the nose and the lower lip, and the lower lip limited by the upper lip and the chin, thus limiting its size variation in three dimensions. These results indicated that the BMI of patients should be considered in future treatment planning, and individualized treatment planning should be made because different soft tissue characteristics vary among different BMI.
In addition, the independent association of LMCs with BMI was assessed in this study, considering various confounders. Multiple variables affecting soft tissue morphology have been identified. Many studies have focused on age, with several studies showing that the upper and lower lips significantly retruded compared to the aesthetic line and became thinner with aging [30,31,32]. Gender is also an important factor in soft tissue morphology, and it has been demonstrated in many different countries and regions that facial soft tissue including the lips of males is thicker than that of females [30,33,34]. Besides, race-related soft tissue differences have also been commonly involved [35,36,37]. Basically, the Negroid population have the thickest and the most protruding lips, followed by the Asian population, with the Caucasian population the thinnest and straightest, and with shorter upper lips. In addition, the incisor position and skeletal patterns are also confounding factors highlighted by numerous studies [6,7,38], which show that upper lip thickness and length are significantly correlated with the protrusion of maxillary incisors, and there are significant differences in the upper lip morphology among different skeletal patterns. After adjusting for confounding variables that may affect the relationship between lip characteristics and BMI, ULL, SPT, ISD and LLL were revealed to be positively correlated with BMI. Previous studies simply explored the relationship between soft tissue thickness and BMI, and concluded that obese subjects have thicker gnathion and pogonion soft tissue thickness. However, these studies did not include sufficient sample size, adequately adjust for confounding factors, or specifically measure and compare lip characteristics among different BMI categories [19,39,40]. In clinical practice, teenage patients, who account for a large proportion of orthodontic treatments, are at the peak of their growth and development, with rapid increases in height and weight and changes in BMI. The results of this study can help doctors predict the possible impact of BMI changes on soft tissue profile in advance. Besides, BMI has been shown to influence skeletal development, with obese/overweight children and adolescents more likely to experience advanced dental and skeletal maturation [41,42,43], thus influencing the timing of intervention and treatment plans.
For adult patients, studies have shown that the mandibular cortex was thicker in obese and overweight patients, and periodontal tissue responded differently to orthodontic force [41,44]. Moreover, it has been found that increased BMI may be related to the decrease of orthodontic treatment compliance, which is worthy of attention [14]. However, it was found that NLA, SSD, BULT and ULT were not independently affected by BMI, and mediation analysis found BMI associated with SSD and BULT through ULL, which is an unprecedented discovery. This may indicate that changes in the length of the upper lip are limited by the adjacent structures, so when the length of the upper lip increases to a certain extent, it is compensated for by changes in other dimensions, such as the basic upper lip thickness, which in turn affects the depth of the upper lip groove. However, the result of mediation analysis between the other two upper lip morphology characteristics, NLA and ULT, with BMI, was not significant. We speculate that the sample size is insufficient to find statistical differences or that there are lip characteristics which have not been measured, and further research could be considered to focus on this.
To the best of our knowledge, the current study is the first to explore the association of BMI with lip morphology characteristics in a Chinese population, and the non-negligible influence of BMI on lip morphology has been found, which provides a further reference for the diagnosis, personalized treatment planning and subsequent scientific research in orthodontics. Based on previous studies and the large sample size of this study, multiple linear regression was used to adequately adjust for covariates, thus increasing the accuracy and authenticity of the results [45,46]. Furthermore, it should not be ignored that special mediation analysis was used to further explore the lip characteristics not independently affected by BMI, and the mediator ULL was found.
The study has several limitations. First, as a cross-sectional study, we could only draw conclusions on whether there was a correlation between BMI and LMCs, rather than a direct causal relationship. Second, due to limitations of the database, we were unable to identify participants younger than 12 y, older than 53 y, with a history of orthodontic treatment or without permanent dentition, hence the conclusions need to be interpreted with caution. Third, covariates were adjusted to control for confounders, but there may still be unmeasured or unknown covariates, such as waistline, body fat percentage and systemic diseases such as diabetes and hypertension, etc. Finally, the sample source of this study is a Chinese population, so the findings should be cautiously generalized to other populations. Future studies can use frontal photographs and three-dimensional facial scanning techniques to focus on more lip features from different perspectives and explore their relationship between BMI. More diverse groups such as children and the elderly should also be considered.
## References
1. Ghorbanyjavadpour F., Rakhshan V.. **Factors associated with the beauty of soft-tissue profile**. *Am. J. Orthod. Dentofac. Orthop.* (2019) **155** 832-843. DOI: 10.1016/j.ajodo.2018.07.020
2. Varatharaju V., Caflisch M., Soroken C., Kiliaridis S., Antonarakis G.S.. **Does age influence self-perception of the soft-tissue profile in children?**. *Am. J. Orthod. Dentofac. Orthop.* (2021) **159** e207-e215. DOI: 10.1016/j.ajodo.2020.10.016
3. Patil H.S., Golwalkar S., Chougule K., Kulkarni N.R.. **Comparative Evaluation of Soft Tissue Chin Thickness in Adult Patients with Skeletal Class II Malocclusion with Various Vertical Growth Patterns: A Cephalometric Study**. *Folia Med.* (2021) **63** 74-80. DOI: 10.3897/folmed.63.e52165
4. Wu S.Q., Pan B.L., An Y., An J.X., Chen L.J., Li D.. **Lip Morphology and Aesthetics: Study Review and Prospects in Plastic Surgery**. *Aesthetic Plast. Surg.* (2019) **43** 637-643. DOI: 10.1007/s00266-018-1268-x
5. Alshahrani I., Kamran M.A., Asiry M.A., Alshahrani A., Almoammar S., Alhaizaey A.. **Evaluation of cephalometric lip morphology in a Saudi sub population: A cross sectional study**. *J. Pak. Med. Assoc.* (2020) **70** 151-153. DOI: 10.5455/JPMA.300315
6. El Asmar R., Akl R., Ghoubril J., El Khoury E.. **Evaluation of the ideal position of the maxillary incisor relative to upper lip thickness**. *Am. J. Orthod. Dentofac. Orthop.* (2020) **158** 264-272. DOI: 10.1016/j.ajodo.2019.08.015
7. Yan X., Zhang X., Chen Y., Long H., Lai W.. **Association of Upper Lip Morphology Characteristics with Sagittal and Vertical Skeletal Patterns: A Cross Sectional Study**. *Diagnostics* (2021) **11**. DOI: 10.3390/diagnostics11091713
8. Daniels S.R.. **The use of BMI in the clinical setting**. *Pediatrics* (2009) **124** S35-S41. DOI: 10.1542/peds.2008-3586F
9. **Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies**. *Lancet* (2004) **363** 157-163. DOI: 10.1016/S0140-6736(03)15268-3
10. Ribeiro C.C.C., Silva M., Nunes A.M.M., Thomaz E., Carmo C.D.S., Ribeiro M.R.C., da Silva A.A.M.. **Overweight, obese, underweight, and frequency of sugar consumption as risk indicators for early childhood caries in Brazilian preschool children**. *Int. J. Paediatr. Dent.* (2017) **27** 532-539. DOI: 10.1111/ipd.12292
11. Aluckal E., Anzil K., Baby M., George E.K., Lakshmanan S., Chikkanna S.. **Association between Body Mass Index and Dental Caries among Anganwadi Children of Belgaum City, India**. *J. Contemp. Dent. Pract.* (2016) **17** 844-848. PMID: 27794156
12. Sfasciotti G.L., Marini R., Pacifici A., Ierardo G., Pacifici L., Polimeni A.. **Childhood overweight-obesity and periodontal diseases: Is there a real correlation?**. *Ann. Stomatol.* (2016) **7** 65-72. DOI: 10.11138/ads/2016.7.3.065
13. Simsek H., Korkmaz Y.N., Buyuk S.K.. **Relationship between obesity and prevalence of dental anomalies: Does body mass index play a role?**. *Eur. J. Paediatr. Dent.* (2019) **20** 95-99. PMID: 31246082
14. Michelogiannakis D., Rossouw P.E., Khan J., Akram Z., Menenakos E., Javed F.. **Influence of increased body mass index on orthodontic tooth movement and related parameters in children and adolescents: A systematic review of longitudinal controlled clinical studies**. *J. Orthod.* (2019) **46** 323-334. DOI: 10.1177/1465312519873669
15. López-Gómez J.J., Pérez Castrillón J.L., de Luis Román D.A.. **Impact of obesity on bone metabolism**. *Endocrinol. Nutr.* (2016) **63** 551-559. DOI: 10.1016/j.endonu.2016.08.005
16. Al-Jewair T., Marwah S., Preston C.B., Wu Y., Yu G.. **Correlation between craniofacial structures, anthropometric measurements, and nasopharyngeal dimensions in black adolescents**. *Int. Orthod.* (2021) **19** 96-106. DOI: 10.1016/j.ortho.2021.01.002
17. Giuca M.R., Giannotti L., Saggese R., Vanni A., Pasini M.. **Evaluation of cephalometric, hormonal and enzymatic parameters in young obese subjects**. *Eur. J. Paediatr. Dent.* (2013) **14** 175-180. PMID: 24294999
18. Chu G., Han M.-Q., Ji L.-L., Li M.-J., Zhou H., Chen T., Guo Y.-C.. **Will different sagittal and vertical skeletal types relate the soft tissue thickness: A study in Chinese female adults**. *Leg Med.* (2020) **42** 101633. DOI: 10.1016/j.legalmed.2019.101633
19. Buyuk S.K., Genc E., Simsek H., Karaman A.. **Analysis of facial soft tissue values and cranial skeletal widths in different body mass index percentile adolescent subjects**. *Cranio* (2019) **37** 223-230. DOI: 10.1080/08869634.2017.1420440
20. Joshi M., Wu L.P., Maharjan S., Regmi M.R.. **Sagittal lip positions in different skeletal malocclusions: A cephalometric analysis**. *Prog. Orthod.* (2015) **16** 8. DOI: 10.1186/s40510-015-0077-x
21. Al Taki A., Yaqoub S., Hassan M.. **Legan-burstone soft tissue profile values in a Circassian adult sample**. *J. Orthod. Sci.* (2018) **7** 18. DOI: 10.4103/jos.JOS_27_18
22. Ayoub F., Saadeh M., Fayyad-Kazan H., Haddad R.. **Stereophotogrammetric analysis of labial morphology in a young adult Middle Eastern population**. *J. Craniomaxillofac. Surg.* (2019) **47** 273-279. DOI: 10.1016/j.jcms.2018.12.001
23. Vandenbroucke J.P., von Elm E., Altman D.G., Gøtzsche P.C., Mulrow C.D., Pocock S.J., Poole C., Schlesselman J.J., Egger M.. **Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration**. *Int. J. Surg.* (2014) **12** 1500-1524. DOI: 10.1016/j.ijsu.2014.07.014
24. Deng C., Wang D., Chen J., Li K., Yang M., Chen Z., Zhu Z., Yin C., Chen P., Cao D.. **Facial soft tissue thickness in Yangtze River delta Han population: Accurate assessment and comparative analysis utilizing Cone-Beam CT**. *Leg Med.* (2020) **44** 101693. DOI: 10.1016/j.legalmed.2020.101693
25. Ricketts R.M.. **The value of cephalometrics and computerized technology**. *Angle Orthod.* (1972) **42** 179-199. PMID: 4504536
26. Bishara S.E., Hession T.J., Peterson L.C.. **Longitudinal soft-tissue profile changes: A study of three analyses**. *Am. J. Orthod.* (1985) **88** 209-223. DOI: 10.1016/S0002-9416(85)90216-7
27. Huang L., Gao X.. **The interaction of obesity and craniofacial deformity in obstructive sleep apnea**. *Dentomaxillofac. Radiol.* (2021) **50** 20200425. DOI: 10.1259/dmfr.20200425
28. Lee Y.J., Park J.T., Cha J.Y.. **Perioral soft tissue evaluation of skeletal Class II Division 1: A lateral cephalometric study**. *Am. J. Orthod. Dentofac. Orthop.* (2015) **148** 405-413. DOI: 10.1016/j.ajodo.2015.03.033
29. Sharma P., Arora A., Valiathan A.. **Age changes of jaws and soft tissue profile**. *Sci. World J.* (2014) **2014** 301501. DOI: 10.1155/2014/301501
30. Johari M., Esmaeili F., Hamidi H.. **Facial Soft Tissue Thickness of Midline in an Iranian Sample: MRI Study**. *Open Dent. J.* (2017) **11** 375-383. DOI: 10.2174/1874210601711010375
31. Nikolis A., Frank K., Guryanov R., Gombolevskiy V., Morozov S., Makhmud K., Chernina V., Gotkin R.H., Green J.B., Cotofana S.. **Differences in Temporal Volume between Males and Females and the Influence of Age and BMI: A Cross-Sectional CT-Imaging Study**. *Facial Plast. Surg.* (2021) **37** 632-638. DOI: 10.1055/s-0041-1725201
32. De Greef S., Claes P., Vandermeulen D., Mollemans W., Suetens P., Willems G.. **Large-scale in-vivo Caucasian facial soft tissue thickness database for craniofacial reconstruction**. *Forensic Sci. Int.* (2006) **159** S126-S146. DOI: 10.1016/j.forsciint.2006.02.034
33. Dong Y., Huang L., Feng Z., Bai S., Wu G., Zhao Y.. **Influence of sex and body mass index on facial soft tissue thickness measurements of the northern Chinese adult population**. *Forensic Sci. Int.* (2012) **222** 396.e1-396.e7. DOI: 10.1016/j.forsciint.2012.06.004
34. Hwang H.S., Park M.K., Lee W.J., Cho J.H., Kim B.K., Wilkinson C.M.. **Facial soft tissue thickness database for craniofacial reconstruction in Korean adults**. *J. Forensic Sci.* (2012) **57** 1442-1447. DOI: 10.1111/j.1556-4029.2012.02192.x
35. Wang J., Zhao X., Mi C., Raza I.. **The study on facial soft tissue thickness using Han population in Xinjiang**. *Forensic Sci. Int.* (2016) **266** 585.e1-585.e5. DOI: 10.1016/j.forsciint.2016.04.032
36. Briers N., Briers T.M., Becker P.J., Steyn M.. **Soft tissue thickness values for black and coloured South African children aged 6–13 years**. *Forensic Sci. Int.* (2015) **252** 188.e1-188.e10. DOI: 10.1016/j.forsciint.2015.04.015
37. Negruţiu B.M., Vaida L.L., Todor B.I., Judea A.S., Lile I.E., Moca A.E., Judea-Pusta C.T.. **An important morphological feature of the face: Upper lip length**. *Rom. J. Morphol. Embryol.* (2019) **60** 537-541. PMID: 31658327
38. McNamara L., McNamara J.A., Jr Ackerman M.B., Baccetti T.. **Hard- and soft-tissue contributions to the esthetics of the posed smile in growing patients seeking orthodontic treatment**. *Am. J. Orthod. Dentofac. Orthop.* (2008) **133** 491-499. DOI: 10.1016/j.ajodo.2006.05.042
39. Baillie L.J., Mirijali S.A., Niven B.E., Blyth P., Dias G.J.. **Ancestry and BMI Influences on Facial Soft Tissue Depths for A Cohort of Chinese and Caucasoid Women in Dunedin, New Zealand**. *J. Forensic Sci.* (2015) **60** 1146-1154. DOI: 10.1111/1556-4029.12799
40. Toneva D., Nikolova S., Georgiev I., Harizanov S., Zlatareva D., Hadjidekov V., Lazarov N.. **Facial soft tissue thicknesses in Bulgarian adults: Relation to sex, body mass index and bilateral asymmetry**. *Folia Morphol.* (2018) **77** 570-582. DOI: 10.5603/FM.a2017.0114
41. Saloom H.F., Boustan R., Seehra J., Papageorgiou S.N., Carpenter G.H., Cobourne M.T.. **The impact of obesity on orthodontic treatment outcome in adolescents: A prospective clinical cohort study**. *Eur. J. Orthod.* (2021) **43** 165-172. DOI: 10.1093/ejo/cjaa032
42. Leonard M.B., Shults J., Wilson B.A., Tershakovec A.M., Zemel B.S.. **Obesity during childhood and adolescence augments bone mass and bone dimensions**. *Am. J. Clin. Nutr.* (2004) **80** 514-523. DOI: 10.1093/ajcn/80.2.514
43. Danze A., Jacox L.A., Bocklage C., Whitley J., Moss K., Hardigan P., Garcia-Godoy C., Jackson T.H.. **Influence of BMI percentile on craniofacial morphology and development in children and adolescents**. *Eur. J. Orthod.* (2021) **43** 184-192. DOI: 10.1093/ejo/cjaa056
44. Yasa Y., Buyuk S.K., Genc E.. **Comparison of mandibular cortical bone among obese, overweight, and normal weight adolescents using panoramic mandibular index and mental index**. *Clin. Oral Investig.* (2020) **24** 2919-2924. DOI: 10.1007/s00784-019-03158-7
45. Yin G., Liao S., Gong D., Qiu H.. **Association of acrylamide and glycidamide haemoglobin adduct levels with diabetes mellitus in the general population**. *Environ. Pollut.* (2021) **277** 116816. DOI: 10.1016/j.envpol.2021.116816
46. Li H., Zheng D., Li Z., Wu Z., Feng W., Cao X., Wang J., Gao Q., Li X., Wang W.. **Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults**. *JAMA Netw. Open* (2019) **2** e1916591. DOI: 10.1001/jamanetworkopen.2019.16591
|
---
title: IL-33 via PKCμ/PRKD1 Mediated α-Catenin Phosphorylation Regulates Endothelial
Cell-Barrier Integrity and Ischemia-Induced Vascular Leakage
authors:
- Deepti Sharma
- Geetika Kaur
- Shivantika Bisen
- Anamika Sharma
- Ahmed S. Ibrahim
- Nikhlesh K. Singh
journal: Cells
year: 2023
pmcid: PMC10001418
doi: 10.3390/cells12050703
license: CC BY 4.0
---
# IL-33 via PKCμ/PRKD1 Mediated α-Catenin Phosphorylation Regulates Endothelial Cell-Barrier Integrity and Ischemia-Induced Vascular Leakage
## Abstract
Angiogenesis, neovascularization, and vascular remodeling are highly dynamic processes, where endothelial cell–cell adhesion within the vessel wall controls a range of physiological processes, such as growth, integrity, and barrier function. The cadherin–catenin adhesion complex is a key contributor to inner blood–retinal barrier (iBRB) integrity and dynamic cell movements. However, the pre-eminent role of cadherins and their associated catenins in iBRB structure and function is not fully understood. Using a murine model of oxygen-induced retinopathy (OIR) and human retinal microvascular endothelial cells (HRMVECs), we try to understand the significance of IL-33 on retinal endothelial barrier disruption, leading to abnormal angiogenesis and enhanced vascular permeability. Using electric cell-substrate impedance sensing (ECIS) analysis and FITC-dextran permeability assay, we observed that IL-33 at a 20 ng/mL concentration induced endothelial-barrier disruption in HRMVECs. The adherens junction (AJs) proteins play a prominent role in the selective diffusion of molecules from the blood to the retina and in maintaining retinal homeostasis. Therefore, we looked for the involvement of adherens junction proteins in IL-33-mediated endothelial dysfunction. We observed that IL-33 induces α-catenin phosphorylation at serine/threonine (Ser/Thr) residues in HRMVECs. Furthermore, mass-spectroscopy (MS) analysis revealed that IL-33 induces the phosphorylation of α-catenin at Thr654 residue in HRMVECs. We also observed that PKCμ/PRKD1-p38 MAPK signaling regulates IL-33-induced α-catenin phosphorylation and retinal endothelial cell-barrier integrity. Our OIR studies revealed that genetic deletion of IL-33 resulted in reduced vascular leakage in the hypoxic retina. We also observed that the genetic deletion of IL-33 reduced OIR-induced PKCμ/PRKD1-p38 MAPK-α-catenin signaling in the hypoxic retina. Therefore, we conclude that IL-33-induced PKCμ/PRKD1-p38 MAPK-α-catenin signaling plays a significant role in endothelial permeability and iBRB integrity.
## 1. Introduction
Angiogenesis is a fundamental process that includes the generation of new blood vessels from pre-existing blood vessels. An appropriate balance between proangiogenic and anti-angiogenic factors highly controls this process. Nevertheless, abnormal angiogenesis or retinal neovascularization is associated with several ocular diseases leading to blindness. Retinal ischemia or hypoxia is a stimulus for neovascularization, leading to a compromised barrier function, dysfunctional and destabilized plexi, or hemorrhaging and retinal detachment [1,2,3,4]. Vascular endothelial growth factor (VEGF), a master regulator of permeability and angiogenesis, is primarily involved in neovascularization-associated ocular disorders. Hence, anti-VEGF therapeutics are presently employed in treating these diseases [5,6]. Although anti-VEGF therapies are effective in certain populations of patients, spontaneous or acquired resistance has been reported in a significant percentage of patients, indicating the association of other vasoactive mediators in the development of pathological angiogenesis in patients with ischemic retinopathies [7,8,9,10]. Several angiogenic cytokines, growth factors, and inflammatory mediators have been implicated in the progression of ocular diseases [11,12]. Therefore, considering the clinical issues associated with anti-VEGF therapies, investigating another effective mediator(s) might pave the way for new therapeutic options in neovascularization-related ocular diseases.
The blood-retinal barrier (BRB) is a highly dynamic and complex barrier that protects the retina from systemic immunological and inflammatory components while preserving retinal homeostasis. Due to the existence of junctional complexes (tight, adherens, and gap junctions) between endothelial and epithelial cells, BRB rigorously controls paracellular permeability [13]. As a result, changes in junction assembly and function significantly impact BRB characteristics, especially barrier permeability. The outer BRB (oBRB) and the inner BRB (iBRB) are the two separate barriers that make up the BRB. The iBRB dysfunction contributes to the pathophysiology of numerous retinal pathologies such as diabetic retinopathy (DR), retinal vein occlusion, retinopathy of prematurity (ROP), retinoblastoma, and retinitis pigmentosa [14,15,16]. The adherens junctions (AJs) of the iBRB include vascular endothelial (VE)-cadherin, p120, β-catenin, and α-catenin. The Ca2+-dependent transmembrane cell adhesion protein VE-cadherin has a conserved cytoplasmic tail that interacts with p120 and β-catenin. Furthermore, β-catenin binds to α-catenin and anchors the cadherin–catenin complex with actin [17]. In several cases, molecular mechanisms regulating the endothelial vascular permeability target the phosphorylation of AJs, their cleavage, and VE-cadherin internalization. Various agents, including histamine [18], tumor necrosis factor-α (TNF-α) [19], platelet-activating factor (PAF) [20], and VEGF [21], have been shown to affect AJ permeability and barrier function via phosphorylation of VE-cadherin and its binding partners at tyrosine residues. Several tyrosine kinases, including SRC kinase, c-SRC tyrosine kinase (CSK), and proline-rich tyrosine kinase 2 (PYK2), have been linked to VE-cadherin and β-catenin phosphorylation [22,23]. VEGF-induced SRC kinase activates the specific tyrosine (Y685) in the VE-cadherin cytoplasmic domain, thereby regulating angiogenesis and permeability [24,25]. In addition to VE-cadherin, VEGF also stimulated the tyrosine phosphorylation of other catenin proteins such as p120, and β-catenin, compromising cytoskeletal organization, cell–cell contact and impairing the barrier function of the endothelium [26,27].
Among various proinflammatory factors, IL-1 family members have been reported to increase oxidative stress, endothelial permeability, and BRB breakdown during the pathogenesis of retinal degenerative diseases [28]. IL-1β and IL-1α are the most widely explored for their angiogenic roles and contribution to retinal disease pathology [29]; however, the role of IL-33 in pathologies of ischemic retinopathies has not been much investigated. The angiogenic role of IL-33 is still under debate, as some reports show it has a proangiogenic role [30], while others exhibit its anti-angiogenic role [31]. In HUVECs, IL-33 stimulated endothelial NO production, resulting in increased endothelial permeability and loss of cadherin-mediated cell–cell contact, promoting angiogenesis [30]. Previously, we have shown that IL-33 regulates OIR/hypoxia-induced endothelial cell sprouting and retinal neovascularization [32]. Increased in vascular permeability loosens adherens junctions (AJs) to allow angiogenic sprouting [33]. Therefore, in the present study, we used human retinal microvascular endothelial cells (HRMVECs) and a murine model of oxygen-induced retinopathy (OIR) to understand the significance of IL-33-signaling on endothelial barrier disruption, leading to abnormal angiogenesis, and enhanced vascular permeability.
## 2.1. Reagents
Anti-α-catenin (sc-9988, dilution 1:500), anti-VE-cadherin (sc-9989, dilution 1:500), and anti-β-catenin (sc-7963, dilution 1:500) were obtained from Santa Cruz Biotechnology (Dallas, TX, USA). Anti-Phospho-p38 MAPK [4511], anti-Phospho-p$\frac{44}{42}$ MAPK (ERK$\frac{1}{2}$) [4370], anti-Phospho-SAPK/JNK [9255], anti-p38 MAPK [9212], anti-p$\frac{44}{42}$ MAPK (ERK$\frac{1}{2}$) [4695], anti-SAPK/JNK [9252], anti-Phospho-PKD/PKCμ (Ser916) [2051], anti-PKD/PKCμ (D4J1N) [90,039], anti-Phospho-PKD/PKCμ (Ser$\frac{744}{748}$) [2054], anti-Phospho-PKC (pan) (βII Ser660) [9371], anti-Phospho-PKCα/β II (Thr$\frac{638}{641}$) [9375], anti-Phospho-PKCδ (Thr505) [9374], anti-Phospho-PKCδ/θ (Ser$\frac{643}{676}$) [9376], anti-Phospho-PKCθ (Thr538) [9377], anti-Phospho-PKCζ/λ (Thr$\frac{410}{403}$) [9378], anti-PKCα [2056], anti-PKCζ (C24E6) [9368], anti-PKD/PKCμ (D4J1N) [90,039], and anti-PKCδ (D10E2) [9616] antibodies were obtained from Cell Signaling Technology (Beverly, MA, USA). VECTASHIELD Antifade mounting medium without DAPI (H-1700), Hoechst 33,342, Prolong Gold antifade reagent (P36984), and Alexa Fluor 568-conjugated goat anti-mouse immunoglobulin G were bought from Invitrogen (Carlsbad, CA, USA). Fluorescein isothiocyanate–dextran (average molecular weight 2,000,000) FD2000S-100MG (average molecular weight 70,000) and 46945-100MG-F, and Triton ™ X-100 (T9284) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Normal Goat Serum Blocking Solution S-1000-20 was purchased from Vector Laboratories, Inc. (Burlingame, CA, USA).
## 2.2. Experimental Animals
Charles River Laboratories provided C57BL/6 mice (Wilmington, MA, USA). The Jackson Laboratory provided IL-33flox/flox mice (#030619) and E2a-Cre mice (#003724). The mice were raised, housed, provided with ad libitum water and food in a 12-h light/12-h dark cycle setting. The animals were kept in the DLAR animal facility at Wayne State University Detroit, Michigan. For this investigation, male and female mouse pups aged postpartum day 12 (P12) to P17 were used. The Wayne State University Animal Care and Use Committee in Detroit, Michigan, approved each animal experiment.
## 2.3. IL-33 Knockout Mice Generation
We crossed IL-33flox/flox mice [34] with E2a-Cre mice to obtain IL-33 knockout animals. In mice, the Cre recombinase triggers germ-line deletion of IL-33 [35]. The male and female mouse pups (IL-33flox/flox and IL-33−/−) of age postpartum day 12 (P12) to P17 were used for the experiments.
## 2.4. Cell Culture
We purchased HRMVECs (ACBRI 181) from the Applied Cell Biology Research Institute. The cells were cultured in an EGM2 medium containing 0.25 μg/mL Amphotericin B and 10 μg/mL Gentamycin. The HRMVECs were maintained at 37 °C in an incubator with $95\%$ air and $5\%$ CO2.
## 2.5. Oxygen-Induced Retinopathy (OIR)
The 7-day old mice pups with dams were placed in a BioSpherix chamber and subjected to 75 ± $2\%$ oxygen for 5 days (P7 to P12) before being returned to room air [36]. Control mice were littermates of the same age who were kept at ambient air ($21\%$ oxygen). The pups were euthanized at P13 and P15, their eyes were enucleated, retinas extracted, retinal tissue extracts were prepared, and analyzed by western blotting using the appropriate antibodies.
## 2.6. Measurement of OIR-Induced Vascular Leakage in Mice Retina
A novel high molecular weight fluorescein-dextran perfusion method was used for the measurement of vascular leakage in a mice model of oxygen-induced retinopathy (OIR) [36]. Briefly, mice pups and dams were placed in a BioSpherix chamber at P7 and subjected to $75\%$ ± $2\%$ oxygen for 5 days (P7 to P12) before being returned to room air [37]. Control mice were littermates of the same age who were kept at ambient air. At P17 mice pups were anesthetized and then perfused with fluorescein-conjugated high molecular weight dextran (2,000,000 molecular weight) through tail vein injection. The eyes were enucleated, fixed in $4\%$ (v/v) paraformaldehyde (PFA) for 24 h at 4 °C. Then the retinas were isolated and a flat mount was prepared and visualized under a Zeiss LSM 800 confocal microscope (Carl Zeiss Microscopy, White Plains, NY, USA). The amount of leakage from iBRB is calculated as (IBRB − IB) × ABRB. Here, IBRB stands for average fluorescent intensity due to iBRB, IB for average fluorescent intensity from non-leaky area, and ABRB for area of iBRB leakage. Since there was little to no leakage in the peripheral retina, we used the peripheral retina as a non-leaky region for background removal. Data-normality tests were performed to exclude physiological differences in the animals’ distribution of FITC-dextran in the vasculature.
## 2.7. FITC-Dextran Flux Assay
To measure the endothelial permeability, HRMVECs were grown on the apical side of the Transwell insert. The cells were allowed to grow to form a monolayer. Cells were growth arrested overnight in serum-free media. Thereafter, FITC-conjugated dextran (~70,000 Da) at a working concentration of 100 μg/mL was added to the apical chamber. IL-33 (agonist) was added to both the apical and basal chamber for 2 h. In the case of inhibitor, cells were treated with inhibitor for 30 min before the agonist treatment. Then 100 μL of medium was transferred to 96-well plate from each (apical and basal) chamber and a BIOTEK SYNERGY H1 microplate reader (with Gen5™ Data Analysis Software v3.11, Santa Clara, CA, USA) was used to measure fluorescent intensity. The FITC-dextran flux was expressed as the % dextran diffused/h/cm2.
## 2.8. Electric Cell-Substrate Impedance Sensing (ESIC)
The effects of IL-33 on the real-time barrier function of human retinal endothelial cell monolayers were studied by measuring overall cellular impedance utilizing (ECIS® Zθ (theta), Applied Biophysics Inc., Troy, NY, USA) technology. In brief, a 96-well array (96W20idf PET) was coated for 30 min with 100 µM cysteine (50 µL/well), followed by coating with $0.02\%$ gelatin (50 µL/well) for 60 min. HRMVECs in EGM-2 were cultured and allowed to form a mature monolayer before being treated with several doses of IL-33 (1 ng/mL to 100 ng/mL). Following that, an alternating current of 1 μA was applied to an electrode at the bottom of the well to measure the total resistance (R) with regard to time and frequency. The optimum frequency corresponding to the maximum total R was chosen to be 4000 Hz based on our previous study [38]. The R value at each time point was adjusted to the baseline R before adding IL-33 and then displayed as a function of time. The data were collected throughout the experiment by calculating the area under the curve (AUC).
## 2.9. Cell-Surface Receptor Internalization
HRMVECs were allowed to rest in a serum-free medium overnight. Quiesced cells were treated with or without IL-33 (20 ng/mL) for the respective periods, then washed and incubated with sulfo-NHSS-SS-biotin in PBS for 30 min at 4 °C. After 30 min, 50 mM Tris (pH 8.0) was added to stop the reaction. The cells were lysed in lysis buffer, and the membrane proteins that had been biotinylated were affinity purified using avidin resins before being examined by western blotting. The receptor internalization is measured by the fraction of receptors present on the cell surface of control and agonist-treated cells.
## 2.10. Immunoprecipitation and Mass Spectroscopy
The quiesced retinal endothelial cells were treated for 60 min with IL-33 (20 ng/mL). Cells were washed before being lysed in lysis buffer and immunoprecipitated with anti α-catenin antibodies. The immunocomplexes were then incubated for 3 h at 4 °C with protein A/G beads. The bound proteins were eluted from the beads and separated on an SDS-PAGE gel before being stained with Coomassie Brilliant Blue R-250 to make the proteins visible. The α-catenin band was excised and digested in-gel with trypsin. At Wayne State University’s Protein/Molecular Structural Analysis Core, the resultant peptides were examined for the phosphorylated amino acid residues using LC-ESI-MS/MS.
## 2.11. Immunofluorescence
HRMVECs were grown to confluence in a six-well plate on a circular glass coverslip. The cells were quiesced overnight and then treated with IL-33 (20 ng/mL) for the indicated periods. In the case of inhibitor studies, cells were treated with the inhibitor for 30 min before the IL-33 treatment. The cells were fixed, permeabilized, blocked, and incubated with mouse anti-α-catenin and mouse anti-VE-cadherin (dilution 1:100) antibodies for overnight at 4 °C. The cells were then washed and incubated with Alexa Fluor 568-conjugated goat anti-mouse secondary antibodies (dilution 1:250) for 1 h at RT. The cells were counter-stained with DAPI (Hoechst 33,342, dilution 1:1000), washed, and mounted using VECTASHIELD mounting media. The images were captured using a Zeiss LSM 800 confocal microscope (Carl Zeiss Microscopy, White Plains, NY, USA).
## 2.12. Western Blotting
Electrophoresis was used to separate an equivalent quantity of protein from cell or tissue extracts on SDS-PAGE gels. The proteins that had been resolved on the gels were then electrophoretically transferred to a nitrocellulose membrane. Either $5\%$ (w/v) nonfat dry milk or bovine serum albumin (BSA) was used to block the membrane. Appropriate primary antibodies were used to probe the nitrocellulose membranes. After washing, the membranes were incubated with horseradish peroxidase-conjugated secondary antibodies. The membrane antigen-antibody complexes were visualized using an improved chemiluminescent detection reagent (Supersignal West Pico Plus, Thermo Fisher Scientific, Waltham, MA, USA).
## 2.13. Statistics
The data were presented as the mean ± standard deviation (SD) of three independent experiments. To compare the differences between two groups, two-tailed t-tests were utilized. To investigate differences between more than two groups, one-way ANOVA with Tukey’s post hoc analysis was performed. We used GraphPad Prism 9 (Prism, Boston, MA, USA) for statistical analysis. All p values < 0.05 were significant.
## 3.1. IL-33 Disrupts Human Retinal Endothelial Cell-Barrier Integrity
The role of IL-33 in HRMVECs barrier function in a real-time manner was investigated using electric cell-substrate impedance sensing (ECIS®, Applied Biophysics Inc., Troy, NY, USA) instrument. HRMVECs were treated with various concentrations of IL-33 (1, 10, 20, 50, and 100 ng/mL) when the (R) reached the plateau phase, where HRMVECs formed a stable and confluent monolayer with mature tight connections (Figure 1A). The barrier integrity of HRMVECs was then assessed based on total R over 10 h. As shown in Figure 1A, IL-33 treatment at 10, 20, 50, and 100 ng/mL resulted in impaired barrier functionality of HRMVECs with little or no effect at 1 ng/mL. The area under the curve (AUC) for each R curve was calculated to determine the influence of IL-33 on HRMVECs R throughout the experiment (Figure 1B). The dose-dependent impact of IL-33 revealed a substantial difference in AUCs compared to the control, lending credence to the hypothesis that IL-33 modulates cell R throughout the whole experiment. The effect of IL-33 on human microvascular endothelial cell-barrier function was further corroborated using fluorescein isothiocyanate (FITC)-labeled dextran flux assay. IL-33 enhanced HRMVECs barrier permeability relative to controls, as evaluated by a fluorescein isothiocyanate (FITC)-labeled dextran flow experiment (Figure 1C).
## 3.2. IL-33 Promotes α-Catenin Phosphorylation and Adherens Junction Disruption
Adherens junctions (AJs) are cell–cell adhesion complexes found in endothelial and epithelial cells that play critical roles in embryogenesis and tissue homeostasis [39,40,41]. To further understand how IL-33 increases EC barrier permeability, we examined the effect of IL-33 on endothelial adherens junction proteins. The steady-state levels of AJ proteins, particularly α-catenin, β-catenin, and VE-cadherin, were unaffected by IL-33 (Figure 2A,B). As a result, we postulated that IL-33 might disrupt AJs by inducing post-translational modifications of AJ proteins, triggering their separation from multimeric protein complexes. Following this viewpoint, we investigated the tyrosine (Tyr) and serine/threonine (Ser/Thr) phosphorylation of AJ proteins in HRMVECs. IL-33 increased Ser/Thr phosphorylation of α-catenin in HRMVECs in a time-dependent manner but had little or no effect on VE-cadherin or β-catenin phosphorylation (Figure 2B,C). The immunofluorescence staining of the HRMVECs monolayer treated with and without IL-33 for various time periods for α-catenin showed that it dissociates from the plasma membrane in response to IL-33 treatment (Figure 2C). α-catenins are essential cytoplasmic molecules that are hypothesized to connect the cadherin cytoplasmic domain to the actin cytoskeleton [42]. To further understand the effect of IL-33 on VE-cadherin endocytosis in HRMVECs, we performed a cell surface-receptor internalization experiment. We discovered that IL-33 promoted VE-cadherin endocytosis in HRMVECs (Figure 2D,E). Based on the data, it seems that IL-33 destroys endothelial AJs in HRMVECs via α-catenin phosphorylation. We next used mass spectrometry to demonstrate that IL-33 induces α-catenin phosphorylation at Thr654 residues in HRMVECs (Figure 3A–C).
## 3.3. PKCμ Regulates α-Catenin Phosphorylation and Endothelial Barrier Disruption
Protein kinase C (PKC) is a serine/threonine protein kinase that plays a range of functions in cell processes, including cell–cell adhesion. PKCα and PKCβ are shown to influence cell–cell junctions and permeability in vascular endothelial cells [43,44]. As a result, we sought to determine whether IL-33 activates any PKCs and, if so, its role in α-catenin phosphorylation and endothelial barrier disruption. The time-course experiment demonstrated that IL-33 increases PKCμ/PRKD1 phosphorylation at Ser$\frac{744}{748}$ residues (Figure 4A,B). We next investigated the effect of PKCμ/PRKD1 deficiency on α-catenin phosphorylation. PKCμ downregulation by its siRNA reduced IL-33-induced α-catenin phosphorylation, indicating that PKCμ modulates IL-33-induced α-catenin phosphorylation in HRMVECs (Figure 4C). The role of PKCμ in HRMEVCs barrier permeability and electrical resistance was then investigated. Figure 5A,B shows that siRNA-mediated PKCμ depletion effectively reduced IL-33-induced endothelial cell permeability. To confirm this, we found that PKCμ depletion in HRMVECs reversed not only IL-33-induced α-catenin dislocation from the plasma membrane (Figure 5C), but also abrogated IL-33-induced reduction in electrical resistance of HRMVECs (Figure 5D) evaluated by ECIS compared to the control group throughout the experiment, as indicated by the AUC in Figure 5E.
## 3.4. p38 MAPK Mediates IL-33-Induced Retinal Endothelial Cell-Barrier Disruption
Previous reports have suggested that MAPKs play a role in endothelial cell permeability in response to external cues [45,46]. Therefore, we investigated the role of MAPKs in IL-33-induced endothelial cell permeability. IL-33 enhanced the phosphorylation of JNK, ERK$\frac{1}{2}$, and p38 MAPK in a time-dependent manner in HRMVECs (Figure 6A). Based on these findings, we looked at the role of JNK, ERK$\frac{1}{2}$, and p38 MAPK in IL-33-induced HRMVECs barrier permeability. We used SP600125 to inhibit JNK activity, FR180204 to reduce ERK activity, and SB203580 to inhibit p38 MAPK activity. SP600125, a selective inhibitor of JNK [47], blocked IL-33-induced JNK phosphorylation in HRMVECs (Figure 6B). FR180204 is a competitive inhibitor of ERK, but it does not affect ERK phosphorylation by upstream kinases [48]. Similarly, SB203580 reduces p38 MAPK catalytic activity but does not affect p38 MAPK phosphorylation by upstream kinases [49]. Studies have shown that FR180204 and SB203580 inhibit AKT phosphorylation at serine 473 residue [48,50]. Therefore, we assessed the activity of FR180204 and SB203580 by looking at their effects on AKT phosphorylation in HRMVECs. Both FR180204 and SB203580 blocked IL-33-induced AKT phosphorylation in HRMVECs (Figure 6B). SB203580, a pharmacological inhibitor of p38 MAPK [49], attenuated IL-33-induced endothelial cell permeability (Figure 6C). We failed to observe any effect of SP600125 (JNK inhibitor) and FR180204 (ERK$\frac{1}{2}$ inhibitor) on IL-33-induced endothelial-cell permeability (Figure 6C). To confirm these results, we also investigated the role of SB203580 (p38 MAPK inactivation) on α-catenin localization at the plasma membrane. We observed that p38 MAPK inactivation inhibited IL-33-induced α-catenin phosphorylation and reversed the IL-33-induced dislocation of α-catenin from the plasma membrane (Figure 6D,E). We next tested the role of PKCμ in p38 MAPK activation. The siRNA-mediated downregulation of PKCμ blocked IL-33-induced p38 MAPK phosphorylation (Figure 6F), suggesting that p38 MAPK is downstream of PKCμ in IL-33-mediated retinal endothelial-cell permeability.
## 3.5. IL-33 Regulates iBRB Integrity in Oxygen-Induced Retinopathy (OIR)
We expanded our research on the function of IL-33 in OIR-induced retinal endothelial barrier breakdown to better comprehend the pathophysiological significance of our findings in HRMVECs. *We* genetically deleted IL-33 to examine its effect on OIR-induced retinal endothelial barrier dysfunction (Figure 7A,B). We observed an induced phosphorylation of PKCμ, p38 MAPK, and α-catenin in hypoxic retinas of IL-33fl/fl mice, which was significantly inhibited in IL-33 knockout mice retina (Figure 7C,D). IL-33-deficient mice also showed a significant reduction in OIR-induced vascular leakage (FITC leakage) compared to IL-33fl/fl mice (Figure 7E,F). These results suggest that IL-33 regulates hypoxia/ischemia-induced retinal endothelial barrier permeability, which is a predisposing cause for inner blood-retinal barrier (iBRB) damage and pathological retinal neovascularization.
## 4. Discussion
A critical component of various proliferative retinopathies is blood vessel dysfunction. The retina is susceptible to fluid buildup produced by excessive blood vessel leakage in the eye, and reducing this leakage is a treatment target in retinal disorders [51]. Here, we demonstrate that IL-33 induces retinal endothelial cell-barrier permeability in two independent models, HRMVECs and mouse oxygen-induced retinopathy (OIR) model. We have shown that IL-33 disrupts human retinal endothelial cell-barrier permeability through PKCμ-p38 MAPK-α-catenin signaling. We also observed that IL-33 via PKCμ-p38 MAPK-α-catenin signaling regulates OIR-induced vascular leakage in retinal vascular beds.
We and others have shown that IL-33, a stress-regulated cytokine produced largely by epithelial and endothelial cells, has a role in angiogenesis [32,52,53,54]. We have also shown that IL-33 has a role in post-ischemic neoangiogenesis [32]. Anti-VEGF medications used to treat pathological neovascularization (NV), but do not specifically block NV, as documented impairment in normal retinal vascular development and retinal function have also been noted [55]. During our studies, we observed that downregulation of IL-33 resulted in reduced NV without impairing intraretinal revascularization [32]. Therefore, depletion or blockage of IL-33 might be helpful in only regulating pathological retinal neovascularization without affecting regular retinal repair. Increased vascular permeability is frequently associated with the early stages of angiogenesis. Therefore, we looked for the role of IL-33 on retinal endothelial barrier permeability using electric cell–substrate impedance sensing (ECIS), and FITC-dextran flux assay. We observed that IL-33 treatment not only induces retinal endothelial cell permeability, but also decreases cell–cell adhesion in human retinal endothelial cells. We have previously shown that IL-33 regulates Jagged1-Notch1 mediated retinal endothelial cell sprouting and neovascularization [32]. The phosphorylation of adherens junction molecules, in particular VE-cadherin, increases the junctional permeability and sufficiently loosens the adhesions to permit efficient sprouting [33]. According to a computational model, the intercalation of cells that facilitates sprout elongation is made possible by regional variations in the adhesive characteristics between cells [56]. Therefore, we investigated how IL-33 affected the expression and phosphorylation of adherens junction proteins. Here, we observed that IL-33 induces Ser/Thr phosphorylation of α-catenin. From mass-spectrometric analysis, we found that IL-33 induces α-catenin phosphorylation at Thr-654 residue. Our immunofluorescence studies show that IL-33 treatment induces α-catenin dissociation from the plasma membrane, and this correlates with α-catenin phosphorylation and endothelial cell-barrier dysfunction. These findings suggest that IL-33-induced α-catenin phosphorylation at Thr-654 contributes to human retinal endothelial cell-barrier dysfunction. Various knockout studies have demonstrated that α-catenin is essential for tissue morphogenesis and cell–cell adhesion [57,58]. However, it is unknown how α-catenin controls cell-adhesion states or other dynamic adhesion modifications that result in angiogenesis.
There are several members of the PKC family, and each isozyme is strongly associated with a variety of cellular functions, including cell adhesion, cytoskeletal rearrangements, membrane traffic, and ion transport [59,60]. PKC regulates the endocytosis and internalization of several cell surface receptors [61,62,63]. In the present study, we observed that IL-33 induces PKCμ/PRKD1 activation in retinal endothelial cells, with little or no effect on other PKCs. According to previous studies, PKCα and PKCδ are crucial PKC isoforms in the Cholecystokinin (CCK)-dependent activation of PKCμ/PRKD1 in pancreatic acini [64]. Our findings suggest that PKCα and PKCδ might be involved in the phosphorylation of PKCμ/PRKD1 since we also observed induced phosphorylation of PKCα and PKCδ by IL-33 in HRMVECs. PKCμ/PRKD1 has been shown to regulate αvβ3 integrin trafficking and HUVECs migration [65]. The impact of PKCμ/PRKD1 on angiogenesis has also been reported [65]. However, the molecular pathways that PKCμ/PRKD1 activates in angiogenesis have yet to be fully discovered. The present study shows that IL-33-induced PKCμ/PRKD1 activation regulates α-catenin phosphorylation in HRMVECs. In addition, our observation also suggests a role for PKCμ/PRKD1 on IL-33-induced HRMEVCs barrier permeability and electrical impedance.
Mitogen-activated protein kinases (MAPKs) regulate numerous biological processes, including cell division, motility, differentiation, survival, and apoptosis [66,67,68]. As a result, the initiation and progression of various diseases depend on the dysregulation of MAPK signaling. The MAPK-signaling pathways are involved in the onset and development of cardiac and vascular diseases. It is activated by various extracellular stimuli, which leads to alterations in intracellular processes. Extracellular stimuli include cellular stress, adhesion molecules, neurohormones, and PKCs [69]. There is mounting evidence that many MAPK family members regulate signaling pathways, which ultimately results in several vascular disorders. The MAPK subfamilies-p38 MAPK, c-Jun NH2-terminal kinases (JNK 1, 2, and 3), and extracellular signal-regulated kinases (ERK$\frac{1}{2}$) regulate signaling in vascular diseases. The role of PKC-regulated MAPK signaling in diabetes complications has received a lot of attention. Several studies have shown that PKCs have a direct role in ERK and JNK activation, which results in cardiomyocyte hypertrophy and remodeling [70,71]. Under hypoxic conditions, the PKCμ/PRKD1-p38 MAPK signaling regulates melatonin-induced osteoblast development [72]. Furthermore, it has also been reported that PKCμ/PRKD1 mediated p38 MAPK phosphorylation regulates hypoxia-induced growth and metabolism of the SCC25 cells [73]. In this regard, our findings indicate that IL-33-induced PKCμ activation regulates p38 MAPK phosphorylation in HRMVECs. Furthermore, our results also emphasize that p38 MAPK activation regulates IL-33-induced retinal endothelial cell permeability.
Our recent study found that oxygen-induced retinopathy (OIR) induces IL-33 levels in hypoxic retinal endothelial cells. Additionally, OIR-induced retinal EC sprouting and neovascularization were prevented by the genetic deletion of IL-33 in mice. Several studies have shown that VEGFA not only stimulates the production of endothelial tip cells (sprouting) but also reduces adherens junctions (AJs) to promote vascular permeability [74]. It was also reported that tyrosine phosphorylation of VE-cadherin and c-SRC by VEGFR2 enhances junctional permeability and loosens adhesions enough to allow for efficient sprouting [33]. According to a computer model, local changes in adhesive affinities between cells allow the intercalation of cells that enable sprout extension [56]. Therefore, we investigated the involvement of PKCμ-p38 MAPK-α-catenin signaling in OIR-induced proliferative retinopathy and the effect of IL-33 deletion on this signaling. In the retina, OIR-induced PKCμ-p38 MAPK mediated α-catenin Ser/Thr phosphorylation, and IL-33 deficiency inhibited this signaling. We also observed that the genetic depletion of IL-33 results in reduced OIR-induced vascular leakage in the hypoxic retina. The findings here may provide more evidence for the significance of IL-33 in retinal endothelial cell permeability and neovascularization. Several knockout studies have shown that α-catenin plays a crucial role in tissue morphogenesis and cell–cell adhesion [57,58]. However, it is unknown how α-catenin regulates the dynamic cell adhesion states and the alterations that result in angiogenesis. Our findings may have highlighted the involvement of IL-33 and its signaling in hypoxia/ischemia-induced α-catenin phosphorylation and retinal endothelial cell-barrier failure.
## 5. Conclusions
In conclusion, our findings show that IL-33 controls hypoxia/ischemia-induced retinal endothelial permeability via the PKCμ-p38 MAPK-α-catenin pathway. These findings suggest that increased IL-33 levels in the hypoxic retina promote endothelial barrier rupture and vascular leakage, contributing to the pathogenesis of angiogenesis-dependent proliferative retinopathies.
## References
1. Campochiaro P.A.. **Molecular pathogenesis of retinal and choroidal vascular diseases**. *Prog. Retin. Eye Res.* (2015) **49** 67-81. DOI: 10.1016/j.preteyeres.2015.06.002
2. Semenza G.L.. **Hypoxia-Inducible Factors in Physiology and Medicine**. *Cell* (2012) **148** 399-408. DOI: 10.1016/j.cell.2012.01.021
3. Fruttiger M.. **Development of the retinal vasculature**. *Angiogenesis* (2007) **10** 77-88. DOI: 10.1007/s10456-007-9065-1
4. Krock B.L., Skuli N., Simon M.C.. **Hypoxia-Induced Angiogenesis: Good and Evil**. *Genes Cancer* (2011) **2** 1117-1133. DOI: 10.1177/1947601911423654
5. Apte R.S., Chen D.S., Ferrara N.. **VEGF in Signaling and Disease: Beyond Discovery and Development**. *Cell* (2019) **176** 1248-1264. DOI: 10.1016/j.cell.2019.01.021
6. Meadows K.L., Hurwitz H.I.. **Anti-VEGF Therapies in the Clinic**. *Cold Spring Harb. Perspect. Med.* (2012) **2** a006577. DOI: 10.1101/cshperspect.a006577
7. Paulus Y.M., Sodhi A.. **Anti-angiogenic therapy for retinal disease**. *Handb. Exp. Pharmacol.* (2017) **242** 271-307. PMID: 27783271
8. Brown D.M., Regillo C.D.. **Anti-VEGF Agents in the Treatment of Neovascular Age-related Macular Degeneration: Applying Clinical Trial Results to the Treatment of Everyday Patients**. *Am. J. Ophthalmol.* (2007) **144** 627-637.e2. DOI: 10.1016/j.ajo.2007.06.039
9. Eghøj M.S., Sørensen T.L.. **Tachyphylaxis during treatment of exudative age-related macular degeneration with ranibizumab**. *Br. J. Ophthalmol.* (2012) **96** 22-23. DOI: 10.1136/bjo.2011.203893
10. McAuley A.K., Sanfilippo P.G., Hewitt A.W., Liang H., Lamoureux E., Wang J.J., Connell P.P.. **Vitreous biomarkers in diabetic retinopathy: A systematic review and meta-analysis**. *J. Diabetes Complicat.* (2014) **28** 419-425. DOI: 10.1016/j.jdiacomp.2013.09.010
11. Al-Shabrawey M., Elsherbiny M., Nussbaum J., Othman A., Megyerdi S., Tawfik A.. **Targeting neovascularization in ischemic retinopathy: Recent advances**. *Expert Rev. Ophthalmol.* (2013) **8** 267-286. DOI: 10.1586/eop.13.17
12. Wang S., Park J.K., Duh E.J.. **Novel Targets Against Retinal Angiogenesis in Diabetic Retinopathy**. *Curr. Diabetes Rep.* (2012) **12** 355-363. DOI: 10.1007/s11892-012-0289-0
13. Shechter R., London A., Schwartz M.. **Orchestrated leukocyte recruitment to immune-privileged sites: Absolute barriers versus educational gates**. *Nat. Rev. Immunol.* (2013) **13** 206-218. DOI: 10.1038/nri3391
14. Luo Y., Xiao W., Zhu X., Mao Y., Liu X., Chen X., Huang J., Tang S., Rizzolo L.J.. **Differential Expression of Claudins in Retinas during Normal Development and the Angiogenesis of Oxygen-Induced Retinopathy**. *Investig. Opthalmol. Vis. Sci.* (2011) **52** 7556-7564. DOI: 10.1167/iovs.11-7185
15. Chen J., Stahl A., Krah N.M., Seaward M.R., Dennison R.J., Sapieha P., Hua J., Hatton C.J., Juan A.M., Aderman C.M.. **Wnt Signaling Mediates Pathological Vascular Growth in Proliferative Retinopathy**. *Circulation* (2011) **124** 1871-1881. DOI: 10.1161/CIRCULATIONAHA.111.040337
16. Strong S., Liew G., Michaelides M.. **Retinitis pigmentosa-associated cystoid macular oedema: Pathogenesis and avenues of intervention**. *Br. J. Ophthalmol.* (2017) **101** 31-37. DOI: 10.1136/bjophthalmol-2016-309376
17. Weis W.I., Nelson W.J.. **Re-solving the Cadherin-Catenin-Actin Conundrum**. *J. Biol. Chem.* (2006) **281** 35593-35597. DOI: 10.1074/jbc.R600027200
18. Shasby D.M., Ries D.R., Shasby S.S., Winter M.C.. **Histamine stimulates phosphorylation of adherens junction proteins and alters their link to vimentin**. *Am. J. Physiol. Lung Cell. Mol. Physiol.* (2002) **282** L1330-L1338. DOI: 10.1152/ajplung.00329.2001
19. Angelini D.J., Hyun S.-W., Grigoryev D.N., Garg P., Gong P., Singh I.S., Passaniti A., Hasday J.D., Goldblum S.E.. **TNF-α increases tyrosine phosphorylation of vascular endothelial cadherin and opens the paracellular pathway through fyn activation in human lung endothelia**. *Am. J. Physiol. Lung Cell. Mol. Physiol.* (2006) **291** L1232-L1245. DOI: 10.1152/ajplung.00109.2006
20. Hudry-Clergeon H., Stengel D., Ninio E., Vilgrain I.. **Platelet-activating factor increases VE-cadherin tyrosine phosphoryla-tion in mouse endothelial cells and its association with the PtdIns3 kinase**. *FASEB J.* (2005) **19** 512-520. DOI: 10.1096/fj.04-2202com
21. Esser S., Lampugnani M.G., Corada M., Dejana E., Risau W.. **Vascular endothelial growth factor induces VE-cadherin ty-rosine phosphorylation in endothelial cells**. *J. Cell Sci.* (1998) **111** 1853-1865. DOI: 10.1242/jcs.111.13.1853
22. Weis S.M., Cheresh D.A.. **Pathophysiological consequences of VEGF-induced vascular permeability**. *Nature* (2005) **437** 497-504. DOI: 10.1038/nature03987
23. Baumeister U., Funke R., Ebnet K., Vorschmitt H., Koch S., Vestweber D.. **Association of Csk to VE-cadherin and inhibition of cell proliferation**. *EMBO J.* (2005) **24** 1686-1695. DOI: 10.1038/sj.emboj.7600647
24. Allingham M.J., van Buul J.D., Burridge K.. **ICAM-1-mediated, Src- and Pyk2-dependent vascular endothelial cadherin ty-rosine phosphorylation is required for leukocyte transendothelial migration**. *J. Immunol.* (2007) **179** 4053-4064. DOI: 10.4049/jimmunol.179.6.4053
25. Wallez Y., Cand F., Cruzalegui F., Wernstedt C., Souchelnytskyi S., Vilgrain I., Huber P.. **Src kinase phosphorylates vascular endothelial-cadherin in response to vascular endothelial growth factor: Identification of tyrosine 685 as the unique target site**. *Oncogene* (2006) **26** 1067-1077. DOI: 10.1038/sj.onc.1209855
26. Lampugnani M.G., Corada M., Andriopoulou P., Esser S., Risau W., Dejana E.. **Cell confluence regulates tyrosine phos-phorylation of adherens junction components in endothelial cells**. *J. Cell Sci.* (1997) **110** 2065-2077. DOI: 10.1242/jcs.110.17.2065
27. Gavard J., Gutkind J.S.. **VEGF controls endothelial-cell permeability by promoting the beta-arrestin-dependent endocytosis of VE-cadherin**. *Nat. Cell Biol.* (2006) **8** 1223-1234. DOI: 10.1038/ncb1486
28. Zhu X., Wang K., Zhang K., Tan X., Wu Z., Sun S., Zhou F., Zhu L.. **Tetramethylpyrazine protects retinal capillary en-dothelial cells (TR-iBRB2) against IL-1β-induced nitrative/oxidative stress**. *Int. J. Mol. Sci.* (2015) **16** 21775-21790. DOI: 10.3390/ijms160921775
29. Yvette W., Ming M.S., Riemke A.B., Riccardo N., Nilisha F.. **IL-1 Family Members Mediate Cell Death, Inflammation and Angiogenesis in Retinal Degenerative Diseases**. *Front. Immunol.* (2019) **10** 1618. PMID: 31379825
30. Choi Y.S., Choi H.J., Min J.K., Pyun B.J., Maeng Y.S., Park H., Kim J., Kim Y.M., Kwon Y.G.. **Interleukin-33 induces an-giogenesis and vascular permeability through ST2/TRAF6-mediated endothelial nitric oxide production**. *Blood* (2009) **114** 3117-3126. DOI: 10.1182/blood-2009-02-203372
31. Küchler A.M., Pollheimer J., Balogh J., Sponheim J., Manley L., Sorensen D.R., Angelis P.M.D., Scott H., Haraldsen G.. **Nuclear interleukin-33 is generally expressed in resting endothelium but rapidly lost upon angiogenic or proinflammatory ac-tivation**. *Am. J. Pathol.* (2008) **173** 1229-1242. DOI: 10.2353/ajpath.2008.080014
32. Sharma D., Bisen S., Kaur G., Van Buren E.C., Rao G.N., Singh N.K.. **IL-33 enhances Jagged1 mediated NOTCH1 intra-cellular domain (NICD) deubiquitination and pathological angiogenesis in proliferative retinopathy**. *Commun. Biol.* (2022) **5** 479. DOI: 10.1038/s42003-022-03432-7
33. Gordon E.J., Fukuhara D., Weström S., Padhan N., Sjöström E.O., van Meeteren L., He L., Orsenigo F., Dejana E., Bentley K.. **The endothelial adaptor molecule TSAd is required for VEGF-induced angiogenic sprouting through junctional c-Src activation**. *Sci. Signal.* (2016) **9** ra72. DOI: 10.1126/scisignal.aad9256
34. Hsu C.-L., Chhiba K.D., Krier-Burris R., Hosakoppal S., Berdnikovs S., Miller M.L., Bryce P.J.. **Allergic inflammation is initiated by IL-33–dependent crosstalk between mast cells and basophils**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0226701
35. Lakso M., Pichel J.G., Gorman J.R., Sauer B., Okamoto Y., Lee E., Alt F.W., Westphal H.. **Efficient in vivo manipulation of mouse genomic sequences at the zygote stage**. *Proc. Natl. Acad. Sci. USA* (1996) **93** 5860-5865. DOI: 10.1073/pnas.93.12.5860
36. Zhang Q., Wang D., Kundumani-Sridharan V., Gadiparthi L., Johnson D.A., Tigyi G.J., Rao G.N.. **PLD1-dependent PKCgamma activation downstream to Src is essential for the development of pathologic retinal neovascularization**. *Blood* (2010) **116** 1377-1385. DOI: 10.1182/blood-2010-02-271478
37. Smith L.E., Wesolowski E., McLellan A., Kostyk S.K., D’Amato R., Sullivan R., D’Amore P.A.. **Oxygen-induced retinopathy in the mouse**. *Investig. Opthalmol. Vis. Sci.* (1994) **35** 101-111
38. El-Tanani S., Yumnamcha T., Singh L.P., Ibrahim A.S.. **Differential Effects of Cytopathic Hypoxia on Human Retinal En-dothelial Cellular Behavior: Implication for Ischemic Retinopathies**. *Int. J. Mol. Sci.* (2022) **23**. DOI: 10.3390/ijms23084274
39. Gumbiner B.M.. **Regulation of cadherin-mediated adhesion in morphogenesis**. *Nat. Rev. Mol. Cell Biol.* (2005) **6** 622-634. DOI: 10.1038/nrm1699
40. Halbleib J.M., Nelson W.J.. **Cadherins in development: Cell adhesion, sorting, and tissue morphogenesis**. *Genes Dev.* (2006) **20** 3199-3214. DOI: 10.1101/gad.1486806
41. Nishimura T., Takeichi M.. **Remodeling of the Adherens Junctions during Morphogenesis**. *Curr. Top. Dev. Biol.* (2009) **89** 33-54. DOI: 10.1016/s0070-2153(09)89002-9
42. Rimm D.L., Koslov E.R., Kebriaei P., Cianci C.D., Morrow J.S.. **Alpha 1(E)-catenin is an actin-binding and -bundling protein mediating the attachment of F-actin to the membrane adhesion complex**. *Proc. Natl. Acad. Sci. USA* (1995) **92** 8813-8817. DOI: 10.1073/pnas.92.19.8813
43. Sandoval R., Malik A.B., Minshall R.D., Kouklis P., Ellis C.A., Tiruppathi C.. **Ca**. *J. Physiol.* (2001) **533** 433-445. DOI: 10.1111/j.1469-7793.2001.0433a.x
44. Wang Y., Pampou S., Fujikawa K., Varticovski L.. **Opposing effect of angiopoietin-1 on VEGF-mediated disruption of en-dothelial cell-cell interactions requires activation of PKC beta**. *J. Cell Physiol.* (2004) **198** 53-61. DOI: 10.1002/jcp.10386
45. Borbiev T., Birukova A., Liu F., Nurmukhambetova S., Gerthoffer W.T., Garcia J.G.N., Verin A.D.. **p38 MAP kinase-dependent regulation of endothelial cell permeability**. *Am. J. Physiol. Lung Cell. Mol. Physiol.* (2004) **287** L911-L918. DOI: 10.1152/ajplung.00372.2003
46. Zhao Z., Zhang X., Dai Y., Pan K., Deng Y., Meng Y., Xu T.. **PPAR-γ promotes p38 MAP kinase-mediated endothelial cell permeability through activating Sirt3**. *BMC Neurol.* (2019) **19**. DOI: 10.1186/s12883-019-1508-y
47. Bennett B.L., Sasaki D.T., Murray B.W., O’Leary E.C., Sakata S.T., Xu W., Leisten J.C., Motiwala A., Pierce S., Satoh Y.. **SP600125, an anthrapyrazolone inhibitor of Jun N-terminal kinase**. *Proc. Natl. Acad. Sci. USA* (2001) **98** 13681-13686. DOI: 10.1073/pnas.251194298
48. Ruiz-Medina B.E., Ross J.A., Kirken R.A.. **Interleukin-2 Receptor β Thr-450 Phosphorylation Is a Positive Regulator for Receptor Complex Stability and Activation of Signaling Molecules**. *J. Biol. Chem.* (2015) **290** 20972-20983. DOI: 10.1074/jbc.M115.660654
49. Kumar S., Jiang M.S., Adams J.L., Lee J.C.. **Pyridinylimidazole Compound SB 203580 Inhibits the Activity but Not the Activation of p38 Mitogen-Activated Protein Kinase**. *Biochem. Biophys. Res. Commun.* (1999) **263** 825-831. DOI: 10.1006/bbrc.1999.1454
50. Lali F.V., Hunt A.E., Turner S.J., Foxwell B.M.. **The pyridinyl imidazole inhibitor SB203580 blocks phosphoinosi-tide-dependent protein kinase activity, protein kinase B phosphorylation, and retinoblastoma hyperphosphorylation in inter-leukin-2-stimulated T cells independently of p38 mitogen-activated protein kinase**. *J. Biol. Chem.* (2000) **275** 7395-7402. PMID: 10702313
51. He T., Liu S., Chen S., Ye J., Wu X., Bian Z., Chen X.. **The p38 MAPK Inhibitor SB203580 Abrogates Tumor Necrosis Factor-Induced Proliferative Expansion of Mouse CD4+Foxp3+ Regulatory T Cells**. *Front. Immunol.* (2018) **9** 1556. DOI: 10.3389/fimmu.2018.01556
52. Daruich A., Matet A., Moulin A., Kowalczuk L., Nicolas M., Sellam A., Rothschild P.-R., Omri S., Gélizé E., Jonet L.. **Mechanisms of macular edema: Beyond the surface**. *Prog. Retin. Eye Res.* (2018) **63** 20-68. DOI: 10.1016/j.preteyeres.2017.10.006
53. Rider P., Voronov E., Dinarello C.A., Apte R.N., Cohen I.. **Alarmins: Feel the Stress**. *J. Immunol.* (2017) **198** 1395-1402. DOI: 10.4049/jimmunol.1601342
54. Shan S., Li Y., Wang J., Lv Z., Yi D., Huang Q., Corrigan C.J., Wang W., Quangeng Z., Ying S.. **Nasal administration of interleukin-33 induces airways angiogenesis and expression of multiple angiogenic factors in a murine asthma surrogate**. *Immunology* (2016) **148** 83-91. DOI: 10.1111/imm.12589
55. Alon T., Hemo I., Itin A., Pe’er J., Stone J., Keshet E.. **Vascular endothelial growth factor acts as a survival factor for newly formed retinal vessels and has implications for retinopathy of prematurity**. *Nat. Med.* (1995) **1** 1024-1028. DOI: 10.1038/nm1095-1024
56. Bentley K., Franco C.A., Philippides A., Blanco R., Dierkes M., Gebala V., Stanchi F., Jones M., Aspalter I.M., Cagna G.. **The role of differential VE-cadherin dynamics in cell rear-rangement during angiogenesis**. *Nat. Cell. Biol.* (2014) **16** 309-321. DOI: 10.1038/ncb2926
57. Lien W.H., Klezovitch O., Fernandez T.E., Delrow J., Vasioukhin V.. **alphaE-catenin controls cerebral cortical size by reg-ulating the hedgehog signaling pathway**. *Science* (2006) **311** 1609-1612. DOI: 10.1126/science.1121449
58. Sarpal R., Pellikka M., Patel R.R., Hui F.Y., Godt D., Tepass U.. **Mutational analysis supports a core role for**. *J. Cell Sci.* (2012) **125** 233-245. DOI: 10.1242/jcs.096644
59. Keenan C., Kelleher D.. **Protein Kinase C and the Cytoskeleton**. *Cell. Signal.* (1998) **10** 225-232. DOI: 10.1016/S0898-6568(97)00121-6
60. Newton A.C.. **Regulation of protein kinase C**. *Curr. Opin. Cell. Biol.* (1997) **9** 161-167. DOI: 10.1016/S0955-0674(97)80058-0
61. Holm P.K., Eker P., Sandvig K., van Deurs B.. **Phorbol Myristate Acetate Selectively Stimulates Apical Endocytosis via Protein Kinase C in Polarized MDCK Cells**. *Exp. Cell Res.* (1995) **217** 157-168. DOI: 10.1006/excr.1995.1075
62. Lennartz M.R.. **Phospholipases and phagocytosis: The role of phospholipid-derived second messengers in phagocytosis**. *Int. J. Biochem. Cell Biol.* (1999) **31** 415-430. DOI: 10.1016/S1357-2725(98)00108-3
63. Hao Q., Wang L., Zhao Z.J., Tang H.. **Identification of Protein Kinase D2 as a Pivotal Regulator of Endothelial Cell Proliferation, Migration, and Angiogenesis**. *J. Biol. Chem.* (2009) **284** 799-806. DOI: 10.1074/jbc.M807546200
64. Gschwendt M., Dieterich S., Rennecke J., Kittstein W., Mueller H.J., Johannes F.J.. **Inhibition of protein kinase C mu by various inhibitors. Differentiation from protein kinase c isoenzymes**. *FEBS Lett.* (1996) **392** 77-80. DOI: 10.1016/0014-5793(96)00785-5
65. Qin L., Zeng H., Zhao D.. **Requirement of protein kinase D tyrosine phosphorylation for VEGF-A165-induced angiogenesis through its interaction and regulation of phospholipase C gamma phosphorylation**. *J. Biol. Chem.* (2006) **281** 32550-32558. DOI: 10.1074/jbc.M604853200
66. Pearson G., Robinson F., Beers Gibson T., Xu B.E., Karandikar M., Berman K., Cobb M.H.. **Mitogen-Activated Protein (MAP) Kinase Pathways: Regulation and Physiological Functions**. *Endocr. Rev.* (2001) **22** 153-183. DOI: 10.1210/edrv.22.2.0428
67. Zhang W., Elimban V., Nijjar M.S., Gupta S.K., Dhalla N.S.. **Role of mitogen-activated protein kinase in cardiac hypertrophy and heart failure**. *Exp. Clin. Cardiol.* (2003) **8** 173-183. PMID: 19649217
68. Zhang W., Liu H.T.. **MAPK signal pathways in the regulation of cell proliferation in mammalian cells**. *Cell Res.* (2002) **12** 9-18. DOI: 10.1038/sj.cr.7290105
69. Montiel M., de la Blanca E.P., Jiménez E.. **P2Y Receptors Activate MAPK/ERK through a Pathway Involving PI3K/PDK1/PKC-Zeta in Human Vein Endothelial Cells**. *Cell. Physiol. Biochem.* (2006) **18** 123-134. DOI: 10.1159/000095180
70. Puente L.G., He J.-S., Ostergaard H.L.. **A Novel PKC Regulates ERK Activation and Degranulation of Cytotoxic T Lymphocytes: Plasticity in PKC Regulation of ERK**. *Eur. J. Immunol.* (2006) **36** 1009-1018. DOI: 10.1002/eji.200535277
71. Hu B., Song J.-T., Ji X.-F., Liu Z.-Q., Cong M.-L., Liu D.-X.. **Sodium Ferulate Protects against Angiotensin II-Induced Cardiac Hypertrophy in Mice by Regulating the MAPK/ERK and JNK Pathways**. *BioMed. Res. Int.* (2017) **2017** 3754942. DOI: 10.1155/2017/3754942
72. Son J.-H., Park B.-S., Kim I.-R., Sung I.-Y., Cho Y.-C., Kim J.-S., Kim Y.-D.. **A novel combination treatment to stimulate bone healing and regeneration under hypoxic conditions: Photobiomodulation and melatonin**. *Lasers Med. Sci.* (2017) **32** 533-541. DOI: 10.1007/s10103-017-2145-6
73. Chen J., Cui B., Fan Y., Li X., Li Q., Du Y., Feng Y., Zhang P.. **Protein kinase D1 regulates hypoxic metabolism through HIF-1α and glycolytic enzymes incancer cells**. *Oncol Rep.* (2018) **40** 1073-1082. PMID: 29901206
74. Vestweber D., Wessel F., Nottebaum A.F.. **Similarities and differences in the regulation of leukocyte extravasation and vas-cular permeability**. *Semin. Immunopathol.* (2014) **36** 177-192. DOI: 10.1007/s00281-014-0419-7
|
---
title: Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors
in Health Insurance Data Using Bayesian Spatial Modelling
authors:
- Boris Kauhl
- Jörg König
- Sandra Wolf
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001453
doi: 10.3390/ijerph20054375
license: CC BY 4.0
---
# Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling
## Abstract
The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.
## 1. Introduction
The COVID-19 pandemic has already and still continues to impact billions of people across the world and has been declared a public health emergency of international concern by the World Health Organization (WHO) [1]. To contain the spread of the virus, lockdowns across the globe were declared, resulting in closure of cities, suspension of schools, and restrictions of international travel, resulting not only in a public health crisis, but also in a humanitarian, economic and social crisis [2,3].
In Germany, the first case was reported in Bavaria at the end of January 2020. At the beginning of March, almost all federal states in Germany reported cases of the disease. The southern counties in Bavaria and Baden-Württemberg in particular were affected by high numbers of cases [4]. From March 16, the first lockdown was imposed: far-reaching exit and contact restrictions applied, which were only gradually lifted again at the end of April.
Several studies from German-speaking countries have investigated the spread of COVID-19 infections from a spatiotemporal perspective. The first study [5] dates from May 2020 and is known as the Ischgl study. Based on a spatial diffusion model, correlations between the occurrence of COVID-19 infections in Germany and population mobility could be established for the first time. The vacation resort of Ischgl in Austria was given special importance as a starting point for infection occurrence in Germany, which primarily brought into focus the importance of mobility as a driver of virus spread. Steiger et al., in their study on the determinants of regional infection incidence at the level of districts and district-free cities in the period from 15 February 8 to July 2020, found that increasing temperature and mobility for basic supplies, especially, reduce the incidence of infection, whereas recreational mobility or precipitation can increase the incidence of infection [6]. In their study, Scarpone et al., analysed spatial associations between COVID-19 case rates and spatial characteristics of infrastructure, sociodemographics, and the built environment [7]. In summary, the results showed, among others, an association between built density, place of residence, transportation infrastructure (e.g., access to intensive care units), and sociodemographic factors (e.g., unemployment) as predictors of regional incidence rates in Germany.
Overall, it is clear that mobility and sociodemographic circumstances in particular have an important influence on the regional incidence of infection. In addition, it has been shown that density, built-up areas, and even weather influence the frequency of contact. Importantly, the determinants overlap spatially and temporally [8] and also depend on the pandemic phase [9]. For example, in the early pandemic phase until mid-April 2020, a socioeconomic gradient with higher incidence in less deprived regions of *Germany is* evident, but this gradient dissipates or reverses in favour of more deprived regions in the south of the country as the pandemic progresses [10]. This highlights the need to consider spatiotemporal dynamics within the observation period when analysing COVID-19 determinants with infection incidence, as the predictors of incidence rates are spatiotemporally dependent on the pandemic phase.
The fast spread of COVID-19 has increased public awareness of the use of geographic information systems (GIS) for pandemic preparedness, resulting in a large number of studies revealing the potential of GIS and spatial statistics—especially cluster detection methods—to detect outbreaks [3,11,12,13]. Likewise, GIS has also been extensively used to identify sociodemographic and environmental characteristics associated with COVID-19, possibly resulting in a better understanding of the population groups most at risk [14,15].
In Germany, most research on the spatial distribution of COVID-19 is restricted to the relatively coarse level of counties [16], masking important variation at the small-area, municipality, or even neighbourhood level, hampering productive outbreak detection and management, despite numerous studies’ having shown the value of microgeographic data on COVID-19 [17,18,19].
Likewise, studies on the spatiotemporal dynamics focus mainly on cluster detection methods, with SaTScan (Software for the spatial, temporal, and space–time scan statistics) being the most widely used statistics software [19,20]. Cluster tests are an important tool here to effectively detect outbreaks.
A large number of studies examined sociodemographic risk factors for COVID-19. However, the majority of studies are based on an ecological study design and not at the individual level [14,15]. While these studies have the advantage in that they may represent the total population, they suffer from ecological fallacy, meaning that the results of a study design based on aggregated data do not necessarily represent associations at the individual level. In contrast, studies based on individual data often suffer from small population samples (e.g., a hospital) [21,22].
In this context, health insurance data might not only provide fairly detailed insights into the spatial and spatiotemporal distribution, since these data can be analysed at microgeographic level, but also provide a rich and detailed data source on individual-level sociodemographic information and pre-existing medical conditions.
The aim of this research is therefore to (i) provide insight into the spatial distribution of COVID-19 hospitalizations based on the data of northeast Germany’s largest statutory health insurance provider and (ii) analyse sociodemographic and medical conditions associated with hospitalization.
## 2.1. Data
AOK *Nordost is* the largest statutory health insurance provider in northeast Germany and covers approximately $25\%$ of the population in the three federal states of Berlin, Brandenburg, and Mecklenburg-Western-Pomerania.
For this study, we used all 1.7 million insurants that were insured in 2021. We defined COVID-19 hospitalization as an insurant having a positive PCR test in a hospital, coded with the international classification of disease (ICD-10) U07.1!. To ensure that we captured only hospitalizations where COVID-19 is likely the primary reason for hospitalization, we additionally restricted our data source to include only individuals that have in addition to U07.1! a diagnosis for viral pneumonia or respiratory syndrome as defined by the ICD-10 codes J12.8, J12.9, J20.8, J20.9, J21.8, J21.9, J22.-. In total, 8402 insurants were hospitalized due to COVID-19.
For the analysis of possible risk factors for COVID-19 hospitalizations, we included sex, age, being unemployed at 1 July 2021, and foreign citizenship. To account for underlying chronic diseases, we included information on whether the insurant had a confirmed diagnosis of diseases, aggregated to ICD-10 chapters to keep the number of possible diagnoses per insurant at a reasonable number. The included ICD-10 chapters consist of I: *Certain infectious* and parasitic diseases, II: Neoplasms, III: Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism, IV: Endocrine, nutritional and metabolic diseases, V: Mental and behavioural disorders, VI: Diseases of the nervous system, VII: Diseases of the eye and adnexa, VIII: Diseases of the ear and mastoid process, IX: Diseases of the circulatory system, X: Diseases of the respiratory system, XI: Diseases of the digestive system, XII: Diseases of the skin and subcutaneous tissue, XIII: Diseases of the musculoskeletal system and connective tissue, XIV: Diseases of the genitourinary system, XV: Pregnancy, childbirth and the puerperium, XVI: Certain conditions originating in the perinatal period, XVII: Congenital malformations, deformations and chromosomal abnormalities, and XVIII: Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified.
At the aggregated level, we used a commercial dataset from WIgeoGIS of the so-called Geomarkets. A *Geomarket is* an administrative unit of approximately 300 households and contains valuable information on demographics, socioeconomic information, and household composition of the respective population. This data source is more useful than free official administrative data, which are only available at the level of municipalities, where large cities such as Germany’s capital, Berlin, represent only one single municipality. In contrast, Geomarkets allow an analysis of intra-urban differences. In total, northeast Germany consists of approximately 16,400 Geomarkets. The insurants were aggregated to the level of Geomarkets based on their respective address coordinates. Several studies demonstrated that area deprivation has a significant impact on COVID-19 [23,24]. We therefore calculated a deprivation index based on the following variables: unemployment rate, proportion of employed persons at the place of residence, purchasing power, persons with high school degrees, and proportion of persons without formal education. The domains of employment, income, and education were weighted equally. The resulting index values range from 1 (least deprived) to 100 (most deprived). The methodology is similar to the calculation of the German index of multiple deprivation by Werner Maier [25].
## 2.2. Statistical Analysis
To visualize the cumulative one-year COVID-19 incidence, we aggregated the insurants to the level of the 16,400 Geomarkets based on their address coordinates. To be able to visualize regional differences at this fine level, we used the Besag–York–Mollie (BYM) model. The BYM model has been extensively used to display disease rates at fine spatial resolution [26]. The input for this model consisted of the sex- and age-adjusted number of hospitalized COVID-19 patients and the expected cases. The basic assumption is that the COVID-19 hospitalizations follow a Poisson distribution, where the expected cases are the global average of the sum of observed cases divided by the global sum of insurants, multiplied by the insurants of each Geomarket. The model adjusts for the uneven distribution of the AOK Nordost insurants by weighting the incidence of a Geomarket by the average of the neighbouring Geomarkets and additionally shrinking the rate towards the global mean. This is performed by providing a neighbourhood matrix of the Geomarkets. We chose queen contiguity, where all Geomarkets are defined as neighbours if they share a common edge or border [27]. The model then smooths out the noise associated with small numbers of COVID-19 hospitalization cases as a function of the data in surrounding areas. A more detailed statistical explanation is given by Lawson et al., 2000 [28]. Additionally, we created a continuous surface to preserve insurant confidentiality, by applying an interpolation method called the stochastic partial differential equation (SPDE) approach. This approach has also been used to create small-area continuous surfaces for several diseases such as HIV prevalence in sub-Saharan Africa [29] or disease management enrolment in Germany [30]. The calculation of the BYM model and the SPDE approach was carried out using the integrated nested Laplace approximation available in the INLA package for R version 4 [31], and the results were then displayed with the R package ggplot2 [32].
## 2.3. Regression Analysis
To calculate possible risk factors for COVID-19 hospitalizations, we used a Bayesian global logistic regression model, using the BYM model to account for spatial relationships in the form of structured and unstructured effects at the level of the 16,400 Geomarkets [30,31]. At the individual level, we used sex, age, foreign citizenship, being unemployed at 1 July 2021, and being in a nursing home. At the aggregated level, we used our deprivation index and average household size. We transformed the deprivation index into quintiles and included the index as categories, where the first quintile—the lowest level of deprivation—is the reference category. The response variable was coded as a binary variable (the insurant was hospitalized for COVID-19 vs. was not hospitalized). The regression coefficients were then exponentiated to allow an interpretation as odds ratios, which are easier to interpret than the plain regression coefficients [33,34].
To check for multicollinearity among the explanatory variables, we started with a non-spatial global regression model and checked for multicollinearity using the HH package in R. The HH package assigns a variance inflation factor (VIF) to all explanatory variables within the regression model. A VIF > 5 indicates the presence of multicollinearity and warrants the removal of one or more of the explanatory variables [35].
## 3.1. Spatial Distribution of Accumulated COVID-19 Incidence 2021
The accumulated one-year incidence of COVID-19 hospitalizations ranged between 0 and 1422 hospitalized insurants per 100,000 insurants. The highest incidence could be observed in the south of Brandenburg in the counties of Elbe-Elster and Spree-Neiße, but also in smaller spots scattered across the whole study area (Figure 1). The lowest incidence could be observed on the coastline of Mecklenburg-Western-Pomerania, including the city of Rostock.
## 3.2. Risk Factors for COVID-19 Hospitalizations
Male insurants had a $67.7\%$ higher risk of hospitalizations than women (Table 1). With every year of age, the risk of hospitalization increased by $3.9\%$. Insurants with foreign citizenship had a $150.2\%$ higher risk than insurants with German citizenship. Being currently unemployed increased the risk by $29.6\%$. Insurants living in a nursing home had a $75.9\%$ higher risk than insurants not living in a nursing home.
Pre-existing chronic conditions significantly associated with hospitalizations were certain infectious and parasitic diseases, where insurants with this disease group had a $23.6\%$ higher risk. Diseases of the blood and blood-forming organs increased the risk by $29.3\%$. Endocrine, nutritional and metabolic diseases increased the risk by $35.5\%$. Diseases of the nervous system increased the risk by $28.4\%$. Diseases of the circulatory system increased the risk by $21.4\%$. Diseases of the respiratory system increased the risk by $23.2\%$. Diseases of the genitourinary system increased the risk by $24.5\%$. Symptoms, signs and findings not elsewhere classified increased the risk by $16.2\%$. Average household size did not have a significant impact on the risk of hospitalization. The effect of deprivation was not linear. Only the second-least-deprived quintile and the medium-deprived quintile had a significant effect on the risk of hospitalization: Insurants living in second-least-deprived Geomarkets had an $11\%$ higher risk than insurants living in the least deprived quintile, and insurants living in the medium-deprived quintile had an $8\%$ higher risk than insurants living in the least deprived quintile.
## 4. Discussion
This is likely one of the most spatially detailed research studies in Germany based on health insurance data on COVID-19 hospitalizations.
We found strong spatial differences. The main sociodemographic risk factors for COVID-19 hospitalizations were male sex, higher age, being unemployed, and living in a nursing home. Pre-existing conditions associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary system, and symptoms, signs and findings not elsewhere classified.
Our results clearly demonstrate the benefits of small-area data on COVID-19 hospitalizations. We aggregated the insurants for the accumulated one-year incidence of 2021 to the level of the 16,400 Geomarkets of our study area, which is more detailed by far than the counties, for which official data of the Robert Koch *Institute is* reported [16,36].
Individual lower socioeconomic status was a risk factor for hospitalization. This is in line with other studies, not only in the German context [37], but in international studies [38]. Our study examined both lower socioeconomic status both at the individual level and at the aggregated level in the form of deprivation at the place of residence at a very detailed spatial resolution. However, we found that mainly individual-level socioeconomic status is a risk factor, but not necessarily living in the least deprived areas.
Similarly, our results confirm that foreign citizenship seems to be a risk factor for more severe consequences from a COVID-19 infection. This has been observed in Germany [39] as well as in other high-income countries [40].
We identified insurants living in nursing homes as another sociodemographic high-risk group. This is not surprising, as persons living in nursing homes generally are fairly old and have a higher number of chronic diseases than average. Logically, these findings are in line with other studies in Germany [41].
While the international literature suggests that area deprivation has an important effect on COVID-19 hospitalization risk [42], we found that insurants living in the second-least and medium-deprived Geomarkets had a higher risk than in the least-deprived Geomarkets. Since our study is based at the microgeographic level of the Geomarkets, this might further reflect the need for more spatially detailed research on COVID-19, as the problem of ecological fallacy grows with the size of the geographical unit for which the data are available [43]. Based on our findings, we might conclude that, at least for our subsample of the population, individual-level socioeconomic status might be more relevant than the place where the insurants live. Since our study included both individual-level socioeconomic status and area-level socioeconomic status, our findings add more depth than previous studies, which mostly included only one measure of socioeconomic status, but seldom both.
## 5. Limitations
Our study has several limitations:The database of AOK Nordost does not contain any information on vaccination status of its insurants. Logically, the positive effect of vaccination could not be quantified. It would have been interesting to quantify the effect of vaccination with regards to date of vaccination, number of doses, and pre-existing conditions on COVID-19 hospitalizations. Such an approach could help to determine in which groups with specific underlying medical conditions vaccination is more effective than in others. Although as cases we selected only those persons who have a laboratory-confirmed diagnosis of COVID-19 as the primary code in addition to a secondary diagnosis of viral pneumonia or respiratory syndrome, it is not clear how high the quality of diagnosis actually is, e.g., COVID-19 being detected as a by-product of another reason for hospital admission. AOK *Nordost is* northeast Germany‘s largest health insurance provider, covering appr. $25\%$ of the inhabitants. However, large sociodemographic differences of members of different health insurance providers exist, with the AOK Nordost having a higher proportion of elderly and chronically ill persons. As a result, our analysis may not be representative of the whole population. While the prevalence rates may be slightly higher than for all statutory health insurants, the regional distribution of diseases is generally comparable to those of all statutory health insurants [26,44,45,46]. As a result, the general distribution of COVID-19 hospitalizations may be slightly higher than for all statutory health insurants, but the regional distribution is expected to still be comparable.
Additionally, with tests for COVID-19 in 2020 and 2021 having been mostly performed at testing sites and not within ambulatory care, we could not see whether a COVID-19 diagnosis was existent before the insurant was hospitalized. This might influence the validity of our results, since our database contains only hospital diagnoses for COVID-19 for those years.
## 6. Conclusions
This is likely one of the most spatially detailed studies on the spatial distribution of COVID-19 hospitalizations and its associated risk factors. We found important regional variations at very fine scales, clearly demonstrating the need for more fine-grained spatial data on possible future pandemics. Our results clearly identified persons with lower socioeconomic status and persons living in nursing homes as important sociodemographic risk groups. Additionally, we identified several disease groups as risk factors for hospitalizations. COVID-19 hospitalizations and associated risk factors have significant policy implications that must be taken into consideration when creating and implementing mitigation and containment strategies. Age, underlying health conditions, and socio-economic status have been identified as key risk factors for severe illness and hospitalization from COVID-19. Therefore, policies that target vulnerable populations, such as elderly individuals and those with underlying health conditions, are crucial in reducing hospitalizations and deaths from the virus. Additionally, policies that address socio-economic disparities, such as increasing access to healthcare and providing financial support for those who have been impacted by the pandemic, can also have a meaningful impact on reducing hospitalizations. These results might serve as a foundation for better outbreak and containment strategies.
## References
1. Sohrabi C., Alsafi Z., O’Neill N., Khan M., Kerwan A., Al-Jabir A., Iosifidis C., Agha R.. **World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)**. *Int. J. Surg.* (2020.0) **76** 71-76. DOI: 10.1016/j.ijsu.2020.02.034
2. **UN Response to COVID-19**
3. Aral N., Bakir H.. **Spatiotemporal Analysis of Covid-19 in Turkey**. *Sustain. Cities Soc.* (2022.0) **76** 103421. DOI: 10.1016/j.scs.2021.103421
4. **Ärzteblatt.de Rückblick 2020: Die Welt im Griff des Virus**
5. Felbermayr G., Hinz J., Chowdhry S.. **Après-ski: The spread of coronavirus from Ischgl through Germany**. *Ger. Econ. Rev.* (2021.0) **22** 415-446. DOI: 10.1515/ger-2020-0063
6. Steiger E., Mussgnug T., Kroll L.E.. **Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0237277
7. Scarpone C., Brinkmann S.T., Große T., Sonnenwald D., Fuchs M., Walker B.B.. **A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: A cross-sectional case study of COVID-19 incidence in Germany**. *Int. J. Health Geogr.* (2020.0) **19** 32. DOI: 10.1186/s12942-020-00225-1
8. Kuebart A., Stabler M.. **Infectious Diseases as Socio-Spatial Processes: The COVID-19 Outbreak In Germany**. *Tijdschr. Econ. Soc. Geogr.* (2020.0) **111** 482-496. DOI: 10.1111/tesg.12429
9. Plümper T., Neumayer E.. **The pandemic predominantly hits poor neighbourhoods? SARS-CoV-2 infections and COVID-19 fatalities in German districts**. *Eur. J. Public Health* (2020.0) **30** 1176-1180. DOI: 10.1093/eurpub/ckaa168
10. Wachtler B., Michalski N., Nowossadeck E., Diercke M., Wahrendorf M., Santos-Hövener C., Lampert T., Hoebel J.. *Sozioökonomische Ungleichheit im Infektionsrisiko mit SARS-CoV-2—Erste Ergebnisse einer Analyse der Meldedaten für Deutschland* (2020.0). DOI: 10.25646/7056
11. Siljander M., Uusitalo R., Pellikka P., Isosomppi S., Vapalahti O.. **Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland**. *Spat. Spatiotemporal. Epidemiol.* (2022.0) **41** 100493. DOI: 10.1016/j.sste.2022.100493
12. Nazia N., Law J., Butt Z.A.. **Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada**. *Spat. Spatiotemporal. Epidemiol.* (2022.0) **43** 100534. DOI: 10.1016/j.sste.2022.100534
13. Lu Y., Cai G., Hu Z., He F., Jiang Y., Aoyagi K.. **Exploring spatiotemporal patterns of COVID-19 infection in Nagasaki Prefecture in Japan using prospective space-time scan statistics from April 2020 to April 2022**. *Arch. Public Health* (2022.0) **80** 176. DOI: 10.1186/s13690-022-00921-3
14. Iyanda A.E., Boakye K.A., Lu Y., Oppong J.R.. **Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: A Negative Binomial and GIS-Based Analysis**. *J. Racial Ethn. Health Disparities* (2022.0) **9** 708-721. DOI: 10.1007/s40615-021-01006-7
15. Lee J., Ramírez I.J.. **Geography of Disparity: Connecting COVID-19 Vulnerability and Social Determinants of Health in Colorado**. *Behav. Med.* (2022.0) **48** 72-84. DOI: 10.1080/08964289.2021.2021382
16. Rohleder S., Bozorgmehr K.. **Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: A small-area analysis in Germany**. *Spat. Spatiotemporal. Epidemiol.* (2021.0) **38** 100433. DOI: 10.1016/j.sste.2021.100433
17. Adin A., Congdon P., Santafé G., Ugarte M.D.. **Identifying extreme COVID-19 mortality risks in English small areas: A disease cluster approach**. *Stoch. Environ. Res. Risk Assess.* (2022.0) **36** 2995-3010. DOI: 10.1007/s00477-022-02175-5
18. Dhewantara P.W., Puspita T., Marina R., Lasut D., Riandi M.U., Wahono T., Ridwan W., Ruliansyah A.. **Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study**. *Transbound. Emerg. Dis.* (2022.0) **69** e362-e373. DOI: 10.1111/tbed.14313
19. Greene S.K., Peterson E.R., Balan D., Jones L., Culp G.M., Fine A.D., Kulldorff M.. **Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June–July 2020**. *Emerg. Infect. Dis.* (2021.0) **27** 1500-1504. DOI: 10.3201/eid2705.203583
20. Fatima M., O’Keefe K.J., Wei W., Arshad S., Gruebner O.. **Geospatial Analysis of COVID-19: A Scoping Review**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph18052336
21. Booth A., Reed A.B., Ponzo S., Yassaee A., Aral M., Plans D., Labrique A., Mohan D.. **Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0247461
22. Sandoval M., Nguyen D.T., Vahidy F.S., Graviss E.A.. **Risk factors for severity of COVID-19 in hospital patients age 18–29 years**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0255544
23. Meurisse M., Lajot A., Devleesschauwer B., van Cauteren D., van Oyen H., van den Borre L., Brondeel R.. **The association between area deprivation and COVID-19 incidence: A municipality-level spatio-temporal study in Belgium, 2020–2021**. *Arch. Public Health* (2022.0) **80** 109. DOI: 10.1186/s13690-022-00856-9
24. Madhav K.C., Oral E., Straif-Bourgeois S., Rung A.L., Peters E.S.. **The effect of area deprivation on COVID-19 risk in Louisiana**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0243028
25. Maier W., Fairburn J., Mielck A.. **Regionale Deprivation und Mortalität in Bayern. Entwicklung eines ’Index Multipler Deprivation’ auf Gemeindeebene**. *Gesundheitswesen* (2012.0) **74** 416-425. DOI: 10.1055/s-0031-1280846
26. Kauhl B., Maier W., Schweikart J., Keste A., Moskwyn M.. **Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany**. *PLoS ONE* (2018.0) **13**. DOI: 10.1371/journal.pone.0190865
27. Odoi A., Busingye D.. **Neighborhood geographic disparities in heart attack and stroke mortality: Comparison of global and local modeling approaches**. *Spat. Spatiotemporal. Epidemiol.* (2014.0) **11** 109-123. DOI: 10.1016/j.sste.2014.10.001
28. Lawson A.B., Biggeri A.B., Boehning D., Lesaffre E., Viel J.F., Clark A., Schlattmann P., Divino F.. **Disease mapping models: An empirical evaluation. Disease Mapping Collaborative Group**. *Stat. Med.* (2000.0) **19** 2217-2241. DOI: 10.1002/1097-0258(20000915/30)19:17/183.0.co;2-e
29. Dwyer-Lindgren L., Cork M.A., Sligar A., Steuben K.M., Wilson K.F., Provost N.R., Mayala B.K., VanderHeide J.D., Collison M.L., Hall J.B.. **Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017**. *Nature* (2019.0) **570** 189-193. DOI: 10.1038/s41586-019-1200-9
30. Kauhl B., Vietzke M., König J., Schönfelder M.. **Exploring regional and sociodemographic disparities associated with unenrollment for the disease management program for type 2 Diabetes Mellitus using Bayesian spatial modelling**. *Res. Health Serv. Reg* (2022.0) **1** 7. DOI: 10.1007/s43999-022-00007-1
31. Lindgren F., Rue H.. **Bayesian Spatial Modelling with R—INLA**. *J. Stat. Soft.* (2015.0) **63** 1-25. DOI: 10.18637/jss.v063.i19
32. Wickham H., Winston C., Henry L., Lin Pedersen T.. **Package ‘ggplot2’. Create Elegant Data Visualisations using the Grammar of Graphics. Version 2.1**. (2016.0)
33. Bland J.M., Altman D.G.. **Statistics notes. The odds ratio**. *BMJ* (2000.0) **320** 1468. DOI: 10.1136/bmj.320.7247.1468
34. Anderson R.P., Jin R., Grunkemeier G.L.. **Understanding logistic regression analysis in clinical reports: An introduction**. *Ann. Thorac. Surg.* (2003.0) **75** 753-757. DOI: 10.1016/S0003-4975(02)04683-0
35. Heiberger R.M.. **Package ‘HH’. Statistival Analysis and Data Display: Heidberger and Holland**
36. Schüler L., Calabrese J.M., Attinger S.. **Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0254660
37. Dragano N., Rupprecht C.J., Dortmann O., Scheider M., Wahrendorf M.. **Higher risk of COVID-19 hospitalization for unemployed: An analysis of health insurance data from 1.28 million insured individuals in Germany**. *Bundesgesundheitsblatt Gesundh. Gesundh.* (2021.0) **64** 314-321
38. Mena G.E., Martinez P.P., Mahmud A.S., Marquet P.A., Buckee C.O., Santillana M.. **Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile**. *Science* (2021.0) **372** eabg5298. DOI: 10.1126/science.abg5298
39. Doblhammer G., Kreft D., Reinke C.. **Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph182010663
40. Hayward S.E., Deal A., Cheng C., Crawshaw A., Orcutt M., Vandrevala T.F., Norredam M., Carballo M., Ciftci Y., Requena-Méndez A.. **Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: A systematic review**. *J. Migr. Health* (2021.0) **3** 100041. DOI: 10.1016/j.jmh.2021.100041
41. Said D., Suwono B., Schweickert B., Schönfeld V., Eckmanns T., Haller S.. **SARS-CoV-2 Outbreaks in Care Homes for the Elderly and Disabled in Germany**. *Dtsch. Arztebl. Int.* (2022.0) **119** 486-487. DOI: 10.3238/arztebl.m2022.0170
42. McGowan V.J., Bambra C.. **COVID-19 mortality and deprivation: Pandemic, syndemic, and endemic health inequalities**. *Lancet Public Health* (2022.0) **7** e966-e975. DOI: 10.1016/S2468-2667(22)00223-7
43. Salkeld D.J., Antolin M.F.. **Ecological Fallacy and Aggregated Data: A Case Study of Fried Chicken Restaurants, Obesity and Lyme Disease**. *Ecohealth* (2020.0) **17** 4-12. DOI: 10.1007/s10393-020-01472-1
44. Kauhl B., Schweikart J., Krafft T., Keste A., Moskwyn M.. **Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression**. *Int. J. Health. Geogr.* (2016.0) **15** 1-12. DOI: 10.1186/s12942-016-0068-2
45. Goffrier B., Schulz M., Bätzing-Feigenbaum J.. **Administrative Prevalence and Incidence of Diabetes Mellitus in Germany, 2009–2015**
46. Akmatov M.K., Steffen A., Holstiege J., Bätzing J.. **Die Chronisch Obstruktive Lungenerkrankung (COPD) in der Ambulanten Versorgung in Deutschland–Zeitliche Trends und Kleinräumige Unterschiede**. (2019.0)
|
---
title: Topical Diacerein Decreases Skin and Splenic CD11c+ Dendritic Cells in Psoriasis
authors:
- Susanne M. Brunner
- Andrea Ramspacher
- Caroline Rieser
- Julia Leitner
- Hannah Heil
- Michael Ablinger
- Julia Tevini
- Monika Wimmer
- Andreas Koller
- Josefina Piñón Hofbauer
- Thomas K. Felder
- Johann W. Bauer
- Barbara Kofler
- Roland Lang
- Verena Wally
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001455
doi: 10.3390/ijms24054324
license: CC BY 4.0
---
# Topical Diacerein Decreases Skin and Splenic CD11c+ Dendritic Cells in Psoriasis
## Abstract
Psoriasis is an inflammatory skin disease characterized by increased neo-vascularization, keratinocyte hyperproliferation, a pro-inflammatory cytokine milieu and immune cell infiltration. Diacerein is an anti-inflammatory drug, modulating immune cell functions, including expression and production of cytokines, in different inflammatory conditions. Therefore, we hypothesized that topical diacerein has beneficial effects on the course of psoriasis. The current study aimed to evaluate the effect of topical diacerein on imiquimod (IMQ)-induced psoriasis in C57BL/6 mice. Topical diacerein was observed to be safe without any adverse side effects in healthy or psoriatic animals. Our results demonstrated that diacerein significantly alleviated the psoriasiform-like skin inflammation over a 7-day period. Furthermore, diacerein significantly diminished the psoriasis-associated splenomegaly, indicating a systemic effect of the drug. Remarkably, we observed significantly reduced infiltration of CD11c+ dendritic cells (DCs) into the skin and spleen of psoriatic mice with diacerein treatment. As CD11c+ DCs play a pivotal role in psoriasis pathology, we consider diacerein to be a promising novel therapeutic candidate for psoriasis.
## 1. Introduction
Psoriasis is a systemic and chronic inflammatory skin disease characterized by increased neovascularization, keratinocyte hyperproliferation, a pro-inflammatory cytokine milieu and immune cell infiltration. It affects 2–$4\%$ of the population with a strong negative impact on quality of life [1,2]. This disease is often associated with comorbidities, such as dyslipidemia, diabetes, and obesity with subsequent cardiovascular complications, and with extra-cutaneous involvements such as arthritis [3,4,5]. The pathogenesis of psoriasis is multifaceted and the exact underlying mechanisms remain elusive. However, the interplay between several immune cell types and diverse cytokines was identified as a pivotal factor in the pathology of psoriasis. Especially T cells, dendritic cells (DCs), and the IL-23/IL-17 axis play a prominent role [6]. Despite continuous advances in elucidating psoriasis pathophysiology and its therapy, existing treatment regimens all have distinct limitations [7], underscoring the need for further research aimed at identifying new drug candidates.
The murine model of imiquimod (IMQ)-induced psoriasiform-like skin inflammation is one of the most frequently used preclinical models of psoriasis [8,9]. IMQ activates Toll-like receptor 7 (TLR7) and shows strong upregulation of the NLRP3 inflammasome leading to a prominent skin inflammation. Importantly, it was shown that IMQ-induced psoriasis is critically dependent on the IL-23/IL-17 axis [8].
Diacerein, an anthraquinone derivative from the rhubarb root, is a slow-acting and anti-inflammatory drug. Deacetylation of diacerein gives rise to its active metabolite rhein, an IL-1 converting enzyme inhibitor. Even though diacerein is considered a non-steroidal anti-inflammatory drug (NSAID), its function differs from classical NSAIDs as it acts on the IL-1β-related pathways rather than on cyclooxygenase 2 (COX2) and prostaglandin E2 (PGE2) regulation [10,11]. Currently, diacerein is approved as an oral formulation for the treatment of osteoarthritis, where IL-1β is a key pathologic player. However, due to severe gastrointestinal side effects, the European Medicines Agency (EMA) established new restrictions for the oral formulation in 2014 [11].
Diacerein reduces the production of caspase-1, resulting in decreased IL-1β maturation and secretion and thus lower AP-1 and NFκB activity with concomitant diminished expression of many pro-inflammatory genes, including IL-1β, TNFα, or IL-6 [10,12,13]. Furthermore, diacerein modulated superoxide anion production, chemotaxis, and phagocytic activity of neutrophils, as well as macrophage migration and phagocytosis in rheumatoid conditions [14]. In keratinocytes, diacerein reverted the effects of IL-1α and IL-1β and dampened the expression of numerous IL-1-responsive genes bearing pro-inflammatory functions [15]. Importantly, the potential of diacerein as a topical treatment was recently shown in two clinical trials. Patients suffering from the rare genodermatosis epidermolysis bullosa were treated topically with $1\%$ diacerein cream. In 22 treated patients, blister numbers decreased significantly after 4 weeks of diacerein application and no treatment-related adverse events were reported [16,17]. Recently, diacerein has been investigated in further multicenter trials, for which results are being eagerly awaited (ClinicalTrials.gov identifiers: NCT03154333, NCT03389308, NCT03472287). In addition, metabolization and deacetylation of diacerein within the skin was demonstrated. Low levels of rhein in human serum and urine substantiated the safety of topical diacerein as compared to oral administration [18].
Since diacerein dampens diverse inflammatory processes in different conditions, we hypothesized that diacerein has a beneficial effect on psoriasis. The possibility to circumvent systemic side effects by topical administration renders this small molecule drug a promising candidate for the treatment of psoriasis. Here, we aimed to evaluate the effect of topical diacerein on IMQ-induced psoriasiform-like skin inflammation in C57BL/6 mice. We tested several doses of diacerein for its effect on psoriasis-associated clinical severity, splenomegaly, and infiltration of immune cells into psoriatic skin and spleen.
## 2.1. Diacerein Alleviates the Clinical Severity of Psoriasiform-like Skin Inflammation
To investigate the therapeutic effect of topical diacerein in psoriasis, we used the model of IMQ-induced psoriasiform-like skin inflammation in C57BL/6N mice. Psoriasis was induced by daily application of IMQ to depilated back skin. Six hours after IMQ application, the skin was topically treated with different doses of diacerein, placebo, or left untreated. Body weight and macroscopic symptoms of skin inflammation were monitored daily.
Compared to non-psoriatic control mice, IMQ treatment resulted in progressive loss of body weight ($p \leq 0.05$). Importantly, diacerein did not influence the IMQ-induced loss in body weight nor the body weight in healthy animals (Supplementary Figure S1).
The progression and the clinical severity of psoriasis were monitored daily using the PASI score, assessing erythema, scaling, and thickening individually. Symptoms first appeared in IMQ-treated mice on day 2 and continued to increase, peaking on day 4 and 5 (thickening) or day 6 (erythema and scaling). Scores were elevated in IMQ-treated mice compared to non-psoriatic control mice for at least one but up to five consecutive days (p ≤ 0.0440) (Supplementary Figure S2, Supplementary Tables S1–S3). Accordingly, the clinical severity of the IMQ-induced psoriasis, represented by the cumulative PASI score, continuously increased until days 5 or 6, respectively (Figure 1, Supplementary Figure S3, Supplementary Table S4).
While erythema and thickening scores were not affected by diacerein treatment, the drug reduced psoriasis-associated scaling (two-way ANOVA treatment main effect, $$p \leq 0.0146$$). Over the course of the 7-day treatment period, $5\%$ diacerein and placebo application showed a trend towards decreased scaling ($$p \leq 0.0762$$ and $$p \leq 0.0747$$, respectively), while application of $2.5\%$ and $10\%$ diacerein diminished the scaling scores ($$p \leq 0.0076$$ and $$p \leq 0.0161$$, respectively) compared to untreated IMQ-induced psoriasis. Multiple comparison tests revealed that each treatment option resulted in decreased scaling scores on days 5 and 6 compared to untreated psoriatic skin (p ≤ 0.0319) (Supplementary Figure S2, Supplementary Tables S1–S3).
Based on the cumulative PASI scores, we observed a more severe psoriasiform-like inflammation on days 4 to 6 compared to non-psoriatic controls (p ≤ 0.0411) in all treatment groups. Remarkably, two-way ANOVA analysis of clinical severity scores revealed a significant interaction between time and treatment in the IMQ groups ($$p \leq 0.0316$$). Analyzing single days revealed that placebo alleviated the inflammation on day 4 ($$p \leq 0.0469$$) and showed a trend towards reduced severity scores on day 5 ($$p \leq 0.0544$$) compared to untreated IMQ-induced psoriasis. In addition, $5\%$ diacerein reduced the clinical severity only on day 6 ($$p \leq 0.0024$$). Further, $2.5\%$ and $10\%$ diacerein attenuated the clinical severity from days 4–6 [$2.5\%$: $$p \leq 0.0023$$/$\frac{0.0039}{0.0007}$; $10\%$: $$p \leq 0.0129$$/$\frac{0.0331}{0.0425}$; (day $\frac{4}{5}$/6)] compared to untreated psoriatic skin. Importantly, analyzing the whole 7-day treatment period, multiple comparison tests showed that $2.5\%$ diacerein diminished the clinical severity compared to untreated IMQ-induced psoriasis (treatment main effect, $$p \leq 0.0072$$) (Figure 1, Supplementary Figures S3 and S4, Supplementary Table S4).
## 2.2. Diacerein Ameliorates Psoriasis-Associated Splenomegaly
As psoriasis is characterized by a systemic inflammation [4,19] which is recapitulated by the IMQ model as splenomegaly [8], we analyzed the spleens of mice at the end of the experiment to investigate a possible systemic effect of diacerein. Two-way ANOVA analysis of the spleen weight revealed a significant interaction between disease and treatment on day 7 ($$p \leq 0.0063$$). *In* general, IMQ treatment induced an enlargement of the spleen compared to non-psoriatic animals (IMQ main effect, $p \leq 0.0001$). Remarkably, multiple comparison analysis showed that $10\%$ diacerein attenuated the IMQ-induced splenomegaly in comparison to untreated ($$p \leq 0.0006$$) and placebo-treated ($$p \leq 0.0005$$) psoriasis (Figure 2A).
As we observed that the IMQ-induced body weight loss and clinical severity were declining towards day 7, we decided to measure the spleen weight additionally at an earlier day when the inflammation was more pronounced. Thus, the experiment was terminated on day 4. Since on day 7 placebo treatment had no effect on the splenomegaly compared to untreated psoriasis (Figure 2A), we did not include an untreated IMQ group in this experiment. Interestingly, we observed that spleen weights on day 4 were similar to weights on day 7 ($p \leq 0.05$). Consistently, diacerein dose-dependently attenuated the IMQ-induced splenomegaly on day 4 in comparison to placebo-treated psoriasis ($5\%$: $$p \leq 0.0246$$; $10\%$: $$p \leq 0.0005$$) (Figure 2B).
Further support for a systemic effect of diacerein on IMQ-induced psoriasis was provided when we determined the levels of rhein, the active metabolite of diacerein, in the treated area of the dorsal skin and in plasma of experimental animals. While we observed only a minor amount of rhein in the skin, the rhein levels in plasma were pronounced (Supplementary Figure S5).
## 2.3. Diacerein Decreases CD11c+ Dendritic Cells in the Skin and Spleen during IMQ-Induced Psoriasis
As topical diacerein could modulate psoriasis-associated clinical symptoms and splenomegaly, we sought to elucidate which immune cell types were predominantly affected by diacerein. Therefore, we assessed the distribution of diverse immune cell populations in single cell suspensions from treated dorsal skin and from spleen by flow cytometry.
As $10\%$ diacerein was the only dose consistently attenuating clinical and systemic parameters of IMQ-induced psoriasis, we only included this dose in the flow cytometric analysis. Furthermore, in an attempt to increase the therapeutic window of diacerein, we utilized a higher dose of IMQ to induce psoriasis, applying 80 mg Aldara® topically to the dorsal skin of mice for three consecutive days. Following IMQ, mice were treated with $10\%$ diacerein, placebo or left untreated. Skin and spleen samples were collected on day 4.
Interestingly, the higher dose of IMQ (80 mg Aldara®) resulted in similar clinical severity scores as compared to the lower dose (62.5 mg Aldara®) ($p \leq 0.05$) (Supplementary Figure S6A,B). Remarkably, treatment with $10\%$ diacerein resulted in lower PASI scores following 80 mg Aldara® compared to 62.5 mg. On the third day of treatment, the difference reached significance ($$p \leq 0.0065$$) (Supplementary Figure S6C). Comparing the groups treated with 80 mg Aldara®, we found that $10\%$ diacerein and placebo had similar attenuating effects on the clinical severity as compared to untreated IMQ-induced psoriasis [two-way ANOVA main treatment effect, $$p \leq 0.0002$$ ($10\%$) and $$p \leq 0.0035$$ (placebo)], with both treatment options diminishing cumulative scores on day 3 [$p \leq 0.0001$ ($10\%$) and $$p \leq 0.0006$$ (placebo)] and day 4 ($p \leq 0.0001$) (Supplementary Figure S6D). Furthermore, spleen weights were similar following psoriasis induction with 80 mg Aldara® compared to 62.5 mg Aldara® ($p \leq 0.05$). Consistently, $10\%$ diacerein showed a strong trend to reduce IMQ-induced splenomegaly ($$p \leq 0.0665$$) compared to untreated psoriasis (Supplementary Figure S7).
At day 4, flow cytometry analysis revealed that IMQ did not alter the number of CD45+ leukocytes in the spleen (Figure 3A) or skin (Figure 4A) compared to non-psoriatic controls but changed the relative distribution of immune cell subsets in each tissue.
Using a nine-marker flow cytometric panel, we observed that the fraction of F$\frac{4}{80}$+ macrophages in the spleen were unaffected by IMQ (Supplementary Figure S8A). Compared to non-psoriatic controls, IMQ decreased the numbers of CD4+ ($p \leq 0.0001$) and CD8+ T cells ($p \leq 0.0001$), eosinophils ($$p \leq 0.0076$$), and red pulp macrophages (RPMs) ($$p \leq 0.0016$$) (Supplementary Figure S8B–D, Figure 3B). Furthermore, IMQ increased the splenic fractions of CD11b-CD11c+ lymphoid DCs ($p \leq 0.0001$), CD11b+ myeloid cells ($$p \leq 0.0004$$), MHCII+ and MHCII- monocytes/macrophages ($p \leq 0.0001$), neutrophils ($$p \leq 0.0002$$), myeloid CD11c+ DCs ($p \leq 0.0001$), and CD19+ B cells ($p \leq 0.00019$) and showed a trend to increased NK cells ($$p \leq 0.0506$$) (Figure 3C, Supplementary Figure S8E–K).
In the skin, the fractions of CD19+ B cells and F$\frac{4}{80}$+ macrophages were unaffected by IMQ treatment (Supplementary Figure S9A,B). Compared to non-psoriatic controls, IMQ reduced the numbers of CD4+ ($p \leq 0.0001$) and CD8+ ($$p \leq 0.0187$$) T cells and CD11b-CD11c+ lymphoid DCs ($$p \leq 0.0140$$) (Supplementary Figure S9C–E) in the skin. Further, IMQ increased the fractions of eosinophils ($$p \leq 0.0266$$), NK cells ($$p \leq 0.0041$$), CD11b+ myeloid cells ($p \leq 0.0001$), neutrophils ($$p \leq 0.0001$$), monocytes/macrophages ($p \leq 0.0001$) and showed a trend to increase CD11b+CD11c+ myeloid DCs in the skin ($$p \leq 0.0689$$) (Supplementary Figure S9F,G, Figure 4B–F).
Interestingly, topical treatment of the psoriatic skin with placebo, but not diacerein, significantly reduced the overall fraction of CD45+ cells in the spleen and skin compared to untreated psoriasis ($$p \leq 0.0017$$ and $$p \leq 0.0122$$;) (Figure 3A and Figure 4A). The majority of immune cell types in the spleen and skin were unaffected by either diacerein or placebo treatment (Supplementary Figures S8 and S9). However, $10\%$ diacerein decreased numbers of RPMs ($$p \leq 0.0210$$) and lymphoid CD11c+ DCs ($$p \leq 0.0214$$) in the spleen compared to placebo treatment (Figure 3B,C). In the skin, both diacerein and placebo treatment diminished the fraction of CD11b+ myeloid cells ($$p \leq 0.0006$$ and $$p \leq 0.0054$$, respectively) (Figure 4B), showed a trend to diminish neutrophil cell numbers ($$p \leq 0.0938$$ and $$p \leq 0.0806$$, respectively) (Figure 4C), and reduced the IMQ-induced increase in MHCII+ ($$p \leq 0.524$$ and $$p \leq 0.0490$$, respectively), as well as MHCII- ($$p \leq 0$$ 0127 and $$p \leq 0.0192$$, respectively) monocytes/macrophages (Figure 4D,E) compared to untreated psoriatic skin. Remarkably, diacerein treatment additionally decreased the number of CD11b+CD11c+ skin myeloid DCs compared to IMQ alone (Tukey’s test, $$p \leq 0.0257$$) or to placebo (unpaired t-test, $$p \leq 0.0113$$), whereas placebo treatment had no effect on immune cell populations compared to untreated psoriatic skin (Figure 4F).
## 3. Discussion
The present study aimed to evaluate the effect of the anti-inflammatory drug diacerein on psoriasis. We demonstrated that diacerein has a beneficial effect by attenuating the clinical severity, reducing the disease-associated splenomegaly, and decreasing the infiltration of lymphoid CD11c+ DCs to the spleen and myeloid CD11c+ DCs to the skin in an IMQ-induced psoriasis mouse model.
In the literature, the anti-inflammatory properties of diacerein have been documented in different inflammatory conditions in vitro [13,14,15] and in vivo, including osteoarthritis [10], cervical hyperkeratosis [20], and epidermolysis bullosa [17,18]. While these observations render diacerein a promising candidate for the treatment of psoriasis, so far, no studies have tested this hypothesis.
Since oral diacerein exhibits low bioavailability [21] and is associated with diarrhea, liver disorder, and urine discoloration [22,23], other application routes are more preferable. Previously, we observed that a topical $1\%$ diacerein formulation in Ultraphil® cream has shown excellent results in a phase $\frac{2}{3}$ randomized, placebo-controlled, double-blind clinical trial in epidermolysis bullosa patients without any study-related adverse events [17]. Furthermore, transdermal delivery of diacerein has been shown to be safe in rodents [24]. Consequently, we tested topical diacerein in experimental psoriasis-like inflammation. As rhein, the active metabolite of diacerein, was detected predominantly in plasma compared to skin, our data prove that the active compound crosses the skin barrier without requiring other vehicles, as reported previously [25,26]. *The* generally higher rhein levels in skin or plasma of IMQ-treated mice in contrast to non-psoriatic controls is likely due to the disruption of the epidermal barrier, which is characteristic of psoriasis [27]. Accordingly, IMQ-treated skin exhibited increased drug permeation and accumulation [28]. Most importantly, none of the diacerein doses tested in the present study caused any adverse or toxicity-related events nor had any impact on body weight of IMQ-treated or non-psoriatic mice. Of interest, the $10\%$ diacerein dose is >200 times the dose applied to epidermolysis bullosa patients [17], considering differences in body surface area (BSA) and calculating the dose conversion between mouse and human according to Nair et al. [ 29]. Since we previously found that only $37\%$ of totally applied rhein was detected in a porcine skin model at 72 h after diacerein application [18], we aimed to increase the amount of the active metabolite in the skin. Furthermore, extrapolation of rhein levels in serum from patients who received treatment on $3\%$ BSA with $1\%$ diacerein, to a treatment of $90\%$ BSA, resulted in levels that were still 150-fold below the reported levels upon oral administration [21]. This emphasizes the safety of topical diacerein, even if applied at relatively high doses.
Following psoriasis induction with IMQ, diacerein was applied with a delay of 6 h between topical treatments to avoid direct cross-reactions of the two drugs. In this treatment regimen, diacerein does not prevent or delay disease onset. Instead, diacerein-treated psoriasis exhibits a similar course of the disease compared to untreated psoriasis with clinical severity scores peaking at day 6 and declining thereafter. However, we observed that any kind of topical treatment, i.e., verum or placebo, relieves clinical symptoms compared to leaving the psoriatic skin untreated. This is not unexpected, as Ultraphil® is commonly used as indifferent therapy of subacute and chronic dermatoses. Nevertheless, diacerein had some advantages over placebo, as $2.5\%$ diacerein was the only treatment option showing a significant reducing effect on the clinical severity over the whole 7-day period. This is in line with the reported anti-inflammatory effect of diacerein on human keratinocytes [15]. Diacerein diminished IL-1α/β-induced gene expression of several cytokines and chemokines, which play an important role in psoriasis [15,30,31,32].
While $2.5\%$ diacerein alleviates clinical symptoms, it does not seem to be sufficient for systemic effects, as only the higher doses of diacerein, i.e., $5\%$ and $10\%$, reduce the IMQ-induced splenomegaly. A possible explanation for the more potent systemic over local effect of diacerein is that rhein levels are high in plasma but low in the skin. It can be speculated that rhein levels in the skin may be too low to exert even more substantial effects on psoriasis severity. A systemic effect of topical diacerein is especially important for the clinical setting as there is increasing awareness that psoriasis is a systemic inflammatory disease [4]. Correspondingly, in patients, psoriasis is not only correlated with spleen enlargement [33,34] but also with splenic inflammation, which is further linked to cardiovascular comorbidities [35]. Interestingly, other inflammatory diseases, such as arthritis, seem to share similar underlying systemic pathological mechanisms with psoriasis [4]. Consequently, arthritis is one of the most common extra-cutaneous involvements in psoriasis, with up to $30\%$ of patients developing psoriatic arthritis [5,36]. Since oral diacerein is approved as an anti-inflammatory treatment for osteoarthritis [10], our data promote a possible effect of topical diacerein on psoriatic arthritis via systemic pathways. However, this hypothesis needs to be tested in future studies.
Since diacerein only showed a slight advantage over placebo treatment regarding the PASI score, we intended to induce a more severe psoriasis by using a higher IMQ dose. Thereby, the placebo effect should be reduced and the therapeutical window of diacerein should be increased to be able to better determine which immune cell populations are affected by diacerein. While 62.5 mg Aldara®, representing 3.125 mg IMQ, is the most frequently used dose in the literature to induce psoriasis in mice, the highest dose tested in experimental studies was 80 mg Aldara® (4.0 mg IMQ) [37]. However, we found that this dose failed to increase the PASI score compared to 62.5 mg Aldara®. While Baek et al. rated the higher IMQ dose as generating a maximal effect in female BALB/c mice, they failed to show corresponding data [37]. Furthermore, in the initial description of the model, van der Fits et al. reported that the 62.5 mg dose was “empirically determined to cause most optimal and reproducible skin inflammation” in BALB/c and C57BL/6, without showing supporting data as to which doses were tested or which sex of animals was used [8]. As IMQ was demonstrated to have sex- and strain-dependent effects in mice [38], it is possible that a 4.0 mg IMQ daily does not reliably increase PASI scores in male C57BL/6N mice.
To elucidate a possible mechanism of action of topical diacerein, we determined which immune cell types in the skin and spleen are affected. Psoriasis induction with IMQ strongly affects the infiltration of diverse immune cell types to skin and spleen compared to non-psoriatic animals. The IMQ-induced changes to the relative distribution of immune cells observed in the present study are largely in agreement with other previously published reports [8,39,40,41]. Deviations might be due to differences in mouse strains, markers used for the identification of cell types, antibodies used for detection, gating strategies, treatment periods with IMQ, and time points of analysis or, regarding the skin, different isolation protocols.
In the psoriatic skin, placebo and diacerein reduced infiltration of myeloid cells, including monocytes/macrophages and neutrophils, explaining the alleviating effect on the PASI score. Both cell types are reported to be involved in psoriasis pathogenesis. During psoriasis, neutrophils were shown to influence the growth and differentiation of epidermal keratinocytes and to recruit activated effector T cells [42]. Importantly, neutrophils found in psoriatic skin of patients produced IL-17 [43], which seems of critical importance in psoriasis [30,44]. Moreover, macrophages were shown to play a role not only in T cell dependent but also independent psoriasiform inflammation [45]. Macrophages isolated from psoriatic skin of human donors increasingly expressed IL-23 [32], which is important for the polarization of IL-17-producing Th17 cells and thus is another pivotal cytokine in psoriasis pathogenesis [30].
In spleens of psoriatic mice, diacerein reduced the number of RPMs. This splenic cell type is one of the most studied lineages of tissue-resident macrophages, however, evidence for their involvement in immunological functions is scarce. So far, RPMs were implicated in differentiation of T cells towards Tregs and were shown to produce type I interferons (IFN) [46]. Of note, plasmacytoid DCs (pDCs) are potent producers of type I IFN and were identified also as initiators of psoriatic inflammation. Psoriasis-relevant IFNα production by pDCs is induced via TLR9 activation [47]. As RPMs also express TLR9, they might contribute to the systemic inflammation associated with psoriasis [46].
Remarkably, diacerein reduced the infiltration of CD11b+CD11c+ myeloid DCs in the skin and of splenic CD11b-CD11c+ lymphoid DCs in psoriatic mice. The important contribution of DCs in psoriasis pathogenesis is emphasized by the observation of highly increased numbers of myeloid CD11c+ DCs in the dermis of psoriasis patients. Furthermore, in agreement with our data, disease improvement correlated with reduced numbers of CD11c+ cells [48]. Myeloid DCs drive psoriasis progression by stimulating and activating autologous T cells in situ [47]. Therefore, our data indicate that diacerein is able to dampen psoriasis inflammation by reducing the cellular interactions of antigen-presenting DCs with T cells, thereby reducing T cell activation. Specifically important for T cell activation by DCs is the expression of costimulatory molecules, e.g., CD40. Increased CD40 expression is characteristic of DC activation and maturation [49,50]. Interestingly, diacerein was shown to reduce IL-1β-induced CD40 expression on keratinocytes [15], indicating a possible role of diacerein in inhibiting DC maturation and thus T cell activation. Pivotal for psoriatic inflammation, CD11c+ DCs were shown to produce TNFα, IL-6, and IL-23 in lesional psoriatic skin [48,51,52]. Antagonization of TNFα with etanercept attenuated psoriasis severity in a clinical trial [53]. Importantly, IL-6 suppresses Treg differentiation and IL-23 drives the polarization of Th17 cells, thus, both cytokines promote psoriasis progression [51]. Mechanistically, diacerein inhibits the IL-1β system but also IL-1-related pathways at different levels [11], resulting in lower AP-1 and NFκB activity, leading to reduced expression of TNFα and IL-6 [10,12,13]. Consequently, diacerein could lower the release of psoriasis-promoting cytokines by DCs. However, whether diacerein has a direct effect on DC maturation and function should be a focus of future studies.
In conclusion, we demonstrated the therapeutic effect of topical diacerein on IMQ-induced psoriasis-like inflammation. Furthermore, we substantiated the safety of topical diacerein. This route of application not only circumvented systemic side effects but instead revealed beneficial systemic effects on psoriasis. This finding not only promotes topical diacerein as a promising candidate drug for the treatment of psoriasis but also for other systemic inflammatory diseases, such as arthritis. Importantly, our study established a reducing effect of topical diacerein on the infiltration of psoriasis-promoting CD11c+ DCs to the spleen and skin. Future studies should focus on the direct effect of diacerein on DCs in more detail.
## 4.1. Experimental Animals
All animal procedures were approved by the local ethical committee of the Land Salzburg according to Austrian legislation on animal experiments (20901-TVG/$\frac{130}{6}$-2019 and 20901-TVG/$\frac{130}{11}$-2021) and conducted according to the Directive of the European Parliament and of the Council of 22 September 2010 ($\frac{2010}{63}$/EU).
Male C57BL/6NCrl mice were purchased from Charles River (Sulzfeld, Germany) at the age of 7 weeks. Experimental animals were housed in groups of 3 mice per cage at the Preclinical Research Unit of the Paracelsus Medical University, Salzburg, Austria, under controlled conditions at a 12 h light/dark cycle (lights on/off $\frac{0700}{1900}$ h) with unrestricted access to food and water. Prior to the start of experiments, animals were allowed to acclimatize to their new environment for at least one week.
## 4.2. Induction of Psoriasiform-like Skin Inflammation
As previously described [54], to induce psoriasiform-like skin inflammation, an approx. 8-cm2 area (4 cm × 2 cm) of the dorsal skin of mice was shaved and depilated (Veet sensitive; Reckitt Benckiser Austria GmbH, Vienna, Austria). The depilation cream was washed off gently but thoroughly. Starting the next morning (0800 h), the bare area of the back skin was treated with Aldara® cream, containing $5\%$ IMQ (3M Pharmaceuticals, Neuss, Germany). Mice received either a daily topical dose of 62.5 mg Aldara® (3.125 mg IMQ) [8,54] for 3 or 6 consecutive days or a daily topical dose of 80 mg Aldara® (4.0 mg IMQ) [37] for 3 consecutive days. Simultaneously, non-psoriatic control mice were treated with a similar amount of a vehicle cream (Vaseline Lanette cream, Fagron, Barsbüttel, Germany). At least 6 h following IMQ or vehicle treatment (1400 h), animals received 100 mg of Ultraphil® cream (Bayer, Vienna, Austria) containing $2.5\%$, $5\%$, or $10\%$ diacerein (Duke Chem SA, Barcelona, Spain), equivalent to 2.5, 5, and 10 mg daily dose of diacerein, respectively. Control animals received a similar amount of plain Ultraphil® cream colorized with $0.005\%$ tartrazine (Placebo) or the skin was left untreated. Topical treatments were performed under light isoflurane (IsoFlo®, Abbott, Vienna, Austria) anesthesia (isoflurane:O2 2:$2\%$ vol). The operator was blinded regarding the content of the Ultraphil® cream. Every day, before any topical treatments, the body weight of mice was recorded and macroscopic images of the dorsal skin were taken. On day 4 or 7, approximately 24 h after the last treatment, mice were deeply anesthetized with a mixture of ketamine (205 mg/kg), xylazine (53.6 mg/kg), and acepromazine (2.7 mg/kg). Blood was collected via cardiac puncture with a heparin-coated syringe and centrifuged at 2500× g for 10 min at 4 °C. Plasma was collected, mixed with a protease inhibitor cocktail (cOmpleteTM; Roche, Penzberg, Germany), and stored at −80 °C. Mice were euthanized by cervical dislocation and the dorsal skin was removed. From the treated area of the dorsal skin, either 8-mm skin biopsies were taken randomly or the complete area was processed into a single cell suspension for flow cytometry. Biopsies were weighed, immediately snap-frozen, and stored at −80 °C. The spleen was excised, weighed, and processed into a single cell suspension for flow cytometry.
## 4.3. Evaluation of Clinical Severity
The severity of the psoriasiform-like skin inflammation was evaluated using a modified clinical Psoriasis Area and Severity Index (PASI) scoring system as previously described [8,54]. The daily macroscopic images of the back skin were used to visually score erythema (redness), scaling (desquamation), and thickening (skin wave formation due to rete ridges) by giving each parameter an independent score between 0 and 4 (with half-point steps). The cumulative score from 0 to 12 (erythema + scaling + thickening) served as an indicator for clinical severity. The semiquantitative scoring was performed independently by two researchers blinded to information on treatments. The mean of values was then calculated.
## 4.4. Quantification of Rhein in Mouse Skin and Plasma
Per mouse, an 8-mm skin biopsy was homogenized in 1 mL fetal bovine serum (Gibco, Carlsbad, CA, USA) using an Ultra-Turrax (IKA, Staufen, Germany). The lysate was centrifuged, the supernatant was collected and stored at −80 °C. Then, 100 µL of skin lysate and 50 µL of plasma were subjected to liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis on a TripleQuad 5500+ (Sciex, Darmstadt, Germany) for rhein quantification as previously described [16,18]. Rhein levels in the skin were normalized to the weight of the skin biopsy.
## 4.5. Preparation of Skin and Spleen Cell Suspensions
Skin samples were cut into pieces and incubated in C10 medium (RPMI 1640, $10\%$ fetal bovine serum, $1\%$ Pen/Strep, $1\%$ L-glutamine, $1\%$ non-essential amino acids, $1\%$ HEPES, $1\%$ sodium pyruvate, $0.0012\%$ ß-mercaptoethanol; Sigma-Aldrich, Darmstadt, Germany) supplemented with collagenase XI (1 mg/mL), hyaluronidase (0.25 mg/mL), and DNase (0.05 mg/mL) (Sigma-Aldrich) for 45 min in a shaking water bath at 37 °C. Samples were minced through a 100-µm pore size nylon mesh. Cells were collected, centrifuged (300× g, 5 min, 4 °C), and washed in C10 medium and counted in a CASY cell counter (OMNI Life Sciences, Bremen, Germany). Spleen samples were minced through a 100-µm pore size nylon mesh, cells were collected and centrifuged (300× g, 5 min, 4 °C). Depending on pellet size, cells were resuspended in 3–6 mL red blood cell lysis buffer (150 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA, pH 7.3) for 5 min on ice. Cells were washed in PBS and counted in a CASY cell counter.
## 4.6. Flow Cytometric Analysis
First, 5 × 106 (skin) and 2 × 106 (spleen) cells, respectively, were stained for the markers summarized in Table 1 to discriminate immune cell populations. Additionally, a fixable viability dye was used to exclude dead cells from analysis (Table 1, Supplementary Figures S10 and S11). Staining was done in PBS, supplemented with $10\%$ lamb serum (Gibco, Carlsbad, CA, USA) to block FcγIII/IIR (CD16/CD32). Cells were stained with an antibody cocktail as listed in Table 1. Data were collected using a BD LSRFortessaTM cell analyzer (BD Biosciences, Mountain View, CA, USA). Unstained and single-stained controls were used to set appropriate PMT voltages and to adjust compensations of non-specific fluorescence signals due to spectral overlap using BD FACSDiva software. Fluorescence Minus One (FMO) controls were used to identify gating boundaries. The gating strategies used for data collection are illustrated in Supplementary Figures S10 and S11 [39,41,55,56,57]. Further, 300,000 events per sample were recorded. Data analysis was performed using FlowJo v.10 software (TreeStar; BD Biosciences). In the skin, CD45+ leukocytes were less than $20\%$ of all live cells, therefore numbers of all assessed immune cell populations in the skin were calculated in relation to CD45+ cells. In contrast, in the spleen, CD45+ leukocytes were more than $96\%$ of all live cells, thus the number of all assessed immune cell populations in the spleen were calculated in relation to all live cells.
## 4.7. Statistical Analysis
Statistical analysis was performed using Graph Pad Prism 9.3.1 (GraphPad Software Inc., San Diego, CA, USA). Data on daily body weight and severity scores were analyzed by two-way repeated measures ANOVA and Sidak’s or Tukey’s multiple comparison test, as appropriate. Data on spleen weight and rhein levels in plasma and skin were analyzed by two-way ANOVA and Sidak’s or Tukey’s multiple comparison test, as appropriate. In data sets of the population size of inflammatory cells in skin and spleen, outliers were identified by Grubb’s test and were excluded from the statistical analysis. Then, data were analyzed by unpaired t-test (vehicle vs. IMQ alone) and one-way ANOVA with Tukey’s multiple comparison test (IMQ groups only). In addition, p values < 0.05 were regarded as statistically significant.
## References
1. Christophers E.. **Psoriasis--epidemiology and clinical spectrum**. *Clin. Exp. Dermatol.* (2001) **26** 314-320. DOI: 10.1046/j.1365-2230.2001.00832.x
2. Grozdev I., Kast D., Cao L., Carlson D., Pujari P., Schmotzer B., Babineau D., Kern E., McCormick T., Cooper K.D.. **Physical and mental impact of psoriasis severity as measured by the compact Short Form-12 Health Survey (SF-12) quality of life tool**. *J. Investig. Dermatol.* (2012) **132** 1111-1116. DOI: 10.1038/jid.2011.427
3. Coimbra S., Catarino C., Santos-Silva A.. **The triad psoriasis-obesity-adipokine profile**. *J. Eur. Acad. Dermatol. Venereol.* (2016) **30** 1876-1885. DOI: 10.1111/jdv.13701
4. Mrowietz U., Elder J., Barker J.. **The importance of disease associations and concomitant therapy for the long-term management of psoriasis patients**. *Arch. Dermatol. Res.* (2006) **298** 309-319. DOI: 10.1007/s00403-006-0707-8
5. Kamata M., Tada Y.. **Efficacy and Safety of Biologics for Psoriasis and Psoriatic Arthritis and Their Impact on Comorbidities: A Literature Review**. *Int. J. Mol. Sci.* (2020) **21**. DOI: 10.3390/ijms21051690
6. Lowes M.A., Suarez-Farinas M., Krueger J.. **Immunology of psoriasis**. *Annu. Rev. Immunol.* (2014) **32** 227-255. DOI: 10.1146/annurev-immunol-032713-120225
7. Kim W.B., Jerome D., Yeung J.. **Diagnosis and management of psoriasis**. *Can. Fam. Physician.* (2017) **63** 278-285. PMID: 28404701
8. van der Fits L., Mourits S., Voerman J.S., Kant M., Boon L., Laman J.D., Cornelissen F., Mus A.M., Florencia E., Prens E.P.. **Imiquimod-induced psoriasis-like skin inflammation in mice is mediated via the IL-23/IL-17 axis**. *J. Immunol.* (2009) **182** 5836-5845. DOI: 10.4049/jimmunol.0802999
9. Swindell W.R., Johnston A., Carbajal S., Han G., Wohn C., Lu J., Xing X., Nair R.P., Voorhees J.J., Elder J.T.. **Genome-wide expression profiling of five mouse models identifies similarities and differences with human psoriasis**. *PLoS ONE* (2011) **6**. DOI: 10.1371/journal.pone.0018266
10. Martel-Pelletier J., Pelletier J.. **Effects of diacerein at the molecular level in the osteoarthritis disease process**. *Ther. Adv. Musculoskelet Dis.* (2010) **2** 95-104. DOI: 10.1177/1759720X09359104
11. Pavelka K., Bruyere O., Cooper C., Kanis J.A., Leeb B.F., Maheu E., Martel-Pelletier J., Monfort J., Pelletier J.P., Rizzoli R.. **Diacerein: Benefits, Risks and Place in the Management of Osteoarthritis. An Opinion-Based Report from the ESCEO**. *Drugs Aging* (2016) **33** 75-85. DOI: 10.1007/s40266-016-0347-4
12. Yaron M., Shirazi I., Yaron I.. **Anti-interleukin-1 effects of diacerein and rhein in human osteoarthritic synovial tissue and cartilage cultures**. *Osteoarthr. Cartil.* (1999) **7** 272-280. DOI: 10.1053/joca.1998.0201
13. Gao Y., Chen X., Fang L., Liu F., Cai R., Peng C., Qi Y.. **Rhein exerts pro- and anti-inflammatory actions by targeting IKKbeta inhibition in LPS-activated macrophages**. *Free Radic Biol. Med.* (2014) **72** 104-112. DOI: 10.1016/j.freeradbiomed.2014.04.001
14. Mian M., Brunelleschi S., Tarli S., Rubino A., Benetti D., Fantozzi R., Zilletti L.. **Rhein: An anthraquinone that modulates superoxide anion production from human neutrophils**. *J. Pharm. Pharmacol.* (1987) **39** 845-847. DOI: 10.1111/j.2042-7158.1987.tb05131.x
15. Mohan G.C., Zhang H., Bao L., Many B., Chan L.S.. **Diacerein inhibits the pro-atherogenic & pro-inflammatory effects of IL-1 on human keratinocytes & endothelial cells**. *PLoS ONE* (2017) **12**. PMID: 28323859
16. Wally V., Kitzmueller S., Lagler F., Moder A., Hitzl W., Wolkersdorfer M., Hofbauer P., Felder T.K., Dornauer M., Diem A.. **Topical diacerein for epidermolysis bullosa: A randomized controlled pilot study**. *Orphanet J. Rare Dis.* (2013) **8** 69. DOI: 10.1186/1750-1172-8-69
17. Wally V., Hovnanian A., Ly J., Buckova H., Brunner V., Lettner T., Ablinger M., Felder T.K., Hofbauer P., Wolkersdorfer M.. **Diacerein orphan drug development for epidermolysis bullosa simplex: A phase 2/3 randomized, placebo-controlled, double-blind clinical trial**. *J. Am. Acad. Dermatol.* (2018) **78** 892-901 e7. DOI: 10.1016/j.jaad.2018.01.019
18. Ablinger M., Felder T.K., Wimmer M., Zauner R., Hofbauer P., Lettner T., Wolkersdorfer M., Lagler F.B., Diem A., Bauer J.W.. **Basal pharmacokinetic parameters of topically applied diacerein in pediatric patients with generalized severe epidermolysis bullosa simplex**. *Orphanet J. Rare Dis.* (2018) **13** 193. DOI: 10.1186/s13023-018-0940-1
19. Di Meglio P., Villanova F., Nestle F.. **Psoriasis**. *Cold Spring Harb. Perspect. Med.* (2014) **4** a015354. DOI: 10.1101/cshperspect.a015354
20. Refaie M.M.M., El-Hussieny M.. **Diacerein inhibits Estradiol-benzoate induced cervical hyperkeratosis in female rats**. *Biomed. Pharmacother.* (2017) **95** 223-229. DOI: 10.1016/j.biopha.2017.08.053
21. Nicolas P., Tod M., Padoin C., Petitjean O.. **Clinical pharmacokinetics of diacerein**. *Clin. Pharmacokinet.* (1998) **35** 347-359. DOI: 10.2165/00003088-199835050-00002
22. Falgarone G., Dougados M.. **Diacerein as a disease-modulating agent in osteoarthritis**. *Curr Rheumatol. Rep.* (2001) **3** 479-483. DOI: 10.1007/s11926-001-0061-y
23. Panova E., Jones G.. **Benefit-risk assessment of diacerein in the treatment of osteoarthritis**. *Drug Saf.* (2015) **38** 245-252. DOI: 10.1007/s40264-015-0266-z
24. Aziz D.E., Abdelbary A.A., Elassasy A.. **Investigating superiority of novel bilosomes over niosomes in the transdermal delivery of diacerein: In vitro characterization, ex vivo permeation and in vivo skin deposition study**. *J. Liposome Res.* (2019) **29** 73-85. DOI: 10.1080/08982104.2018.1430831
25. Aziz D.E., Abdelbary A., Elassasy A.. **Fabrication of novel elastosomes for boosting the transdermal delivery of diacerein: Statistical optimization, ex-vivo permeation, in-vivo skin deposition and pharmacokinetic assessment compared to oral formulation**. *Drug Deliv.* (2018) **25** 815-826. DOI: 10.1080/10717544.2018.1451572
26. El-Say K.M., Abd-Allah F.I., Lila A.E., Hassan Ael S., Kassem A.E.. **Diacerein niosomal gel for topical delivery: Development, in vitro and in vivo assessment**. *J. Liposome Res.* (2016) **26** 57-68. DOI: 10.3109/08982104.2015.1029495
27. Jabeen M., Boisgard A.S., Danoy A., El Kholti N., Salvi J.P., Boulieu R., Fromy B., Verrier B., Lamrayah M.. **Advanced Characterization of Imiquimod-Induced Psoriasis-Like Mouse Model**. *Pharmaceutics* (2020) **12**. DOI: 10.3390/pharmaceutics12090789
28. Sun L., Liu Z., Lin Z., Cun D., Tong H.H., Yan R., Wang R., Zheng Y.. **Comparison of normal versus imiquimod-induced psoriatic skin in mice for penetration of drugs and nanoparticles**. *Int. J. Nanomed.* (2018) **13** 5625-5635. DOI: 10.2147/IJN.S170832
29. Nair A.B., Jacob S.. **A simple practice guide for dose conversion between animals and human**. *J. Basic Clin. Pharm.* (2016) **7** 27-31. DOI: 10.4103/0976-0105.177703
30. Deng Y., Chang C., Lu Q.. **The Inflammatory Response in Psoriasis: A Comprehensive Review**. *Clin. Rev. Allergy Immunol.* (2016) **50** 377-389. DOI: 10.1007/s12016-016-8535-x
31. Katayama H.. **Development of psoriasis by continuous neutrophil infiltration into the epidermis**. *Exp. Dermatol.* (2018) **27** 1084-1091. DOI: 10.1111/exd.13746
32. Bridgewood C., Fearnley G.W., Berekmeri A., Laws P., Macleod T., Ponnambalam S., Stacey M., Graham A., Wittmann M.. **IL-36gamma Is a Strong Inducer of IL-23 in Psoriatic Cells and Activates Angiogenesis**. *Front. Immunol.* (2018) **9** 200. DOI: 10.3389/fimmu.2018.00200
33. Balato N., Napolitano M., Ayala F., Patruno C., Megna M., Tarantino G.. **Nonalcoholic fatty liver disease, spleen and psoriasis: New aspects of low-grade chronic inflammation**. *World J. Gastroenterol.* (2015) **21** 6892-6897. DOI: 10.3748/wjg.v21.i22.6892
34. Kaiser H., Kvist-Hansen A., Krakauer M., Gortz P.M., Henningsen K.M.A., Wang X., Becker C., Zachariae C., Skov L., Hansen P.R.. **Association between Vascular Inflammation and Inflammation in Adipose Tissue, Spleen, and Bone Marrow in Patients with Psoriasis**. *Life* (2021) **11**. DOI: 10.3390/life11040305
35. Hjuler K.F., Gormsen L.C., Vendelbo M.H., Egeberg A., Nielsen J., Iversen L.. **Systemic Inflammation and Evidence of a Cardio-splenic Axis in Patients with Psoriasis**. *Acta Derm. Venereol.* (2018) **98** 390-395. DOI: 10.2340/00015555-2873
36. Ocampo D.V., Gladman D.. **Psoriatic arthritis**. *F1000Res* (2019) **8** 1665. DOI: 10.12688/f1000research.19144.1
37. Baek J.O., Byamba D., Wu W.H., Kim T.G., Lee M.G.. **Assessment of an imiquimod-induced psoriatic mouse model in relation to oxidative stress**. *Arch. Dermatol. Res.* (2012) **304** 699-706. DOI: 10.1007/s00403-012-1272-y
38. Swindell W.R., Michaels K.A., Sutter A.J., Diaconu D., Fritz Y., Xing X., Sarkar M.K., Liang Y., Tsoi A., Gudjonsson J.E.. **Imiquimod has strain-dependent effects in mice and does not uniquely model human psoriasis**. *Genome Med.* (2017) **9** 24. DOI: 10.1186/s13073-017-0415-3
39. Lou F., Sun Y., Wang H.. **Protocol for Flow Cytometric Detection of Immune Cell Infiltration in the Epidermis and Dermis of a Psoriasis Mouse Model**. *STAR Protoc.* (2020) **1** 100115. DOI: 10.1016/j.xpro.2020.100115
40. Surcel M., Huica R.I., Munteanu A.N., Isvoranu G., Pirvu I.R., Ciotaru D., Constantin C., Bratu O., Caruntu C., Neagu M.. **Phenotypic changes of lymphocyte populations in psoriasiform dermatitis animal model**. *Exp. Ther. Med.* (2019) **17** 1030-1038. DOI: 10.3892/etm.2018.6978
41. Palamara F., Meindl S., Holcmann M., Luhrs P., Stingl G., Sibilia M.. **Identification and characterization of pDC-like cells in normal mouse skin and melanomas treated with imiquimod**. *J. Immunol.* (2004) **173** 3051-3061. DOI: 10.4049/jimmunol.173.5.3051
42. Terui T., Ozawa M., Tagami H.. **Role of neutrophils in induction of acute inflammation in T-cell-mediated immune dermatosis, psoriasis: A neutrophil-associated inflammation-boosting loop**. *Exp. Dermatol.* (2000) **9** 1-10. DOI: 10.1034/j.1600-0625.2000.009001001.x
43. Dyring-Andersen B., Honore T.V., Madelung A., Bzorek M., Simonsen S., Clemmensen S.N., Clark R.A., Borregaard N., Skov L.. **Interleukin (IL)-17A and IL-22-producing neutrophils in psoriatic skin**. *Br. J. Dermatol.* (2017) **177** e321-e322. DOI: 10.1111/bjd.15533
44. Blauvelt A., Chiricozzi A.. **The Immunologic Role of IL-17 in Psoriasis and Psoriatic Arthritis Pathogenesis**. *Clin. Rev. Allergy Immunol.* (2018) **55** 379-390. DOI: 10.1007/s12016-018-8702-3
45. Clark R.A., Kupper T.. **Misbehaving macrophages in the pathogenesis of psoriasis**. *J. Clin. Investig.* (2006) **116** 2084-2087. DOI: 10.1172/JCI29441
46. Kurotaki D., Uede T., Tamura T.. **Functions and development of red pulp macrophages**. *Microbiol. Immunol.* (2015) **59** 55-62. DOI: 10.1111/1348-0421.12228
47. Kim T.G., Kim D.S., Kim H.P., Lee M.G.. **The pathophysiological role of dendritic cell subsets in psoriasis**. *BMB Rep.* (2014) **47** 60-68. DOI: 10.5483/BMBRep.2014.47.2.014
48. Lowes M.A., Chamian F., Abello M.V., Fuentes-Duculan J., Lin S.L., Nussbaum R., Novitskaya I., Carbonaro H., Cardinale I., Kikuchi T.. **Increase in TNF-alpha and inducible nitric oxide synthase-expressing dendritic cells in psoriasis and reduction with efalizumab (anti-CD11a)**. *Proc. Natl. Acad. Sci. USA* (2005) **102** 19057-19062. DOI: 10.1073/pnas.0509736102
49. Tai Y., Wang Q., Korner H., Zhang L., Wei W.. **Molecular Mechanisms of T Cells Activation by Dendritic Cells in Autoimmune Diseases**. *Front. Pharmacol.* (2018) **9** 642. DOI: 10.3389/fphar.2018.00642
50. Podojil J.R., Miller S.. **Molecular mechanisms of T-cell receptor and costimulatory molecule ligation/blockade in autoimmune disease therapy**. *Immunol. Rev.* (2009) **229** 337-355. DOI: 10.1111/j.1600-065X.2009.00773.x
51. Goodman W.A., Levine A.D., Massari J.V., Sugiyama H., McCormick T.S., Cooper K.D.. **IL-6 signaling in psoriasis prevents immune suppression by regulatory T cells**. *J. Immunol.* (2009) **183** 3170-3176. DOI: 10.4049/jimmunol.0803721
52. Lee E., Trepicchio W.L., Oestreicher J.L., Pittman D., Wang F., Chamian F., Dhodapkar M., Krueger J.G.. **Increased expression of interleukin 23 p19 and p40 in lesional skin of patients with psoriasis vulgaris**. *J. Exp. Med.* (2004) **199** 125-130. DOI: 10.1084/jem.20030451
53. Leonardi C.L., Powers J.L., Matheson R.T., Goffe B.S., Zitnik R., Wang A., Gottlieb A.B.. **Etanercept as monotherapy in patients with psoriasis**. *N. Engl. J. Med.* (2003) **349** 2014-2022. DOI: 10.1056/NEJMoa030409
54. Locker F., Vidali S., Holub B.S., Stockinger J., Brunner S.M., Ebner S., Koller A., Trost A., Reitsamer H.A., Schwarzenbacher D.. **Lack of Galanin Receptor 3 Alleviates Psoriasis by Altering Vascularization, Immune Cell Infiltration, and Cytokine Expression**. *J. Investig. Dermatol.* (2018) **138** 199-207. PMID: 28844939
55. Yu Y.R., O'Koren E.G., Hotten D.F., Kan M.J., Kopin D., Nelson E.R., Que L., Gunn M.D.. **A Protocol for the Comprehensive Flow Cytometric Analysis of Immune Cells in Normal and Inflamed Murine Non-Lymphoid Tissues**. *PLoS ONE* (2016) **11**. DOI: 10.1371/journal.pone.0150606
56. Rose S., Misharin A., Perlman H.. **A novel Ly6C/Ly6G-based strategy to analyze the mouse splenic myeloid compartment**. *Cytom. A* (2012) **81** 343-350. DOI: 10.1002/cyto.a.22012
57. Liu Z., Gu Y., Shin A., Zhang S., Ginhoux F.. **Analysis of Myeloid Cells in Mouse Tissues with Flow Cytometry**. *STAR Protoc.* (2020) **1** 100029. DOI: 10.1016/j.xpro.2020.100029
|
---
title: Development of the Menu Assessment Scoring Tool (MAST) to Assess the Nutritional
Quality of Food Service Menus
authors:
- Claire Elizabeth Pulker
- Leisha Michelle Aberle
- Lucy Meredith Butcher
- Clare Whitton
- Kristy Karying Law
- Amy Louise Large
- Christina Mary Pollard
- Georgina S. A. Trapp
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001456
doi: 10.3390/ijerph20053998
license: CC BY 4.0
---
# Development of the Menu Assessment Scoring Tool (MAST) to Assess the Nutritional Quality of Food Service Menus
## Abstract
Preventing the rise in obesity is a global public health priority. Neighbourhood environments can help or undermine people’s efforts to manage their weight, depending on availability of nutritious and nutrient-poor ‘discretionary’ foods. The proportion of household food budgets spent on eating outside the home is increasing. To inform nutrition policy at a local level, an objective assessment of the nutritional quality of foods and beverages on food service menus that is context-specific is needed. This study describes the development and piloting of the Menu Assessment Scoring Tool (MAST), used to assess the nutritional quality of food service menus in Australia. The MAST is a desk-based tool designed to objectively assess availability of nutrient-poor and absence of nutritious food and beverages on food service menus. A risk assessment approach was applied, using the best available evidence in an iterative way. MAST scores for 30 food service outlets in one Local Government Authority in Perth, Western Australia highlight opportunities for improvements. MAST is the first tool of its kind in Australia to assess the nutritional quality of food service menus. It was practical and feasible to use by public health nutritionists/dietitians and can be adapted to suit other settings or countries.
## 1. Introduction
Obesity is a common, complex, chronic, relapsing, diet-related condition [1]. Global prevalence continues to increase, with two billion people classified as obese in 2015 [2]. In Australia, two-thirds of adults were classified as overweight or obese in 2017–2018 [3]. Over one hundred different factors can contribute to weight gain and obesity, including biological, psychological, environmental, and social [4]. People who are classified as obese have an increased risk of chronic diseases including cardiovascular disease, type 2 diabetes, some cancers [5], as well as mental ill-health and eating disorders [6]. Preventing the rise in prevalence of excess body weight is a global public health priority [1,7]. In addition, supporting people who are classified as overweight or obese to reduce weight by 5–10 percent and maintain the weight loss can lead to significant risk reduction [5].
Public health interventions designed to reduce and prevent obesity include food and nutrition policies to improve population diets [8], as healthy dietary patterns are essential for achieving and managing a healthy weight [9]. Australian government policy addresses obesity via three dietary guidelines: Guideline 1: Achieve and maintain a healthy weight; Guideline 2: *Enjoy a* wide variety of nutritious foods; and Guideline 3: Limit intake of foods containing saturated fat, added salt, added sugars and alcohol [9]. In Australia, less than five percent of the population eat the types and amounts of foods recommended in the Australian Guide to Healthy Eating [9] to maintain a healthy body weight. Specifically, the most recent Australian Health Survey found that only four percent of adults ate the minimum recommended amount of vegetables and 35 percent of total dietary energy intake was from energy-dense-nutrient-poor or ‘discretionary’ foods (referred to as ‘nutrient-poor’ hereon in) [10].
Eating behaviour is influenced by multi-level factors, including individual (e.g., food preferences), social (e.g., social and cultural norms), environmental (e.g., neighbourhood food environments), and macro-level (e.g., government policies and programmes) [11]. A range of policy interventions to improve eating behaviours are needed, including those which aim to improve neighbourhood food environments [12].
## 1.1. Neighbourhood Food Environments
The food environment is multifactorial, including food access, provision, and price, as well as how food is labelled and marketed [13]. The term ’neighbourhood food environment’ is used to describe a mixture of food retail and food service outlets and is not limited to a residential neighbourhood [14]. Neighbourhood food environments can influence food purchases and consumption by the types of food outlets present and their location (the community food environment); and by the choice of foods available, their price, placement and promotion (the consumer food environment) [13]. They can help or undermine people’s efforts to manage their weight, depending on availability of nutritious and nutrient-poor foods [15]. There is a growing body of literature that describes Australian community [16,17] and consumer food environments [18]. However, little is known about Australian food service outlets, apart from cost and presence of nutrition information in selected fast food chains [19,20], and the nutritional quality of children’s menus from non-chain restaurants and cafes [21].
Food service outlets (e.g., cafes, coffee shops, restaurants, fast food or quick service, takeaway, pubs, hotels, and taverns) are important considerations for nutrition policy because they contribute an increasing proportion of total dietary intake in many countries, as more people are ‘eating out’ or ‘ordering in’ ready-to-eat meals or snacks. Australians purchased an average of 2.73 meals from outside of the home each week in 2018 [22]. A third of Australian households’ food expenditure was on restaurant meals and fast foods in 2015–2016 [23]. Similarly, in the United Kingdom (UK) household expenditure on food and drinks eaten outside the home comprised 32 percent of the total spent on food in 2019–2020 [24]. In the United States (US), households spent over half of their food budget on prepared foods from outside of the home in 2021 [25]. Meals purchased in restaurants or fast food outlets contributed a substantial proportion of US dietary energy intake but had poor nutritional quality [26]. Ecological studies indicate that growth in availability of fast food outlets may be associated with the increase in obesity, but this has not yet been established at a local or individual level [27].
## 1.2. Food Service Outlet Assessment Tools
Most of the existing tools to assess the impact of food service outlets on food environments and dietary health [28] require on-site audits. Food and nutrition policy interventions requiring on-site assessment of food outlets are time consuming, expensive, and unfeasible in Australia due to the widely dispersed urban areas [29,30,31]. To be practical and feasible for use on a large scale, a new tool to assess the impact of food service outlets on population diets without the need to conduct site visits is needed.
The Portuguese Kids’ Menu Healthy Score (KIMEHS) was developed as a desk-based tool and provides an easy and fast way to assess children’s menus for consistency with the Mediterranean diet [31]. KIMEHS penalises the availability of unhealthy items and assigns reward points for the availability of healthy items, providing clear signals for improvement to food outlet operators [31]. While some dietary advice is universal (e.g., to consume fruit and vegetables; and limit salt, sugar and fat) there are some culturally specific differences regarding the types and amounts of foods recommended between countries [32]. The Australian Guide to Healthy Eating was designed to inform common dietary patterns reflective of the country’s multi-cultural heritage, and recommendations are not specific to the Mediterranean dietary pattern [9]. Menu nutritional quality is an essential component of an objective tool to assess the impact of food service outlets on population diet, and can inform national and local policy interventions [33]. An objective menu assessment tool that is tailored for the Australian context is therefore needed.
## 1.3. East Metropolitan Health Service Assessment of Food Service Outlets
The East Metropolitan Health Service (EMHS), based in metropolitan Perth, is one of four publicly-funded area health services within Western Australian (WA). EMHS provides tertiary, secondary, specialist, and community and population health services that aim to maintain and improve the health of one-third of the WA population [34]. The EMHS Obesity Prevention Strategy uses evidence-based approaches to address overweight and obesity and its determinants via 28 priority actions [35]. It includes an action to “classify the dietary risk of food outlets, identify issues of concern, and inform and influence local public health responses” ([35], p. 16).
As part of the implementation of the EMHS Obesity Prevention Strategy, the Food Outlet Dietary Risk assessment tool (FODR) was developed to objectively assess and score the dietary risk of all consumer-facing food outlets present in EMHS neighbourhood food environments. It assigns scores for six public health nutrition attributes: availability of nutrient-poor foods; availability of nutritious foods; acceptability and appeal; accessibility; type of business operation; and complex food outlet considerations [36]. A risk rating similar to that used for food safety is assigned to each food outlet, from low to very high. Food outlet data for FODR can be obtained during routine environmental health officer site visits, or from desk-based research using the best available evidence. When pilot testing the FODR tool, the research team identified that a consistent method to objectively assess the nutritional quality of food service menus, specific to the Australian context, was needed.
This study aims to describe the development and piloting of the Menu Assessment Scoring Tool (MAST), to assess the nutritional quality of food service menus in Australia. MAST was designed using a risk assessment approach [36], to be used by public health nutritionists and dietitians to conduct desk-based audits of food service outlets. Risk assessment principles applied in the development of MAST were similar to those used in food safety risk assessment and included using the best available evidence in an iterative way and recognising the inherent uncertainty in risk assessment [37].
## 2.1.1. Database of Classified Food Outlets
EMHS staff collected registered food business data from all 13 Local Government Authorities (LGAs) in 2018. A database of 6963 food businesses was constructed by the research team, and a classification framework applied (Supplementary Table S1). Food businesses classified as consumer-facing food retail (e.g., supermarkets, convenience stores) and food service outlets (e.g., cafes, restaurants) ($$n = 4136$$) were identified [36]. Other types of food service provision, including in schools, hospitals, or sports and recreation centres were classified as ‘institutional food’ to reflect the different ways in which people interact with them as well as different approaches to policy responses (e.g., mandatory nutrition criteria for school meals), and not included in this study.
## 2.1.2. Principles for Assessment of Food Service Menus
MAST was developed to be fit-for-purpose in the Australian context, applying the following principles during its development: [1] menu items were classified as either nutritious (i.e., the types of foods recommended in the Australian Guide to Healthy Eating [9]) or nutrient-poor, consistent with national guidelines; [2] a risk assessment approach was adopted using the best available evidence, with menu items assumed to be nutrient-poor unless available information demonstrated otherwise; [3] the desk-based tool needed to be quick and easy to use; [4] face validity of menu scores assigned by the research team of qualified public health nutritionists and dietitians was required; and [5] areas for improvement should be signalled to food outlet operators. MAST is used to assess availability of nutritious and nutrient-poor food and beverages (referred to as food hereon in) on food service menus.
## 2.1.3. MAST Categorisation of Menu Items
Details provided in the Australian Guide to Healthy Eating [9] and the accompanying Eat for Health Educator’s Guide [37] formed the basis of menu item classification as ‘nutritious’ or ‘nutrient-poor’ for MAST.
The Australian Guide to Health Eating recommends enjoying a wide variety of nutritious foods from five food groups every day, including: grain (cereal) foods; vegetables and legumes/beans; fruit; milk, yoghurt, cheese and alternatives; and lean meats and poultry, fish, eggs and alternatives. Plenty of water is recommended, and small amounts of healthy fats and oils can be used when preparing meals [9]. Discretionary, or energy-dense-nutrient-poor foods, should only be eaten sometimes and small amounts. Examples include sweetened drinks, processed meats, sausages, pies and pastries, commercially fried foods including chips, biscuits, cakes, puddings, and alcoholic beverages [37]. The Eat for Health Educator Guide recommends that the main ingredients of mixed foods or meals are identified and classified as nutritious five food group foods or energy-dense-nutrient-poor discretionary items for dietary analysis [37]. However, this approach does not categorise whole menu items as either nutritious or nutrient-poor and is problematic when only short meal descriptions are provided on food service menus. Nutritious five food group foods and water are referred to as ‘nutritious’ foods and discretionary energy-dense nutrient-poor foods are referred to as ‘nutrient-poor’ foods hereon in.
Lack of consistency in classifying foods as ‘discretionary’ has been identified among Australian health professionals, food industry, policy makers, and consumers [38]. Therefore, to achieve consistency among the research team and support wider translation of MAST, detailed definitions of food groups were developed. They were adapted from the Eat for Health Educator Guide and the 38 food groups used by Wang and colleagues to assess menu items available from an online delivery platform in Sydney in 2020 [38]. During its development, six public health nutritionists and dietitians and two dietetics students used MAST to assess menus from food service outlets present in Perth, WA. Development of food groups and definitions was done in a collaborative and iterative way to reflect the challenges of assessing a wide variety of food service menus, and to resolve any discrepancies in classification arising among the research team.
## 2.1.4. MAST Scoring System
The MAST scoring system was adapted from the work of Tavares et al. [ 2021], which recognised that food service outlets supportive of healthy eating make nutritious choices available and limit nutrient-poor choices [39]. The Healthiness Indicator, developed in Brazil to characterise food outlets selling food for immediate consumption, assigns a reward point for each of the nutritious food groups sold, and for each of the nutrient-poor food groups not sold [39].
Using the risk assessment approach, MAST assigns a penalty point for the availability of each nutrient-poor food category, and for the absence of each corresponding nutritious food category. When one meal meets the definition of each nutrient-poor food category, one penalty point is allocated. When there are no meals available that meet the definition of each corresponding nutritious food category, one penalty point is allocated. There is no change to the penalty points allocated when more than one meal meets the definition of each nutrient-poor or corresponding nutritious food category. The scores assigned by MAST range from $0\%$ to $100\%$. The best-case scenario produces a score of $0\%$ which indicates there are no nutrient-poor food groups present on the menu, only nutritious food groups. The worst-case scenario products a score of $100\%$ which indicates there are nutrient-poor food groups present on the menu, but no nutritious food groups (Figure 1).
Categorical risk (e.g., low, medium, high) is not assigned by the MAST score for food service menus. This is because the MAST score contributes two of the six components of the Australian FODR tool (i.e., availability of nutrient-poor food; availability of nutritious food) which was developed to assess and score the dietary risk of all consumer-facing food outlets present in EMHS neighbourhood food environments.
**Figure 1:** *Schematic of the Menu Assessment Scoring Tool (MAST) scoring system.*
## 2.1.5. Sourcing Online Menus
Some food service outlets provided menus in multiple online locations, including one or more online delivery platforms (e.g., Deliveroo® (Balaclava, VIC, Australia), UberEats® (Port Melbourne, VIC, Australia)), company websites, company social media platforms (e.g., Facebook® (Sydney, NSW, Australia)), company mobile phone apps, or user-generated review websites (e.g., TripAdvisor® (Sydney, NSW, Australia)). To determine whether the menus available at food service outlets differed across online locations, a sample of five major chain food service outlets which sold a range of foods were identified for comparison. Chains were a focus for comparison because they have the most influence over neighbourhood food environments due to contributing a high proportion of food service sales, an approach recommended by the International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS) [40]. The five chains sold burger-based products, chicken-based products, pizza, sandwiches, and Mexican-based products. The largest online delivery platforms were identified as UberEats®, Meunlog® (Sydney, NSW, Australia) Deliveroo®, and Doordash® (Melbourne, VIC, Australia) (all apps were iOS version 16.1) and used in the analysis. In total, 26 menus were collected in October 2022 for comparison as follows: 5 from UberEats®, 4 from Menulog®, 4 from Deliveroo®, 4 from Doordash®, 4 from company websites, and 5 from company mobile phone apps. A standard location in the EMHS geographic catchment was used across all online delivery platforms, websites, and apps. All five chains received identical MAST scores for each of the menus available online. Based on this comparison, the research team agreed that using one online delivery platform as the default for obtaining food service menus would provide robust MAST scores.
## 2.2.1. Identify Food Service Outlets Present
One LGA agreed to provide an updated database of registered food businesses in 2021, which formed the setting for pilot testing of MAST. The data were cleaned and classified and included 24 food retail and 38 food service outlets.
## 2.2.2. Source Menus Online
Online searches were conducted to locate menus for each of the 38 food service outlets. Online delivery platforms were used as the primary source of menus, supplemented by food outlet websites and social media. Two food service outlets appeared to have closed, no recent menus could be located for a further six, and 30 menus were obtained.
## 2.2.3. Pilot Testing MAST Categorisation of Menu Items
The menus were imported into NVivo (QSR International Pty Ltd., Release 1.6.1, Doncaster, Victoria, Australia) and saved in a predetermined filing system. A MAST coding framework was developed and applied to each menu, which identified availability of six nutrient-poor food categories (which include 15 nutrient-poor food groups), and five nutritious food categories (which include 12 nutritious food groups). NVivo served as a storage platform for web captures of menus and visual records of the assessment decision making process.
For menu items that could be tailored by the customer, e.g., by adding a topping or changing the side dish, the default option was used. For menus with meal promotional packages or ‘meal deals’ the item was analysed together and not split into components. Menu items for children (e.g., kid’s menus) were included if listed on the main menu. MAST assigns a penalty point for each nutrient-poor food category available, so only one item per food category was coded in NVivo to indicate presence. MAST assigns a penalty point for each corresponding nutritious food category that is absent, so only one item per food category was coded in NVivo if available.
Availability of the food categories was then entered into REDCap® (REDCap consortium, Version 12.4.21, Nashville, Tennessee, United States of America). REDCap® housed the MAST tool and functioned as the quantitative data capture application. The presence or absence of each food group was entered into REDCap® for each menu, using a MAST survey to assign scores as previously described, and the software then calculated the final score (Figure 2). Use of software and applications ensured a rigorous process was used by the research team to score the menus, which could be easily adapted during the iterative process undertaken.
## 2.2.4. Pilot Testing the MAST Scoring System
The MAST scoring system was pilot tested with diverse food service menus during its development. Examples included: fine-dining restaurants requiring selection of a separate side dish (e.g., salad) to accompany the main dish (e.g., steak); cafes selling a wide range of teas and coffees but a small number of food offerings; fast food outlets with menu items that could be tailored with a choice of toppings or fillings; and specialty food service outlets such as ice cream and gelato shops. The MAST scores were reviewed to see what signals were given to food outlet operators for improvement.
Initial testing of the scoring system found that food outlets with nutrient-poor products from only one or two food categories (e.g., burgers, ice cream, fish and chips) would be advantaged over food outlets with more diverse menus. This was because they were allocated fewer penalty points due to the absence of multiple nutrient-poor food categories, despite selling few if any nutritious menu items. Based on this feedback, the final MAST scoring system was applied in three stages. Step one is to assign penalty points for the availability of each of six nutrient-poor food categories, and for the absence of each of five corresponding nutritious food categories, with the total used as the numerator for the MAST score (i.e., a maximum score of 11). Step two is to identify how many of the food categories received a penalty point, which is used as the denominator for the MAST score. In step three, the numerator is divided by the denominator and multiplied by 100 to give a percentage. This method produces more appropriate signals for improvement to a wide range of food service outlets.
**Figure 2:** *Menu Assessment Scoring Tool (MAST) process.*
## 3.1. The Final MAST
The final 27 food groups defined in MAST are mapped to six food categories of vegetables, fruits, grain (cereals), meat and alternatives, dairy and alternatives, and beverages and miscellaneous; and further defined as either nutritious (five food categories, 12 food groups) or nutrient-poor (6 food categories, 15 food groups) to reflect differences in ingredients and methods of preparation (Table 1).
Water was not included as a nutritious food group, because tap water is freely available in all Australian food outlets as a cultural norm. Alcoholic beverages were not included as a nutrient-poor food group, because the availability of alcohol is assessed separately in the overall FODR tool, via assessment of the type of liquor licence granted. It was therefore omitted from MAST to avoid duplication. If necessary, MAST may be modified for other settings to include water as a nutritious food group and alcoholic beverages as a nutrient-poor food group in the beverages and miscellaneous food category. For each food group, a definition and examples or exclusions are provided (Supplementary File S2).
**Table 1**
| Food Category | Nutrient-Poor Food Groups | Nutritious Food Groups |
| --- | --- | --- |
| Vegetables | Vegetable-based mixed dishFried potato (or similar)Examples: Deep fried vegetable patties or croquettes, fried or oil-cooked potato products, salads with large amounts of creamy dressing | Vegetable-based mixed dishBaked, steamed or roasted potatoesExamples: baked, steamed or roasted potatoes, salads with modest amount of dressing, vegetable-based curries, stews, or casseroles, vegetable patties, stir fries with or without meat/alternatives, broths, blended and chunky soups |
| Fruit | Fried fruitJuice and fruit-based smoothiesExamples: Fried fruit, fruit-based smoothies or juice containing added sugar or visible discretionary ingredients e.g., ice cream, cream, syrups/flavours, chocolate confectionary toppings, and any other sweeteners | FruitJuice and fruit-based smoothiesExamples: fresh fruit, canned/stewed/dried fruit, fruit or vegetable juices or smoothies with fruit as major component, with no added sugars |
| Grain (cereals) | Cereal-based mixed dishSweet or savoury baked goods/desserts(homemade or similar)Examples: deep fried foods, meat pies, quiche, sausage rolls, pizza, pasta/lasagne with cream-based sauce, ramen, soy-sauce based soups, garlic bread, cookies, cakes, cake-type desserts, muffins, slices, sweet pies, scones, scrolls, brioche, pancakes/crepes with discretionary toppings, waffles, souffle, pastries | Cereal-based mixed dishBreads and cerealsExamples: All breads and cereals, pasta, noodles, roti, bread rolls, flat breads, oatmeal/ porridge, crumpets, mixed meals with cereal products as the major ingredient and no discretionary ingredients including pasta, pizza, burgers, sandwiches/rolls/buns, sushi, wraps, rice, noodles, dumplings, steamed dim sum, burrito, taco |
| Meat and alternatives | Meat and poultry, and/or meat and/or poultry based mixed dishSeafood and/or seafood based mixed dishMeat alternatives and/or meat alternatives-based mixed dishExamples: Cured/salted/smoked/processed meats such as bacon, ham, salami, luncheon meats, sausages, dishes that are deep fried, contain coconut-based or cream-based sauce, beef stroganoff, chicken parmigiana, schnitzel, processed plant-based products mimicking meat | Meat and poultry, and/or meat and/or poultry based mixed dishSeafood and/or seafood based mixed dishMeat alternatives and/or meat alternatives-based mixed dishExamples: all raw, steamed and grilled seafood, lean meat, poultry, legumes, eggs, tofu, mixed dishes with meat/alternatives as the major ingredient and no discretionary ingredients including curry, stew, stir fry, casserole, meatballs, kebabs using cubes of meat, broths, blended and chunky soups, omelette, frittata |
| Dairy and alternatives | Discretionary milk-based beveragesFried dairy-based foodsIced confectionery and dairy-based dessertsExamples: Milk-based drinks made with syrups, confectionery, ice cream, whipped cream, or sago pearls, dairy-based desserts that are deep fried, ice blocks, slushies, snow cones, jelly, frozen yoghurt, ice cream, gelato, sorbet, rice pudding, fromage frais, mousse, custard, iced drink desserts, panna cotta | Flavoured milk/milk alternative based beveragesDairy and alternativesExamples: Milk within dishes, milk on cereal, yoghurt, cheese and/or their alternatives, all fat levels of plain milk, rice milk, almond milk, macadamia milk, soy milk, drinkable yoghurts, milk-based smoothies, chocolate flavoured milk, iced/hot coffee or chocolate without ice cream/syrup/confectionery, latte, cappuccino, flat white, dirty chai |
| Beverages and miscellaneous | Energy DrinksSweetened and Rehydration BeveragesConfectioneryExamples: energy drinks, soft drinks, cordial, non-dairy chain tea varieties, iced tea, mineral water with added sugar, lollies, chocolate, nougat, fruit leather, sesame snaps, peanut brittle, chocolate coated fruit/nuts/seeds, chocolate hazelnut spreads, chocolate sauces | |
## 3.2. MAST Scores Assigned
MAST scores for 30 food service outlets present within the food environment of one LGA in the EMHS geographic catchment are summarised below (Table 2).
The mean MAST score across all 30 food service outlets was $71\%$ (range 29–$100\%$). Almost all food service outlets ($\frac{24}{30}$) had a MAST score of $56\%$ or higher, indicating the menus were high risk and poor nutritional quality. The mean MAST scores for each type of food service outlet were: restaurants $56\%$ (range 43–$67\%$); cafes and coffee shops $56\%$ (range 36–$78\%$); pubs, hotels, and taverns $69\%$ (range 57–$78\%$); and fast food and takeaway $81\%$ (29–$100\%$). Only one food service outlet, which was classified as fast food or takeaway, had a MAST score below $30\%$, indicating few nutrient-poor food items were available on the menu. Five food service outlets, all classified as fast food or takeaway, had a MAST score of $100\%$ indicating no nutritious menu items were available.
## 4. Discussion
MAST was developed as an objective tool for assessing the nutritional quality of food service menus in Australia, using a risk assessment approach. To ensure MAST was fit-for-purpose in the local context, a number of principles were identified as important during its development and pilot tested. They informed refinements to MAST, which were made using a collaborative and iterative process by the research team of qualified public health nutritionists and dietitians.
The first principle identified was to classify menu items as either nutritious or nutrient-poor, consistent with national guidelines, to attribute dietary risk. Food classification requires the skills and expertise of public health nutritionists or dietitians [36], however discrepancies in classification can still occur due to the absence of detailed definitions of mixed foods and meals [41]. Creating detailed definitions for MAST ensured an agreed, consistent approach was used, aligned to current government policy. This will support wider dissemination and translation in Australia. Refining MAST definitions to include examples of common dishes found on menus, and resolving discrepancies in classification between the research team, aimed to improve accuracy and ease of use. The definitions were aligned to the Australian Dietary Guidelines, published in 2013, which are currently under review. When revised guidelines are published at the end of 2025 [42], food groups and definitions included in MAST will need to be updated to reflect any changes. The MAST could also be adapted for use in other countries by aligning the definitions to national food-based dietary guidelines.
The second principle used in the development of MAST was to adopt a risk assessment approach, using the best available evidence [43]. Menu items were assumed to be nutrient-poor unless available information demonstrated otherwise. The widely used NEMS-R also adopted this approach to identifying healthy menu items [44]. For example, in the absence of nutrition information or a regulated claim (e.g., low-fat) on menus, the NEMS-R only assigned salads as healthy if low-fat or fat-free dressings were used and a maximum of two salad ingredients were over $50\%$ fat [44]. Pilot testing of MAST identified some practical considerations, including sourcing of food service outlet menus online. Some smaller, non-chain food service outlets had minimal or no online presence which meant they were unable to be assessed using MAST. For future analysis, the number of missing menus will inform the approach used. When there are only a small number of missing menus, EMHS or LGA staff may visit the venues and request menus for assessment. When the number of missing menus is larger, the mean MAST score for each type of food service outlet will be assigned as the best available evidence.
The third principle that was essential for feasibility of applying MAST to EMHS neighbourhood food environments, was for the desk-based tool to be quick and easy to use. Using menus that were sourced online reduced the time needed to undertake site visits, which have been reported to take up to 40 min per outlet when using the NEMS-R [30]. Also, application of MAST does not require every menu item to be classified, as only one item from each food category is coded to demonstrate whether it is available or absent. The two steps which used NVivo software for coding and a REDCap® survey for scoring added to the time taken for assessment but ensured consistency and transparency. Verification of coding between different EMHS staff could be undertaken, and changes made during the iterative development process. It was also envisaged that the documents saved during the process of deriving MAST scores could be used to provide tailored feedback to food service outlets seeking to improve menu nutritional quality in future. Therefore, the process adopted for MAST was considered time efficient and robust.
The fourth principle for developing MAST was for face validity in the scores assigned by the research team. Face validity was assessed by identifying whether MAST appeared to do the job it aimed to do, from the users’ perspectives [45]. MAST was developed by EMHS staff over a period of 18 months, involving contributions from six public health nutritionists and dietitians and two student dietitians. The collaborative and iterative process included regular meetings to discuss the scores assigned and resolve any discrepancies in classification of menu items. For example, due to the cultural status of meat in Australia some staff assumed any mixed dish that included meat would be coded to the meat and alternatives food category. However, for dishes where cereal or grain foods were a main ingredient including burgers and pizza, MAST assigns them to this food category. Changes to MAST definitions over the period of development led to common understanding and agreement among the research team. MAST was deemed to have achieved face validity by EMHS staff (i.e., the users) because the tool categorised menu items using definitions adapted from the Australian government’s Eat for Health Educator Guide [37]. It could be applied to objectively identify presence or absence of menu items that were consistent with government recommendations.
The fifth principle that was identified as important when developing MAST was to signal areas for improvement to food service outlet operators. The way the MAST score is calculated means that when one meal meets the definition of a nutritious food category penalty points are not allocated. In contrast, if numerous meals meet the definition of a nutrient-poor food category only one penalty point is allocated. Therefore, the strongest signal to food service outlet operators is to add nutritious meals to the menu rather than to reduce the number of nutrient-poor meals, although both changes are recommended. An Australian study which used an adapted NEMS-R to assess 28 rural food service outlets recommended urgent action to introduce and promote nutritious meals, as the outlets were dominated by nutrient-poor foods [46]. People engaged in weight management strategies reported avoiding food service outlets due to poor availability of nutritious food, and abundant availability of nutrient-poor options [15]. Supporting food service outlets to improve availability of nutritious food on menus should be a policy priority for regions aiming to address obesity, which is reinforced by MAST. Using MAST to screen all food service outlets present in a neighbourhood food environment could inform design of future interventions to support food service outlets to make positive changes.
Strengths of MAST include the ability to use the objective results to create tailored reports for food service operators willing to make changes to menus. The results can highlight where menus are supportive of healthy eating, and areas for improvement. A toolkit of suggestions can be constructed based on EMHS staff experience of supporting implementation of a mandated food and nutrition policy across health service sites [47]. A quality improvement approach could be used that identifies quick wins (e.g., adding steamed vegetables as a side dish) or easy wins (e.g., removing processed meat from an otherwise nutritious sandwich), which would require resourcing at EMHS or LGAs. MAST could also be adapted for use in different settings, including to encourage more nutritious food options at community food events organised by LGAs, such as farmer’s markets and food truck events. MAST can be used as a stand-alone tool to assess the nutritional quality of menus, or to contribute scores to a more comprehensive assessment of dietary risk such as the Australian FODR assessment tool.
The study also has some limitations. Food service outlets change over time, and menus also change depending on seasonality, ingredient cost and availability, and customer preferences. MAST assessment can be repeated over time but will not capture all of these ad hoc changes. MAST is used to assess nutritional quality of menus using only the information provided. Therefore, cooking methods which include adding significant amounts of fat, sugar and salt may not be identified. MAST does not include assessment of prices, promotions, or portion size. Sourcing menus from online food delivery platforms means there may be some differences in the offer available, compared to visiting the physical food service outlet. This is more likely to occur for smaller and independently operated food service outlets, who may have less capacity to ensure online information is updated regularly. The indicative MAST score for food service menus does not incorporate the comprehensive assessments used in other settings with mandated government policies, such as the Healthy Options WA Food and Nutrition Policy in hospitals [48]. MAST was developed as a rapid desk-based assessment of food service menus, suitable for use in large-scale surveillance of neighbourhood food environments. It can be used as a screening tool for risk, rather than a tool that provides a precise and detailed assessment of every menu item. As demonstrated in this study, MAST was able to detect variation between food outlets despite this limitation. It was developed to be used alongside the FODR tool which also assesses acceptability and appeal; accessibility; type of business operation; and complex food outlet considerations [36]. The risk-based approach, which assumed items were nutrient-poor unless information was available to demonstrate otherwise, may have resulted in over-estimation of risk in some food service outlets. However, given that presence of only one nutrient-poor item was required for a penalty point to be assigned, the chance of over-estimation of risk is unlikely.
## 5. Conclusions
The new MAST is the first tool of its kind in Australia to assess the nutritional quality of a wide range of food service menus. MAST was used to accurately and objectively assess availability of nutritious and nutrient-poor food on food service menus. MAST was practical and feasible to use by qualified public health nutritionists and dietitians and can be adapted to suit other settings or countries. Measuring availability of nutrient-poor food and absence of nutritious food contributes two of six components of the Australian FODR tool which assesses the dietary risk of consumer-facing food outlets. Pilot testing MAST with one LGA within Perth, WA identified the typically poor nutritional quality of menus. Findings indicate the challenges to eating healthily faced by people who are managing their weight. Assessing the nutritional quality of menus from neighbourhood food environments across the EMHS geographic catchment using MAST will inform local policy responses addressing obesity.
## References
1. **The National Obesity Strategy 2022–2032**
2. Swinburn B.A., Kraak V.I., Allender S., Atkins V.J., Baker P.I., Bogard J.R., Brinsden H., Calvillo A., De Schutter O., Devarajan R.. **The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report**. *Lancet* (2019.0) **393** 791-846. DOI: 10.1016/S0140-6736(18)32822-8
3. **National Health Survey: First Results, 2017–2018, Cat.No. 4364.0.55.001**
4. Butland B., Jebb S., Kopelman P., McPherson K., Thomas S., Mardell J., Parry V.. *Foresight. Tackling Obesities: Future Choices. Project Report* (2007.0)
5. Jebb S.. **Obesity: Causes and consequences**. *Women’s Health Med.* (2004.0) **1** 38-41. DOI: 10.1383/wohm.1.1.38.55418
6. Hay P., Mitchison D.. **Eating Disorders and Obesity: The Challenge for Our Times**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11051055
7. **Obesity and Overweight**
8. Hawkes C., Jewell J., Allen K.. **A food policy package for healthy diets and the prevention of obesity and diet-related non-communicable diseases: The NOURISHING framework**. *Obes. Rev.* (2013.0) **14** 159-168. DOI: 10.1111/obr.12098
9. 9.
National Health and Medical Research Council
Australian Dietary GuidelinesCommonwealth of AustraliaCanberra, Australia2013. *Australian Dietary Guidelines* (2013.0)
10. **Australian Health Survey: Consumption of Food Groups from the Australian Dietary Guidelines, 2011–2012, Cat. No. 4364.0.55.012**
11. Story M., Kaphingst K.M., Robinson-O’Brien R., Glanz K.. **Creating Healthy Food and Eating Environments: Policy and Environmental Approaches**. *Annu. Rev. Public Health* (2008.0) **29** 253-272. DOI: 10.1146/annurev.publhealth.29.020907.090926
12. Mozaffarian D., Angell S.Y., Lang T., Rivera J.A.. **Role of government policy in nutrition—Barriers to and opportunities for healthier eating**. *BMJ* (2018.0) **361** k2426. DOI: 10.1136/bmj.k2426
13. Glanz K., Sallis J.F., Saelens B.E., Frank L.D.. **Healthy nutrition environments: Concepts and measures**. *Am. J. Health Promot.* (2005.0) **19** 330-333. DOI: 10.4278/0890-1171-19.5.330
14. Lake A.A.. **Neighbourhood food environments: Food choice, foodscapes and planning for health**. *Proc. Nutr. Soc.* (2018.0) **77** 239-246. DOI: 10.1017/S0029665118000022
15. Neve K.L., Isaacs A.. **How does the food environment influence people engaged in weight management? A systematic review and thematic synthesis of the qualitative literature**. *Obes. Rev.* (2021.0) **23** e13398. DOI: 10.1111/obr.13398
16. Bivoltsis A., Christian H., Ambrosini G.L., Hooper P., Pulker C.E., Thornton L., Trapp G.S.A.. **The community food environment and its association with diet, health or weight status in Australia: A systematic review with recommendations for future research**. *Health Promot. J. Aust.* (2022.0) 1-38. DOI: 10.1002/hpja.679
17. Needham C., Sacks G., Orellana L., Robinson E., Allender S., Strugnell C.. **A systematic review of the Australian food retail environment: Characteristics, variation by geographic area, socioeconomic position and associations with diet and obesity**. *Obes. Rev.* (2020.0) **21** e12941. DOI: 10.1111/obr.12941
18. Pulker C., Thornton L.E., Trapp G.. **What is known about consumer nutrition environments in Australia? A scoping review of the literature**. *Obes. Sci. Pract.* (2018.0) **4** 318-337. DOI: 10.1002/osp4.275
19. Wellard L., Glasson C., Chapman K., Miller C.. **Fast facts: The availability and accessibility of nutrition information in fast food chains**. *Health Promot. J. Aust.* (2011.0) **22** 184-188. DOI: 10.1071/HE11184
20. Wellard L., Havill M., Hughes C., Watson W.L., Chapman K.. **Energy-dense fast food products cost less: An observational study of the energy density and energy cost of Australian fast foods**. *Aust. N. Z. J. Public Health* (2015.0) **39** 544-545. DOI: 10.1111/1753-6405.12430
21. Trapp G.S.A., Pulker C.E., Hurworth M., Law K.K., Brinkman S., Pollard C.M., Harray A.J., Sambell R., Mandzufas J., Anzman-Frasca S.. **The Nutritional Quality of Kids’ Menus from Cafes and Restaurants: An Australian Cross-Sectional Study**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14132741
22. Cameron A.J., Oostenbach L.H., Dean S., Robinson E., White C.M., Vanderlee L., Hammond D., Sacks G.. **Consumption Frequency and Purchase Locations of Foods Prepared Outside the Home in Australia, 2018 International Food Policy Study**. *J. Nutr.* (2022.0) **152** 76S-84S. DOI: 10.1093/jn/nxab437
23. Hogan L.. *Food Demand in Australia: Trends and Issues 2018* (2018.0)
24. **National Statistics. Family Food 2019/2020**
25. **Food Expenditure Series**
26. Liu J., Rehm C.D., Micha R., Mozaffarian D.. **Quality of Meals Consumed by US Adults at Full-Service and Fast-Food Restaurants, 2003–2016: Persistent Low Quality and Widening Disparities**. *J. Nutr.* (2020.0) **150** 873-883. DOI: 10.1093/jn/nxz299
27. De Vogli R., Kouvonen A., Gimeno D.. **‘Globesization’: Ecological evidence on the relationship between fast food outlets and obesity among 26 advanced economies**. *Crit. Public Health* (2011.0) **21** 395-402. DOI: 10.1080/09581596.2011.619964
28. Glanz K., Johnson L., Yaroch A.L., Phillips M., Ayala G.X., Davis E.L.. **Measures of retail food store environments and sales: Review and implications for healthy eating initiatives**. *J. Nutr. Educ. Behav.* (2016.0) **48** 280-288.e281. DOI: 10.1016/j.jneb.2016.02.003
29. Alston L., Versace V., Brown E., Nichols M., Whelan J., Bolton K.A., Sacks G., Needham C., Orellana L., Allender S.. **Understanding the healthfulness of outlets providing lunch and dinner meals: A census of a rural food retail environment in Victoria, Australia**. *Aust. N. Z. J. Public Health* (2021.0) **45** 65-70. DOI: 10.1111/1753-6405.13057
30. Carins J.E., Rundle-Thiele S., Storr R.J.. **Appraisal of short and long versions of the Nutrition Environment Measures Survey (NEMS-S and NEMS-R) in Australia**. *Public Health Nutr.* (2018.0) **22** 564-570. DOI: 10.1017/S1368980018002732
31. Rocha A., Viegas C.. **KIMEHS—Proposal of an Index for Qualitative Evaluation of Children’s Menus—A Pilot Study**. *Foods* (2020.0) **9**. DOI: 10.3390/foods9111618
32. Herforth A., Arimond M., Álvarez-Sánchez C., Coates J., Christianson K., Muehlhoff E.. **A Global Review of Food-Based Dietary Guidelines**. *Adv. Nutr.* (2019.0) **10** 590-605. DOI: 10.1093/advances/nmy130
33. **NOURISHING Framework—Set Incentives and Rules to Create a Healthy Retail and Food Service Environment**
34. **About Us**
35. Pulker C., Law K., Pollard C.. *Obesity Prevention Strategy 2020–2025* (2020.0)
36. Pulker C.E., Trapp G.S.A., Fallows M., Hooper P., McKee H., Pollard C.M.. **Food Outlets Dietary Risk (FODR) assessment tool: Study protocol for assessing the public health nutrition risks of community food environments**. *Nutr. J.* (2020.0) **19** 122. DOI: 10.1186/s12937-020-00641-w
37. **Eat for health**. *Educator Guide* (2013.0)
38. Wang C., Korai A., Jia S.S., Allman-Farinelli M., Chan V., Roy R., Raeside R., Phongsavan P., Redfern J., Gibson A.A.. **Hunger for Home Delivery: Cross-Sectional Analysis of the Nutritional Quality of Complete Menus on an Online Food Delivery Platform in Australia**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13030905
39. Tavares L.F., Perez P.M.P., dos Passos M.E.A., de Castro Junior P.C.P., da Silva Franco A., de Oliveira Cardoso L., de Castro I.R.R.. **Development and Application of Healthiness Indicators for Commercial Establishments That Sell Foods for Immediate Consumption**. *Foods* (2021.0) **10**. DOI: 10.3390/foods10061434
40. Sacks G., Swinburn B., Kraak V., Downs S., Walker C., Barquera S., Friel S., Hawkes C., Kelly B., Kumanyika S.. **A proposed approach to monitor private-sector policies and practices related to food environments, obesity and non-communicable disease prevention**. *Obes. Rev.* (2013.0) **14** 38-48. DOI: 10.1111/obr.12074
41. Lee A., Rangan A., Allman-Farinelli M., Chen J., Grech A., McDonald S., Wilson A.. **A Rapid Review of Evidence: Discretionary Food and Drinks**. (2018.0)
42. **Anticipated Timelines for the Revision of the Australian Dietary Guidelines**
43. 43.
Food Standards Australia New Zealand
Risk Analysis in Food RegulationFSANZCanberra, Australia2013. *Risk Analysis in Food Regulation* (2013.0)
44. Saelens B.E., Glanz K., Sallis J.F., Frank L.D.. **Nutrition Environment Measures Study in restaurants (NEMS-R): Development and evaluation**. *Am. J. Prev. Med.* (2007.0) **32** 273-281. DOI: 10.1016/j.amepre.2006.12.022
45. Dickie S., Woods J., Machado P., Lawrence M.. **A novel food processing-based nutrition classification scheme for guiding policy actions applied to the Australian food supply**. *Front. Nutr.* (2023.0) **10** 1071356. DOI: 10.3389/fnut.2023.1071356
46. Whelan J., Millar L., Bell C., Russell C., Grainger F., Allender S., Love P.. **You Can’t Find Healthy Food in the Bush: Poor Accessibility, Availability and Adequacy of Food in Rural Australia**. *Int. J. Environ. Res. Public Health* (2018.0) **15**. DOI: 10.3390/ijerph15102316
47. Law K.K., Pulker C.E., Healy J.D., Pollard C.M.. **“Just So You Know, It Has Been Hard”: Food Retailers’ Perspectives of Implementing a Food and Nutrition Policy in Public Healthcare Settings**. *Nutrients* (2021.0) **13**. DOI: 10.3390/nu13062053
48. **Healthy Options WA Food and Nutrition Policy**
|
---
title: Integrating Structured and Unstructured EHR Data for Predicting Mortality by
Machine Learning and Latent Dirichlet Allocation Method
authors:
- Chih-Chou Chiu
- Chung-Min Wu
- Te-Nien Chien
- Ling-Jing Kao
- Chengcheng Li
- Chuan-Mei Chu
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001457
doi: 10.3390/ijerph20054340
license: CC BY 4.0
---
# Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method
## Abstract
An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient’s age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.
## 1. Introduction
The World Federation of Societies of Intensive and Critical Care Medicine defines an intensive care unit (ICU) as an organized system of care for critically ill patients that provides intensive and specialized medical and nursing care, enhanced monitoring capabilities, and multiple physiological organ support modality to sustain life during a period of multiple organ dysfunction syndrome (MODS) [1]. The hospital has established an intensive care unit (ICU) for patients with severe or life-threatening conditions. ICU mortality and costs are the highest of all hospital units [2]. It is difficult for medical and nursing staff to deal with rapidly changing patient conditions if there is not enough real-time information for clinicians to make accurate and timely decisions [3]. Different types of judgment errors can have many negative consequences, and incorrect decisions or delayed diagnosis can have a significant impact on patient prognosis, medical resource availability, and healthcare costs [4]. Recently, when the COVID-19 pandemic flooded intensive care units around the world, their significance was highlighted. In times such as these, more active research on how to manage scarce critical care resources is required to provide additional tools to support medical decision-making and effective clinical practice benchmarks [5]. In the United States, more than 5 million patients are admitted to the ICU annually, and $40\%$ of these patients die during their hospital stay, with $22\%$ spending their entire hospital stay in the ICU [6]. Predicting mortality in ICU patients is one of the most important tasks in critical care research, not only to aid health professionals in clinical decision-making, but also as a basis for managing hospital resource utilization. Patients admitted to the ICU require close and constant monitoring to prevent rapid deterioration of their health. Intensive monitoring through ICU equipment generates a large number of medical records, requiring an efficient and accurate data analysis system [7].
The electronic health record (EHR) is a digital version of the paper chart. Numerous researchers have utilized EHR database data in the past to predict patient mortality, admission time, disease diagnosis, disease onset, etc., to prevent and intervene in early disease in patients which is crucial to critical care. As an essential risk assessment tool, the predictive model has been developed and utilized in numerous healthcare fields. The Sequential Organ Failure Assessment (SOFA), a new Simplified Acute Physiology Score (SAPSII), and the Multiple Organ Dysfunction Score (MODS) have also been used widely in clinical practice to predict mortality [8,9,10]. Predictive models facilitate the early identification of patients at risk for a disease or event and provide effective intervention measures for those who are most likely to benefit from the identification of specific risk factors. Much research has been conducted to determine how data analysis and prediction can assist medical and nursing staff in the process of diagnosis and treatment to heighten alertness to the progression of patient condition [11,12,13,14,15]. The results of the statistical data’s predictive power derived from the basic vital signs and simple demographic data such as age save the most resources and are the most useful. Vital signs were chosen as features mainly because most vital signs can be easily measured using non-invasive equipment, and vital signs are the most basic health indicators that are easily understood by all healthcare professionals [16,17,18,19].
Unstructured data comprise $80\%$ of EHR data [20]. It is undeniable that overlooking the deficiencies of qualitative data in the EHR may not only result in the omission of key factors caused by the absence of handwritten diagnostic data, but may also result in the omission of clues in the initial judgement being overlooked or diminished. Although these variables can be used to partially predict the mortality of ICU patients, quantitative variables are utilized in the majority of these studies. After all, the existing statistical predictive modeling is relatively mature with respect to the processing of quantitative data, whereas distinctive challenges exist in the standardization and utilization of qualitative data [21,22,23].
Machine learning is a subfield of artificial intelligence concerned with teaching computers to learn from data and improve with experience. It focuses on the issue of how to design computer programs that can automatically improve output accuracy based on experience [24]. There has recently been an increase in the use of machine learning applications in clinical medicine. These include preclinical data processing, bedside diagnostic assistance, patient stratification, treatment decision-making, and early warning for primary and secondary prevention [25]. Machine learning can improve clinical decision-making in many ways, by providing early warning, facilitating diagnosis, conducting widespread screening, personalizing treatment, and assessing patient response to treatment. Many different fields and clinical applications are gradually adopting machine learning from mature preclinical scenarios [26,27].
The development of machine learning includes text mining, natural language processing and Latent Dirichlet Allocation (LDA) which are used to identify and extract information or relationships from unstructured data and have become popular techniques for literary analysis [28,29]. LDA is a Bayesian probability generation model in the field of natural language processing proposed by Blei et al. [ 30] that has several advantages for literature analysis. LDA is a powerful tool for processing massive amounts of data that can capture text-specific dimensions without relying on assumptions. Furthermore, it incorporates multiple steps of text analysis, such as data sampling with minimal human intervention to yield more realistic and objective topic modeling outcomes [31,32].
However, it takes a lot of time and money to process the unstructured data that make up medical big data. This is particularly so for the digital part, typically a vital component, presented in the large number of clinical notes made during treatment and hospital stay. In accordance with the rules of unstructured data processing, numbers are frequently removed to reduce their utility. By incorporating unstructured data as input, we are not using raw physiological data, but rather the perception and judgment of medical and nursing professionals in the form of free text annotations. These allow us to access higher-level concepts that are not present in the physiological data. The text data format is relatively consistent, and this allows circumvention of the LDA digital deletion limitation. This is the most noticeable feature of free text records, which contain information about patients’ admission to and diagnosis in the ICU. Data about observations and first signs of condition and diagnoses are added as soon as possible after admission of the patient to the ICU, with minimal interference from the earlier patient data. Clinicians can also use the topic obtained as a follow-up reference. Our recent study combined 16 structured variables and 10 topic modeling semi-structured variables from the Medical Information Mart for Intensive Care (MIMIC-III) dataset to predict mortality in ICU patients. The results show that semi-structured data contain useful information that can help clinicians make critical clinical decisions [33].
In this study, we utilized the MIMIC-III database to develop a model for predicting mortality in ICU patients. Our approach involved integrating structured data, which are basic and easily collected from ICU patients, with unstructured data derived from the initial clinical diagnosis of the patient’s physician at the time of admission. We used the LDA approach to topic modeling of diagnostic records and applied machine learning techniques to combine both structured and unstructured data to build a robust mortality risk prediction model. This model can provide patients, their families, and healthcare professionals with valuable additional information for making informed medical decisions. Our findings could have significant implications for improving patient outcomes and advancing critical care medicine.
## 2.1. Proposed Framework
Figure 1 depicts the framework of this study. The structured data collected after patients were admitted to the ICU was integrated (six vital sign measurements in the first 24 h, the Glasgow Coma Scale (GCS), and patient age) with unstructured data (initial clinical diagnosis records at ICU admission). The machine learning model was used to predict mortality of the ICU patient. Finally, five different metrics were used to assess predictive performance. The period of mortality in ICU patients is defined as follows:
## 2.2. Data Collection and Preprocessing
The rapid development of digital health systems has occurred in recent years. However, concerns surrounding personal privacy and security have made it difficult to integrate and apply this information to scientific research. To ensure the convenience and completeness of data collection, this study has focused on obtaining complete patient dynamic information from databases that are easier to obtain than ICU data. The data were obtained from MIMIC-III clinical database in our research. MIMIC-III uses integrated comprehensive clinical data from patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts [34]. The MIMIC-III database used contained information on 46,520 patients and 58,976 admission-related data items, including patient vital signs, drugs, laboratory measurement values, and observation records. There were 38,597 adult patients, $56\%$ of whom were male, and the median age was 65.8. The median of the length of admission was 6.9 days, the mortality rate during admission was $11.5\%$, and the median of the length of stay in the ICU was 2.1 days. Furthermore, the following data were generated per patient per stay in the intensive care unit: 6643 patient observation records, 83 patient medical document records, and 559 laboratory test result records. Table 1 shows the database compilation. The National Institutes of Health (NIH) online course was completed, as well as an exam for protecting human research participants and the submission of an access application (Certification Number: 35628530).
To reflect the universality of the analytical results and to ensure that they were comparable with the conclusions of the related literature, this study followed the patient selection principles of previous related studies and specific diseases in patients were not analyzed; instead, the data from all patients were used [18,32,35]. First, the inclusion of only the initial ICU admission and exclusion of all subsequent ICU readmissions ensured that the outcome was measured the same way for all patients. This highlighted the early predictive ability of the model and prevented possible information omissions when the dataset was separated for training and testing (12,456 admissions were deleted). Second, the subjects used in this study were all adults older than 16 years (7878 admissions were deleted). Lastly, only data from patients who stayed in the ICU for longer than 24 h were utilized (2138 admissions were deleted). In the case of patients who stayed in the ICU for at least one day, only data from their first day were considered. If multiple measurements had been taken on the same day, an average of the values was taken. In addition, the data preprocessing method of Guo et al. [ 36] was used for the processing of missing values in this study. Three-stage missing value processing was carried out and patients with more than $30\%$ missing variable values were excluded (8954 admissions were deleted) and a total of 27,550 participants were included in this study. Figure 2 depicts the extraction of data in their entirety.
In this study, information from the MIMIC-III database admission and chart events tables were used for the variable selection part. In reference to previous related studies [16,17,18,37], only six basic vital signs from the patient files were used: Heart Rate, Respiratory Rate, Systolic Blood Pressure, Diastolic Blood Pressure, Temperature, and Oxygen saturation, along with Glasgow Coma Scale and the patient’s age at admission as a predictor of variables in the first part. Topic model variables extracted from unstructured data of the initial diagnosis made by physicians when the patients were admitted to the hospital, were among the predictive variables in the second part.
## 2.3. Baseline Characteristics
Ultimately, the ICU records of 27,550 patients were utilized after the data in the MIMIC-III database had been preprocessed. Table 2 shows patient demographic information. The average age of the patients in this study was 64, of which $56\%$ were male, their average hospital stay was 10.39 days, and their average intensive care unit stay was 4.48 days. In addition, over $84\%$ of patients were admitted to the hospital for emergency care. Medicare insurance covered more than $50\%$ of patients. Table 2 also displays the statistical values of the eight structural variables utilized in the study.
Among these were the diagnosis in the initial clinical notes about the patient made by the physician. As shown in Table 3, the diagnosis field provides the clinician with a written record of the initial diagnosis on admission. The admitting clinician usually specifies a diagnosis and does not use system ontology. Diagnoses may be very useful (e.g., congestive heart failure\biventricular implantable cardioverter defibrillator placement) or extremely vague (e.g., fever). This text section can provide useful information about the condition of the patient on admission. The information in the diagnosis field from the Admission Table was used in this study and a machine learning model was used to investigate the impact of structured EHR data and unstructured data on ICU patient mortality. Structured EHR data included variables such as vital signs and lab tests, and clinical note content includes topic features extracted from clinical notes using the LDA method.
## 2.4. Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) is an unsupervised topic modeling algorithm that derives topics in a corpus. The model is a standard “bag of words” model, wherein each text item is viewed as a word frequency vector and the text is viewed as a set made up of various word groups [30]. Typically, an LDA topic generation model is built in three steps: First, a topic is extracted from the topic distribution for each text item. Second, a vocabulary corresponding to the extracted topics is taken from the vocabulary distribution. The steps are then repeated until every word in the text has been extracted. Because each text item contains multiple topics, several corresponding key words can be chosen for each topic. In other words, the same vocabulary can appear across multiple topics. Topic modeling methods mine significant topics from collected documents using probabilistic procedures and applications. As a result, by effectively processing a large amount of unstructured data in the text, the topic modeling method can help identify the latent semantics of complex articles [38,39]. LDA assumes that each document in the collection is created in two steps, the first by selecting a distribution of topics for that document, and the second by assigning a random topic and its corresponding distribution of words to each position in the document that may contain a word. This is repeated for the entire corpus. As a result, the main feature of LDA is that all documents share the same topic to varying degrees. Based on this theory, an LDA model can be applied to a set of documents using the Gibbs sampling algorithm to infer their underlying topics. The algorithm iterates over all the words in the document and calculates the most representative words for each topic. Each word can appear multiple times in the same document and can be repeated in different documents at the same time. At each iteration, the algorithm can modify the topic that best represents it, and after using Gibbs sampling with the training set, a model is built that produces a topic distribution for each document [40].
In a similar context-based textual analysis, probabilistic topic modeling conceptualizes a document as a collection of words derived from underlying thematic topics that define a probability distribution of words related to a topic, where the relative importance of each word in respective topics is defined by the conditional probability P(Wordi|Topicj) in category probability distribution. Because an article is a weighted mixture of multiple topics, its conditional probability can be determined, and file content is generated based on the proportion of words related to each topic. This matrix is decomposed by topic modeling approaches based on latent topic structures that link latent words to related documents. A precise solution to this inverted inference is not generally tractable and requires an iterative optimization solution such as that given by Gibbs sampling. The probabilistic LDA framework will interpret correlation structures as conditional probabilities P(Wordi|Topicj) and P(Topicj|Documentk), which are closely related to other dimensionality reduction techniques for providing low-rank data approximations. An insight into the underlying topic structure allows for a more convenient, efficient, and interpretable approach to information retrieval, classification, and document data exploration [41]. [ 1]P(Wordi|Documentk)=∑$j = 1$JP(Wordi|Topicj)×P(Topicj|Documentk) In the medical field, topic modeling research primarily focuses on the organization of clinical text, such as in newspapers and scientific literature, as well as clinical discharge records. However, recent studies have modeled laboratory results, claims data, and clinical concepts [42,43]. In this study, the aim is to learn the topic structure of clinical data through algorithms and apply it to clinical decision-making prediction. Unlike a top-down rule-based approach that isolates preconceived clinical concepts from electronic medical records, this bottom-up approach recognizes patterns in the data with more consistency. Additionally, in this paper, reference is made to an algorithm used in a previous study for the handling of non-quantified data [44,45], where Grid *Search is* used to confirm the best LDA model and tests multiple sets of topics. The LDA model was then applied to the ten topics derived from the results to categorize these key words into different topics. Any word that appears in a keyword set is related to the topic. Furthermore, certain words are more likely to appear under each topic, and there is a probability that each word will appear under respective topics. Figure 3 and Table 4 show the ten topics and keywords chosen for topic modeling in this study.
## 2.5. Machine Learning
In this study, the organized dataset was divided into two parts with $80\%$ of the data being used for training the model and the remaining $20\%$ for testing. Eight commonly used machine learning algorithms were used to establish the ICU mortality prediction model: Adaptive Boosting (AdaBoost), Bagging, Gradient Boosting, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Multilayer Perceptron (MLP), Support Vector Classification (SVC), eXtreme Gradient Boosting (XGBoost). All data mining tasks of this research were conducted using the Python programming language. Table 5 shows the 8 machine learning models with their specific parameters’ settings. The following sections provide detailed descriptions of the various machine learning classification algorithms.
## 2.6. The Synthetic Minority Oversampling Technique (SMOTE)
When the class distribution is highly skewed, machine learning problems become unbalanced. Unbalanced classification problems are prevalent in a variety of application domains and pose challenges for conventional learning algorithms [61]. *In* general, an imbalanced dataset can negatively affect the results of a model. *In* general, gold-standard datasets are unbalanced, which reduces model predictive ability [62]. In the evaluation of model performance, over- and underfitting are the most common issues. When a model has a high accuracy score during training but a low accuracy one during verification, overfitting has occurred. The greatest reduction in model overfitting can be achieved by increasing the size of the training set and decreasing the number of neural network layers. The failure of a model to classify data or make predictions during the training phase signifies underfitting [63]. SMOTE is a potent classification imbalance solution that produces consistent results across domains. The SMOTE algorithm adds synthetic data to the minority class to create a balanced dataset [61]. Class imbalance refers to the disparity between the classes of data used to train a predictive model, a prevalent issue that is not exclusive to medical data. Classification algorithms have a tendency to favor the majority class when it has significantly fewer observations than the class with negative outcome. Predictive performance can be improved by the manipulation of data, algorithms, or both [64]. The methodology involves the under- and oversampling of larger and smaller samples.
Table 6 displays the descriptive statistics for the data used in this study. The data in the table indicate a significant imbalance between the ratio of patient survival and mortality. Because these unbalanced datasets frequently produce inaccurate model prediction [65], the addition of minority class samples, or the deletion of majority class samples, is frequently performed to correct this [15]. The Synthetic Minority Oversampling Technique (SMOTE) randomly generates new minority class samples from the nearest neighbor line connecting the minority class samples and the technique is extensively used to process skewed data [63,66]. In this study, SMOTE technology was used to increase the sample size for the side with fewer samples to balance the data [15]. This was necessary because the number of samples of patients dying in the ICU was much smaller than the number of samples of patients surviving. In other words, a synthetic minority sampling technique was used to preprocess extremely unbalanced datasets.
In this study, a range of SMOTE methods of varying percentages was used to examine a selection of cases. A fresh training dataset was produced based on the information in Table 6. Non-survivors’ samples were increased by a factor of eight or nine using the SMOTE technology on a dataset of patients who died within 30 days of admission, from 3028 patients to 24,224 patients. This increased the proportion of the minority group in the baseline dataset from $10.99\%$ to $49.69\%$.
## 2.7. Performance Evaluation
To make a thorough comparison of the impact of the integration of structured and unstructured data on the prediction of mortality in ICU patients, in this study, five different metrics were chosen as evaluation tools for modeling. These included AUROC, Precision, Recall, F1-Score, and Accuracy. Appendix A shows the confusion matrix.
## 3.1. Prediction of Mortality in ICU
The k-fold cross-validation method was used to assess the performance of the model after training. The dataset was initially divided into k sections, with each section containing instances of equal size. The final measure of performance was the average of all test results across all components. This method has the benefit of training and validating all instances of the entire dataset, resulting in more accurate predictions with less bias. However, it is computationally costly, and validation is time-consuming. The model was constructed using 10-fold cross-validation, which has been utilized in a number of healthcare and medical studies [67,68]. In this study, the patient’s mortality was predicted at 3 days, 30 days, and 365 days after admission based on data collected within 24 h of admission. AUROC, which compares the true-positive rate to the false-positive rate, is the most prevalent metric used to evaluate the performance of diagnostic tools. Table 7 lists the eight distinct machine learning methods employed in this study, as well as AUROC for the ICU mortality prediction task across three all time periods. Our AUROC findings revealed that the mortality rate in 3 days can exceed $80\%$, and in 30 days and 365 days can exceed $75\%$. The results indicated that the best AUROC is $88.20\%$ in our research, and that could accurately predict patient death within 3 days at 24 h after admission. Compared with using structured quantitative data alone, adding unstructured data makes the model increase by 2–$5\%$ on average in AUROC, which is a great improvement in the prediction of mortality of patients in intensive care units. Figure 4 shows that ICU data can be used to predict 3-day mortality with better precision. This clearly shows that the model developed in this study can predict the vital status of patients with great precision. Gradient Boosting, as indicated by the data in the chart, is the best model for predicting ICU patient mortality across all time periods.
For a comprehensive understanding of the impact of unstructured data on the prediction of mortality in ICU patients, the prediction results of models using only structured data within 24 h of ICU patient admission were compared with those using both structured and unstructured data. As illustrated in Figure 4, the pertinent prediction results are sorted. Within 24 h of ICU patient admission, the ROC of model prediction results using both structured and unstructured data is greater than that predicted using only structured data across all time periods. Table 7 also demonstrates that Gradient Boosting has a higher AUROC than other machine learning algorithms, regardless of ICU patient mortality across all time periods. Moreover, the prediction accuracy of the model made using both structured and unstructured data, within 24 h of patient admission to the ICU, is generally higher than that of the model using structured data alone. Indeed, basic observations and judgments of the patient at the time are of reference value and will significantly influence the accuracy of constructed model predictions. Overall, this indicates that a model constructed using both structured and unstructured data from ICU patients after admission can predict early patient death after admission with considerable accuracy. By incorporating unstructured data as input, it is possible to gain access to higher-level concepts not present in physiological data.
We summarize the use of four different metrics (Precision, Recall, F1-Score, and Accuracy) for a more complete picture of the differences in prediction accuracy of models constructed using different machine learning methods in Appendix B, which also show an evaluation of the structured ICU patient basic vital signs within 24 h of admission. These basic observations and judgement depend on whether or not the model was made using unstructured data about patient condition collected at time of admission to the ICU to predict time of patient death. According to the data in the table, the results obtained by using both structured and unstructured data of ICU patients after admission (and the eight different machine learning methods) are slightly better at predicting ICU patient mortality than those using structured data alone under different evaluation metrics. Furthermore, XGBoost has the highest prediction accuracy ($97.23\%$) of the algorithms used, followed by LightGBM ($95.61\%$), and Bagging has the highest prediction recall ($95.13\%$)
## 3.2. Feature Importance
The most promising features are typically chosen, and the unimportant ones are usually eliminated using feature selection methods. The feature importance score reflects the information gained by each feature during construction of the decision tree [69]. An advantage of using Gradient *Boosting is* that, once the prediction model has been constructed, the variable importance can be obtained with relative ease by sorting the calculated variable importance scores. The feature importance framework ranks input variables according to their contribution to the predictive model and gives insight into which features are crucial for the task [70]. The more a variable is utilized in the decision tree, the more important it will become. In this study, the importance of each feature is determined by applying a feature importance scoring method to a model trained with gradient boosting. In addition, a percentage rating is provided for how frequently each feature is used to determine the output label. Relevant research notes [71,72] provide additional information on how the Gradient Boosting method determines the significance of input variables. The variance importance within 24 h of ICU patient admission is outlined in Table 8.
According to Table 8, the Glasgow Coma Scale (X7), Age (X8), and Heart Rate (X1) are relatively important variables for prediction of ICU patient mortality using structural data from ICU patients recorded within 24 h of admission. The addition of initial clinical diagnosis records (unstructured data) produced variable results about the feature significance of patient mortality prediction. In addition to the Glasgow Coma Scale (X7), chest pain (TOPIC6) and coronary artery disease (TOPIC1) were also relatively significant. In the model constructed using data from ICU patients within 24 h of admission, bleed mass (TOPIC7) was a relatively important variable for 3-day mortality, altered mental status (TOPIC10) for 30-day mortality, and sepsis (TOPIC 8) for 365-day mortality. Important variables to consider are the Glasgow Coma Scale (X7), chest pain (TOPIC6), and coronary artery disease (TOPIC1), regardless of the mortality prediction for different ICU patient time periods.
## 4.1. Principal Findings
Previous studies have focused on building predictive models using quantitative variables from EHR databases to predict mortality, length of stay, and disease diagnosis in ICU patients. However, such studies have largely ignored the potential value of qualitative data due to challenges in standardization and utilization. By overlooking unstructured data in EHR, clinicians may miss critical information and clues provided by the physician’s initial observations. To fully utilize unstructured data, this study employs NLP techniques, specifically the LDA model, to analyze clinical notes. Our study integrates structured data, such as basic vital signs, with unstructured data, derived from physicians’ initial clinical diagnoses at the time of ICU admission, to predict patient mortality. Additionally, our model successfully identifies significant variables for predicting clinical outcomes during different ICU periods. We hope that our analysis results can enhance medical staff’s understanding of patient conditions, optimize medical resource allocation, and provide patients, families, and medical staff with more information for informed decision-making. The main contributions of this study include: [1] investigating the impact of integrating structured and unstructured clinical records on ICU patient outcomes using a machine learning model, and [2] predicting patient mortality and risk factors to inform potential preventive measures in medical practice.
In previous studies, researchers have achieved comparable or even superior accuracy by employing excessive numbers of features. For instance, Xia et al. [ 13] used 50 features to achieve an AUROC of 0.85, and Liu et al. [ 73] employed 99 features to achieve an AUROC of 0.78. However, in our study, we achieved an accuracy of $97\%$ and an AUROC of 0.88 for the mortality model using only six vital signs, the GCS, age, and the initial written clinical records and diagnosis made on patient admission to the ICU. We utilized eight commonly used machine learning classification algorithms, each with a known degree of accuracy in predicting ICU patient mortality. Our AUROC findings revealed that the mortality rate in 3 days can exceed $80\%$, and in 30 days and 365 days can exceed $75\%$. Our study found that Gradient Boosting provided the most accurate prediction model. XGBoost had the highest prediction accuracy, indicating that our proposed method could predict mortality in ICU patients very well. Our results also demonstrated that the initial written notes of clinical observations and diagnoses made at the time of patient admission to the ICU contain a wealth of useful information that can aid ICU medical and nursing staff in making crucial clinical decisions. Furthermore, our study only utilized structured and unstructured data of ICU patients within 24 h of admission; our prediction model was found to be more suitable for predicting short-term mortality, as it could predict 3-day mortality with more accuracy than 30-day and 365-day mortality.
Using the LDA method, the analysis of unstructured data recorded by ICU admission clinicians during initial observation and diagnosis yielded significant results. Other important variables to consider in addition to the Glasgow Coma Scale (X7) are patient age at admission (X8), chest pain (TOPIC6) and coronary artery disease (TOPIC1). Overall, the LDA method can extract significant medical characteristics from patient topics. Furthermore, these medical characteristics can be utilized in a variety of situations to provide personalized clinical advice to individual patients [35]. In addition, various imputation techniques were applied to the dataset to determine the optimal solution for the issue at hand. Because the number of ICU patients who died in this study was significantly lower than the number of those who survived, the majority-to-minority ratio was 97 to 3 (3-day mortality) and the data demonstrate an extremely high category imbalance. SMOTE technology was used to increase the sample size of the side with the smaller number of samples to achieve data parity.
## 4.2. Limitations
To begin, all the data used in this paper came from a large retrospective clinical database, and the findings were generalized across groups of patients rather than specific people. To ensure the thorough collection of relevant data, this study only took into account complete patient dynamic information from databases where ICU data were easy to obtain. This study used MIMIC-III data collected at Beth Israel Deaconess Medical Center in Boston, Massachusetts. Future studies should evaluate data collected from more medical facilities across a wider geographic area. Because this study was limited to ICU medical data accumulated by a large medical facility in a big city, the findings cannot be safely applied to ICU patients in smaller medical facilities. More comprehensive results and verification could be obtained by comparing these results with those from data obtained from rural or other general small medical facilities.
Second, the model’s performance may be undermined in other critical care settings due to a lack of high-quality care notes for a large number of patients. The information entered by physicians during patient consultations is valuable for disease and treatment research. Because these notes are highly telegraphic and contain many spelling errors, inconsistent punctuation, and non-standard word order, the existing natural language analysis tools struggle to process them [74]. Common spelling errors and other noise in medical notes can affect interpretation quality, resulting in counterintuitive results, which is a limitation and challenge for related research [5]. Furthermore, because this study is retrospective, conclusions about predictive algorithm performance in a hospital setting cannot be drawn. Future studies could evaluate and analyze these constraints in greater depth to make this kind of study more objective and thorough.
## 5. Conclusions
As the COVID-19 pandemic continues to strain ICUs worldwide, the critical importance of such facilities has become increasingly apparent. Consequently, there is a pressing need for more active research to manage scarce critical care resources and provide additional tools to support medical decision-making and effective benchmarks for clinical practice. In this study, not only using structured data from ICU patients’ first 24 h (including six vital sign measurements, the GCS, and patient age at admission), but also focusing on unstructured data from initial state observations and diagnoses made upon admission. The effectiveness of using LDA method and different machine learning technologies in the prediction of ICU patient mortality was discussed. These unstructured data contained a wealth of information that could effectively assist in later clinical decision making. However, the model developed in this study primarily focused on predicting ICU patient mortality, and further investigation is warranted to explore other clinical tasks such as length of stay, complication, and disease prediction. Moreover, it is evident that physician-produced clinical care records may capture the concepts required for mortality prediction with greater pertinence and accuracy than is currently achievable using traditional statistical techniques. Therefore, it is recommended that four directions be pursued for further research.
First, clinicians should integrate a large amount of information to evaluate and predict the current and future status of the patient, making the environment of critical care cognitively more demanding. It is essential to gain a comprehensive understanding of the specific need for clinical ICU predictive systems, the types and properties of predictions that are valued by the clinician, and the optimal time scale for such predictions. Despite the fact that findings show that our proposed method produced good predictive results for ICU patient mortality, additional research is required to evaluate its benefits on clinical care and its effectiveness to elucidate the prediction principles.
Second, the data in this study were restricted to patients admitted to the ICU for the first time and exclude patient readmission records and reports. Reduction in readmissions has long been identified by the United States government as a priority area for healthcare policy reform. Hospital readmission has also been promoted as a metric that can aid in the reduction in healthcare cost. More types of readmission research, such as the predictive performance of readmission models, could be conducted, as well as the impact of patient-level predictors on readmission, and studies of the relationship between healthcare environment quality and readmission [75,76]. Because ICU patient readmission frequently results in excessive use of medical resources and financial risk to medical facilities, analyzing the morbidity and mortality of readmitted ICU patients will benefit both patients and medical facilities [77]. Future research could collect data from multiple ICU admissions and make a comprehensive evaluation of time-series issues and also provide additional levels of analytical results as a reference for patients, medical and nursing staff, and the families of the patients.
Third, because the MIMIC-III database contains accumulated medical data from ICU patients at the medical facilities of a large city, the results of the analysis cannot be safely applied to ICU patients at smaller medical facilities. If follow-up studies are made using ICU patient data from rural and other general medical facilities as a comparison, more comprehensive results and verification would be available. Patient data should be collected from different medical centers, including outpatient, inpatient, and emergency facilities, as well as ICUs. This will allow a more comprehensive model to be constructed for evaluation and expand applicability. In addition, classification and analysis could be conducted on the basis of various diseases, such as diabetes, and disciplines such as chest medicine.
Finally, unstructured or semi-structured data account for more than $80\%$ of the information in electronic health records. If qualitative information is ignored, clues and key factors may be missed if the initial observation-based judgments of the physician are not taken into account. In this study, unstructured data from the initial state observation and diagnosis made by physicians at ICU admission were added to the commonly used structural variables in the traditional ICU prediction model and the LDA method was used for model construction. Future research can also collect and integrate various types of unstructured data, such as the hospital consultation process, the needs of the patient, and their social media message content, to improve prediction accuracy of the model. Other new topic modeling tools, such as BERT, can also be used to assess the power of the proposed prediction plan.
## References
1. Marshall J.C., Bosco L., Adhikari N.K., Connolly B., Diaz J.V., Dorman T., Fowler R.A., Meyfroidt G., Nakagawa S., Pelosi P.. **What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine**. *J. Crit. Care* (2017) **37** 270-276. DOI: 10.1016/j.jcrc.2016.07.015
2. Mahbub M., Srinivasan S., Danciu I., Peluso A., Begoli E., Tamang S., Peterson G.D.. **Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients**. *PLoS ONE* (2022) **17**. PMID: 34990485
3. Chen W., Long G., Yao L., Sheng Q.Z.. **AMRNN: Attended multi-task recurrent neural networks for dynamic illness severity prediction**. *World Wide Web* (2019) **23** 2753-2770. DOI: 10.1007/s11280-019-00720-x
4. Romana S., Bernhard F.. **Iatrogenic events contributing to paediatric intensive care unit admission**. *Swiss Med. Wkly.* (2021) **151** 7
5. Caicedo-Torres W., Gutierrez K.. **ISeeU2: Visually interpretable mortality prediction inside the ICU using deep learning and free-text medical notes**. *Expert Syst. Appl.* (2022) **202** 117190. DOI: 10.1016/j.eswa.2022.117190
6. Romano M.. **The Role of Palliative Care in the Cardiac Intensive Care Unit**. *Healthcare* (2019) **7**. DOI: 10.3390/healthcare7010030
7. El-Rashidy N., El-Sappagh S., Abuhmed T., Abdelrazek S., El-Bakry H.M.. **Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model**. *IEEE Access* (2020) **8** 133541-133564. DOI: 10.1109/ACCESS.2020.3010556
8. Vincent J.L., Moreno R., Takala J., Willatts S., De Mendonça A., Bruining H., Reinhart C.K., Suter P., Thijs L.G.. **The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine**. *Intensive Care Med.* (1996) **22** 707-710. DOI: 10.1007/BF01709751
9. Legall J.R., Lemeshow S., Saulnier F.. **A new simplified acute physiology score (SAPS-II) based on a European North-American multicenter study**. *Jama J. Am. Med. Assoc.* (1993) **270** 2957-2963. DOI: 10.1001/jama.1993.03510240069035
10. Baue A.E., Durham R., Faist E.. **Systemic inflammatory response syndrome (SIRS), multiple organ dysfunction syndrome (MODS), multiple organ failure (MOF): Are we winning the battle?**. *Shock* (1998) **10** 79-89. DOI: 10.1097/00024382-199808000-00001
11. Ibrahim Z.M., Wu H.H., Hamoud A., Stappen L., Dobson R.J.B., Agarossi A.. **On classifying sepsis heterogeneity in the ICU: Insight using machine learning**. *J. Am. Med. Inform. Assoc.* (2020) **27** 437-443. DOI: 10.1093/jamia/ocz211
12. Darabi S., Kachuee M., Fazeli S., Sarrafzadeh M.. **TAPER: Time-Aware Patient EHR Representation**. *IEEE J. Biomed. Health Inform.* (2020) **24** 3268-3275. DOI: 10.1109/JBHI.2020.2984931
13. Gong M.G., Pan K., Xie Y., Qin A.K., Tang Z.D.. **Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition**. *Neural Netw.* (2020) **125** 131-141. DOI: 10.1016/j.neunet.2020.02.001
14. Sheikhalishahi S., Balaraman V., Osmani V.. **Benchmarking machine learning models on multi-centre eICU critical care dataset**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0235424
15. Loreto M., Lisboa T., Moreira V.P.. **Early prediction of ICU readmissions using classification algorithms**. *Comput. Biol. Med.* (2020) **118** 8. DOI: 10.1016/j.compbiomed.2020.103636
16. Baker S., Xiang W., Atkinson I.. **Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: A hybrid neural network approach**. *Sci. Rep.* (2020) **10** 1-12. DOI: 10.1038/s41598-020-78184-7
17. Davidson S., Villarroel M., Harford M., Finnegan E., Jorge J., Young D., Watkinson P., Tarassenko L.. **Day-to-day progression of vital-sign circadian rhythms in the intensive care unit**. *Crit. Care* (2021) **25** 13. DOI: 10.1186/s13054-021-03574-w
18. Alghatani K., Ammar N., Rezgui A., Shaban-Nejad A.. **Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation**. *JMIR Med. Inform.* (2021) **9** e21347. DOI: 10.2196/21347
19. Sarang B., Bhandarkar P., Raykar N., O’Reilly G.M., Soni K.D., Wärnberg M.G., Khajanchi M., Dharap S., Cameron P., Howard T.. **Associations of On-arrival Vital Signs with 24-hour In-hospital Mortality in Adult Trauma Patients Admitted to Four Public University Hospitals in Urban India: A Prospective Multi-Centre Cohort Study**. *Inj. Int. J. Care Inj.* (2021) **52** 1158-1163. DOI: 10.1016/j.injury.2021.02.075
20. Hashir M., Sawhney R.. **Towards unstructured mortality prediction with free-text clinical notes**. *J. Biomed. Inform.* (2020) **108** 103489. DOI: 10.1016/j.jbi.2020.103489
21. Tootooni M.S., Pasupathy K.S., Heaton H.A., Clements C.M., Sir M.Y.. **CCMapper: An adaptive NLP-based free-text chief complaint mapping algorithm**. *Comput. Biol. Med.* (2019) **113** 13. DOI: 10.1016/j.compbiomed.2019.103398
22. Ye J.C., Yao L., Shen J.H., Janarthanam R., Luo Y.. **Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes**. *BMC Med. Inform. Decis. Mak.* (2020) **20**. DOI: 10.1186/s12911-020-01318-4
23. Zhang D.D., Yin C.C., Zeng J.C., Yuan X.H., Zhang P.. **Combining structured and unstructured data for predictive models: A deep learning approach**. *BMC Med. Inform. Decis. Mak.* (2020) **20**. DOI: 10.1186/s12911-020-01297-6
24. Mitchell T.. *Machine Learning* (1997) **1**
25. Adlung L., Cohen Y., Mor U., Elinav E.. **Machine learning in clinical decision making**. *Med* (2021) **2** 642-665. DOI: 10.1016/j.medj.2021.04.006
26. Rajkomar A., Dean J., Kohane I.. **Machine learning in medicine**. *N. Engl. J. Med.* (2019) **380** 1347-1358. DOI: 10.1056/NEJMra1814259
27. Purushotham S., Meng C.Z., Che Z.P., Liu Y.. **Benchmarking deep learning models on large healthcare datasets**. *J. Biomed. Inform.* (2018) **83** 112-134. DOI: 10.1016/j.jbi.2018.04.007
28. Cheng X., Cao Q., Liao S.S.. **An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation**. *J. Inf. Sci.* (2022) **48** 304-320. DOI: 10.1177/0165551520954674
29. Xue J., Chen J.X., Chen C., Zheng C.D., Li S.J., Zhu T.S.. **Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0239441
30. Blei D.M., Ng A.Y., Jordan M.I.. **Latent dirichlet allocation**. *J. Mach. Learn. Res.* (2003) **3** 993-1022
31. Breuninger T.A., Wawro N., Breuninger J., Reitmeier S., Clavel T., Six-Merker J., Pestoni G., Rohrmann S., Rathmann W., Peters A.. **Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation**. *Microbiome* (2021) **9** 61. DOI: 10.1186/s40168-020-00969-9
32. Gangavarapu T., Jayasimha A., Krishnan G.S., Kamath S.S.. **Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes**. *Knowl. Based Syst.* (2020) **190** 105321. DOI: 10.1016/j.knosys.2019.105321
33. Chiu C.C., Wu C.M., Chien T.N., Kao L.J., Qiu J.T.. **Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques**. *Healthcare* (2022) **10**. DOI: 10.3390/healthcare10061087
34. Johnson A.E., Pollard T.J., Shen L., Lehman L.W.H., Feng M., Ghassemi M., Moody B., Szolovits P., Anthony Celi L., Mark R.G.. **MIMIC-III, a freely accessible critical care database**. *Sci. Data* (2016) **3** 160035. DOI: 10.1038/sdata.2016.35
35. Yu R., Zheng Y., Zhang R., Jiang Y., Poon C.C.Y.. **Using a Multi-Task Recurrent Neural Network With Attention Mechanisms to Predict Hospital Mortality of Patients**. *IEEE J. Biomed. Health Inf.* (2020) **24** 486-492. DOI: 10.1109/JBHI.2019.2916667
36. Guo C.H., Lu M.L., Chen J.F.. **An evaluation of time series summary statistics as features for clinical prediction tasks**. *BMC Med. Inform. Decis. Mak.* (2020) **20**. DOI: 10.1186/s12911-020-1063-x
37. Sayed M., Riano D., Villar J.. **Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10173824
38. Kozlowski D., Semeshenko V., Molinari A.. **Latent Dirichlet allocation model for world trade analysis**. *PLoS ONE* (2021) **16**. DOI: 10.1371/journal.pone.0245393
39. Li Y., Rapkin B., Atkinson T.M., Schofield E., Bochner B.H.. **Leveraging Latent Dirichlet Allocation in processing free-text personal goals among patients undergoing bladder cancer surgery**. *Qual. Life Res.* (2019) **28** 1441-1455. DOI: 10.1007/s11136-019-02132-w
40. Celard P., Vieira A.S., Iglesias E.L., Borrajo L.. **LDA filter: A Latent Dirichlet Allocation preprocess method for Weka**. *PLoS ONE* (2020) **15**. DOI: 10.1371/journal.pone.0241701
41. Chen J.H., Goldstein M.K., Asch S.M., Mackey L., Altman R.B.. **Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets**. *J. Am. Med. Inform. Assoc.* (2017) **24** 472-480. DOI: 10.1093/jamia/ocw136
42. Pivovarov R., Perotte A.J., Grave E., Angiolillo J., Wiggins C.H., Elhadad N.. **Learning probabilistic phenotypes from heterogeneous EHR data**. *J. Biomed. Inform.* (2015) **58** 156-165. DOI: 10.1016/j.jbi.2015.10.001
43. Choi Y., Chiu C.Y.-I., Sontag D.. **Learning low-dimensional representations of medical concepts**. *AMIA Summits Transl. Sci. Proc.* (2016) **2016** 41-50. PMID: 27570647
44. Gabriel R.A., Kuo T.-T., McAuley J., Hsu C.-N.. **Identifying and characterizing highly similar notes in big clinical note datasets**. *J. Biomed. Inform.* (2018) **82** 63-69. DOI: 10.1016/j.jbi.2018.04.009
45. Teng F., Ma Z., Chen J., Xiao M., Huang L.F.. **Automatic Medical Code Assignment via Deep Learning Approach for Intelligent Healthcare**. *IEEE J. Biomed. Health Inform.* (2020) **24** 2506-2515. DOI: 10.1109/JBHI.2020.2996937
46. Kim D.H., Choi J.Y., Ro Y.M.. **Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection**. *Comput. Biol. Med.* (2015) **63** 238-250. DOI: 10.1016/j.compbiomed.2014.09.006
47. Lee Y.W., Choi J.W., Shin E.H.. **Machine learning model for predicting malaria using clinical information**. *Comput. Biol. Med.* (2021) **129** 104151. DOI: 10.1016/j.compbiomed.2020.104151
48. Ali S., Majid A., Javed S.G., Sattar M.. **Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data**. *Comput. Biol. Med.* (2016) **73** 38-46. DOI: 10.1016/j.compbiomed.2016.04.002
49. Sarmah C.K., Samarasinghe S.. **Microarray gene expression: A study of between-platform association of Affymetrix and cDNA arrays**. *Comput. Biol. Med.* (2011) **41** 980-986. DOI: 10.1016/j.compbiomed.2011.08.007
50. Ramos-Gonzalez J., Lopez-Sanchez D., Castellanos-Garzon J.A., de Paz J.F., Corchado J.M.. **A CBR framework with gradient boosting based feature selection for lung cancer subtype classification**. *Comput. Biol. Med.* (2017) **86** 98-106. DOI: 10.1016/j.compbiomed.2017.05.010
51. Song J.Z., Liu G.X., Jiang J.Q., Zhang P., Liang Y.C.. **Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm**. *Int. J. Mol. Sci.* (2021) **22**. DOI: 10.3390/ijms22020939
52. Li L.J., Lin Y.K., Yu D.X., Liu Z.Y., Gao Y.J., Qiao J.P.. **A Multi-Organ Fusion and LightGBM Based Radiomics Algorithm for High-Risk Esophageal Varices Prediction in Cirrhotic Patients**. *IEEE Access* (2021) **9** 15041-15052. DOI: 10.1109/ACCESS.2021.3052776
53. Cuadrado-Godia E., Jamthikar A.D., Gupta D., Khanna N.N., Araki T., Maniruzzaman M., Saba L., Nicolaides A., Sharma A., Omerzu T.. **Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach**. *Comput. Biol. Med.* (2019) **108** 182-195. DOI: 10.1016/j.compbiomed.2019.03.020
54. Ergun U., Serhatioglu S., Hardalac F., Guler I.. **Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression**. *Comput. Biol. Med.* (2004) **34** 389-405. DOI: 10.1016/S0010-4825(03)00085-4
55. Kavitha M.S., Kurita T., Ahn B.C.. **Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques**. *Comput. Biol. Med.* (2018) **94** 55-64. DOI: 10.1016/j.compbiomed.2018.01.005
56. Guler E.C., Sankur B., Kahya Y.P., Raudys S.. **Visual classification of medical data using MLP mapping**. *Comput. Biol. Med.* (1998) **28** 275-287. DOI: 10.1016/S0010-4825(98)00010-9
57. Nanayakkara S., Fogarty S., Tremeer M., Ross K., Richards B., Bergmeir C., Xu S., Stub D., Smith K., Tacey M.. **Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study**. *PLoS Med.* (2018) **15**. DOI: 10.1371/journal.pmed.1002709
58. Akbari G., Nikkhoo M., Wang L., Chen C.P., Han D.S., Lin Y.H., Chen H.B., Cheng C.H.. **Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach**. *Sensors* (2021) **21**. DOI: 10.3390/s21124017
59. Hou N., Li M., He L., Xie B., Wang L., Zhang R., Yu Y., Sun X., Pan Z., Wang K.. **Predicting 30-days mortality for MIMIC-III patients with sepsis-3: A machine learning approach using XGboost**. *J. Transl. Med.* (2020) **18** 462. DOI: 10.1186/s12967-020-02620-5
60. Luo X.Q., Yan P., Duan S.B., Kang Y.X., Deng Y.H., Liu Q., Wu T., Wu X.. **Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury**. *Front. Med.* (2022) **9** 853102. DOI: 10.3389/fmed.2022.853102
61. Raghuwanshi B.S., Shukla S.. **Classifying imbalanced data using SMOTE based class-specific kernelized ELM**. *Int. J. Mach. Learn. Cybern.* (2021) **12** 1255-1280. DOI: 10.1007/s13042-020-01232-1
62. Zhang Y., Jiang Z.W., Chen C., Wei Q.Q., Gu H.M., Yu B.. **DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier**. *Interdiscip. Sci. Comput. Life Sci.* (2022) **14** 311-330. DOI: 10.1007/s12539-021-00488-7
63. Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P.. **SMOTE: Synthetic minority over-sampling technique**. *J. Artif. Intell. Res.* (2002) **16** 321-357. DOI: 10.1613/jair.953
64. Mpanya D., Celik T., Klug E., Ntsinjana H.. **Machine learning and statistical methods for predicting mortality in heart failure**. *Heart Fail. Rev.* (2021) **26** 545-552. DOI: 10.1007/s10741-020-10052-y
65. Javan S.L., Sepehri M.M., Javan M.L., Khatibi T.. **An intelligent warning model for early prediction of cardiac arrest in sepsis patients**. *Comput. Methods Programs Biomed.* (2019) **178** 47-58. DOI: 10.1016/j.cmpb.2019.06.010
66. Blagus R., Lusa L.. **Joint use of over-and under-sampling techniques and cross-validation for the development and assessment of prediction models**. *BMC Bioinform.* (2015) **16**. DOI: 10.1186/s12859-015-0784-9
67. Liu B., Fang L., Liu F., Wang X., Chen J., Chou K.-C.. **Identification of real microRNA precursors with a pseudo structure status composition approach**. *PLoS ONE* (2015) **10**. DOI: 10.1371/journal.pone.0121501
68. Liu B., Fang L., Liu F., Wang X., Chou K.-C.. **iMiRNA-PseDPC: MicroRNA precursor identification with a pseudo distance-pair composition approach**. *J. Biomol. Struct. Dyn.* (2016) **34** 223-235. DOI: 10.1080/07391102.2015.1014422
69. Upadhyay D., Manero J., Zaman M., Sampalli S.. **Gradient Boosting Feature Selection With Machine Learning Classifiers for Intrusion Detection on Power Grids**. *IEEE Trans. Netw. Serv. Manag.* (2021) **18** 1104-1116. DOI: 10.1109/TNSM.2020.3032618
70. Adler A.I., Painsky A.. **Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection**. *Entropy* (2022) **24**. DOI: 10.3390/e24050687
71. Hastie T., Tibshirani R., Friedman J.. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction* (2001)
72. Friedman J.H.. **Greedy function approximation: A gradient boosting machine**. *Ann. Stat.* (2001) **29** 1189-1232. DOI: 10.1214/aos/1013203451
73. Liu D., Wu Y.L., Li X., Qi L.. **Medi-Care AI: Predicting medications from billing codes via robust recurrent neural networks**. *Neural Netw.* (2020) **124** 109-116. DOI: 10.1016/j.neunet.2020.01.001
74. Savkov A., Carroll J., Koeling R., Cassell J.. **Annotating patient clinical records with syntactic chunks and named entities: The Harvey Corpus**. *Lang. Resour. Eval.* (2016) **50** 523-548. DOI: 10.1007/s10579-015-9330-7
75. Qiu L.F., Kumar S., Sen A., Sinha A.. **Impact of the Hospital Readmission Reduction Program on hospital readmission and mortality: An economic analysis**. *Prod. Oper. Manag.* (2022) **31** 2341-2360. DOI: 10.1111/poms.13724
76. Senot C.. **Continuity of care and risk of readmission: An investigation into the healthcare journey of heart failure patients**. *Prod. Oper. Manag.* (2019) **28** 2008-2030. DOI: 10.1111/poms.13027
77. Lin Y.W., Zhou Y.Q., Faghri F., Shawl M.J., Campbell R.H.. **Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long shortterm memory**. *PLoS ONE* (2019) **14**. PMID: 31283759
|
---
title: Comparison of Frailty Assessment Tools for Older Thai Individuals at the Out-Patient
Clinic of the Family Medicine Department
authors:
- Pimonpan Rattanapattanakul
- Adchara Prommaban
- Peerasak Lerttrakarnnon
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001464
doi: 10.3390/ijerph20054020
license: CC BY 4.0
---
# Comparison of Frailty Assessment Tools for Older Thai Individuals at the Out-Patient Clinic of the Family Medicine Department
## Abstract
This study evaluated the validity of the screening tools used to evaluate the frailty status of older Thai people. A cross-sectional study of 251 patients aged 60 years or more in an out-patient department was conducted using the Frailty Assessment Tool of the Thai Ministry of Public Health (FATMPH) and the Frail Non-Disabled (FiND) questionnaire, and the results were compared with Fried’s Frailty Phenotype (FFP). The validity of the data acquired using each method was evaluated by examining their sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s kappa coefficient. Most of the participants were female ($60.96\%$), and most were between 60 and 69 years old ($65.34\%$). The measured prevalences of frailty were $8.37\%$, $17.53\%$, and $3.98\%$ using FFP, FATMPH, and FiND tools, respectively. FATMP had a sensitivity of $57.14\%$, a specificity of $86.09\%$, a PPV of $27.27\%$, and an NPV of $95.65\%$. FiND had a sensitivity of $19.05\%$, a specificity of $97.39\%$, a PPV of $40.00\%$, and an NPV of $92.94\%$. The results of the Cohen’s kappa comparison of these two tools and FFP were 0.298 for FATMPH and 0.147 for FiND. The predictive values of both FATMPH and FiND were insufficient for assessing frailty in a clinical setting. Additional research on other frailty tools is necessary to improve the accuracy of frailty screening in the older population of Thailand.
## 1. Introduction
Frailty is defined as a reduction in the ability to cope with everyday or acute stressors, particularly among older adults [1]. Frailty results in an increased vulnerability brought about by age-associated declines in physiological reserves and functioning across multiple organ systems [1]. The consequences of this condition heighten an individual’s susceptibility to increased dependency and vulnerability, as well as to an increased risk of death [1,2]. The health care system is affected by increases in health care needs, admissions to hospital, and admissions to long-term care. However, frailty is a dynamic process which can emerge from pre-frail or robust statuses [3]. Validated assessment tools and appropriate interventions are important to reduce morbidity and mortality. A systematic review and meta-analysis of a survey of the models used to evaluate frailty among ≥ 50-year-olds in 62 countries found that $12\%$ of prevalence used physical frailty models and $24\%$ used deficit accumulation models. The prevalences of the consideration of pre-frailty were $46\%$ and $49\%$ for the physical frailty models and the deficit accumulation models, respectively [4]. In terms of geographical location, using physical frailty models, the highest prevalence of physical frailty was found in Africa ($22\%$) and the lowest prevalence was in Europe ($8\%$), while the pre-frailty prevalence was highest in the Americas ($50\%$) and lowest in Europe ($42\%$). However, using deficit accumulation models, the prevalence of frailty was found to be highest in Oceania ($31\%$) and lowest in Europe ($22\%$), while pre-frailty prevalence was highest in Oceania ($51\%$) and lowest in Europe and Asia ($49\%$). The population-level frailty prevalence among community-dwelling adults varied by age, gender, and frailty classification [4].
Several studies have reported that frailty is related to a variety of negative health outcomes and diseases. In 2013, cognitive frailty was described as a group of heterogeneous clinical symptoms based on the presence of both physical frailty and cognitive impairment, excluding consistent Alzheimer’s disease or other dementias. The prevalence of cognitive frailty among community-dwelling older adults was reported to be $9\%$ in a systematic review and meta-analysis [5]. Similarly, the prevalences of frailty and pre-frailty were found to be $20.1\%$ and $49.1\%$, respectively, in a systematic review and meta-analysis study of community-dwelling older adults with diabetes. Older adults with diabetes were more susceptible to being frail than those without diabetes [6]. Additional factors were found to have an influence on frailty; for example, fruit and vegetable consumption was associated with a lower risk of frailty [7].
There are many measurement tools available which can provide frailty scores when used to screen for or assess the degree of frailty; however, no single score metric is considered the gold standard [2,8]. It has been recommended that geriatricians in the Asia-Pacific region use a validated measurement tool to identify frailty [2]. There are three major approaches used, i.e., the physical frailty phenotype model of Fried et al. and its rapid screening tool, FRAIL; the deficit accumulation model of Rockwood and Mitnitski, which captures multimorbidity; and mixed physical and psychosocial models, such as the Tilburg Frailty Indicator [9] and the Edmonton Frailty Scale [10]. Another approach by Aguayo GA et al. [ 8] consists of the use of four models, including a phenotype of the frailty model, a multidimensional model, an accumulation of deficits model, and a disability model.
The most commonly used method in the literature is the physical frailty phenotype [11]. The phenotype diagnosis is based on three of the following five criteria: weight loss, exhaustion, physical inactivity, slow walking speed, and weak grip strength [12]. The present study reviews five phenotypic criteria that have been measured in different ways across various studies which could potentially affect the estimates of the prevalence of frailty and the predictive ability of the aforementioned phenotype, potentially leading to different classifications and results [11]. Kutner and Zhang [13] commented on the replacement of the performance-based measures (i.e., grip strength and walking speed) in the original frailty phenotype definition with self-reported items.
In Thailand, a study by Boribun N. et. al. [ 14] found that the prevalence of frailty in Thai community-dwelling older adults was $24.6\%$, based on the Frail Non-Disabled (FiND) questionnaire. A 2020 study by Sukkriang and Punsawad [15], which used various frailty assessment tools, found that the prevalence of frailty of older individuals in Thai communities was $11.7\%$, using Fried’s Frailty Phenotype (Cardiovascular Heart Study) criteria, and studied the validity of various frailty assessment tools. The Clinical Frailty Scale (CFS) used in the same study had a sensitivity of $56\%$ and a specificity of $98.41\%$; the simple FRAIL questionnaire had a sensitivity of $88\%$ and a specificity of $85.71\%$; the PRISMA-7 questionnaire sensitivity was $76\%$; and the specificity was $86.24\%$. The Timed Up and Go (TUG) test had a sensitivity of $72\%$ and a specificity of $82.54\%$. The Gerontopole frailty screening tool (GFST) sensitivity was $88\%$ and the specificity was $83.56\%$. The study by Sriwong et al. [ 2022] [16] developed a Thai version of the Simple Frailty Questionnaire (T-FRAIL) and modified it to improve its diagnostic properties in the preoperative setting. Their study found that the incidence of frailty diagnosed using the Thai Frailty Index was $40.0\%$. The identification of frailty using a score of two points or more provided the best Youden index, at 63.1, with a sensitivity of $77.5\%$ ($95\%$ CI 69.0–84.6) and a specificity of $85.6\%$ ($95\%$ CI 79.6–90.3).
There is currently a need for simple, valid, accurate, and reliable methods and tools for detecting frailty which are appropriate for the Thai population. Our team works in an academic hospital and has developed evidence data in our clinic in the hospital. Therefore, the present study was conducted in this clinic. This study compared selected frailty assessment tools, including Fried’s Frailty phenotype (FFP), which is the most commonly used assessment tool used for reference; the Frailty Assessment Tool of the Thai Ministry of Public Health (FATMPH), which is recommended in the Thai check-up manual but lacks published validation; and the FiND questionnaire, which is used in communities but, as yet, there is no evidence of its use at the Out-Patient Department (OPD) of Maharaj Nakorn Chiang Mai Hospital (a university-level hospital).
## 2.1. Samples
This cross-sectional study included 251 older patients (age 60 years or older) who came to the OPD of the Family Medicine Department, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, during the period of December 2016–March 2017. The patients signed a consent form declaring their agreement to participate in this research. This study was approved by the Research Ethics Committee of the Faculty of Medicine of Chiang Mai University (no. $\frac{380}{2016}$). The inclusion criteria for participants were: [1] Thais 60 years or older and who had been seen at the OPD for more than 1 year, [2] the ability to communicate orally in Thai and read the Thai language, [3] the ability to walk by themselves or with walking aids. The exclusion criteria were: [1] being bed ridden, [2] being handicapped in both hands, [3] currently having a serious illness, and [4] having impaired cognition.
The sample size was calculated to be 230 using the following formula:n = Z2α/2 × Se(1 − Se)/d2 × Prev where n = sample size, Se = sensitivity (0.9), Prev = prevalence (0.15) [17], d = precision of the estimate (1.0), and alpha = 0.1.
## 2.2.1. Fried’s Frailty Phenotype
The five criteria of Fried’s Frailty Phenotype (FFP) assessment were used as the reference assessment tool in this study, following Fried et al. [ 12], with slight modification. These criteria were:[1]Weight loss. My weight has decreased at least 4.5 kg in the past year or I have had an unintentional weight loss of at least $5\%$ of my previous year’s body weight (no = 0, yes = 1).[2]Exhaustion. Self-reported results of the Center for Epidemiologic Studies Depression scale (CES–D). Two statements were provided: (2.1) I felt that everything I did was an effort and (2.2) I could not get going. The question is then asked, “How often in the last week did you feel this way?” The alternative answers are: 0 = rarely or none of the time (<1 day), 1 = some or a little of the time (1–2 days), 2 = a moderate amount of the time (3–4 days), or 3 = most of the time. Answers of “2” or “3” to either of these questions were categorized as frail by the exhaustion criterion (no = 0, yes = 1).[3]Slowness. My walking speed is $20\%$ below baseline (adjusted for gender and height) (no = 0, yes = 1).[4]Weakness. Grip strength is $20\%$ below baseline (adjust for gender and body mass index) (no = 0, yes = 1).[5]Low activity was evaluated with the following question: How often do you engage in activities that require a low or moderate amount of energy such as gardening, cleaning the car, or walking? ( more than once a week = 1, once a week = 2, one to three times a month = 3 and hardly ever or never = 4) [18].
A combined FFP score of 0 was considered a “non-frail” phenotype; a score of 1 or 2 was considered a “pre-frail” phenotype; and a score of 3 or more was considered a “frail” phenotype.
## 2.2.2. Frailty Assessment Tool of the Thai Ministry of Public Health
The Frailty Assessment Tool of the Thai Ministry of Public Health (FATMPH) is a modification of Fried’s Frailty Phenotype, and is included in the Elderly Screening/Assessment Manual [2015] [19]. The assessment tool has 5 criteria: four questions are self-reports and one is based on measurement by medical staff:[1]In the past year, has your weight has decreased by more than 4.5 kg? ( no = 0, yes = 1)[2]Do you feel tired all the time? ( no = 0, yes = 1)[3]Are you unable to walk alone and need someone for support? ( no = 0, yes = 1)[4]The participants walked in a straight line for a distance of 4.5 m. Time was measured from when they started walking (Time < 7 $s = 0$, time ≥ 7 s or could not walk = 1).[5]The participant had an obvious weakness in their hands, arms, and legs (no = 0, yes = 1).
A FATMPH score of 0 was considered a phenotype of “non-frail”; a score of 1 or 2 was considered a phenotype of “pre-frail”; and a score of 3 or more was consider a phenotype of “frail”.
## 2.2.3. Frail Non-Disabled (FiND) Questionnaire
The Frail Non-Disabled (FiND) questionnaire is designed to differentiate between frailty and disability. FiND was used for community-dwelling older Thai adults by Boribun N et. al. [ 14]. The content validity index (CVI) was 0.8 and Cronbach’s alpha was 0.89 [13]. The FiND questionnaire consists of 5 questions: Do you have any difficulty walking 400 m? ( no or some difficulty = 0, much difficulty or unable = 1)Do you have any difficulty climbing up a flight of stairs? ( no or some difficulty = 0, much difficulty or unable = 1)During the last year, have you involuntarily lost more than 4.5 kg? ( no = 0, yes = 1)How often in the last week did you feel that everything you did was an effort or that you could not get going? ( 2 times or less = 0, 3 or more times = 1)*What is* your level of physical activity? ( at least 2–4 h per week = 0, mainly sedentary = 1) A combined score of A + B + C + D + $E = 0$ was considered as “non-frail”; A + $B = 0$ and C + D + E ≥ 1 was considered as “frail”; and A + B ≥ 1 was considered as “disabled”.
## 2.3. Data Collection
Data were collected using questionnaires and assessed using various tools. *The* general characteristics recorded included age, sex, religion, education, income, source of payment of medical expenses, history of family disease, present weight, weight one year ago, height, and body mass index. All participants were assessed using the Thai-language version of FATMPH, FFP, and FiND. The inter-rater reliability was 1.0 between researchers and assistants.
## 2.4. Statistical Analysis
The data were analyzed using Stata 12.0 and are presented as frequency, percentage, mean, and standard deviation (SD). The frailty assessment tools were analyzed for their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV); Cohen’s kappa was used to measure the reliability of these assessment tools.
## 2.5. Evaluation Consequence
All frail participants who were involved in any of the study of the assessment tools were advised to undergo comprehensive geriatric assessment. The appropriate interventions were then provided to these individuals.
## 3. Results
The demographic characteristics of the 251 older participants from the OPD are shown in Table 1. Most were female and ranged in age from 60 to 69. The majority of the participants were married or living with a partner, had lower than a high school education, and were Buddhist. Half the participants were government officials. Most participants had an income of more than 10,000 baht per month. Their major source of income was from pensions, which provided an adequate income.
The health status of the participants is shown in Table 2. Several medical conditions were identified among the participants. The most prevalent was hypertension, followed (in declining order of incidence) by dyslipidemia, diabetes mellitus, hyperuricemia, glaucoma or cataracts, chronic kidney disease, benign prostatic hypertrophy, coronary artery disease, cerebrovascular disease, and malignancy, followed by others.
In this study, frailty status was evaluated using frailty assessment tools including FFP, FATMPH, and FiND. The frail and non-frail phenotypes were defined based on the combined results of all the assessment tools. The study found that the overall prevalence of frailty was $8.37\%$ based on FFP, most of whom were female ($90.47\%$). The frailty phenotype prevalence determined using FATMPH was $17.53\%$ (female = $65.91\%$); using FiND, the frailty phenotype prevalence determined was $3.98\%$ (female = $80.00\%$) (Table 3 and Table 4).
The sensitivity, specificity, positive predictive value, and negative predictive value of the FATMPH and FiND tools were analyzed and compared with the standard FFP tool. As shown in Table 5, FATMHP had a sensitivity of $57.14\%$, a specificity of $86.09\%$, a positive predictive value (PPV) of $27.27\%$, and a negative predictive value (NPV) of $95.65\%$. FiND had a sensitivity of $19.05\%$, a specificity of $97.39\%$, a PPV of $40.00\%$ and an NPV of $92.94\%$. The comparison of FATMPH and FiND with FFP found the *Cohen kappa* statistics to be 0.298 for FATMPH and 0.147 for FiND.
## 4. Discussion
Fried’s Frailty Phenotype (FFP) is a well-known and regularly utilized tool for identifying frailty in older individuals [20]. In Thailand, FATMPH was developed as a frailty assessment tool based on FFP. Even though the *Fried criteria* were not initially intended to be used as a self-reported questionnaire, researchers now usually employ modified questionnaires based on this frailty phenotype [21,22]. The Frail Non-Disabled (FiND) questionnaire, a self-administered frailty screening instrument designed to differentiate frailty from disability, was developed as a screening tool [23]. We focused on the comparison of both FATMPH and FiND with FFP, which is currently used to assess older patients at the OPD of the Family Medicine Department of the Maharaj Nakorn Chiang Mai Hospital Faculty of Medicine. Most of the participants had a chronic disease ($92.43\%$), most frequently hypertension ($65.75\%$). The prevalence of frailty in this study was $8.37\%$ using FFP, which is lower than the prevalence of frailty among community-based elderly people ($9.9\%$) [24]. Differences in frailty prevalence were due at least in part to differences in the assessment tools used, as well as the different geographical locations covered in this study. Frailty prevalence increased with age and was higher for females than males [3]. The relatively low prevalence of frailty in the study may be due to the fact that most of the participants were in the younger group of the elderly participants (60–69 years, $65.34\%$).
A screening test is defined as a medical test or procedure performed on members (subjects) of a defined asymptomatic population or population subgroup to assess the likelihood of their members having a particular disease or condition [25]. A screening test has only two possible outcomes: positive, suggesting that the subject has the disease or condition; or negative, suggesting that the subject does not have the disease or condition [26]. In prior research, a Korean version of the FRAIL scale (K-FRAIL) was found to be consistent with the multidimensional frailty index and to be a concise tool for screening for frailty in a clinical setting in Korea [24].
In Thailand, many frailty assessment tools have been established for use both for community-dwelling individuals [14,27,28] and in hospitals [16,29]. There have, however, been few studies in Thailand that have included a comparison and validation of the frailty assessment tools used for older Thai adults in order to evaluate their diagnostic efficacy. A previous comparative study of the Thai version of the Simple Frailty Questionnaire (T-FRAIL) and the Thai Frailty Index (TFI) found that T-FRAIL was valid and reliable for frailty detection in elderly patients at a surgery out-patient clinic [16]. Another study of community-dwelling elderly compared several screening tests, including CFS, the simple FRAIL questionnaire, the PRISMA-7 questionnaire, the TUG, and the GFST with Fried’s Frailty Phenotype method. That study found the simple FRAIL questionnaire and the GFST were the most appropriate tests for screening frailty due to their high sensitivity [15].
The present study is the first study to compare the use of FATMPH and FiND with FFP regarding patients in an OPD for older Thai adults. The comparison of FATMPH and FiND found that the sensitivity of FATMPH ($57.14\%$) was higher than that of FiND ($19.05\%$), but that the specificity of FATMPH ($86.09\%$) was lower than that of FiND ($97.39\%$). FATMPH and FiND were both had a lower sensitivity than CFS ($56\%$), the simple FRAIL questionnaire ($88\%$), the PRISMA-7 questionnaire ($76\%$), the TUG ($72\%$), and the GFST ($88\%$), as reported in the study by Sukkriang and Punsawad [15], as well as the modified T-Frails, including T-Frail M1 ($83.3\%$) and T-Frail M2 ($85.8\%$), as reported in the study by Sriwong [16]. However, the categorizations of FiND (non-frail, frail, and disabled) are different from that of both FATMPH and FFP (non-frail, pre-frail, and frail), which could affect the sensitivity of the tests and which might be a reason that FiND had the lowest sensitivity in the present study. FATMPH had a higher sensitivity than FiND because it was modified from FFP, but its sensitivity as a screening tool remains poor. In addition, FATMPH and FiND both had high specificity, similar to other tools used in previous studies [15,16]. Most of the screening tools had a specificity of higher than $85\%$: CSF, at $98.41\%$, as found in a previous study [15]; and FiND, at $97.39\%$, as found in the present study. The sensitivity of both FATMPH and FiND were lower than $85\%$, suggesting that neither is an adequate screening tool [30], while the high specificities of both CSF and FiND suggest they are appropriate for confirming the absence of the condition. FiND is a self-assessment questionnaire suitable for use for individuals in communities, as well as in primary care, whereas FFP is appropriate in primary care and acute care for both individuals in communities and in clinical settings, although the assessment time of FFP is longer than that of FiND [31]. The final judgement of whether or not these methods are appropriate will depend on the context. If the score is used as part of a sequence of screening steps, sensitivity is likely to be more important than specificity, while if the score is used to guide treatment initiation, specificity is equally important [32].
The reliability of FATMPH and FiND were compared with FFP and evaluated using Cohen’s kappa statistic. The kappa values of FATMPH and FiND were 0.289 ($95\%$ CI = 0.132–0.445) and 0.147 ($95\%$ CI = 0.004–0.241), respectively. The levels of agreement of these values were fair (0.21 ≤ K ≤ 0.40) and slight (0.00 ≤ K ≤ 0.20) [33], respectively. Additionally, in a research context, this measure depends on the prevalence of the condition (with a very low prevalence, κ will be very low, even with high agreement between the raters) [32]. FATMPH’s kappa agreement level was higher than FiND because FATMPH was modified from FFP. Aguayo GA et al. [ 8], in a study of the agreement between 35 published frailty scores in the general population, found a very wide range of agreement (Cohen’s kappa = 0.10–0.83). The frailty phenotype properties were impacted by the modified frailty phenotype criteria [11]. The prevalence of frailty was $31.2\%$ for modified self-reported walking, $33.6\%$ for modified self-reported strength, and $31.4\%$ for modified self-reported walking and strength [11]. The agreement with the primary phenotype was 0.651 for modified self-reported walking, 0.913 for modified self-reported strength, and 0.441 for modified self-report walking and strength [11]. FATMPH had a lower agreement (0.268) than that of the Modified Frailty Phenotype. We think that the physical inactivity criteria of FATMPH, i.e., the “Can you walk by yourself or do you need someone help you? ( no = 0, yes = 1)” should be re-evaluated, as it appears to be very similar to the walk speed criteria (4.5 m walk time; <7 $s = 0$, ≥7 s or cannot walk = 1). FFP has two measurements (grip strength and walking speed), but FATMPH uses only walking speed and includes fewer detailed questions. Frailty scores show marked heterogeneity because they are based on different concepts of frailty and research results based on different frailty scores cannot be compared or pooled [8].
A limitation of our study is that it was not representative of all community-dwelling older Thais because the participants were all older patients at the OPD of an academic hospital (Maharaj Nakorn Chiang Mai Hospital) and most were urban residents receiving regular government welfare payments. Further study of validated frailty assessment tools such as multicenter studies, as well as other assessment tools, are necessary to ensure their suitability for the Thai population context.
## 5. Conclusions
Our academic hospital-based study using the Thai-language version of the Frailty Assessment Tool of the Thai Ministry of Public Health (FATMPH) and the FiND questionnaire found that both have only a fair to slight agreement with Fried’s Frailty Phenotype (FFP). Additionally, their predictive power is low and, thus, insufficient for frailty detection in a clinical setting. Further multicenter study of these and other assessment tools is needed to improve frailty screening in older Thai populations.
## References
1. 1.
WHO Clinical Consortium on Healthy Ageing
Report of Consortium Meeting 1–2 December 2016 in Geneva, SwitzerlandWorld Health OrganizationGeneva, Switzerland2017. *Report of Consortium Meeting 1–2 December 2016 in Geneva, Switzerland* (2017.0)
2. Dent E., Lien C., Lim W.S., Wong W.C., Wong C.H., Ng T.P., Woo J., Dong B., de la Vega S., Poi P.J.H.. **The Asia-Pacific Clinical Practice Guidelines for the Management of Frailty**. *J. Am. Med. Dir. Assoc.* (2017.0) **18** 564-575. DOI: 10.1016/j.jamda.2017.04.018
3. Clegg A., Young J., Iliffe S., Rikkert M.O., Rockwood K.. **Frailty in elderly people**. *Lancet* (2013.0) **381** 752-762. DOI: 10.1016/S0140-6736(12)62167-9
4. O’Caoimh R., Sezgin D., O’Donovan M.R., Molloy D.W., Clegg A., Rockwood K., Liew A.. **Prevalence of frailty in 62 countries across the world: A systematic review and meta-analysis of population-level studies**. *Age Ageing* (2021.0) **50** 96-104. DOI: 10.1093/ageing/afaa219
5. Qiu Y., Li G., Wang X., Zheng L., Wang C., Wang C., Chen L.. **Prevalence of cognitive frailty among community-dwelling older adults: A systematic review and meta-analysis**. *Int. J. Nurs. Stud.* (2022.0) **125** 104112. DOI: 10.1016/j.ijnurstu.2021.104112
6. Kong L.N., Lyu Q., Yao H.Y., Yang L., Chen S.Z.. **The prevalence of frailty among community-dwelling older adults with diabetes: A meta-analysis**. *Int. J. Nurs. Stud.* (2021.0) **119** 103952. DOI: 10.1016/j.ijnurstu.2021.103952
7. Ghoreishy S.M., Asoudeh F., Jayedi A., Mohammadi H.. **Fruit and vegetable intake and risk of frailty: A systematic review and dose response meta-analysis**. *Ageing Res. Rev.* (2021.0) **71** 101460. DOI: 10.1016/j.arr.2021.101460
8. Aguayo G.A., Donneau A.-F., Vaillant M.T., Schritz A., Franco O.H., Stranges S., Malisoux L., Guillaume M., Witte D.R.. **Agreement Between 35 Published Frailty Scores in the General Population**. *Am. J. Epidemiol.* (2017.0) **186** 420-434. DOI: 10.1093/aje/kwx061
9. Gobbens R.J., van Assen M.A., Luijkx K.G., Wijnen-Sponselee M.T., Schols J.M.. **The Tilburg Frailty Indicator: Psychometric properties**. *J. Am. Med. Dir. Assoc.* (2010.0) **11** 344-355. DOI: 10.1016/j.jamda.2009.11.003
10. Rolfson D.B., Majumdar S.R., Tsuyuki R.T., Tahir A., Rockwood K.. **Validity and reliability of the Edmonton Frail Scale**. *Age Ageing* (2006.0) **35** 526-529. DOI: 10.1093/ageing/afl041
11. Theou O., Cann L., Blodgett J., Wallace L.M., Brothers T.D., Rockwood K.. **Modifications to the frailty phenotype criteria: Systematic review of the current literature and investigation of 262 frailty phenotypes in the Survey of Health, Ageing, and Retirement in Europe**. *Ageing Res. Rev.* (2015.0) **21** 78-94. DOI: 10.1016/j.arr.2015.04.001
12. Fried L.P., Tangen C.M., Walston J., Newman A.B., Hirsch C., Gottdiener J., Seeman T., Tracy R., Kop W.J., Burke G.. **Frailty in older adults: Evidence for a phenotype**. *J. Gerontol. A Biol. Sci. Med. Sci.* (2001.0) **56** M146-M156. DOI: 10.1093/gerona/56.3.M146
13. Kutner N.G., Zhang R.. **Frailty in Dialysis-Dependent Patients with End-Stage Renal Disease**. *JAMA Intern. Med.* (2013.0) **173** 78-79. DOI: 10.1001/2013.jamainternmed.750
14. Boribun N., Lerttrakarnnon P., Siviroj P.. **Prevalence and associated factors of the frailty among community-dwelling elders in Sermngam district, Lampang province**. *J. Med. Health Sci.* (2017.0) **24** 45-54
15. Sukkriang N., Punsawad C.. **Comparison of geriatric assessment tools for frailty among community elderly**. *Heliyon* (2020.0) **6** e04797. DOI: 10.1016/j.heliyon.2020.e04797
16. Sriwong W.T., Mahavisessin W., Srinonprasert V., Siriussawakul A., Aekplakorn W., Limpawattana P., Suraarunsumrit P., Ramlee R., Wongviriyawong T.. **Validity and reliability of the Thai version of the simple frailty questionnaire (T-FRAIL) with modifications to improve its diagnostic properties in the preoperative setting**. *BMC Geriatr.* (2022.0) **22**. DOI: 10.1186/s12877-022-02863-5
17. Hajian-Tilaki K.. **Sample size estimation in diagnostic test studies of biomedical informatics**. *J. Biomed. Inform.* (2014.0) **48** 193-204. DOI: 10.1016/j.jbi.2014.02.013
18. Romero-Ortuno R., Walsh C.D., Lawlor B.A., Kenny R.A.. **A frailty instrument for primary care: Findings from the Survey of Health, Ageing and Retirement in Europe (SHARE)**. *BMC Geriatr.* (2010.0) **10**. DOI: 10.1186/1471-2318-10-57
19. 19.
Department of Medical Services, Ministry of Public Health
Screening/Evaluation of Elderly Manual2nd ed.The War Veterans Organization of Thailand Officer of Printing MillBangkok, Thailand2015. *Screening/Evaluation of Elderly Manual* (2015.0)
20. Op het Veld L.P.M., van Rossum E., Kempen G.I.J.M., de Vet H.C.W., Hajema K., Beurskens A.J.H.M.. **Fried phenotype of frailty: Cross-sectional comparison of three frailty stages on various health domains**. *BMC Geriatr.* (2015.0) **15**. DOI: 10.1186/s12877-015-0078-0
21. Macklai N.S., Spagnoli J., Junod J., Santos-Eggimann B.. **Prospective association of the SHARE-operationalized frailty phenotype with adverse health outcomes: Evidence from 60+ community-dwelling Europeans living in 11 countries**. *BMC Geriatr.* (2013.0) **13**. DOI: 10.1186/1471-2318-13-3
22. Barreto Pde S., Greig C., Ferrandez A.M.. **Detecting and categorizing frailty status in older adults using a self-report screening instrument**. *Arch. Gerontol. Geriatr.* (2012.0) **54** e249-e254. DOI: 10.1016/j.archger.2011.08.003
23. Cesari M., Demougeot L., Boccalon H., Guyonnet S., Van Kan G.A., Vellas B., Andrieu S.. **A self-reported screening tool for detecting community-dwelling older persons with frailty syndrome in the absence of mobility disability: The FiND questionnaire**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0101745
24. Collard R.M., Boter H., Schoevers R.A., Oude Voshaar R.C.. **Prevalence of frailty in community-dwelling older persons: A systematic review**. *J. Am. Geriatr. Soc.* (2012.0) **60** 1487-1492. DOI: 10.1111/j.1532-5415.2012.04054.x
25. Jung H.-W., Yoo H.-J., Park S.-Y., Kim S.-W., Choi J.-Y., Yoon S.-J., Kim C.-H., Kim K.-I.. **The Korean version of the FRAIL scale: Clinical feasibility and validity of assessing the frailty status of Korean elderly**. *Korean J. Intern. Med.* (2016.0) **31** 594-600. DOI: 10.3904/kjim.2014.331
26. Maxim L.D., Niebo R., Utell M.J.. **Screening tests: A review with examples**. *Inhal. Toxicol.* (2014.0) **26** 811-828. DOI: 10.3109/08958378.2014.955932
27. Chittrakul J., Siviroj P., Sungkarat S., Sapbamrer R.. **Physical Frailty and Fall Risk in Community-Dwelling Older Adults: A Cross-Sectional Study**. *J. Aging Res.* (2020.0) **2020** 3964973. DOI: 10.1155/2020/3964973
28. Wanaratna K., Muangpaisan W., Kuptniratsaikul V., Chalermsri C., Nuttamonwarakul A.. **Prevalence and Factors Associated with Frailty and Cognitive Frailty Among Community-Dwelling Elderly with Knee Osteoarthritis**. *J. Community Health* (2019.0) **44** 587-595. DOI: 10.1007/s10900-018-00614-5
29. Limpawattana P., Khammak C., Manjavong M., So-ngern A.. **Frailty as a Predictor of Hospitalization and Low Quality of Life in Geriatric Patients at an Internal Medicine Outpatient Clinic: A Cross-Sectional Study**. *Geriatrics* (2022.0) **7**. DOI: 10.3390/geriatrics7050089
30. Ruiz M., Reske T., Cefalu C., Estrada J.. **Management of elderly and frail elderly cancer patients: The importance of comprehensive geriatrics assessment and the need for guidelines**. *Am. J. Med. Sci.* (2013.0) **346** 66-69. DOI: 10.1097/MAJ.0b013e31826d59aa
31. **Frailty Screening and Assessment Tools Comparator**
32. Sim J., Wright C.C.. **The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements**. *Phys. Ther.* (2005.0) **85** 257-268. DOI: 10.1093/ptj/85.3.257
33. Watson P.F., Petrie A.. **Method agreement analysis: A review of correct methodology**. *Theriogenology* (2010.0) **73** 1167-1179. DOI: 10.1016/j.theriogenology.2010.01.003
|
---
title: A Comparative Study of Periodontal Health Status between International and
Domestic University Students in Japan
authors:
- Masanobu Abe
- Ai Ohsato
- Yuko Fujihara
- Kazuto Hoshi
- Shintaro Yanagimoto
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001467
doi: 10.3390/ijerph20053866
license: CC BY 4.0
---
# A Comparative Study of Periodontal Health Status between International and Domestic University Students in Japan
## Abstract
Background: In our previous study, international university students showed a significantly higher dental caries morbidity rate than domestic students. On the other hand, the periodontal health status of international university students has not been clarified yet. In this study, we compared the periodontal health status of international and domestic university students in Japan. Methods: We conducted a retrospective review of the clinical data of the university students that visited a dental clinic in the division for health service promotion at a university in Tokyo for screening between April 2017 and March 2019. Bleeding on probing (BOP), calculus deposition and probing pocket depth (PPD) were investigated. Results: The records of 231 university students (79 international and 152 domestic university students) were analyzed; $84.8\%$ of international students were from Asian countries ($$n = 67$$). The international university students showed a higher percentage of BOP than domestic students ($49.4\%$ and $34.2\%$, respectively: $p \leq 0.05$) and they showed more extensive calculus deposition (calculus grading score [CGS]) than domestic university students (1.68 and 1.43, respectively: $p \leq 0.01$), despite no significant difference in PPD. Conclusions: The current study shows that international university students have poorer periodontal health than domestic students in Japan, even though the result might include many uncertainties and possible biases. To prevent severe periodontitis in the future, regular checkups and thorough oral health care are essential for the university students, especially those from foreign countries.
## 1. Introduction
Periodontal diseases, along with dental caries, are an important public health problem in terms of their high prevalence, affecting approximately $90\%$ of the world’s population [1,2,3]. Periodontal disease is classified into gingivitis and periodontitis. Gingivitis, the mildest form of periodontal disease, is caused by a bacterial biofilm that accumulates on teeth adjacent to the gingiva. Gingivitis does not affect the supporting structures of the teeth and is reversible. On the other hand, periodontitis, the advanced stage of periodontal disease, causes loss of connective tissue and bone support and is the leading cause of tooth loss in adults. In addition to pathogenic microorganisms in biofilms, genetic and environmental factors such as smoking are known to contribute to the cause of these diseases [3].
Severe periodontitis is reported to have the sixth highest prevalence in the world ($11\%$) [4]. Although dental caries used to be the leading cause of tooth loss in Japan [5,6], now periodontal disease has overtaken dental caries as the leading cause of tooth loss. Specifically, $30.2\%$ of men and $29.0\%$ of women lose their teeth due to dental caries, and $40.4\%$ of men and $34.9\%$ of women lose their teeth due to periodontal disease [7]. Unlike dental caries, periodontal disease often does not cause severe pain, thus regular checkups by dentists are essential. Furthermore, it has been shown that periodontal disease not only causes tooth loss, but also affects overall health [8]. Various diseases, including respiratory diseases [9,10], cardiovascular diseases [11,12,13], rheumatoid arthritis [14], diabetes [15], and others [16,17,18,19], have been reported to be associated with periodontal disease. Although the periodontal disease is more prevalent in middle-aged and older adult, more than one-third of university students aged less than 20 years are already aware of gum bleeding which is known as a major symptom of periodontal disease. Importantly, the gum bleeding is found to be closely associated with common systemic disorders in late adolescence such as asthma [20,21,22]. The importance of periodontal disease prevention from young age has been increasing [2].
According to the Japan Student Services Organization (JASSO: https://www.jasso.go.jp, accessed on 10 January 2023), the number of foreign students at Japanese institutions of higher education and Japanese language education is currently on the rise, although it is temporarily declining due to the COVID-19 pandemic. In a study by Ohsato et al. [ 23], authors analyzed the medical records of 554 subjects (138 international and 416 domestic university students) and found no significant difference in dental treatment history between international and domestic university students ($49.3\%$ and $48.8\%$, respectively). However, the incidence of dental caries was significantly higher in international university students than in domestic university students ($60.1\%$ and $49.0\%$, respectively). The indices of decayed, missing, and filled teeth (DMFT) were also significantly higher in international university students than in domestic university students (5.0 and 4.0, respectively). International university students were found to have a significantly higher dental caries morbidity rate than domestic students in Japan [23]. On the other hand, the differences in periodontal health between international and domestic university students are not yet well defined. In this study, the periodontal health status of international and domestic university students was compared.
## 2.1. Study Design and Population
Clinical data of university students who visited a dental clinic at The University of Tokyo for screening purposes (not for symptomatic or dental treatment purposes) between April 2017 and March 2019 were retrospectively analyzed. Students who held Japanese nationality or who were permanent residents of Japan were classified into the group of domestic university students in Japan. Of the 374 university students who visited the dental clinic for initial dental checkups, the records of 231 university students under 25 years of age (including 79 international students) were included in the analysis. No specific undergraduate or graduate school students were targeted.
Periodontal health status was determined by three dentists’ individual examinations on separate students for three parameters: probing pocket depth (PPD), bleeding on probing (BOP), and calculus grading scale (CGS) in the fully erupted permanent dentition excluding wisdom teeth. One identical dental hygienist was present during all examinations to ensure that the examinations were performed properly. A community periodontal index (CPI) probe (YDM, Tokyo, Japan) was used to measure each tooth at six sites (mesiobuccal, mid-buccal, distobuccal, distolingual, mid-lingual, and mesiolingual) for the evaluation of PPD and BOP [24]. The PPD value of each tooth was determined as the deepest of the six locations listed above. The PPD value of each student was determined as the mean value of the PPD of each tooth. For BOP, if bleeding was observed in even one location, the student was classified as having BOP. CGS was determined as follows: NONE: no calculus deposition (scored as 1), MILD: calculus deposition on less than one-half of the tooth surface (scored as 2), SEVERE: calculus deposition on more than one-half of the tooth surface and/or extending below the gingival margin (scored as 3) [23].
This study was approved by the Research Ethics Committee of the University of Tokyo (approval number 13–146): “Retrospective analyses of medical and health record information retained by the division for health service promotion, the University of Tokyo.”
## 2.2. Statistical Analyses
Statistical analysis was performed using the χ2 test for BOP evaluation and the student’s t-test for PPD and CGS evaluation. A value of $p \leq 0.05$ (two-sided) was accepted as statistically significant. All the analyses were conducted using the statistical software program: Statistical Package for Social Sciences (SPSS version 21.0, IBM Corporation, Armonk, NY, USA). No statistical sample size calculations were conducted.
## 3.1. Region of Origin of International University Students
The records of all university students under 25 years of age who visited a dental clinic for checkups (not for symptomatic or dental treatment) between April 2017 and March 2019 were analyzed. Of the total 231 university students, 152 were domestic students and 79 were international students. Of the international students, $84.8\%$ were from Asian countries ($$n = 67$$), which was the highest percentage, followed by North America and Europe, both $5.1\%$ ($$n = 4$$). In Asia, China accounted for $83.6\%$ ($$n = 56$$) of all Asian international university students, followed by South Korea with $6.0\%$ ($$n = 4$$), Singapore and Thailand both with $3.0\%$ ($$n = 2$$), and Hong Kong, Taiwan, and Malaysia with $1.5\%$ ($$n = 1$$). Among international university students, $45.6\%$ ($$n = 36$$) were male and $54.4\%$ ($$n = 43$$) were female; among domestic university students, $77.6\%$ ($$n = 118$$) were male, and $22.4\%$ ($$n = 34$$) were female (Figure 1).
## 3.2. Difference in Bleeding on Probing (BOP) between International and Domestic University Students in Japan
The mean number of remaining teeth for all university students was 27.6 (maximum number of teeth: 28 excluding wisdom teeth). The mean number of remaining teeth for international students was 27.2, while that for domestic students was 27.7. The periodontal status of those remaining teeth was evaluated in this study. Overall, 91 of 231 ($39.4\%$) university students showed BOP. By gender, 60 of 154 ($39.0\%$) males and 31 of 77 ($40.3\%$) females had BOP. There were no significant differences between males and females. 39 of 79 ($49.4\%$) international university students and 52 of 152 ($34.2\%$) domestic university students showed BOP. The international university students showed a higher percentage of BOP than domestic university students ($p \leq 0.05$). Among international university students, females tended to exhibit BOP at a higher rate than males ($55.8\%$ and $41.7\%$, respectively: $$p \leq 0.21$$). On the other hand, among domestic university students, males showed BOP at a higher rate than females ($38.1\%$ and $20.6\%$, respectively: $$p \leq 0.057$$), although the difference was not significant (Figure 2, Supplementary Table S1).
## 3.3. Differences in Calculus Deposition between International and Domestic University Students
The mean calculus grading score (CGS) of the total ($$n = 231$$) was 1.52. By gender, the mean CGS for males ($$n = 154$$) was 1.55, and for females ($$n = 77$$) was 1.45, showing no significant difference. The mean CGS of international university students ($$n = 79$$) was 1.68 and that of domestic university students ($$n = 152$$) was 1.43. The international university students showed more extensive calculus deposition than domestic students ($p \leq 0.01$). Among international university students, males tended to have higher CGS than females (1.83 and 1.56, respectively: $$p \leq 0.10$$). Similarly, among domestic university students, males tended to have higher CGS than females (1.46 and 1.32, respectively: $$p \leq 0.24$$) (Figure 3, Supplementary Table S1).
## 3.4. Difference in Probing Pocket Depth (PPD) Status between International and Domestic University Students
The mean PPD of the total university students ($$n = 231$$) was 1.68 mm. By gender, the mean PPD for males ($$n = 154$$) was 1.67 mm and for females ($$n = 77$$) was 1.70 mm, showing no significant difference. The mean PPD of international university students ($$n = 79$$) was 1.77 mm and that of domestic university students ($$n = 152$$) was 1.64 mm, showing no significant difference between the two groups. There was no gender difference in PPD for either international university students or domestic university students (Figure 4, Supplementary Table S1).
## 3.5. The Association between BOP and PPD in International and Domestic University Students
The mean PPD of the university students with BOP was 1.98 mm, and the mean PPD of the students without BOP was 1.49 mm, showing a large difference ($p \leq 0.001$). For international university students, the mean PPD for students with BOP was 2.05 mm, while the mean PPD for students without BOP was 1.49 mm. The mean PPD of international students with BOP was significantly larger than that of international students without BOP ($p \leq 0.001$). The mean PPD for domestic university students with BOP was 1.93 mm, and the mean PPD for domestic university students without BOP was 1.48 mm. The mean PPD of domestic students with BOP was significantly larger than that of domestic students without BOP ($p \leq 0.001$). Within the population that showed BOP, there was no significant difference in PPD between international and domestic students. Even among the population without BOP, there was no significant difference in PPD between international and domestic students (Figure 5, Supplementary Table S2).
## 4. Discussion
The current study showed that international university students in Japan had a higher rate of bleeding on probing (BOP) and more extensive calculus deposition than domestic university students. Although probing pocket depth (PPD) was found to be at a physiological level for both international and domestic students, and no differences were observed, students with BOP showed significantly larger PPD values than those without BOP, regardless of international or domestic students.
Oral diseases such as caries, periodontal disease, tooth loss, oral infections, oral cancer, and malocclusion are among the most prevalent diseases worldwide and carry serious health and economic burdens that significantly reduce the quality of life of those affected, and their impact is immeasurable [25]. Oral diseases, like most non-communicable diseases (NCDs), are chronic and susceptible to social context, such as economic status. Chronic untreated oral diseases often have serious consequences, not only in terms of pain and other painful symptoms and progression to systemic diseases (e.g., sepsis), but also in terms of reduced quality of life and work productivity. The cost of treating oral diseases also imposes a significant financial burden on households and health care systems [25]. Unfortunately, oral diseases have not been given much importance in global health policy, including in Japan, despite the fact that they are a global public health problem. In recent years, however, the need to treat oral diseases as an urgent priority for global health has begun to be stated [2,25,26,27,28]. Among oral diseases, periodontal disease is of particular public health importance because it occurs with such high frequency that it is estimated to affect $90\%$ of the world’s population [3].
Periodontal disease is the most common disease affecting tooth-supporting structures and is therefore a common cause of tooth loss [11,29,30,31]. In Japan, periodontal disease has replaced dental caries as the leading cause of tooth loss [7]. Importantly, periodontal disease has also been shown to be associated with a variety of systemic diseases including respiratory diseases [9,10], cardiovascular diseases [11,12,13], rheumatoid arthritis [14], diabetes [15], and a lot of other disorders [16,17,18,19,32]. Therefore, the importance of prevention and treatment of periodontal disease has been recognized by society and has become a focus of public health in recent years [25,28,33]. The relationship between systemic diseases and periodontal disease has been discussed mainly in middle-aged and older adults. Recently, however, it has been shown that late adolescents who have gingival bleeding are significantly more likely to suffer from systemic diseases such as asthma, otitis media/externa [21]. Based on the above, it is not surprising that the relationship between periodontal disease and lifestyle-related diseases such as diabetes and stroke, which are common in middle-aged and older adults, has already begun during late adolescence.
Although subjective symptoms of periodontal disease usually become apparent after the age of 40s, it is common for young people to develop gingivitis, an early stage of periodontal disease, and in a survey of 17- to 19-year-old university students, $36.5\%$ of them complained of gingival bleeding [21]. This result suggests that one out of every three persons in their late teens already has gingivitis. In addition, Dental Health Division of Health Policy Bureau Ministry of Health in Japan reported that periodontal pockets rapidly become deeper after the age of 20 years [20]. This suggests that periodontal disease has already begun in late adolescence, indicating the need for periodontal disease countermeasures for young people [22].
According to the Japan Student Services Organization (JASSO: https://www.jasso.go.jp accessed on 10 January 2023), an independent administrative agency under the jurisdiction of the Ministry of Education, Culture, Sports, Science and Technology, the number of foreign students at Japanese institutions of higher education and Japanese language education is on the rise (although the situation is currently exceptional due to the COVID-19 pandemic), with the largest number of students from Asian countries. In this study, foreign students from Asia accounted for the largest proportion of foreign students ($84.8\%$), with students from China accounting for a particularly large share ($83.6\%$) of all foreign students from Asia. Therefore, the exclusion of international students from North America and Europe (together comprising $5.1\%$ of all international students) did not significantly change the results of the current study. It is not clear whether the percentage of foreign students from Europe and North America will increase in the future but, at present, foreign students from Asia are by far the largest group of foreign students in Japan. Therefore, it is necessary to consider many social factors such as differences in culture, customs, insurance systems, and medical services in order to provide better oral healthcare services to international students from Asian countries. Since there are still few studies comparing the oral health status of international students and domestic students in Japan, more data needs to be accumulated in the future [23,34].
In the current study, $34.2\%$ of domestic university students showed BOP, more than a third of the subjects, which was similar to our previous survey [21]. On the other hand, international university students showed a higher percentage of BOP ($49.4\%$) than domestic university students. A large difference was observed between international and domestic university students in BOP (Figure 2). Among international university students, women tended to have a higher percentage of BOP than men, while among domestic university students, male tended to have a higher percentage of BOP than female, consistent with the result of a previous survey [1]. Although this difference needs to be examined with a larger number of subjects, it is interesting because cultural differences and social backgrounds may be involved. In a study by Ohsato et al. [ 23], severe calculus deposition was observed in international university students ($51.9\%$) compared with domestic students ($31.7\%$) in Japan [23]. Similar results were obtained in the present study, with international university students showing more extensive calculus deposition than domestic students ($p \leq 0.01$) (Figure 3). This difference can be attributed to differences in food culture, socioeconomic differences, and lifestyle habits such as brushing teeth and dental visits. University students with BOP had greater PPD values than those without BOP, although, even for students with BOP, the depth of the gingival sulcus was at the physiological level: gingival pocket (Figure 5). The presence of BOP without periodontal pockets indicates the presence of gingivitis. Gingivitis is in a reversible stage in which healthy periodontal tissue can be restored [3,25,35,36,37]. Thus, some measures are needed to prevent the transition from gingivitis to periodontitis.
What measures should be taken to prevent periodontitis in young people? Recently, it has been shown that the frequency and duration of tooth brushing affect gingival health in late adolescence [1,38]. In a survey of 9098 university students aged 17–19 years, regarding the frequency of tooth brushing, it was reported that the risk of gingival bleeding for university students who brush their teeth “less than once” is 2.36 times that of those who brush their teeth “three or more times,” and even for those who brush their teeth “twice” the risk of gingival bleeding is 1.45 times that of those who brush “three or more times.” Regarding the duration of tooth brushing, it is known that university students who brush their teeth “1 minute or less” have 1.57 times the risk of gingival bleeding compared to those who brush “4 minutes or more” and those who brush “2 to 3 minutes” have 1.26 times the risk compared to those who brush “4 minutes or more.” University students who brush their teeth less frequently and for less time have a higher risk of gingival bleeding. This result implies that the risk of periodontal disease decreases as the frequency and duration of tooth brushing increases. Therefore, in addition to dental checkups, it is important to raise oral hygiene and oral health awareness among the younger generation. However, unfortunately, the working-age population from high school graduation (age 18) to age 40 does not have opportunities to receive dental examinations or oral care instruction, except for special examinations limited to targeted occupations in Japan. Considering that periodontal disease begins in late adolescence and becomes apparent in the forties, it seems essential to establish a seamless oral hygiene management system that compensates for this gap period in Japan [2].
The current study revealed that international university students in Japan have poorer periodontal health status than domestic university students. Although the number of university students included in the study was not large and more studies with a larger number of students are needed, the results suggest that regular checkups and thorough oral care are essential for university students, especially international students, in order to prevent periodontitis. It has been suggested that the relationship between periodontal disease and systemic health status already occurs in late adolescence [21,39,40,41]. From the viewpoint of preventing systemic diseases, oral health care in late adolescence will become increasingly important in the future.
## 5. Conclusions
International university students in Japan showed higher percentage of bleeding on probing (BOP) and more extensive calculus deposition than domestic university students, despite no significant difference in probing pocket depth (PPD). Students with BOP have significantly greater PPD values than those without BOP in both international and domestic students, although the values were at physiological levels. To prevent periodontitis, we have to pay more attention to the periodontal health care of university students, especially international students.
## References
1. Abe M., Mitani A., Hoshi K., Yanagimoto S.. **Large Gender Gap of Oral Hygiene Behavior and Its Impact on Gingival Health in Late Adolescence**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17124394
2. Abe M., Mitani A., Yao A., Zong L., Zhang C.D., Hoshi K., Yanagimoto S.. **Oral Health in Japan: State-of-the-Art and Perspectives**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph19148232
3. Pihlstrom B.L., Michalowicz B.S., Johnson N.W.. **Periodontal diseases**. *Lancet* (2005) **366** 1809-1820. DOI: 10.1016/S0140-6736(05)67728-8
4. Marcenes W., Kassebaum N.J., Bernabe E., Flaxman A., Naghavi M., Lopez A., Murray C.J.. **Global burden of oral conditions in 1990–2010: A systematic analysis**. *J. Dent. Res.* (2013) **92** 592-597. DOI: 10.1177/0022034513490168
5. Aida J., Ando Y., Akhter R., Aoyama H., Masui M., Morita M.. **Reasons for permanent tooth extractions in Japan**. *J. Epidemiol.* (2006) **16** 214-219. DOI: 10.2188/jea.16.214
6. Morita M., Kimura T., Kanegae M., Ishikawa A., Watanabe T.. **Reasons for extraction of permanent teeth in Japan**. *Community Dent. Oral Epidemiol.* (1994) **22** 303-306. DOI: 10.1111/j.1600-0528.1994.tb02056.x
7. Suzuki S., Sugihara N., Kamijo H., Morita M., Kawato T., Tsuneishi M., Kobayashi K., Hasuike Y., Sato T.. **Reasons for Tooth Extractions in Japan: The Second Nationwide Survey**. *Int. Dent. J.* (2022) **72** 366-372. DOI: 10.1016/j.identj.2021.05.008
8. Nazir M.A.. **Prevalence of periodontal disease, its association with systemic diseases and prevention**. *Int. J. Health Sci.* (2017) **11** 72-80
9. Moraschini V., Calasans-Maia J.A., Calasans-Maia M.D.. **Association between asthma and periodontal disease: A systematic review and meta-analysis**. *J. Periodontol.* (2018) **89** 440-455. DOI: 10.1902/jop.2017.170363
10. Dong J., Li W., Wang Q., Chen J., Zu Y., Zhou X., Guo Q.. **Relationships Between Oral Microecosystem and Respiratory Diseases**. *Front. Mol. Biosci.* (2021) **8** 718222. DOI: 10.3389/fmolb.2021.718222
11. Moreillon P., Que Y.A.. **Infective endocarditis**. *Lancet* (2004) **363** 139-149. DOI: 10.1016/S0140-6736(03)15266-X
12. Byun S.H., Lee S., Kang S.H., Choi H.G., Hong S.J.. **Cross-Sectional Analysis of the Association between Periodontitis and Cardiovascular Disease Using the Korean Genome and Epidemiology Study Data**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17145237
13. Hansen P.R., Holmstrup P.. **Cardiovascular Diseases and Periodontitis**. *Adv. Exp. Med. Biol.* (2022) **1373** 261-280. PMID: 35612803
14. Kaur S., White S., Bartold P.M.. **Periodontal disease and rheumatoid arthritis: A systematic review**. *J. Dent. Res.* (2013) **92** 399-408. DOI: 10.1177/0022034513483142
15. Borgnakke W.S., Ylostalo P.V., Taylor G.W., Genco R.J.. **Effect of periodontal disease on diabetes: Systematic review of epidemiologic observational evidence**. *J. Clin. Periodontol.* (2013) **40** S135-S152. DOI: 10.1111/jcpe.12080
16. Abe M., Mori Y., Inaki R., Ohata Y., Abe T., Saijo H., Ohkubo K., Hoshi K., Takato T.. **A Case of Odontogenic Infection by Streptococcus constellatus Leading to Systemic Infection in a Cogan’s Syndrome Patient**. *Case Rep. Dent.* (2014) **2014** 793174. PMID: 25506439
17. Abe M., Mori Y., Saijo H., Hoshi K., Ohkubo K., Ono T., Takato T.. **The efficacy of dental therapy for an adult case of Henoch-Schönlein Purpura**. *Oral Sci. Int.* (2012) **9** 59-62. DOI: 10.1016/S1348-8643(12)00027-4
18. Inagaki Y., Abe M., Inaki R., Zong L., Suenaga H., Abe T., Hoshi K.. **A Case of Systemic Infection Caused by Streptococcus pyogenes Oral Infection in an Edentulous Patient**. *Diseases* (2017) **5**. DOI: 10.3390/diseases5030017
19. Inaki R., Igarashi M., Abe M., Saijo H., Hoshi K., Takato T.. **A case of infective endocarditis by Streptococcus mutans bacteremia induced by asymptomatic chronic dental caries in a wisdom tooth**. *Oral Sci. Jpn.* (2014) **9** 95-96
20. 20.
Dental Health Division of Health Policy Bureau Ministry of Health, L. a. W., Japan
Report on the Survey of Dental Diseases 2016Ministry of Health, Labour and WelareTokyo, Japan2016(In Japanese). *Report on the Survey of Dental Diseases 2016* (2016)
21. Abe M., Mitani A., Yao A., Takeshima H., Zong L., Hoshi K., Yanagimoto S.. **Close Associations of Gum Bleeding with Systemic Diseases in Late Adolescence**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17124290
22. Abe M., Mitani A., Zong L., Hoshi K., Yanagimoto S.. **The Challenge of Early Prevention of Periodontal Diseases in Japan**. *J. Adv. Oral Res.* (2021) **13** 5-6. DOI: 10.1177/23202068211022586
23. Ohsato A., Abe M., Ohkubo K., Yoshimasu H., Zong L., Hoshi K., Takato T., Yanagimoto S., Yamamoto K.. **A Comparative Study of Oral Health Status between International and Japanese University Student Patients in Japan**. *Healthcare* (2018) **6**. DOI: 10.3390/healthcare6020052
24. 24.
World Health Organization
World Health Organization Oral Health Surveys: Basic Methods5th ed.World Health OrganizationGeneva, Switzerland2013. *World Health Organization Oral Health Surveys: Basic Methods* (2013)
25. Peres M.A., Macpherson L.M.D., Weyant R.J., Daly B., Venturelli R., Mathur M.R., Listl S., Celeste R.K., Guarnizo-Herreno C.C., Kearns C.. **Oral diseases: A global public health challenge**. *Lancet* (2019) **394** 249-260. DOI: 10.1016/S0140-6736(19)31146-8
26. Patel J., Wallace J., Doshi M., Gadanya M., Ben Yahya I., Roseman J., Srisilapanan P.. **Oral health for healthy ageing**. *Lancet Healthy Longev* (2021) **2** e521-e527. DOI: 10.1016/S2666-7568(21)00142-2
27. Winkelmann J., Listl S., van Ginneken E., Vassallo P., Benzian H.. **Universal health coverage cannot be universal without oral health**. *Lancet Public Health* (2023) **8** e8-e10. DOI: 10.1016/S2468-2667(22)00315-2
28. Watt R.G., Daly B., Allison P., Macpherson L.M.D., Venturelli R., Listl S., Weyant R.J., Mathur M.R., Guarnizo-Herreno C.C., Celeste R.K.. **Ending the neglect of global oral health: Time for radical action**. *Lancet* (2019) **394** 261-272. DOI: 10.1016/S0140-6736(19)31133-X
29. Scannapieco F.A., Cantos A.. **Oral inflammation and infection, and chronic medical diseases: Implications for the elderly**. *Periodontology* (2016) **72** 153-175. DOI: 10.1111/prd.12129
30. Si Y., Fan H., Song Y., Zhou X., Zhang J., Wang Z.. **Association between periodontitis and chronic obstructive pulmonary disease in a Chinese population**. *J. Periodontol.* (2012) **83** 1288-1296. DOI: 10.1902/jop.2012.110472
31. D’Aiuto F., Gkranias N., Bhowruth D., Khan T., Orlandi M., Suvan J., Masi S., Tsakos G., Hurel S., Hingorani A.D.. **Systemic effects of periodontitis treatment in patients with type 2 diabetes: A 12 month, single-centre, investigator-masked, randomised trial**. *Lancet Diabetes Endocrinol.* (2018) **6** 954-965. DOI: 10.1016/S2213-8587(18)30038-X
32. Goldenberg R.L., Culhane J.F., Iams J.D., Romero R.. **Epidemiology and causes of preterm birth**. *Lancet* (2008) **371** 75-84. DOI: 10.1016/S0140-6736(08)60074-4
33. Dubar M., Delatre V., Moutier C., Sy K., Agossa K.. **Awareness and practices of general practitioners towards the oral-systemic disease relationship: A regionwide survey in France**. *J. Eval. Clin. Pract.* (2019) **26** 1722-1730. DOI: 10.1111/jep.13343
34. Ohshima M., Zhu L., Yamaguchi Y., Kikuchi M., Nakajima I., Langham C.S., Lin W., Otsuka K., Komiyama K.. **Comparison of periodontal health status and oral health behavior between Japanese and Chinese dental students**. *J. Oral Sci.* (2009) **51** 275-281. DOI: 10.2334/josnusd.51.275
35. Bawaskar H.S., Bawaskar P.H.. **Oral diseases: A global public health challenge**. *Lancet* (2020) **395** 185-186. DOI: 10.1016/S0140-6736(19)33016-8
36. Peres M.A., Daly B., Guarnizo-Herreno C.C., Benzian H., Watt R.G.. **Oral diseases: A global public health challenge—Authors’ reply**. *Lancet* (2020) **395** 186-187. DOI: 10.1016/S0140-6736(19)32997-6
37. Vergnes J.N., Mazevet M.. **Oral diseases: A global public health challenge**. *Lancet* (2020) **395** 186. DOI: 10.1016/S0140-6736(19)33015-6
38. Abe M., Mitani A., Zong L., Zhang C.D., Hoshi K., Yanagimoto S.. **High frequency and long duration of toothbrushing can potentially reduce the risk of common systemic diseases in late adolescence**. *Spec. Care Dent. Off. Publ. Am. Assoc. Hosp. Dent. Acad. Dent. Handicap. Am. Soc. Geriatr. Dent.* (2022) **42** 317-318. DOI: 10.1111/scd.12670
39. Abe M., Mitani A., Yao A., Hoshi K., Yanagimoto S.. **Systemic Disorders Closely Associated with Malocclusion in Late Adolescence: A Review and Perspective**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph19063401
40. Abe M., Mitani A., Yao A., Zhang C.D., Hoshi K., Yanagimoto S.. **Close Association between Awareness of Teeth-Alignment Disorder and Systemic Disorders in Late Adolescence**. *Healthcare* (2021) **9**. DOI: 10.3390/healthcare9040370
41. Abe M., Mitani A., Yao A., Zong L., Hoshi K., Yanagimoto S.. **Awareness of Malocclusion Is Closely Associated with Allergic Rhinitis, Asthma, and Arrhythmia in Late Adolescents**. *Healthcare* (2020) **8**. DOI: 10.3390/healthcare8030209
|
---
title: Determinants of Deteriorated Self-Perceived Health Status among Informal Settlement
Dwellers in South Africa
authors:
- Tholang Mokhele
- Chipo Mutyambizi
- Thabang Manyaapelo
- Amukelani Ngobeni
- Catherine Ndinda
- Charles Hongoro
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001468
doi: 10.3390/ijerph20054174
license: CC BY 4.0
---
# Determinants of Deteriorated Self-Perceived Health Status among Informal Settlement Dwellers in South Africa
## Abstract
Self-perceived health (SPH) is a widely used measure of health amongst individuals that indicates an individual’s overall subjective perception of their physical or mental health status. As rural to urban migration increases, the health of individuals within informal settlements becomes an increasing concern as these people are at high health and safety risk due to poor housing structures, overcrowding, poor sanitation and lack of services. This paper aimed to explore factors related to deteriorated SPH status among informal settlement dwellers in South Africa. This study used data from the first national representative Informal Settlements Survey in South Africa conducted by the Human Sciences Research Council (HSRC) in 2015. Stratified random sampling was applied to select informal settlements and households to participate in the study. Multivariate logistic regression and multinomial logistic regression analyses were performed to assess factors affecting deteriorated SPH among the informal settlement dwellers in South Africa. Informal settlement dwellers aged 30 to 39 years old (OR = 0.332 $95\%$CI [0.131–0.840], $p \leq 0.05$), those with ZAR 5501 and more household income per month (OR = 0.365 $95\%$CI [0.144–0.922], $p \leq 0.05$) and those who reported using drugs (OR = 0.069 $95\%$CI [0.020–0.240], $p \leq 0.001$) were significantly less likely to believe that their SPH status had deteriorated compared to the year preceding the survey than their counterparts. Those who reported always running out of food (OR = 3.120 $95\%$CI [1.258–7.737], $p \leq 0.05$) and those who reported having suffered from illness or injury in the past month preceding the survey (OR = 3.645 $95\%$CI [2.147–6.186], $p \leq 0.001$) were significantly more likely to believe that their SPH status had deteriorated compared to the year preceding the survey than their counterparts. In addition, those who were employed were significantly (OR = 1.830 $95\%$CI [1.001–3.347], $$p \leq 0.05$$) more likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those who were unemployed with neutral SPH as a base category. Overall, the results from this study point to the importance of age, employment, income, lack of food, drug use and injury or illness as key determinants of SPH amongst informal settlement dwellers in South Africa. Given the rapid increasing number of informal settlements in the country, our findings do have implications for better understanding the drivers of deteriorating health in informal settlements. It is therefore recommended that these key factors be incorporated into future planning and policy development aimed at improving the standard of living and health of these vulnerable residents.
## 1. Introduction
Rural–urban migration in Africa and South Africa, in particular, is a key contributor to the increase in people living in informal settlements. Whilst moving to these urban settlements holds the promise of a better lifestyle and economic opportunities, urban informal settlements in South Africa are often characterised by overcrowding, safety issues, unemployment, hunger, poor basic services delivery and inequalities [1,2,3,4]. The risks imposed by physical housing structures and living environments in informal settlements have considerable impacts on the health and well-being of these vulnerable groups, potentially exposing them to various diseases [2,5,6] and making them especially vulnerable during pandemics such as the COVID-19 pandemic [7,8]. It is anticipated that the implementation of Universal Health Coverage in South Africa, namely National Health Insurance (NHI), will have positive effects on the health of these informal settlement dwellers. For instance, it was reported that the Health Transformation Plan (HTP) had good effects on the health level of informal settlement residents in Iran by ensuring that they had insurance coverage and reducing many economic, social as well as cultural problems, with reduced out-of-pocket expenditures [9]. Previous studies show that informal settlement dwellers are more likely to self-report ill health and, due to the spatial and social marginalisation, are at an increased risk of experiencing mental health issues [7,10,11]. These vulnerable communities in informal settlements often find themselves further marginalised through labour policies that are not designed to accommodate them [8].
Self-perceived health (SPH), also commonly called self-reported health, self-rated health or self-assessed health, is a widely used and acceptable measure of health across individuals that has been applied both in international and South African studies [11]. Various studies have validated it as a good measure of health that is consistent with objective measures of health [11] and also as a strong predictor of mortality [12,13], morbidity [13,14] and healthcare use [15]. The World Health Organization (WHO) recognises it as one of the best measures of health [16]. SPH does not focus on one specific dimension of health, but rather it is used as an indicator of an individual’s overall subjective perception of their physical or mental health status. Thus, the presence of any health condition is a predictor of self-perception of health [17,18]. SPH is commonly measured using a single item health measure on a three- or five-point scale ranging from good to bad. Options can take the form of “very good, good, fair, bad, very bad”. Using this scale, individuals are then required to rate their health.
The factors that influence SPH include health-related predictors, clinically diagnosed health status, history of chronic illnesses, lifestyle factors, socio-economic status and social factors [19,20,21,22]. Studies have described health status in relation to living environments within informal settlements in South Africa [2,3,4,6,10,23,24,25]. These studies show that a majority of informal settlement dwellers suffer a disproportionate burden of sickness and disease. Studies that have assessed the determinants of health in poor urban communities in South Africa have focused on a specific disease or a specific community [26,27,28].
There are some studies that have been undertaken to explore factors affecting poor SPH, even though some were not focused on informal settlements. For instance, Kasenda et al. [ 29] investigated the prevalence of poor SPH and its determinants among 962 participants in Malawi. Kasenda et al. [ 29] found that poor SPH was associated with being female, increasing age, decreasing education, frequent health care attendance as well as living with disability. Kasenda et al. [ 29] further reported that prevalence of poor SPH in Malawi was in line with findings from other countries.
Mlangeni et al. [ 30] explored factors associated with poor SPH amongst individuals from KwaZulu-Natal using data from the 2012 South African national household survey. Mlangeni et al. [ 30] reported that fair/poor SPH was significantly associated with being older, HIV-positive, being an excessive drinker, being educated, being employed and not accessing care regularly. Mlangeni et al. [ 30] recommended that education, job opportunities, social services for poor living conditions and poor well-being, provision of health insurance as well as incorporating health promotion initiatives as part of social support and public services for substance abusers should be considered.
Patterson et al. [ 31] assessed self-rated physical health and related factors in youth residing in slums or informal settlements in Uganda. Patterson et al. [ 31] found that poor self-rated physical health was significantly associated with older age, lower education, having been injured due to their drinking and having initiated alcohol use early, among others. Patterson et al. [ 31] further indicated that poor living conditions in the slums are exacerbated by a range of health concerns and risk behaviours, which impact youth’s physical health, which can adversely impact their long-term health and longevity if no interventions are undertaken.
To the best of our knowledge, no nationally representative study has assessed the factors associated with SPH in informal settlements in South Africa, let alone deteriorated or poor SPH. The evaluation of factors associated with SPH in the context of living environments is essential for the design of strategies to improve health.
This paper aims to expand on the existing body of literature on health in South African informal settlements by exploring the factors related to deteriorated SPH status among informal settlement dwellers in South Africa. The need to address these issues is entrenched in the United Nations Sustainable Development Goals (SGDs)—a set of internationally agreed goals and targets for sustainable development by 2030. SDG 3, which targets good health and well-being, can only be met through strategies that include informal settlements [32]. For SDG 3 to be met, living conditions need to be addressed as set out in SDG 11, which seeks to make cities inclusive, safe, resilient and sustainable. A study of this nature is also important because there is a lack of longitudinal studies that assess the impact of informal settlement upgrading or informal settlement housing and basic infrastructural service improvements on health in South Africa [2]. As the study focuses on informal settlements targeted for upgrades, it forms the basis for future studies that seek to explore the health benefits of these settlement upgrades. Furthermore, with the continued growth of informal settlements, it is important to assess the factors that influence SPH. Findings from this study could provide a narrative for policies and interventions targeted at improving population health in informal settlements.
## 2.1. Data
This paper used data from the first national representative Informal Settlements Survey in South Africa conducted by the Human Sciences Research Council (HSRC) in 2015. For more details on the methods employed in the survey, please see Ndinda et al. [ 33]. Briefly, a stratified random sampling method was employed. The total number of informal settlements targeted for upgrading per province was recorded. This was used as an informal settlement sapling frame. The total number of informal settlements differed by province and only $10\%$ were sampled in each province. The number of households in each of the visited informal settlements across the country was generated using satellite imagery. This number of households per informal settlement was used as the sampling frame for household sampling. The total number of households differed by informal settlement and only a fixed number of 45 households were sampled in each informal settlement. This means that both informal settlements and households did not have equal chance of being sampled or selected. The data were weighted to correct this potential bias due to unequal sampling probabilities as well as in order to have a national representative of informal settlements targeted for upgrading in South Africa. The weights were applied using the realised sample in both cases, that is, visited informal settlements and interviewed households. A total of 75 informal settlements were successfully visited across the country (Figure 1). See Appendix A (Figure A1, Figure A2, Figure A3 and Figure A4) for some visual materials about the informal settlements. About 2380 household heads were interviewed using a semi-structured household questionnaire from these informal settlements. The informal settlement weight was calculated as the inverse of the probability of the informal settlement being realised in a province, while the household weight was calculated as the inverse of the probability of the household being interviewed in an informal settlement. The final weight was the product of informal settlement weight and household weight.
A paper-based semi-structured household questionnaire was used for collection of the data and was administered by research assistants. The household questionnaire consisted of geographic particulars, household roster (demographics, education and economic activity of household members), living standard measure, health and nutrition, housing and tenure, access to services and crime and safety (Supplementary File [questionnaire] attached).
In terms of exclusion and inclusion criteria, although a total of 2380 household respondents were interviewed in the whole survey, only 2242 respondents responded to the main outcome question, which asked about how their health was compared with one year prior to their taking the survey. Therefore, the final sample size that was considered for analysis for this paper was 2242. This is due to the fact that respondents were allowed to answer questions that they were willing to answer and they were told of their rights to not answer questions that they were not willing to answer.
## 2.2. Measures
For the outcome variable, the SPH was considered. Respondents were asked how their health was compared with one year prior to their taking the survey with response options being: 1 = somewhat better, 2 = much better, 3 = about the same, 4 = much worse and 5 = somewhat worse. These options were further dichotomised into two: 1 = worse or deteriorated (much worse and somewhat worse) and 0 = better/about the same (somewhat better, much better and about the same) for multivariate logistic regression analysis. The reason behind dichotomising the outcome variable and using multivariate logistic regression was that this study focused on determinants of deteriorated SPH other than general SPH. A similar practice was noticed where the focus was on one aspect of SPH in previous studies [29,30,31,34,35,36,37]. For consideration of ordered regression logistic regression, the outcome variable was categorised into three groups: much worse or deteriorated (much worse and somewhat worse), neutral/about the same (about the same) and better/improved (somewhat better and much better).
Explanatory variables included demographic factors such as sex (male or female), age (18–29, 30–39, 40–49, 50–59 and 60+) and marital status (married/cohabiting, divorced/widowed/separated and single/never married). Socioeconomic factors included education (no/primary school, secondary school and matric/higher), employment (unemployed or employed), household income per month (ZAR 0-ZAR 2000, ZAR 2001-ZAR 5500 and ZAR 5501 and more), whether the household has ever run out of food (yes or no) and Living Standard Measure (low, medium and high). Living Standard Measure was developed using Multiple Correspondence Analysis (MCA). The following 19 asset variables with yes response n > 100 were considered from 35 assets: fridge, deep freezer, VCR/DVD, cell phone, washing machine, internet access, electric/gas stove without oven, TV, radio, HI-FI, microwave oven MNET/DSTV, car, iron, electric/gas stove with oven, fan, mattress, bicycle and tools (see Appendix B). All asset variables were coded 0 = no and 1 = yes. Health-related and behavioural factors included illness or injury suffered in the past month prior to taking the survey (yes/no), tobacco use (yes/no), alcohol use (yes/no) and drug use (yes/no).
## 2.3. Data Analysis
Data were analysed in Stata version 15.0 [38]. As indicated, the data were weighted to correct potential bias due to unequal sampling probabilities and to be able to generalise findings to a national representative of informal settlements targeted for upgrading in South Africa. The Stata “svy” command was used to incorporate these weights during data analysis. Differences in categorical variables were compared using Chi-square tests. Multivariate logistic regression analysis was performed to assess factors affecting deteriorated SPH among the informal settlement dwellers in South Africa. Furthermore, ordered regression logistic regression was considered to attain a better understanding of factors associated with deteriorated/worse SPH compared to the other two groups classified as neutral/about the same and better/improved separately, unlike in the case of the multivariate logistic regression wherein the two were grouped together. The Stata “omodel” command was performed to test the proportional odds assumption, and the results revealed that the proportional odds assumption was violated. Multinomial logistic regression analysis, which has been used for ordered outcome variables in previous studies [39,40,41,42], was therefore considered for further analysis. As the focus of this study was on determinants of deteriorated SPH, the two models were run with better/improved SPH being used as base category in the first model while the neutral SPH was the base category in the second model. Odds Ratios (ORs) were reported from the multivariate logistic regression and multinomial logistic regression. Confidence Intervals (CIs) were set at $95\%$, with a p value ≤ 0.05 considered statistically significant in all analyses.
## 3.1. Background Characteristics of Respondents
The study sample used for this paper consisted of 2242 respondents. Males constituted $54.5\%$ of the sample while females accounted for $45.5\%$ (Table 1). There was no significant difference between males and females with $$p \leq 0.489.$$ The dominant age group was those aged 30 to 39 years-old at about $30\%$, followed by those aged 40 to 49 years-old at $26.1\%$. In terms of marital status, just below half ($48.1\%$) were married or cohabiting, followed by $43.8\%$ who were single or never married. No/primary school and secondary school accounted for around $37\%$ each. The majority of respondents, at $58.9\%$, fell under the ZAR 0 to ZAR 2000 household income band. Almost one third ($31.6\%$) of the informal settlement dwellers were smokers.
## 3.2. Deteriorated SPH Status among Informal Settlement Dwellers
Table 2 highlights deteriorated SPH and explanatory factors among informal settlement dwellers across the country. Deteriorated SPH status was significantly higher among those with no/primary school ($19.6\%$) and those who did not use drugs ($15.3\%$) compared to their relevant counterparts. Informal settlement dwellers who never ran out of food ($10.0\%$) and those who did not experience illness or injury in the past month prior to taking the survey ($11.4\%$) were significantly less likely to believe that their SPH deteriorated compared to the year prior to taking the survey of their relevant counterparts.
## 3.3. Factors Influencing Deteriorated SPH Status among Informal Settlement Dwellers
Informal settlement dwellers aged 30 to 39 years-old were significantly less (OR = 0.332 $95\%$CI [0.131–0.840], $p \leq 0.05$) likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those aged 18 to 29 years-old (Table 3). Informal settlement dwellers with ZAR 5501 and more household income were significantly less (OR = 0.365 $95\%$CI [0.144–0.922], $p \leq 0.05$) likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those in the ZAR 0 to ZAR 2000 household income band. Those who reported always running out of food were significantly more (OR = 3.120 $95\%$CI [1.258–7.737], $p \leq 0.05$) likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those who never ran out of food. Residents who reported having suffered from illness or injury in the past month preceding the survey were significantly more (OR = 3.645 $95\%$CI [2.147–6.186], $p \leq 0.001$) likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those who did not. Those who reported using drugs were significantly less (OR = 0.069 $95\%$CI [0.020–0.240], $p \leq 0.001$) likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those who did not use drugs.
Furthermore, multinomial logistic regression models showed that similar factors (age, ran out of food, injury or illness and drug use) were significantly associated with deteriorated SPH status among informal settlement dwellers, as was the case with multivariate logistic regression analysis (Table 4). The only difference is that household income was not significant in multinomial logistic regression models, and instead, employment was significant when neutral SPH was used as a base category. For instance, employed residents were significantly (OR = 1.830 $95\%$CI [1.001–3.347], $$p \leq 0.05$$) more likely to believe that their SPH status had deteriorated compared to the year preceding the survey than those who were unemployed with neutral SPH as a base category.
## 4. Discussion
This paper’s aim was to investigate factors related to deteriorated SPH status among informal settlement residents in a national survey conducted in 2015 in South Africa. This study found that informal settlement residents within a certain age range (between 30 and 39 years), higher income bracket (>R5501) and demonstrating previous use of drugs were significantly less likely to report that their SPH had deteriorated compared to the previous year than their respective counterparts.
Age has been found to be associated with SPH in previous studies [43,44]. This association between age and SPH is not consistent across all studies in the sense that the age ranges associated with SPH varies in different studies. For example, those aged 85 years and older were found to have higher SPH than those aged 64 to 75 years in one study [45], while other studies found no significant differences in SPH between those aged 75 and older and those aged between 35 and 44 years [46], and other studies generally found similarities in SPH across different age subgroups [22]. Bonner et al. [ 30] found that between $75\%$ and $86\%$ of those aged 40 years and older reported good health. Most participants in this study fell between 30 and 49 years old at $55.9\%$ of the total number. This relatively younger cohort might partly explain the significant perception that SPH had not deteriorated.
Contrary to the findings of this study that residents that were employed were significantly more likely to report deteriorated SPH, Chola and Alaba [34] found that those employed were significantly more likely to report good SPH, while Mlangeni [30] also found those employed were significantly less likely to have fair/poor SPH compared to those who were unemployed. This finding might be caused by the fact that informal settlement residents are predominantly poor, so even those that are employed might be earning less, hence they are not far apart in terms of better wealth compared to their unemployed counterparts. However, this finding needs to be explored further as it is commonly known that poor residents who are unemployed are more likely report poor SPH, especially in the informal settlement setting.
A higher income being associated with perceptions that health status had not deteriorated is consistent with previous studies. Research has shown that negative perceptions of environmental hazards were associated with poor self-perception in a low-income community [46]. Moreover, factors such as lower socio-economic status, living in slums, living in a low-income household and poverty were also associated with poor self-rated health [47,48]. Higher income seems to have had a protective effect against poor SPH status.
The finding of those who reported using drugs having perceptions that health status had not deteriorated is inconsistent with what is found in the literature. Previous studies reported that the more drugs a person used, the greater is the likelihood of reporting poor SPH. In certain instances, users of opioids were found to have poorer self-rated health than other drug users [49], and those who frequently used drugs to cope had higher odds of reporting to be poor SPH [50]. A possible explanation for those who reported using drugs in our study having perceptions that their SPH status had not deteriorated could be perhaps they had consumed drugs at the time of the interview. This inebriated state would have been useful to mask the actual perceptions. In addition, a very small number of informal residents who indicated they used drugs reported that their SPH had deteriorated compared to the previous year preceding the survey. Therefore, this could also contribute to the inconsistent findings of this paper.
Those who reported running out of food and those who had suffered from illness or injury in the past month were more likely to believe that their SPH status had deteriorated compared to the preceding year. People who are diagnosed to have clinical evidence of ill health or those who report morbidity are generally more likely to report poor SPH [51,52]. Poor SPH has also been shown to be associated with frailty and prefrailty in urban-living older adults [53]. The evidence suggests that factors which are more immediate and personal to the individual, such as if they are currently living with an ailment or not or if they are on any treatment, have a significant impact on the overall perception of wellbeing.
SPH should be viewed as reflecting people’s lived experiences, their perceptions of health, access to healthcare and how these interact with lifestyle factors, and should also include biological factors such as sex [54]. This means that a more holistic view of health will have to be adopted, since it has been shown that people who live in informal settlements are constantly navigating structural constraints imposed by lack of access to amenities. More specifically, the state of the informal settlements earmarked for upgrading sampled in this study were characterized by a lack of basic services wherein as much as $52\%$ did not have access to electricity, $55\%$ used communal taps and $53\%$ used pit latrines [42]. This state of lack is likely to lead to distress and low self-esteem, which have been shown to be negatively associated with good health [22]. Therefore, when reporting on SPH, it is important to include variables that characterize and seek to incorporate both the physical and social environments [55].
The foremost goal of conducting this kind of research is to identify vulnerable groups and all the possible ways through which individuals and communities experience poor health [54]. The findings in this study identify some of the specific factors that can be targeted in designing interventions to improve the wellbeing of informal settlement residents in South Africa. These factors can be broadly categorized as structural (higher income, employment and running out of food) and individual (age, use of drugs and injury and illness) to help with the development of these interventions. The findings from this study also provide an overview of the general health conditions of residents of informal settlements targeted for upgrading in South Africa. It is therefore recommended that these key factors be incorporated into future planning and policy development aimed at improving the standard of living and health of these vulnerable residents. In addition, based on the findings of this research, the authors recommend that deteriorated or poor SPH should be considered as an indicator for poor health status especially where physical health examination is not financially feasible. Since the urban poor also make up the majority of the labour in the cities, labour legislation that makes provision for decent housing could help alleviate the structural and environmental influences on ill health and poor SPH [8].
Among the limitations of this study, it is important to bear in mind that SPH is subject to both recall bias and social desirability bias. However, the social desirability bias could have been likely mitigated by the need for improved services, thus generating a higher likelihood of more accurate responses. The people who participated are skewed towards unemployed and lower income groups; therefore, overestimating poor SPH is highly possible. However, the findings from this study provide a general picture of deteriorated SPH and related factors among informal settlements in South Africa.
## 5. Conclusions
SPH is a widely used and validated measure of health that is applied in various literatures. This study contributes to the existing body of the literature on health in South African informal settlements by providing insight into the factors associated with deteriorated SPH status amongst informal settlement dwellers in South Africa. Informal settlement dwellers aged 30 to 39 years old, those with ZAR 5501 and more household income and those who reported using drugs were significantly less likely to believe that their SPH status had deteriorated compared to the year preceding the survey. Those who were employed, reported always running out of food and residents who reported having suffered from illness or injury in the past month preceding the survey were more likely to believe that their SPH status had deteriorated compared to the year preceding the survey. Given the rapidly increasing number of informal settlements across the country, especially in the metropolitan areas such as in Gauteng, Western Cape and KwaZulu-Natal, the evidence provided in this study is important for the development of interventions that work towards health improvement, such as health promotion and treatment programmes that aim to reduce illness and injury. It is therefore recommended that these key factors be incorporated into future planning and policy development aimed at improving the standard of living and health of these vulnerable residents. It is also recommended that deteriorated or poor SPH should be considered as another form of assessment of poor health status among informal settlement residents especially where regular physical health examinations are not possible.
## References
1. Zerbo A., Delgado R.C., González P.A.. **Vulnerability and everyday health risks of urban informal settlements in Sub-Saharan Africa**. *J. Glob. Health* (2020) **4** 46-50. DOI: 10.1016/j.glohj.2020.04.003
2. Weimann A., Oni T.. **A systematised review of the health impact of urban informal settlements and implications for upgrading interventions in South Africa, a rapidly urbanising middle-income country**. *Int. J. Environ. Health Res.* (2019) **16**. DOI: 10.3390/ijerph16193608
3. Ndinda C., Ndhlovu T.P.. **Attitudes towards foreigners in informal settlements targeted for upgrading in South Africa: A gendered perspective**. *Agenda* (2016) **30** 131-146. DOI: 10.1080/10130950.2016.1212598
4. Ndinda C., Hongoro C., Labadarios D., Mokhele T., Khalema E., Weir-Smith G., Sobane K.. **Status of informal settlements targeted for upgrading implications for policy and practice**. *HSRC Rev.* (2017) **15** 16-19
5. Sverdlik A.. **Ill-health and poverty: A literature review on health in informal settlements**. *Environ. Urban.* (2011) **23** 123-155. DOI: 10.1177/0956247811398604
6. Cunnan P., Maharaj B.. **Against the odds: Health care in an informal settlement in Durban**. *Dev. S. Afr.* (2000) **17** 667-686. DOI: 10.1080/713661430
7. 7.
UN-Habitat
Habitat III Issue Paper 22—Informal SettlementsUN-HabitatNew York, NY, USA2015. *Habitat III Issue Paper 22—Informal Settlements* (2015)
8. Collantes C.F.. **“Unforgotten” informal communities and the COVID-19 pandemic: Sitio San Roque under Metro Manila’s lockdown**. *Int. J. Hum. Rights Healthc.* (2021) **14** 279-292. DOI: 10.1108/IJHRH-09-2020-0073
9. Behzadifar M., Saran M., Behzadifar M., Martini M., Bragazzi N.L.. **The ‘Health Transformation Plan’ in Iran: A policy to achieve universal health coverage in slums and informal settlement areas**. *Int. J. Health Plann. Manag.* (2021) **36** 267-272. DOI: 10.1002/hpm.3082
10. Ndinda C., Ndhlovu P.T., Juma P., Asiki G., Kyobutungi C.. **Evolution of NCD policies in South Africa**. *BMC Public Health* (2018) **18**. DOI: 10.1186/s12889-018-5832-8
11. Wu S., Wang R., Zhao Y., Ma X., Wu M., Yan X., He J.. **The relationship between self-rated health and objective health status: A population-based study**. *BMC Public Health* (2013) **13**. DOI: 10.1186/1471-2458-13-320
12. Burström B., Fredlund P.. **Self rated health: Is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes?**. *J. Epidemiol. Community Health* (2001) **55** 836-840. DOI: 10.1136/jech.55.11.836
13. Kaplan G.A., Goldberg D.E., Everson S.A., Cohen R.D., Salonen R., Tuomilehto J., Salonen J.. **Perceived health status and morbidity and mortality: Evidence from the Kuopio ischaemic heart disease risk factor study**. *Int. J. Epidemiol.* (1996) **25** 259-265. DOI: 10.1093/ije/25.2.259
14. Møller L., Kristensen T.S., Hollnagel H.. **Self rated health as a predictor of coronary heart disease in Copenhagen, Denmark**. *J. Epidemiol. Community Health.* (1996) **50** 423-428. DOI: 10.1136/jech.50.4.423
15. Miilunpalo S., Vuori I., Oja P., Pasanen M., Urponen H.. **Self-rated health status as a health measure: The predictive value of self-reported health status on the use of physician services and on mortality in the working-age population**. *J. Clin. Epidemiol.* (1997) **50** 517-528. DOI: 10.1016/S0895-4356(97)00045-0
16. 16.
World Health Organization
Health Interview Surveys: Towards International Harmonization of Methods and InstrumentsWHO Regional Office for EuropeCopenhagen, Denmark1996. *Health Interview Surveys: Towards International Harmonization of Methods and Instruments* (1996)
17. Ho S.Y., Mak K.K., Thomas G.N., Schooling M., Fielding R., Janus E.D., Lam T.H.. **The relation of chronic cardiovascular diseases and diabetes mellitus to perceived health, and the moderating effects of sex and age**. *Soc. Sci. Med.* (2007) **65** 1386-1396. DOI: 10.1016/j.socscimed.2007.05.032
18. Piko B., Barabás K., Boda K.. **Frequency of common psychosomatic symptoms and its influence on self-perceived health in a Hungarian student population**. *Eur. J. Public Health* (1997) **7** 243-247. DOI: 10.1093/eurpub/7.3.243
19. Piko B.. **Health-related predictors of self-perceived health in a student population: The importance of physical activity**. *J. Community Health* (2000) **25** 125-137. DOI: 10.1023/A:1005129707550
20. Cau B.M., Falcão J., Arnaldo C.. **Determinants of poor self-rated health among adults in urban Mozambique**. *BMC Public Health* (2016) **16**. DOI: 10.1186/s12889-016-3552-5
21. Eboreime-Oikeh O., Otiene G., Okumbe G.. **Determinants of inequalities in self-perceived health among the urban poor in Kenya: A gender perspective**. *Glob. J. Med. Public Health* (2016) **5** 1-12
22. Shields M., Shooshtari S.. **Determinants of self-perceived health**. *Health Rep.* (2001) **13** 35-52. PMID: 15069807
23. Govender T., Barnes J.M., Pieper C.H.. **Living in low-cost housing settlements in Cape Town, South Africa—The epidemiological characteristics associated with increased health vulnerability**. *J. Urban Health* (2010) **87** 899-911. DOI: 10.1007/s11524-010-9502-0
24. Shortt N.K., Hammett D.. **Housing and health in an informal settlement upgrade in Cape Town, South Africa**. *J. Hous. Built. Environ.* (2013) **28** 615-627. DOI: 10.1007/s10901-013-9347-4
25. Sanni S., Hongoro C., Ndinda C., Wisdom J.P.. **Assessment of the multi-sectoral approach to tobacco control policies in South Africa and Togo**. *BMC Public Health* (2018) **18** 1-12. DOI: 10.1186/s12889-018-5829-3
26. Van Rooyen J., Kruger H., Huisman H., Wissing M., Margetts B., Venter C., Vorster H.. **An epidemiological study of hypertension and its determinants in a population in transition: The THUSA study**. *J. Hum. Hypertens.* (2000) **14** 779-787. DOI: 10.1038/sj.jhh.1001098
27. Collishaw S., Gardner F., Aber J.L., Cluver L.. **Predictors of mental health resilience in children who have been parentally bereaved by AIDS in urban South Africa**. *J. Abnorm. Child Psychol.* (2016) **44** 719-730. DOI: 10.1007/s10802-015-0068-x
28. De Wet T., Plagerson S., Harpham T., Mathee A.. **Poor housing, good health: A comparison of formal and informal housing in Johannesburg, South Africa**. *Int. J. Public Health* (2011) **56** 625-633. DOI: 10.1007/s00038-011-0269-1
29. Kasenda S., Meland E., Hetlevik Ø., Thomas Mildestvedt T., Dullie L.. **Factors associated with self-rated health in primary care in the South-Western health zone of Malawi**. *BMC Prim. Care* (2022) **23**. DOI: 10.1186/s12875-022-01686-y
30. Mlangeni L., Mabaso M., Makola L., Zuma K.. **Predictors of poor self-rated health in KwaZulu-Natal, South Africa: Insights from a cross-sectional survey**. *Open Public Health J.* (2019) **12** 164-171. DOI: 10.2174/1874944501912010164
31. Patterson Q.A., Culbreth R.E., Kasirye R., Kebede S., Bitarabeho J., Swahn M.H.. **Self-rated physical health, health-risk behaviors, and disparities: A cross-sectional study of youth in the slums of Kampala, Uganda**. *Glob. Public Health* (2022) **17** 2962-2976. DOI: 10.1080/17441692.2021.2007974
32. **Sustainable Development Goals**. (2017)
33. Ndinda C., Hongoro C., Labadarios D., Mokhele T., Khalema E., Weir-Smith G., Douglas M., Ngandu S., Parker W., Tshitangano F.. *A Baseline Assessment for Future Impact Evaluation for Informal Settlements Targeted for Upgrading: Study Report* (2016)
34. Chola L., Alaba O.. **Association of neighbourhood and individual social capital, neighbourhood economic deprivation and self-rated health in South Africa—A multi-level analysis**. *PLoS ONE* (2013) **8**. DOI: 10.1371/journal.pone.0071085
35. Ocampo-Chaparro J.M., Zapata-Ossa H.J., Cubides-Munévar A.M., Curcio C.L., Villegas J.D., Reyes-Ortiz C.A.. **Prevalence of poor self-rated health and associated risk factors among older adults in Cali, Colombia**. *Colomb. Med.* (2013) **44** 224-231. DOI: 10.25100/cm.v44i4.1362
36. Pinilla-Roncancio M., González-Uribe C., Lucumí D.I.. **Do the determinants of self-rated health vary among older people with disability, chronic diseases or both conditions in urban Colombia?**. *Cad. Saúde Pública* (2020) **36** e00041719. DOI: 10.1590/0102-311x00041719
37. Höfelmann D.A., Garcia L.P., de Freitas L.R.S.. **Self-rated health in Brazilian adults and elderly: Data from the National Household Sample Survey 2008**. *Salud Publica Mex.* (2014) **56** 603-611. DOI: 10.21149/spm.v56i6.7386
38. 38.
Stata Corp
Stata Statistical Software: Release 15Stata Corp LLCCollege Station, TX, USA2017. *Stata Statistical Software: Release 15* (2017)
39. Rahman M.M., Ngadan D.P., Arif M.T.. **Factors affecting satisfaction on antenatal care services in Sarawak, Malaysia: Evidence from a cross sectional study**. *SpringerPlus* (2016) **5** 725. DOI: 10.1186/s40064-016-2447-3
40. Jalil A., Zakar R., Zakar M.Z., Fischer F.. **Patient satisfaction with doctor-patient interactions: A mixed methods study among diabetes mellitus patients in Pakistan**. *BMC Health Serv. Res.* (2017) **17**. DOI: 10.1186/s12913-017-2094-6
41. Sewpaul R., Mabaso M., Dukhi N., Naidoo I., Vondo N., Davids A., Mokhele T., Reddy P.. **Determinants of social distancing among South Africans from twelve days into the COVID-19 lockdown**. *Front. Public Health* (2021) **9** 1-15. DOI: 10.3389/fpubh.2021.632619
42. Mutyambizi C., Mokhele T., Ndinda C., Hongoro C.. **Access to and satisfaction with basic services in informal settlements: Results from a baseline assessment survey**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17124400
43. Bonner W.I.A., Weiler R., Orisatoki R., Lu X., Andkhoie M., Ramsay D., Yaghoubi M., Steeves M., Szafron M., Farag M.. **Determinants of self-perceived health for Canadians aged 40 and older and policy implications**. *Int. J. Equity Health* (2017) **16** 94. DOI: 10.1186/s12939-017-0595-x
44. Lin M.H., Chen L.J., Huang S.T., Meng L.C., Lee W.J., Peng L.N., Hsiao F.Y., Chen L.K.. **Age and sex differences in associations between self-reported health, physical function, mental function and mortality**. *Arch. Gerontol. Geriatr.* (2022) **98** 104537. DOI: 10.1016/j.archger.2021.104537
45. Damian J., Ruigomez A., Pastor V., Martin-Moreno J.M.. **Determinants of self assessed health among Spanish older people living at home**. *J. Epidemiol. Community Health* (1999) **53** 412-416. DOI: 10.1136/jech.53.7.412
46. Ou J.Y., Peters J.L., Levy J.I., Bongiovanni R., Rossini A., Scammell M.K.. **Self-rated health and its association with perceived environmental hazards, the social environment, and cultural stressors in an environmental justice population**. *BMC Public Health* (2018) **18**. DOI: 10.1186/s12889-018-5797-7
47. Caicedo-Velásquez B., Restrepo-Méndez M.C.. **The role of individual, household and area of residence factors on poor self-rated health in Colombian adults: A multilevel study**. *Biomedica* (2020) **40** 296-308. DOI: 10.7705/biomedica.4818
48. Giatti L., Barreto S.M., César C.C.. **Unemployment and self-rated health: Neighborhood influence**. *Soc. Sci. Med.* (2010) **71** 815-823. DOI: 10.1016/j.socscimed.2010.05.021
49. Rosholm J.U., Christensen K.. **Relationship between drug use and self-reported health in elderly Danes**. *Eur. J. Clin. Pharmacol.* (1997) **53** 179-183. DOI: 10.1007/s002280050359
50. Mauro P.M., Canham S.L., Martins S.S., Spira A.P.. **Substance-use coping and self-rated health among US middle-aged and older adults**. *Addict. Behav.* (2015) **42** 96-100. DOI: 10.1016/j.addbeh.2014.10.031
51. Ozcan S., Amiel S.A., Rogers H., Choudhary P., Cox A., de Zoysa N., Hopkins D., Forbes A.. **Poorer glycaemic control in type 1 diabetes is associated with reduced self-management and poorer perceived health: P cross-sectional study**. *Diabetes Res. Clin. Pract.* (2014) **106** 35-41. DOI: 10.1016/j.diabres.2014.07.023
52. Wong Y.Y.E., Almeida O.P., McCaul K.A., Yeap B.B., Hankey G.J., van Bockxmeer F.M., Flicker L.. **Elevated homocysteine is associated with poorer self-perceived physical health in older men: The health in men study**. *Maturitas* (2012) **73** 158-163. DOI: 10.1016/j.maturitas.2012.06.007
53. Zhao J., Chhetri J.K., Ji S., Ma L., Dan X., Chan P.. **Poor self-perceived health is associated with frailty and prefrailty in urban living older adults: A cross-sectional analysis**. *Geriatr. Nurs. Minneap.* (2020) **41** 754-760. DOI: 10.1016/j.gerinurse.2020.05.003
54. Tadiri C.P., Gisinger T., Kautzky-Willer A., Kublickiene K., Herrero M.T., Norris C.M., Raparelli V., Pilote L.. **Determinants of perceived health and unmet healthcare needs in universal healthcare systems with high gender equality**. *BMC Public Health* (2021) **21**. DOI: 10.1186/s12889-021-11531-z
55. Meireles A.L., Xavier C.C., de Souza Andrade A.C., de Lima Friche A.A., Proietti F.A., Caiaffa W.T.. **Self-rated health in urban adults, perceptions of the physical and social environment, and reported comorbidities: The BH health study**. *Cad. Saúde Pública* (2015) **31** 120-135. DOI: 10.1590/0102-311X00076114
|
---
title: 'Ellagic Acid Prevents Particulate Matter-Induced Pulmonary Inflammation and
Hyperactivity in Mice: A Pilot Study'
authors:
- Sunyoung Jeong
- Sungryong Bae
- Eui-Cheol Shin
- Jong-Hwa Lee
- Jung-Heun Ha
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001477
doi: 10.3390/ijerph20054523
license: CC BY 4.0
---
# Ellagic Acid Prevents Particulate Matter-Induced Pulmonary Inflammation and Hyperactivity in Mice: A Pilot Study
## Abstract
The inhalation of fine particulate matter (PM) is a significant health-related environmental issue. Previously, we demonstrated that repeated PM exposure causes hyperlocomotive activity in mice, as well as inflammatory and hypoxic responses in their lungs. In this study, we evaluated the potential efficacy of ellagic acid (EA), a natural polyphenolic compound, against PM-induced pulmonary and behavioral abnormalities in mice. Four treatment groups were assigned in this study ($$n = 8$$): control (CON), particulate-matter-instilled (PMI), low-dose EA with PMI (EL + PMI), and high-dose EA with PMI (EH + PMI). EA (20 and 100 mg/kg body weight for low dose and high dose, respectively) was orally administered for 14 days in C57BL/6 mice, and after the eighth day, PM (5 mg/kg) was intratracheally instilled for 7 consecutive days. PM exposure induced inflammatory cell infiltration in the lungs following EA pretreatment. Moreover, PM exposure induced inflammatory protein expression in the bronchoalveolar lavage fluid and the expression of inflammatory (tumor necrosis factor alpha (Tnfα), interleukin (Il)-1b, and Il-6) and hypoxic (vascular endothelial growth factor alpha (Vegfα), ankyrin repeat domain 37 (Ankrd37)) response genes. However, EA pretreatment markedly prevented the induction of expression of inflammatory and hypoxic response genes in the lungs. Furthermore, PM exposure significantly triggered hyperactivity by increasing the total moving distance with an increase in moving speed in the open field test. On the contrary, EA pretreatment significantly prevented PM-induced hyperactivity. In conclusion, dietary intervention with EA may be a potential strategy to prevent PM-induced pathology and activity.
## 1. Introduction
Air pollution continues to threaten public health in many cities and endangers the basic right to breathe. Diesel exhaust particles (DEPs) consist of a carbon core that adsorbs a mixture of sulfate, nitrate, metals, and organic chemicals, including polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. DEPs are one of the major components of urban air pollution [1]. DEPs comprise mainly fine particulate matter (PM) with diameters less than 2.5 µm (PM 2.5), including nanoparticles, which can reach the lower lobe of the lung and even systemic circulation. Exposure to air pollutants, including DEPs, is inevitable, and there is cumulative evidence that indicates that continuous exposure to DEPs triggers detrimental effects on the pulmonary [2,3], renal [4,5], hepatic [6], cardiovascular [7,8], and nervous [9,10] systems. In particular, DEP inhalation significantly induces pulmonary inflammation, oxidative stress, and malfunction in mammals [11,12].
In addition, increased exposure to air pollutants triggers behavioral disorders in humans and experimental animals. The worsening degree of air pollution is closely intertwined with the early onset of attention deficit hyperactivity disorder in Taiwan [13,14]. In the US and Denmark, increased inhalation of air pollutants increases the incidence of psychiatric disorders, such as depression, bipolar disorder, and schizophrenia [15]. Although the exact developmental mechanisms of direct pathological causes of behavioral disorders are poorly understood, environmental challenges may be significant initiators of behavioral disorders [16,17]. In addition, in experimental mice, exposure of dams to air pollutants during pregnancy triggered hyperactivity in the pups [18,19]. Furthermore, we have previously demonstrated that PM instillation in relatively young adulthood (8~10 weeks) triggers hyperactivity in mice [20,21]. Interestingly, dietary intervention with phenolic components successfully prevented PM-induced hyperactivity in experimental mice [21].
If exposure to air pollution is inevitable, then dietary intervention with functional materials may be an excellent preventive means to attenuate and/or prevent air-pollutant-induced physiological disturbances [22,23,24,25]. Polyphenolic components are strong candidates for coping with exposure to air pollutants, given that polyphenols are abundant in plants and possess multiple biological functions, including anti-inflammatory [26,27,28], antiendoplasmic reticulum stress [29,30], and antioxidative effects [31,32,33]. Among polyphenols, ellagic acid (EA) may be a promising candidate to mitigate and/or prevent air-pollutant-induced pathophysiological responses in humans. EA is a conjugated form of two distinctive gallic acids, known as strong antioxidants, bridged by two lactone rings [34]. Plants (e.g., berries, grapes, and pomegranates) produce EA as a metabolite of tannin hydrolysis [34]. EA attenuates dyslipidemia [35], weight gain [36], insulin resistance [37], carcinogenesis [38,39], inflammatory responses [40,41], and oxidative stress [42,43]. Therefore, owing to its biological functionalities, EA may be a promising polyphenol candidate that can mitigate the effects of air pollutant inhalation.
EA is an excellent candidate for controlling pathophysiological phenomena during the inhalation of air pollutants. Inhalation of air pollutants directly induces pulmonary disturbances such as inflammation. Therefore, dietary supplements against exposure to air pollutants should be effective in mitigating pathological events in the lungs. According to previous reports, EA significantly ameliorated pulmonary damage triggered by various toxicants to the pulmonary system, such as hydrochloric acid [40], carbon tetrachloride [44], elastase [45], bleomycin with cyclophosphamide [46], and ovalbumin-induced asthma [47] in multiple animal models. The protective role of EA against pulmonary toxicants mainly relies on its anti-inflammatory and/or antioxidant effects [40,44,45,46,47]. Pretreatment with EA significantly attenuated LPS-induced acute pulmonary pathology and significantly reduced inflammatory cell infiltration and cytokine production (TNFα, IL-1β, and IL-6) in experimental mice [48].
Based on a literature review, EA may have protective functions against the effects of exposure of mammals to air pollutants (i.e., PM). However, animal models of PM exposure by instillation have only been established recently; therefore, robust experimental data are not yet available. Moreover, the preventive role of EA against pulmonary PM exposure has not yet been fully elucidated. In this study, to understand the protective effects of EA against acute pulmonary PM exposure, EA was orally administered at 20 and 100 mg/kg for 7 days before initiation of PM instillation. After 1 week of EA administration, PM (5 mg/kg) was instilled for 7 consecutive days while maintaining the aforementioned EA administration. To determine the beneficial effects of EA on PM exposure, pulmonary immune cell infiltration, PM loading, cytokine secretion, and mRNA expression were analyzed. Moreover, behavioral alterations caused by PM exposure and EA pretreatment were examined using an open field test (OFT).
## 2.1. Animal Experiments
All experimental animal procedures were previewed and approved by the Institutional Animal Care and Use Committee (protocol # 2002-0023) of the Korea Institute of Toxicology and accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Seven-week-old male C57BL/6NCrlOri mice (Orient Bio Inc., Seongnam, Republic of Korea) were acquired, acclimatized for 7 days, and maintained in a controlled room at a temperature of 22 °C and humidity of $50\%$ with a 12 h light/dark cycle. The mice were allowed free access to a purified diet (PMI Nutrition International LLC, St. Louis, MO, USA) and filtered distilled water. After the acclimatization period, the experimental mice were weighed and randomly assigned to four groups ($$n = 8$$/group) as follows:[1]Control (CON): $5\%$ dimethyl sulfoxide (DMSO; Sigma-Aldrich, St. Louis, MO, USA) was administered orally for 14 days, and after the eighth day of DMSO administration, distilled water was instilled for another 7 days.[2]PM-instilled (PMI): PM (5 mg/kg; standard reference material 2975; National Institute of Standards and Technology, Gaithersburg, MD, USA) was instilled for 7 days.[3]Low-dose of EA with PMI (EL + PMI): EA (20 mg/kg; Sigma-Aldrich) was administered orally for 14 days, and after the eighth day of EA administration, PM (5 mg/kg) was instilled for another 7 days.[4]High-dose of EA with PMI (EH + PMI): EA (100 mg/kg) was administered orally for 14 days, and after the eighth day of EA administration, PM (5 mg/kg) was instilled for another 7 days.
Fifteen days after the first EA administration, the mice were euthanized by isoflurane inhalation. After sacrifice, the final body, liver, and lung weights were measured. PM instillation was performed 1 h after EA treatment in the EL + PMI and EH + PMI groups.
## 2.2. Histological Analysis and Collection of Bronchoalveolar Lavage Fluid (BALF)
The left lung was fixed in $10\%$ (v/v) neutral-buffered formalin (Sigma-Aldrich) and further processed for hematoxylin and eosin staining as previously described [20,21]. BALF was collected as previously described [20,21], and cells were counted using a cell counter (NC-250; ChemoMetec, Gydevang, Denmark). In addition, cell types in the BALF were distinguished after smearing with the cytospin slide (Thermo Fisher Scientific, Waltham, MA, USA) and staining with Diff-Quik solution (Dade Diagnostics, Aguada, Puerto Rico) as previously described [20,21].
## 2.3. Enzyme-Linked Immunosorbent Assay (ELISA)
Mouse TNFα (Invitrogen, Waltham, MA, USA), IL-6 (Invitrogen), and H2O2 (Biovision, Milpitas, CA, USA) levels in the BALF were analyzed using commercially available ELISA kits. Serum corticosterone levels were determined using an ELISA kit (Abcam, Cambridge, MA, USA). All ELISA procedures were performed in accordance with the manufacturer’s instructions.
## 2.4. Quantitative Real-Time PCR (qRT-PCR)
Total RNA extraction, cDNA synthesis, and relative qRT-PCR analyses were performed as previously described [21]. Primers used for qRT-PCR are indicated in Table 1 and previous literature [20,21].
## 2.5. Open Field Test
An open field test (OFT) was performed 1 h after the last PM instillation. Experimental mice were individually placed in the center of a plexiglass container (42 cm width × 42 cm depth × 42 cm height). The illumination of the plexiglass container was controlled by placing a 100 W lamp 2 m above the floor. The mice were acclimatized for 10 min in an OFT environment, and behavioral indices were recorded continuously for 10 min. Mice movements were recorded using an automated computer system (Ethovision, Noldus, The Netherlands). The distance, duration, and velocity of movements were calculated and expressed in inches, seconds, and inch/s, respectively.
## 2.6. Statistical Analysis
All data from the experiments are summarized and expressed as the mean ± standard deviation for each group. Equal variance of experimental data was assessed using the D’Agostino and *Pearson omnibus* test. If the datasets were normally distributed, one-way analysis of variance (ANOVA) with Tukey’s post hoc test was applied. Otherwise, Kruskal–Wallis with Dunn’s post hoc test was executed. Statistical significance was set at $p \leq 0.05.$ *Statistical analysis* was performed using GraphPad PRISM 5 (GraphPad Software, San Diego, CA, USA).
## 3.1. Alterations in Body and Relative Organ Weights
Pulmonary PM exposure (PMI, EL + PMI, and EH + PMI) did not significantly alter the final body weight at sacrifice (Figure 1A). In contrast, delta body weight and relative lung weight were significantly increased by PM instillation (Figure 1B,D). Interestingly, EA treatment significantly attenuated the delta body weight against PM exposure regardless of the EA concentration (Figure 1B). However, PM-induced lung weight did not change with EA treatment (Figure 1D). There were no significant differences in the relative liver weight among the experimental groups (Figure 1C). EA treatment did not affect the final body weight or the relative lung and liver weights. The overall changes in patterns of delta body weight and relative lung weight exhibited similar trends to our previous studies on quercetin treatment [21]. In our previous findings, PM instillation increased delta body weight [20,21]; however, quercetin treatment inhibited the PM-induced increase in delta body weight [21], similar to the effect exerted by EA treatment in the current study.
## 3.2. Pulmonary PM Loading and Inflammatory Cytokine Secretion
According to our previous findings, PM instillation directly induces pulmonary inflammatory responses in rodents [20]. Similar to our previous findings, in this study, PM exposure resulted in black particles or black pigment-laden alveolar macrophages in the alveolar areas (Figure 2B, blue arrows). In addition, infiltrated inflammatory cells were noted in the peribronchiolar, perivascular, and interstitial regions (Figure 2B, red arrows), similar to our previous reports [20]. Pulmonary PM loading induces infiltration of immune cells and cytokine secretion in the BALF [20], and we postulated that EA treatment would attenuate the recruitment of immune cells and cytokine secretion in the BALF. As expected, PM exposure markedly increased the total number of immune cells and inflammatory cytokines in the BALF. After PM exposure, total immune cells in the BALF were increased ~3-fold compared with CON with induction of the absolute number of neutrophils and macrophages (Figure 3A–C). In addition, a significant increase in the number of eosinophils and lymphocytes was induced in the PMI group compared with the CON group (Figure 3D,E). However, the total number of immune cells was not ameliorated in the EL + PMI and EH + PMI groups compared with the PMI group (Figure 3A–C), similar to the results in our previous study on quercetin treatment [21]. In contrast, the number of eosinophils and lymphocytes gradually decreased in the EA-treated groups compared with that in the PMI group in an EA dose-dependent manner (Figure 3D,E).
PM exposure also upregulated pulmonary cytokine secretion in the BALF. After PM instillation, pulmonary TNFα and IL-6 protein secretion was remarkably elevated, consistent with our previous findings [20,21]. PM exposure elevated pulmonary TNFα and IL-6 secretion in the BALF by approximately 2.6- and 19-fold, respectively, compared with the CON group (Figure 4A,B). However, EA treatment did not prevent the induction of pulmonary inflammatory cytokine secretion in the BALF (Figure 4A,B). In our previous study, quercetin treatment also failed to prevent PM-induced recruitment of immune cells and cytokine secretion in the BALF [21]. Therefore, PM instillation may directly and strongly induce pulmonary inflammation, and EA and quercetin [21] may not fully prevent inflammatory events such as physical PM loading and inflammatory cytokine secretion in the BALF. Moreover, we measured hydrogen peroxide levels in the BALF to determine whether PM exposure may induce pulmonary oxidative stress. PM exposure for 1 week did not increase hydrogen peroxide secretion in the BALF (Figure 4C), consistent with our earlier findings [20,21].
## 3.3. EA Treatment Prevented PM-Induced Expression of Inflammatory and Hypoxic Response Genes
EA treatment did not reduce the recruitment of immune cells or cytokine secretion in the BALF. However, in our previous study, we observed that quercetin exerted anti-inflammatory effects by decreasing pulmonary cytokine mRNA expression [21]. Similarly, pulmonary cytokine mRNA expression was increased in the PMI group compared with that in the CON group. The mRNA expression of pulmonary cytokines such as Tnfα, Il-1b, and Il-6 increased 6.2-, 3.1-, and 1.4-fold, respectively, in the PMI group compared with the CON group (Figure 5A–C). However, EA treatment decreased PM-induced pulmonary Tnfα mRNA expression by 1.9-fold and 2.9-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5A). In addition, EA treatment remarkably reduced PM-induced pulmonary Il-1b mRNA expression by 1.6-fold and 2.1-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5B). Furthermore, EA treatment also reduced PM-induced pulmonary Il-6 mRNA expression by 0.8-fold and 0.9-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5C).
Inflammatory and hypoxic responses are often coincidental physiological events that occur in a site- and cell-type-specific manner [49]. To understand whether [1] PM exposure induced pulmonary hypoxic responses and [2] EA treatment prevented PM-induced hypoxic responses, mRNA expression of hypoxic response genes (e.g., Vegfα and Ankrd37) was assessed in the lung tissue by qRT-PCR. As expected, pulmonary Vegfα mRNA expression in the PMI group was elevated 4.2-fold compared with that in the CON group; however, pulmonary Vegfα mRNA expression was markedly attenuated by 1- and 1.7-fold in the EL + PMI and EH + PMI groups, respectively, compared with that in the PMI group (Figure 5D). In addition, pulmonary Ankrd37 mRNA expression in the PMI group increased by 2.1-fold compared with the CON group; however, pulmonary Ankrd37 mRNA expression was significantly attenuated by 1.2- and 1.4-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5E). Similar trends were also observed in a previous experiment in which PM exposure elevated the mRNA expression of genes for an inflammatory and hypoxic response, which was significantly reduced by quercetin treatment [21].
## 3.4. EA Treatment Prevented PM-Induced Hyperactivity
To evaluate the behavioral effects of EA on PM-exposed mice, an OFT was implemented. In this study, mice in the PMI group exhibited hyperactivity compared with the CON group. The total moving distance, including both the outer and central parts of the plexiglass container, were increased in the PMI group compared with the CON group (Figure 6A–C). Interestingly, PM-treated mice spent a significantly increased amount of time in the central area, whereas quercetin treatment decreased [21]; however, in this experiment, all treatments did not significantly alter the time spent in the central part of the plexiglass container (Figure 6F). Although there were no statistical differences, PM exposure increased the time spent in the central part of the plexiglass container by approximately $30.4\%$ ($$p \leq 0.19$$) compared with the CON group, while EL + PMI and EH + PMI decreased the central staying time by approximately $75.5\%$ and $85.6\%$, respectively, compared with the PMI group. In conjunction with, in all groups, no significant changes were noted in the staying time on the border of the plexiglass container (Figure 6E). The total movement speed of the PMI group was significantly elevated with hyperactivity (mean and maximum speed) at the border of the plexiglass container (Figure 6G,H,J). For the PMI group, the maximum speed at the central area did not significantly increase (Figure 6K); however, the mean speed at the central area significantly increased (Figure 6I). In contrast, in the EL + PMI and EH + PMI groups, the hyperactivity observed in the PMI group was reduced. Subsequently, we proposed that amelioration of PM-induced hyperactivity with EA treatment may involve serum corticosterone, a gold standard to assess stress levels. To answer our extended research question, serum corticosterone levels were analyzed using an ELISA kit. However, there were no distinguishable differences in serum corticosterone levels among the experimental groups (Figure 6L), as in our previous study [21].
## 4. Discussion
In this study, we investigated the potential protective effects of EA against PM-induced pulmonary pathology and locomotor hyperactivity in experimental rodents. PM exposure is an inevitable and chronic event; therefore, dietary intervention with supplementation of functional phenolic compounds may be an ideal means to prevent and/or attenuate PM-induced pulmonary pathology and behavioral alterations. To understand whether EA pretreatment effectively attenuated PM-induced pulmonary pathology and hyperactivity, we used our previous pilot experimental conditions [20,21]. Briefly, mice were supplemented with vehicle control or EA (20 or 100 mg/kg) for 7 days, and then PM was instilled with continuous dietary interventions for the following 7 days. Pulmonary PM loading was a physical and inevitable event because EA pretreatment failed to prevent pulmonary PM accumulation and recruitment of immune cells in the BALF. EA pretreatment partially prevented PM-induced pulmonary cytokine and hypoxic mRNA expression and hyperactivity.
PM instillation significantly elevated PM loading in the lung and pulmonary inflammatory responses, similar to our previous findings [20,21]. Based on the histological evaluation, black materials from the PM were markedly accumulated in the alveolar lumen and interstitial tissue in all PMI groups, regardless of the EA pretreatment. In the BALF, PM instillation significantly induced infiltration of immune cells such as neutrophils and macrophages, as noted in previous publications [20,21]. Moreover, cytokine secretions in the BALF, such as those of IL-6 and TNFα, were remarkably elevated in all PMI groups. EA treatment did not significantly prevent IL-6 and TNFα induction in the BALF. Jeong et al. also reported that dietary intervention with quercetin did not prevent PM loading in the lung and cytokine secretion in the BALF [21]. Probably, the PM loading concentration in our experimental protocol was in excess, as evidenced by pulmonary PM loading; therefore, dietary intervention may not be sufficient to prevent pulmonary cytokine secretions in BALF.
However, EA pretreatment significantly attenuated PM-induced pulmonary cytokine and hypoxic mRNA expression in our experiments. As expected, PM instillation significantly induced the mRNA expression of pulmonary cytokines (Il-1b, Tnfα, and Il-6), as increased cytokine secretion was observed in the BALF. Moreover, the expression of hypoxic response genes (e.g., Ankrd37 and Vegfα) was markedly elevated in the PMI group. The induction of inflammatory and hypoxic responses verified our previous results [21]. However, EA treatment significantly reduced PM-induced inflammatory and hypoxic changes in mRNA expression. Key regulatory proteins for inflammatory and hypoxic responses are NF-κB and HIF1α, respectively, which are closely intertwined at the molecular level [50]. The NFκB and HIF1α pathways share a common molecular denominator, the IKK complex; therefore, the induction of NFκB by phosphorylation may trigger hypoxic signal induction of HIF1α, and vice versa. Our previous [21] and current findings suggest that dietary intervention with phenolic compounds (e.g., quercetin and EA) may attenuate PM-induced pulmonary inflammatory and hypoxic mRNA expression. In future studies, the expression of NFκB and HIF1α pathways should be scrutinized to understand whether dietary intervention can prevent PM-induced pulmonary inflammation and/or hypoxic events.
EA pretreatment significantly attenuated PM-induced locomotor hyperactivity in experimental mice. The PMI group had increased total, border, and center moving distances and mean speeds and increased maximum speed at the border compared with the CON group. Interestingly, EA pretreatment decreased the distinctive PM-induced hyperactivity by attenuating moving distances in total (EH + PMI), border (all EA treatments), and center (EH + PMI), mean speeds in total (EH + PMI), border (EH + PMI), and center (all EA treatments), and maximum speed in the border (EH + PMI). Previous findings using cohort studies have also demonstrated that PM exposure in early developmental periods triggers attention deficit hyperactivity disorder-like hyperactivity [14,51]. In addition, high-DEP exposure prenatally and 1 week after birth led to increased hyperactivity in experimental mice [18]. Moreover, maternal PM exposure significantly triggered hyperactivity in pups in a mouse model [19]. In this study, we demonstrated that PM instillation in relatively young adulthood (8~10 weeks) also increased locomotor activity in mice, consistent with our previous findings [20,21]. Interestingly, dietary intervention with phenolic components, such as EA and quercetin [21], successfully prevented PM-induced hyperactivity in experimental mice. An increased chance of inhalation of air pollutants is closely intertwined with an elevation in abnormal behaviors, such as depression, bipolar disorder, and schizophrenia [15]. Therefore, finding and applying functional dietary resources (e.g., EA and quercetin) as preventive measures against air pollutants may be a possible and sustainable strategy to maintain normal health.
Our current findings have significant advantages and disadvantages when extrapolating to the clinical field. Our experimental conditions included limited dietary intervention, PM exposure time, and PM concentration. Exposure of humans to PM may be long-term; however, our experimental protocol was executed in a relatively short-term period (14 days of dietary intervention and 7 days of PM exposure) with relatively higher concentrations of PM. Dietary intervention with phenolic compounds (EA and quercetin [21]) did not significantly prevent inflammatory cytokine secretion in the BALF. It seems that our experimental conditions may not fully account for potential pathological events and dietary interventions in humans. In addition, we detected hydrogen peroxide to gauge the pulmonary oxidative stress level in the BALF because prolonged inflammation may induce oxidative stress. Under hypoxic conditions, oxidative stress is generally elevated by ROS induction of reactive oxygen species [52]. Therefore, we postulated that hydrogen peroxide would be increased by PM exposure because of the induction of hypoxic Ankrd37 and Vegfa mRNA expression in the lungs. However, hypoxic mRNA expression in the lung and hydrogen peroxide secretion in the BALF did not match because hydrogen peroxide concentrations in the BALF were similar among all experimental groups. In future studies, we need to optimize the experimental conditions to make robust conclusions regarding whether PM exposure triggers pulmonary hypoxic responses. In addition, hyperactivity was noted in the PMI group, but EA pretreatment significantly normalized hyperactivity in mice. Our previous study used an identical experimental setting; quercetin also prevented PM-induced hyperactivity [21]. Therefore, we hypothesized that the stress hormone corticosterone would be altered by PM exposure; however, serum corticosterone levels were unchanged among all treatments, regardless of dietary intervention or PM treatment. Therefore, in the future, we may try to find any behavior-related hormones that are controlled by PM exposure and dietary intervention.
Although there are restrictions, there are numerous advantages to our experimental setting. In our current and previous experiments [21], in a relatively short period of time, we remarkably observed the preventive potency of EA and quercetin [21] against pulmonary inflammatory and hypoxic mRNA expression induced by PM exposure. Therefore, in the future, the application of optimized and lower PM concentrations to reflect current air pollution with longer experimental periods may result in the positive suppression of PM-induced infiltration of inflammatory cells and cytokine secretion in the BALF. Another promising finding was the behavioral alterations observed in our mouse model. Similar to other PM exposure models in the early life phases [18,19,53], we also found that PM exposure in early adulthood induced hyperactivity in mice. A relatively short period of dietary intervention with EA and quercetin [21] effectively normalized hyperlocomotive activity. Therefore, dietary intervention may be an acceptable approach for maintaining normal behavior amidst PM exposure.
EA is a widely accepted dietary polyphenol with multiple beneficial effects, especially in reducing biological inflammatory reactions [41,54,55,56]. In our experiments, EA pretreatment prevented PM-induced pulmonary cytokine mRNA expression over a relatively short period (14 days). Other studies have demonstrated that EA has significant efficacy in attenuating pulmonary inflammation, oxidative stress, and fibrosis (Table 2). In an acute lung injury (ALI) mouse model triggered by hydrochloric acid, oral EA treatment reduced neutrophil recruitment in the BALF and the lungs [40]. In this model, EA decreased the proinflammatory cytokine IL-6 and increased the anti-inflammatory cytokine IL-10 in the BALF [40]. In addition, EA treatment exerted an anti-inflammatory effect in an LPS-induced ALI model [48]. EA treatment also attenuated elastase-induced immune cells and cytokine secretion in the BALF in an emphysema model [45]. In a murine asthma model, EA treatment also prevented pulmonary inflammation by suppressing pulmonary NFκB activation [47]. Furthermore, EA has anti-inflammatory, antioxidative [44,46], and antifibrosis effects [46] in experimental rodents.
In this study, EA pretreatment significantly prevented PM-induced pulmonary inflammatory and hypoxic mRNA expression, along with the normalization of hyperlocomotive activity. However, inflammatory cytokine and hydrogen peroxide secretion in the BALF did not alter with either PM exposure or EA pretreatment. Our study is a novel endeavor in at least two aspects: [1] investigating the pulmonary pathophysiology of PM instillation and [2] investigating whether dietary intervention with EA could thwart PM-induced pathology. To date, dietary preventive means in PM-exposed animal experiments have just begun [21]; therefore, there is limited information on which experimental settings are suitable for potential clinical application. Our experimental period may have been relatively short, considering PM exposure in humans has a longer incidence. We also used a relatively higher PM concentration compared with those that humans are practically exposed to. Therefore, in future studies, we may optimize our experimental protocols by increasing the PM exposure duration and using lower PM concentrations. Although our experimental setting has some limitations, prevention of pulmonary inflammatory and hypoxic mRNA expression by EA pretreatment may also prevent PM-induced protein expression and function. Another obvious finding was that dietary intervention with EA pretreatment normalized PM-induced hyperactivity.
## 5. Conclusions
This study investigated the effectiveness of EA, a natural polyphenolic compound, in preventing the adverse effects of PM exposure in C57BL/6 mice. Four groups of mice were assigned (CON, PMI, EL + PMI, and EH + PMI); EA was orally administered for 14 days in C57BL/6 mice, and after the eighth day, PM (5 mg/kg) was intratracheally instilled for 7 consecutive days. The experimental results demonstrated that EA pretreatment with EA prevented PM-inducible pulmonary inflammatory and hypoxic mRNA induction, as well as hyperactivity in the experimental mice. This study suggests that EA may be a promising approach for mitigating the pathophysiological impacts of PM exposure.
## References
1. Wichmann H.E.. **Diesel exhaust particles**. *Inhal. Toxicol.* (2007) **19** 241-244. DOI: 10.1080/08958370701498075
2. Ichinose T., Furuyama A., Sagai M.. **Biological effects of diesel exhaust particles (DEP). II. Acute toxicity of DEP introduced into lung by intratracheal instillation**. *Toxicology* (1995) **99** 153-167. DOI: 10.1016/0300-483X(94)03013-R
3. Iwai K., Adachi S., Takahashi M., Moller L., Udagawa T., Mizuno S., Sugawara I.. **Early oxidative DNA damages and late development of lung cancer in diesel exhaust-exposed rats**. *Environ. Res.* (2000) **84** 255-264. DOI: 10.1006/enrs.2000.4072
4. Nemmar A., Al-Salam S., Zia S., Yasin J., Al Husseni I., Ali B.H.. **Diesel exhaust particles in the lung aggravate experimental acute renal failure**. *Toxicol. Sci.* (2010) **113** 267-277. DOI: 10.1093/toxsci/kfp222
5. Morsi A.A., Fouad H., Alasmari W.A., Faruk E.M.. **The biomechanistic aspects of renal cortical injury induced by diesel exhaust particles in rats and the renoprotective contribution of quercetin pretreatment: Histological and biochemical study**. *Environ. Toxicol.* (2022) **37** 310-321. DOI: 10.1002/tox.23399
6. Ito Y., Yanagiba Y., Ramdhan D.H., Hayashi Y., Li Y., Suzuki A.K., Kamijima M., Nakajima T.. **Nanoparticle-rich diesel exhaust-induced liver damage via inhibited transactivation of peroxisome proliferator-activated receptor alpha**. *Environ. Toxicol.* (2016) **31** 1985-1995. DOI: 10.1002/tox.22199
7. Miller M.R., Newby D.E.. **Air pollution and cardiovascular disease: Car sick**. *Cardiovasc. Res.* (2020) **116** 279-294. DOI: 10.1093/cvr/cvz228
8. Olumegbon L.T., Lawal A.O., Oluyede D.M., Adebimpe M.O., Elekofehinti O.O., Umar H.U.. **Hesperetin protects against diesel exhaust particles-induced cardiovascular oxidative stress and inflammation in Wistar rats**. *Environ. Sci. Pollut. Res. Int.* (2022) **29** 52574-52589. DOI: 10.1007/s11356-022-19494-3
9. Kang Y.J., Tan H.Y., Lee C.Y., Cho H.. **An Air Particulate Pollutant Induces Neuroinflammation and Neurodegeneration in Human Brain Models**. *Adv. Sci.* (2021) **8** e2101251. DOI: 10.1002/advs.202101251
10. Kioumourtzoglou M.A., Schwartz J.D., Weisskopf M.G., Melly S.J., Wang Y., Dominici F., Zanobetti A.. **Long-term PM2.5 Exposure and Neurological Hospital Admissions in the Northeastern United States**. *Environ. Health Perspect.* (2016) **124** 23-29. DOI: 10.1289/ehp.1408973
11. Nemmar A., Al-Salam S., Yuvaraju P., Beegam S., Ali B.H.. **Emodin mitigates diesel exhaust particles-induced increase in airway resistance, inflammation and oxidative stress in mice**. *Respir. Physiol. Neurobiol.* (2015) **215** 51-57. DOI: 10.1016/j.resp.2015.05.006
12. Nemmar A., Al-Salam S., Beegam S., Yuvaraju P., Hamadi N., Ali B.H.. **In Vivo Protective Effects of Nootkatone against Particles-Induced Lung Injury Caused by Diesel Exhaust Is Mediated via the NF-kappaB Pathway**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10030263
13. Fan H.C., Chen C.M., Tsai J.D., Chiang K.L., Tsai S.C., Huang C.Y., Lin C.L., Hsu C.Y., Chang K.H.. **Association between Exposure to Particulate Matter Air Pollution during Early Childhood and Risk of Attention-Deficit/Hyperactivity Disorder in Taiwan**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph192316138
14. Chang Y.C., Chen W.T., Su S.H., Jung C.R., Hwang B.F.. **PM**. *Environ. Res.* (2022) **214** 113769. DOI: 10.1016/j.envres.2022.113769
15. Khan A., Plana-Ripoll O., Antonsen S., Brandt J., Geels C., Landecker H., Sullivan P.F., Pedersen C.B., Rzhetsky A.. **Environmental pollution is associated with increased risk of psychiatric disorders in the US and Denmark**. *PLoS Biol.* (2019) **17**. DOI: 10.1371/journal.pbio.3000353
16. Weitekamp C.A., Hofmann H.A.. **Effects of air pollution exposure on social behavior: A synthesis and call for research**. *Environ. Health* (2021) **20** 72. DOI: 10.1186/s12940-021-00761-8
17. Roberts S., Arseneault L., Barratt B., Beevers S., Danese A., Odgers C.L., Moffitt T.E., Reuben A., Kelly F.J., Fisher H.L.. **Exploration of NO**. *Psychiatry Res.* (2019) **272** 8-17. DOI: 10.1016/j.psychres.2018.12.050
18. Thirtamara Rajamani K., Doherty-Lyons S., Bolden C., Willis D., Hoffman C., Zelikoff J., Chen L.C., Gu H.. **Prenatal and early-life exposure to high-level diesel exhaust particles leads to increased locomotor activity and repetitive behaviors in mice**. *Autism Res.* (2013) **6** 248-257. DOI: 10.1002/aur.1287
19. Cui J., Fu Y., Lu R., Bi Y., Zhang L., Zhang C., Aschner M., Li X., Chen R.. **Metabolomics analysis explores the rescue to neurobehavioral disorder induced by maternal PM**. *Ecotoxicol. Environ. Saf.* (2019) **169** 687-695. DOI: 10.1016/j.ecoenv.2018.11.037
20. Jeong S., Lee J.H., Ha J.H., Kim J., Kim I., Bae S.. **An Exploratory Study of the Relationships between Diesel Engine Exhaust Particle Inhalation, Pulmonary Inflammation and Anxious Behavior**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18031166
21. Jeong S., Bae S., Yu D., Yang H.S., Yang M.J., Lee J.H., Ha J.H.. **Dietary Intervention with Quercetin Attenuates Diesel Exhaust Particle-Instilled Pulmonary Inflammation and Behavioral Abnormalities in Mice**. *J. Med. Food* (2023) **26** 2. DOI: 10.1089/jmf.2022.K.0104
22. Tong H.. **Dietary and pharmacological intervention to mitigate the cardiopulmonary effects of air pollution toxicity**. *Biochim. Biophys. Acta* (2016) **1860** 2891-2898. DOI: 10.1016/j.bbagen.2016.05.014
23. Romieu I., Castro-Giner F., Kunzli N., Sunyer J.. **Air pollution, oxidative stress and dietary supplementation: A review**. *Eur. Respir. J.* (2008) **31** 179-197. DOI: 10.1183/09031936.00128106
24. Peter S., Holguin F., Wood L.G., Clougherty J.E., Raederstorff D., Antal M., Weber P., Eggersdorfer M.. **Nutritional Solutions to Reduce Risks of Negative Health Impacts of Air Pollution**. *Nutrients* (2015) **7** 10398-10416. DOI: 10.3390/nu7125539
25. Jung S.H., Bae C.H., Kim J.H., Park S.D., Shim J.J., Lee J.L.. **Lactobacillus casei HY2782 and Pueraria lobata Root Extract Complex Ameliorates Particulate Matter-Induced Airway Inflammation in Mice by Inhibiting Th2 and Th17 Immune Responses**. *Prev. Nutr. Food Sci.* (2022) **27** 188-197. DOI: 10.3746/pnf.2022.27.2.188
26. Yahfoufi N., Alsadi N., Jambi M., Matar C.. **The Immunomodulatory and Anti-Inflammatory Role of Polyphenols**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10111618
27. Gonzalez-Gallego J., Garcia-Mediavilla M.V., Sanchez-Campos S., Tunon M.J.. **Fruit polyphenols, immunity and inflammation**. *Br. J. Nutr.* (2010) **104** S15-S27. DOI: 10.1017/S0007114510003910
28. Yoon H.J., Yoon D.S., Baek H.J., Kang B., Jung U.J.. **Dietary Sinapic Acid Alleviates Adiposity and Inflammation in Diet-Induced Obese Mice**. *Prev. Nutr. Food Sci.* (2022) **27** 407-413. DOI: 10.3746/pnf.2022.27.4.407
29. Lee H., Im S.W., Jung C.H., Jang Y.J., Ha T.Y., Ahn J.. **Tyrosol, an olive oil polyphenol, inhibits ER stress-induced apoptosis in pancreatic beta-cell through JNK signaling**. *Biochem. Biophys. Res. Commun.* (2016) **469** 748-752. DOI: 10.1016/j.bbrc.2015.12.036
30. Yan B., Chen L., Wang Y., Zhang J., Zhao H., Hua Q., Pei S., Yue Z., Liang H., Zhang H.. **Preventive Effect of Apple Polyphenol Extract on High-Fat Diet-Induced Hepatic Steatosis in Mice through Alleviating Endoplasmic Reticulum Stress**. *J. Agric. Food Chem.* (2022) **70** 3172-3180. DOI: 10.1021/acs.jafc.1c07733
31. Pandey K.B., Rizvi S.I.. **Plant polyphenols as dietary antioxidants in human health and disease**. *Oxid. Med. Cell. Longev.* (2009) **2** 270-278. DOI: 10.4161/oxim.2.5.9498
32. Stagos D.. **Antioxidant Activity of Polyphenolic Plant Extracts**. *Antioxidants* (2019) **9**. DOI: 10.3390/antiox9010019
33. Park S.K.. **Antioxidant Activities of Bioactive Compounds Isolated from Rheum emodi Wall (Himalayan Rhubarb) Based on LC-DAD-ESI/MS and Preparative LC/MS System**. *Prev. Nutr. Food Sci.* (2022) **27** 223-233. DOI: 10.3746/pnf.2022.27.2.223
34. Evtyugin D.D., Magina S., Evtuguin D.V.. **Recent Advances in the Production and Applications of Ellagic Acid and Its Derivatives. A Review**. *Molecules* (2020) **25**. DOI: 10.3390/molecules25122745
35. Lee K.H., Jeong E.S., Jang G., Na J.R., Park S., Kang W.S., Kim E., Choi H., Kim J.S., Kim S.. **Unripe Rubus coreanus Miquel Extract Containing Ellagic Acid Regulates AMPK, SREBP-2, HMGCR, and INSIG-1 Signaling and Cholesterol Metabolism In Vitro and In Vivo**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12030610
36. Shiojima Y., Takahashi M., Kikuchi M., Akanuma M.. **Effect of ellagic acid on body fat and triglyceride reduction in healthy overweight volunteers: A randomized, double-blind, placebo-controlled parallel group study**. *Funct. Foods Health Dis.* (2020) **10** 180-194. DOI: 10.31989/ffhd.v10i4.702
37. Ghadimi M., Foroughi F., Hashemipour S., Nooshabadi M.R., Ahmadi M.H., Yari M.G., Kavianpour M., Haghighian H.K.. **Decreased insulin resistance in diabetic patients by influencing Sirtuin1 and Fetuin-A following supplementation with ellagic acid: A randomized controlled trial**. *Diabetol. Metab. Syndr.* (2021) **13** 16. DOI: 10.1186/s13098-021-00633-8
38. Umesalma S., Sudhandiran G.. **Ellagic acid prevents rat colon carcinogenesis induced by 1, 2 dimethyl hydrazine through inhibition of AKT-phosphoinositide-3 kinase pathway**. *Eur. J. Pharmacol.* (2011) **660** 249-258. DOI: 10.1016/j.ejphar.2011.03.036
39. Mohammadinejad A., Mohajeri T., Aleyaghoob G., Heidarian F., Kazemi Oskuee R.. **Ellagic acid as a potent anticancer drug: A comprehensive review on in vitro, in vivo, in silico, and drug delivery studies**. *Biotechnol. Appl. Biochem.* (2022) **69** 2323-2356. DOI: 10.1002/bab.2288
40. Cornelio Favarin D., Martins Teixeira M., Lemos de Andrade E., de Freitas Alves C., Lazo Chica J.E., Arterio Sorgi C., Faccioli L.H., Paula Rogerio A.. **Anti-inflammatory effects of ellagic acid on acute lung injury induced by acid in mice**. *Mediat. Inflamm.* (2013) **2013** 164202. DOI: 10.1155/2013/164202
41. Marin M., Maria Giner R., Rios J.L., Recio M.C.. **Intestinal anti-inflammatory activity of ellagic acid in the acute and chronic dextrane sulfate sodium models of mice colitis**. *J. Ethnopharmacol.* (2013) **150** 925-934. DOI: 10.1016/j.jep.2013.09.030
42. Zeb A.. **Ellagic acid in suppressing in vivo and in vitro oxidative stresses**. *Mol. Cell. Biochem.* (2018) **448** 27-41. DOI: 10.1007/s11010-018-3310-3
43. Karimi M.Y., Fatemi I., Kalantari H., Mombeini M.A., Mehrzadi S., Goudarzi M.. **Ellagic Acid Prevents Oxidative Stress, Inflammation, and Histopathological Alterations in Acrylamide-Induced Hepatotoxicity in Wistar Rats**. *J. Diet. Suppl.* (2020) **17** 651-662. DOI: 10.1080/19390211.2019.1634175
44. Aslan A., Hussein Y.T., Gok O., Beyaz S., Erman O., Baspinar S.. **Ellagic acid ameliorates lung damage in rats via modulating antioxidant activities, inhibitory effects on inflammatory mediators and apoptosis-inducing activities**. *Environ. Sci. Pollut. Res. Int.* (2020) **27** 7526-7537. DOI: 10.1007/s11356-019-07352-8
45. Mansouri Z., Dianat M., Radan M., Badavi M.. **Ellagic Acid Ameliorates Lung Inflammation and Heart Oxidative Stress in Elastase-Induced Emphysema Model in Rat**. *Inflammation* (2020) **43** 1143-1156. DOI: 10.1007/s10753-020-01201-4
46. Saba S., Khan S., Parvez B., Chaudhari F., Ahmad S., Anjum S.. **Ellagic acid attenuates bleomycin and cyclophosphamide-induced pulmonary toxicity in Wistar rats**. *Food Chem. Toxicol.* (2013) **58** 210-219. DOI: 10.1016/j.fct.2013.03.046
47. Zhou E., Fu Y., Wei Z., Yang Z.. **Inhibition of allergic airway inflammation through the blockage of NF-kappaB activation by ellagic acid in an ovalbumin-induced mouse asthma model**. *Food Funct.* (2014) **5** 2106-2112. DOI: 10.1039/C4FO00384E
48. Guan S., Zheng Y., Yu X., Li W., Han B., Lu J.. **Ellagic acid protects against LPS-induced acute lung injury through inhibition of nuclear factor kappa B, proinflammatory cytokines and enhancement of interleukin-10**. *Food Agric. Immunol.* (2017) **28** 1347-1361. DOI: 10.1080/09540105.2017.1339670
49. Taylor C.T., Colgan S.P.. **Regulation of immunity and inflammation by hypoxia in immunological niches**. *Nat. Rev. Immunol.* (2017) **17** 774-785. DOI: 10.1038/nri.2017.103
50. Pham K., Parikh K., Heinrich E.C.. **Hypoxia and Inflammation: Insights from High-Altitude Physiology**. *Front. Physiol.* (2021) **12** 676782. DOI: 10.3389/fphys.2021.676782
51. Thygesen M., Holst G.J., Hansen B., Geels C., Kalkbrenner A., Schendel D., Brandt J., Pedersen C.B., Dalsgaard S.. **Exposure to air pollution in early childhood and the association with Attention-Deficit Hyperactivity Disorder**. *Environ. Res.* (2020) **183** 108930. DOI: 10.1016/j.envres.2019.108930
52. McGarry T., Biniecka M., Veale D.J., Fearon U.. **Hypoxia, oxidative stress and inflammation**. *Free Radic. Biol. Med.* (2018) **125** 15-24. DOI: 10.1016/j.freeradbiomed.2018.03.042
53. Di Domenico M., Benevenuto S.G.M., Tomasini P.P., Yariwake V.Y., de Oliveira Alves N., Rahmeier F.L., da Cruz Fernandes M., Moura D.J., Nascimento Saldiva P.H., Veras M.M.. **Concentrated ambient fine particulate matter (PM**. *Neurotoxicology* (2020) **79** 127-141. DOI: 10.1016/j.neuro.2020.05.004
54. BenSaad L.A., Kim K.H., Quah C.C., Kim W.R., Shahimi M.. **Anti-inflammatory potential of ellagic acid, gallic acid and punicalagin A&B isolated from Punica granatum**. *BMC Complement. Altern. Med.* (2017) **17**. DOI: 10.1186/s12906-017-1555-0
55. Bains M., Kaur J., Akhtar A., Kuhad A., Sah S.P.. **Anti-inflammatory effects of ellagic acid and vanillic acid against quinolinic acid-induced rat model of Huntington’s disease by targeting IKK-NF-kappaB pathway**. *Eur. J. Pharmacol.* (2022) **934** 175316. DOI: 10.1016/j.ejphar.2022.175316
56. Bidanchi R.M., Lalrindika L., Khushboo M., Bhanushree B., Dinata R., Das M., Nisa N., Lalrinzuali S., Manikandan B., Saeed-Ahmed L.. **Antioxidative, anti-inflammatory and anti-apoptotic action of ellagic acid against lead acetate induced testicular and hepato-renal oxidative damages and pathophysiological changes in male Long Evans rats**. *Environ. Pollut.* (2022) **302** 119048. DOI: 10.1016/j.envpol.2022.119048
|
---
title: Meta-Analysis of Exploring the Effect of Curcumin Supplementation with or without
Other Advice on Biochemical and Anthropometric Parameters in Patients with Metabolic-Associated
Fatty Liver Disease (MAFLD)
authors:
- Gracjan Różański
- Hanna Tabisz
- Marta Zalewska
- Wojciech Niemiro
- Sławomir Kujawski
- Julia Newton
- Paweł Zalewski
- Joanna Słomko
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001478
doi: 10.3390/ijerph20054266
license: CC BY 4.0
---
# Meta-Analysis of Exploring the Effect of Curcumin Supplementation with or without Other Advice on Biochemical and Anthropometric Parameters in Patients with Metabolic-Associated Fatty Liver Disease (MAFLD)
## Abstract
Metabolic (dysfunction)-associated fatty liver disease (MAFLD), previously known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease. MAFLD is characterized by the excessive presence of lipids in liver cells and metabolic diseases/dysfunctions, e.g., obesity, diabetes, pre-diabetes, or hypertension. Due to the current lack of effective drug therapy, the potential for non-pharmacological treatments such as diet, supplementation, physical activity, or lifestyle changes is being explored. For the mentioned reason, we reviewed databases to identify studies that used curcumin supplementation or curcumin supplementation together with the use of the aforementioned non-pharmacological therapies. Fourteen papers were included in this meta-analysis. The results indicate that the use of curcumin supplementation or curcumin supplementation together with changes in diet, lifestyle, and/or physical activity led to statistically significant positive changes in alanine aminotransferase (ALT), aspartate aminotransferase (AST), fasting blood insulin (FBI), homeostasis model assessment of insulin resistance (HOMA-IR), total triglycerides (TG), total cholesterol (TC), and waist circumference (WC). It appears that these therapeutic approaches may be effective in alleviating MAFLD, but more thorough, better designed studies are needed to confirm this.
## 1. Introduction
Metabolic-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide [1,2]. It is becoming a huge public health problem as its prevalence is on the rise, generating large costs (the estimated annual cost in *Europe is* EUR 35 billion, while it is EUR 89 billion in the US) [3,4]. MAFLD is characterized by excessive (>$5\%$ of liver weight) fat accumulation in hepatocytes that is not caused by a viral infection, alcohol consumption, or medication [5]. In addition, the condition coexists with other diseases or metabolic disorders such as being overweight or obese, type 2 diabetes or pre-diabetes, insulin resistance, dyslipidemia, or hypertension [6,7,8,9] and is also a factor that increases the risk of liver- and cardiovascular-disease-related mortality [10,11]. Another serious risk is the possibility of disease progression to non-alcoholic steatohepatitis (NASH), which may occur in 23–$44\%$ of MAFLD patients, resulting in fibrosis and even cirrhosis, which within 5–7 years leads to liver failure in 40–$60\%$ of cases and within 3–7 years to hepatocellular carcinoma (HCC) in 2.4–$12\%$ of patients [12]. In view of the potential progression of MAFLD and the high costs, early diagnosis, prevention, treatment of risk factors, and lifestyle modification are important (Figure 1) [3]. In 2016, the European Association for the Study of the Liver specifically recommended dietary changes and a gradual increase in aerobic exercise or resistance training as interventions leading to lifestyle changes in patients with MAFLD. The recommendations for diet and physical activity, among other reasons, are due to the fact that no effective pharmacological therapy is currently available [13].
The aim of this study is to review the effects of curcumin supplementation only or combined with dietary, physical activity, and/or lifestyle changes on biochemical and anthropometric parameters in the course of MAFLD. The indicated aim of the study is based on the existing knowledge of the pathomechanism of MAFLD and the known therapeutic properties of curcumin, whose (disease pathomechanism and curcumin properties) are described in the following section.
## 2. Pathophysiology
It is now recognized that factors leading to MAFLD include a poor diet, a sedentary lifestyle, and genetic and environmental factors. Therefore, the mechanisms that lead to the development of MAFLD are complex, leading to it being referred to as the “multiple hits hypothesis”. Although current knowledge points to specific factors leading to MAFLD, the exact pathomechanism is not yet fully understood. However, its basis is considered to be insulin resistance (IR), which results in increased de novo hepatic lipogenesis (DNL) and reduced inhibition of adipose tissue lipolysis, leading to the increased influx of fatty acids (FA) into hepatocytes and their storage as triglycerides. In addition, IR also causes dysfunction of adipose tissue resulting in altered production and secretion of adipokines and proinflammatory cytokines. High levels of free fatty acids, free cholesterol, and other lipid metabolites result in lipotoxicity, which is also an important part of the pathomechanism of MAFLD. This leads to increased levels of reactive oxygen species, which cause dysfunction of the endoplasmic reticulum and mitochondria. Changes in the intestinal microbiota may also be involved in the increased levels of free fatty acids, leading to increased permeability of the small intestine, which results in enhanced absorption of FA. This results in the activation of pro-inflammatory pathways and the release of pro-inflammatory cytokines such as IL-6 and TNF-α [6].
## 3. Curcumin
Curcumin is a polyphenol belonging to the group of curcuminoids. It occurs in the rhizomes of the plant called turmeric (Curcuma longa), which belongs to the ginger family. Turmeric is naturally found in Asia, mainly in India. It is mainly known for its culinary applications due to its taste, aroma, and intense yellow color. However, turmeric has been used in medicine for thousands of years due to its curcumin content [14]. Curcumin is characterized by many desirable properties. It has anti-inflammatory, antioxidant, and anticancer properties, among others [15]. Furthermore, importantly, it is safe and rarely causes adverse symptoms. For this reason, it is used to treat or support the treatment of many diseases, e.g., cardiovascular diseases, inflammatory bowel diseases, breast, stomach, pancreatic and lung tumors, dermatoses, allergic asthma, and liver diseases [16,17,18,19,20,21].
## 4. Material and Methods
The protocol for this systematic review and meta-analysis was based on the preferred reporting items of systematic reviews and meta-analysis (PRISMA) statement [22]. The design of the present work was fully specified in advance. It was registered in the PROSPERO (International Prospective Register of Systematic Reviews, CRD42022310950, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=310950, accessed on 24 February 2023).
## 4.1. Types of Participants
Participants meeting the inclusion criterion of being adults (>18 years old) suffering from MAFLD were eligible for the study group. No additional criteria, such as gender or nationality, were defined. Participants were excluded from the study when they did not manifest MAFLD.
## 4.2. Types of Interventions
Interventions using curcumin supplementation only or curcumin supplementation with other changes e.g., diet and/or physical activity and/or other lifestyle modifications) and presenting the results of biochemical and/or anthropometric parameters’ outcomes before and after supplementation were included. Studies involving animals were excluded.
## 4.3. Types of Comparisons
There were no specific comparison criteria.
## 4.4. Types of Outcomes
The outcome of at least one biochemical parameter or anthropometric measurement is presented in the study, measured at baseline (pre-intervention) and post-intervention. The biochemical and anthropometric parameters measured in each study are presented in Table 1 and Table 2.
## 4.5. Types of Studies
Only randomized controlled trials published in peer-reviewed journals in English were included. The precise duration of the undertaken intervention was not specified. The exclusion criterion was a non-human study. The detailed PICOS criteria are described in Table 3.
## 4.6. Search Strategy and Study Selection
We reviewed available publications using databases such as PubMed, Web of Science, and Scopus using the words “NAFLD” or “MAFLD” “metabolic-associated fatty liver disease” or “non-alcoholic fatty liver disease” and “curcumin” or “turmeric”. We limited the results to papers in English and published by March 2022 (Figure 2).
## 4.7. Quality and Risk of Bias Assessment
An assessment of the quality of the studies meeting the inclusion criteria was performed using the Cochrane risk of bias tools. The following elements of the studies were analyzed: selection bias (random sequence generation and allocation concealment), performance bias (blinding of participants and personnel), detection bias (blinding of outcome assessment), attrition bias (incomplete outcome data), reporting bias (selective reporting), and other bias (Table 4) [37]. Funnel plots have been used to provide a visual assessment of the association between treatment estimate and study size. Publication bias was considered significant when the p-value was less than 0.05 in either Begg’s test [38] (Supplementary Figures S1 and S2).
## 4.8. Statistical Analysis
The meta-packages of R were used to perform the analyses [38,39,40,41]. A random effects model was conducted to estimate the pooled effect, for values of I2 ≥ $50\%$, while a fixed effects model was conducted for values of I2 ≤ $50\%$. The effect size was calculated as the mean difference (MD) changes from baseline along with $95\%$ confidence intervals (CI). A p-value < 0.05 was defined as statistically significant. The results of the conducted analyses are presented as a forest plot. The heterogeneity among the included studies was evaluated by the I2 statistic. An I2 value of >$50\%$ corresponds to high heterogeneity, values between 25–$50\%$ define heterogeneity as moderate, while I2 < $25\%$ indicates low heterogeneity.
Cytoscape (version: 3.8.1) was used to create network graphs presenting the studies’ results [42].
**Table 4**
| Unnamed: 0 | Random Sequence Generation | Allocation Concealment | Blinding of Participant and Personnel | Blinding of Outcome Assessment | Incomplete Outcome Data | Selective Reporting | Other Bias |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Rahmani, 2016 [23] | + | + | + | + | + | + | + |
| Kelardeh, 2017 [24] | +/− | + | + | + | + | + | +/− |
| Navekar, 2017 [25] | + | + | + | + | + | + | + |
| Chashmniam, 2019 [26] | + | + | + | + | + | + | + |
| Mirhafez, 2019 [27] | +/− | + | + | + | + | + | + |
| Hariri, 2020 [28] | + | + | + | + | + | + | + |
| Kelardeh, 2020 [29] | +/− | + | + | + | + | + | +/− |
| Saberi-Karimian, 2020 [30] | + | + | + | + | + | + | + |
| Panahi, 2016 [31] | +/− | + | − | − | + | + | + |
| Panahi, 2017 [32] | + | + | + | + | + | + | + |
| Jazayeri-Tehrani, 2019 [33] | +/− | + | + | + | + | + | + |
| Saadati, 2019 (a) [34] | + | + | + | + | + | + | + |
| Saadati, 2019 (b) [35] | + | + | + | + | + | + | + |
| Cicero, 2020 [36] | + | + | + | + | + | + | + |
## 5.1. Study Selection
In total, 221 studies were analyzed to meet the inclusion criteria. Ultimately, 14 randomized controlled trials were included in this meta-analysis. Table 1 and Table 2 show the characteristics of the trials included in the meta-analysis including a division by type of applied intervention.
## 5.2. Participant and Study Characteristics
Eight hundred and seventy-four NAFLD patients (429 in the treatment group and 418 in the control group) were included in the meta-analysis. Table 1 shows the characteristics of the studies in which only curcumin was supplemented. Table 2 presents the characteristics of studies in which, in addition to curcumin supplementation, physical activity and/or dietary advice and/or lifestyle changes were also used.
## 5.3. Interventions
In 8 of the study groups included in the analysis, only curcumin supplementation was used; in the other 8 studies groups, curcumin supplementation was also used, but combined with physical activity and/or dietary advice and/or lifestyle changes. Two studies out of fourteen used both of the abovementioned types of intervention; therefore, the total number of study groups amounted to 16. The duration of the intervention was 8 or 12 weeks. The doses of curcumin used ranged from 80–1500 mg/day. Detailed characteristics of the studies are presented in Table 1 and Table 2.
## 5.4. Effect of Curcumin Supplementation and Curcumin Supplementation with Physical Activity and/or Dietary Advice and/or Lifestyle Changes on the Levels of Biochemical Parameters
ALT and AST levels were controlled in nine studies [23,24,26,27,28,32,33,34,35]. Fasting blood insulin levels (FBI) and HOMA-IR were controlled in five studies [25,31,33,35,36]. TG, TC, and LDL-C levels were controlled in eight studies [23,26,27,30,31,33,35,36]. Waist circumference (WC) was controlled in seven studies [24,28,30,32,33,35,36].
Regarding ALT and AST, decreases in their levels were observed (Table 5, Figure 3 and Figure 4). The heterogeneity of the effect measures regarding ALT (I2 = $6.0\%$, $$p \leq 0.39$$) and AST (I2 = $17.5\%$, $$p \leq 0.28$$) was low.
Decreases were also observed for parameters related to glucose metabolism (FBI and HOMA-IR), Table 5, Figure 5 and Figure 6. The heterogeneity for FBI (I2 = $9.3\%$, $$p \leq 0.35$$) and HOMA-IR (I2 = $0\%$, $$p \leq 0.66$$) was low.
Decreases in levels were also observed among parameters related to lipid metabolism (TG, TC, and LDL-C), Table 5, Figure 7, Figure 8 and Figure 9. The heterogeneity for TG (I2 = $0\%$, $$p \leq 0.72$$) was low, but for TC (I2 = $64.6\%$, $p \leq 0.01$) and LDL-C (I2 = $70.8\%$, $p \leq 0.01$), it was high.
Among the anthropometric parameters, a reduction in WC was observed (Table 5, Figure 10). The heterogeneity for WC (I2 = $0\%$, $$p \leq 0.93$$) was low.
The network presented in Figure 11. summarizes the results from single random effects models. The size of circular nodes is proportionally related to the overall sample size of the intervention groups from studies included in the model assessing the effects of the intervention on the parameter. The color of the node denotes the parameters group (white for anthropometrical parameters, orange for liver-function-related parameters, ruby for c, red for blood pressure indicators, and turquoise for parameters related to glucose and insulin metabolism). The width of the arrows is proportional to the number of studies included into a model assessing the effects of the intervention on the particular parameter (k), which is also denoted as a label. The color of the arrows indicates the results of random effects models: light green arrows denote statistically significant beneficial effects of supplementation with curcumin with or without other advice on parameters, while light grey arrows denote statistically non-significant effects.
## 6. Discussion
Our meta-analysis summarizes the findings of fourteen RCTs that used curcumin supplementation or curcumin supplementation with physical activity and/or dietary advice and/or lifestyle changes. The studies conducted to date using curcumin indicate its many positive effects in the course of numerous diseases [14,44]. Physical activity, part of a broader lifestyle, also has many health benefits [45]. Recommendations from the World Health Organization (WHO) indicate that adults, in order to maintain optimal health, should perform at least 150–300 min of moderately intense exercise or 75–150 min of vigorous exercise weekly [46]. Diet is also an important component affecting health, and in the context of MAFLD, poor diet is one of the key elements leading to the development of the disease [6,47]. Due to the lack of an effective drug therapy for MAFLD, attempts are being made to use supplementation, diet, physical activity, and lifestyle changes as treatment, but it is not clear which combinations of the aforementioned elements of therapy would give the greatest effectiveness.
Our results indicate that in many studies, in addition to curcumin supplementation, patients with MAFLD were also advised on changing their diet and lifestyle or implementing physical activity. This is a very important fact in the context of interpreting the results of the studies, as each of these elements may additionally influence the change in the parameters studied making it impossible to unequivocally assess the efficacy of curcumin supplementation. Therefore, our meta-analysis highlighted the fact that, in the indicated studies, other types of interventions were used in addition to curcumin supplementation.
Our results suggest that curcumin supplementation or curcumin supplementation together with a combined change in dietary habits and/or implementation of physical activity and/or lifestyle changes causes a decrease in ALT, AST, FBI, LDL-C, TC, and TG and a decrease in HOMA-IR and WC levels.
The results of our study are in keeping with previous meta-analyses in several cases, but there are also a few differences in the results. In the case of ALT, our results are consistent with the studies of Ngu et al. [ 48], Goodarzi et al. [ 49], Yang et al. [ 50], and Jalali et al. [ 51], who also reported statistically significant decreases. In contrast, Wei et al. [ 52] obtained a decrease that was not statistically significant, but it should be noted that only two studies were included in the analysis. In the case of AST, our results are in accordance with all of the aforementioned publications [48,49,50,51,52], in which the authors also reported statistically significant decreases. FBI has so far been analyzed in two meta-analyses. Jalali et al. [ 51] reported a statistically significant decrease, which is consistent with our results, while Wei et al. [ 52] reported a decrease, but it was not statistically significant. For HOMA-IR, we reported a statistically significant decrease, similar to Yang et al. [ 50], Jalali et al. [ 51], and Wei et al. [ 52] in their meta-analyses. Among the lipid metabolism parameters (TG, TC, and LDL-C), decreases have been reported in previous meta-analyses, but they have not always been statistically significant. For TG, Yang et al. [ 50], Jalali et al. [ 51], and Wei et al. [ 52] also obtained statistically significant decreases in their analyses, while Ngu et al. [ 48] reported a statistically insignificant decrease. Regarding TC, Ngu et al. [ 48], Yang et al. [ 50], and Jalali et al. [ 51] obtained statistically significant decreases, which was also reported in our study. In contrast, Wei et al. [ 52] reported a statistically insignificant decrease in TC. For LDL-C, we obtained a statistically significant decrease, as well as Jalali et al. [ 51] and Wei et al. [ 52], while Ngu et al. [ 48] and Yang et al. [ 50] reported statistically insignificant decreases. Waist circumference has only previously been analyzed in the study of Baziar et al. [ 53], who obtained a statistically significant decrease.
Our study has several strengths. First, it provides evidence of the positive effects of curcumin supplementation and curcumin supplementation with added physical activity and/or dietary recommendations and/or lifestyle changes on the levels of certain blood biochemical parameters and waist circumference.
Our study includes more RCTs than most previously published meta-analyses and also highlights the fact that some studies used other interventions (dietary recommendations, physical activity, and lifestyle changes) in addition to curcumin supplementation, which, to the best of our knowledge, has been omitted in previous publications.
In opposition to the strengths, there are some limitations. Firstly, not all of the studies controlled for the same biochemical parameters. Secondly, there were differences in the duration of the different interventions. Third, the doses of curcumin used and the form of curcumin varied between studies. Fourth, additional recommendations for curcumin supplementation were not always described in detail, with the information being limited to only general information about their type. Fifth, the study groups, especially in some studies, were small.
## 7. Conclusions
This meta-analysis, based on RCTs, provides evidence that curcumin supplementation only or curcumin supplementation with physical activity and/or dietary advice and/or lifestyle changes lead to decreases in ALT, AST, FBI, LDL-C, TC, and TG in blood levels and a decrease in HOMA-IR and WC levels. However, this was due to the use of curcumin doses in the range of 80–1500 mg and additional recommendations, which were not always described in detail. The studies conducted to date do not clearly identify the appropriate dose of curcumin, either used alone or in combination with additional physical activity and/or diet and/or lifestyle recommendations. It is also not possible to determine, on the basis of current studies, the effect of the mentioned additional recommendations on the effect induced by curcumin and therefore also its dose.
Therefore, further well designed studies among MAFLD patients, using curcumin only and with additional recommendations, are needed. The effect of physical activity, diet and lifestyle on the effect induced by curcumin supplementation is also worthy of analysis. An element to be taken into consideration in future studies is the dose of curcumin. Other factors that may influence the results of the study, such as the diet, physical activity, lifestyle, or education of the patients participating in the study, should be considered and described in detail. It is important that the aforementioned interventions are detailed and communicated to the participants to ensure that the patients follow the recommendations with full understanding and according to the established rules, as this may affect the final results. Recommendations cannot be based on general indications, such as “increase physical activity” or “follow a healthy diet”, as this is a strong limitation of the study, with it not allowing for an accurate assessment of the impact of the applied interventions. Each recommendation should be precisely defined, preferably (where possible) in a measurable way, such as ‘30 min a day of walking 5 times a week’ or ‘consume 200 g of salmon per week’. Through the subjects following specific guidelines, it prevents variation in the interventions used, within the group, due to misunderstanding or patients’ own interpretation of the recommendations. Measurable recommendations also make it possible to assess the extent to which patients have followed them.
In conclusion, despite the limitations of the studies carried out to date, it seems that only curcumin supplementation or with the addition of physical activity and/or dietary advice and/or lifestyle changes can be helpful in the treatment of patients with MAFLD.
## References
1. Younossi Z.M.. **Non-Alcoholic Fatty Liver Disease—A Global Public Health Perspective**. *J. Hepatol.* (2019) **70** 531-544. DOI: 10.1016/j.jhep.2018.10.033
2. Williams C.D., Stengel J., Asike M.I., Torres D.M., Shaw J., Contreras M., Landt C.L., Harrison S.A.. **Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study**. *Gastroenterology* (2011) **140** 124-131. DOI: 10.1053/j.gastro.2010.09.038
3. Słomko J., Zalewska M., Niemiro W., Kujawski S., Słupski M., Januszko-Giergielewicz B., Zawadka-Kunikowska M., Newton J., Hodges L., Kubica J.. **Evidence-Based Aerobic Exercise Training in Metabolic-Associated Fatty Liver Disease: Systematic Review with Meta-Analysis**. *J. Clin. Med.* (2021) **10**. DOI: 10.3390/jcm10081659
4. Welsh J.A., Karpen S., Vos M.B.. **Increasing Prevalence of Nonalcoholic Fatty Liver Disease Among United States Adolescents, 1988–1994 to 2007–2010**. *J. Pediatr.* (2013) **162** 496-500. DOI: 10.1016/j.jpeds.2012.08.043
5. Pavlides M., Cobbold J.. **Non-Alcoholic Fatty Liver Disease**. *Medicine* (2019) **47** 728-733. DOI: 10.1016/j.mpmed.2019.08.007
6. Buzzetti E., Pinzani M., Tsochatzis E.A.. **The Multiple-Hit Pathogenesis of Non-Alcoholic Fatty Liver Disease (NAFLD)**. *Metabolism* (2016) **65** 1038-1048. DOI: 10.1016/j.metabol.2015.12.012
7. Tilg H., Effenberger M.. **From NAFLD to MAFLD: When Pathophysiology Succeeds**. *Nat. Rev. Gastroenterol. Hepatol.* (2020) **17** 387-388. DOI: 10.1038/s41575-020-0316-6
8. Chalasani N., Younossi Z., Lavine J.E., Charlton M., Cusi K., Rinella M., Harrison S.A., Brunt E.M., Sanyal A.J.. **The Diagnosis and Management of Nonalcoholic Fatty Liver Disease: Practice Guidance from the American Association for the Study of Liver Diseases: Hepatology, Vol. XX, No. X, 2017**. *Hepatology* (2018) **67** 328-357. DOI: 10.1002/hep.29367
9. Mantovani A., Byrne C.D., Bonora E., Targher G.. **Nonalcoholic Fatty Liver Disease and Risk of Incident Type 2 Diabetes: A Meta-Analysis**. *Diabetes Care* (2018) **41** 372-382. DOI: 10.2337/dc17-1902
10. Targher G., Day C.P., Bonora E.. **Risk of Cardiovascular Disease in Patients with Nonalcoholic Fatty Liver Disease**. *N. Engl. J. Med.* (2010) **363** 1341-1350. DOI: 10.1056/NEJMra0912063
11. Haddad T.M., Hamdeh S., Kanmanthareddy A., Alla V.M.. **Nonalcoholic Fatty Liver Disease and the Risk of Clinical Cardiovascular Events: A Systematic Review and Meta-Analysis**. *Diabetes Metab. Syndr.* (2017) **11** S209-S216. DOI: 10.1016/j.dsx.2016.12.033
12. Kumar R., Priyadarshi R.N., Anand U.. **Non-Alcoholic Fatty Liver Disease: Growing Burden, Adverse Outcomes and Associations**. *J. Clin. Transl. Hepatol.* (2020) **8** 76-86. DOI: 10.14218/JCTH.2019.00051
13. **EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease**. *J. Hepatol.* (2016) **64** 1388-1402. DOI: 10.1016/j.jhep.2015.11.004
14. Hewlings S., Kalman D.. **Curcumin: A Review of Its’ Effects on Human Health**. *Foods* (2017) **6**. DOI: 10.3390/foods6100092
15. Sohn S.-I., Priya A., Balasubramaniam B., Muthuramalingam P., Sivasankar C., Selvaraj A., Valliammai A., Jothi R., Pandian S.. **Biomedical Applications and Bioavailability of Curcumin—An Updated Overview**. *Pharmaceutics* (2021) **13**. DOI: 10.3390/pharmaceutics13122102
16. Chong L., Zhang W., Nie Y., Yu G., Liu L., Lin L., Wen S., Zhu L., Li C.. **Protective Effect of Curcumin on Acute Airway Inflammation of Allergic Asthma in Mice through Notch1-GATA3 Signaling Pathway**. *Inflammation* (2014) **37** 1476-1485. DOI: 10.1007/s10753-014-9873-6
17. Ramírez-Tortosa M.C., Mesa M.D., Aguilera M.C., Quiles J.L., Baró L., Ramirez-Tortosa C.L., Martinez-Victoria E., Gil A.. **Oral Administration of a Turmeric Extract Inhibits LDL Oxidation and Has Hypocholesterolemic Effects in Rabbits with Experimental Atherosclerosis**. *Atherosclerosis* (1999) **147** 371-378. DOI: 10.1016/S0021-9150(99)00207-5
18. Quiles J.L., Mesa M.D., Ramírez-Tortosa C.L., Aguilera C.M., Battino M., Gil A., Ramírez-Tortosa M.C.. **Curcuma Longa Extract Supplementation Reduces Oxidative Stress and Attenuates Aortic Fatty Streak Development in Rabbits**. *Arterioscler. Thromb. Vasc. Biol.* (2002) **22** 1225-1231. DOI: 10.1161/01.ATV.0000020676.11586.F2
19. Burge K., Gunasekaran A., Eckert J., Chaaban H.. **Curcumin and Intestinal Inflammatory Diseases: Molecular Mechanisms of Protection**. *Int. J. Mol. Sci.* (2019) **20**. DOI: 10.3390/ijms20081912
20. Giordano A., Tommonaro G.. **Curcumin and Cancer**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11102376
21. Różański G., Kujawski S., Newton J.L., Zalewski P., Słomko J.. **Curcumin and Biochemical Parameters in Metabolic-Associated Fatty Liver Disease (MAFLD)—A Review**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13082654
22. Moher D., Liberati A., Tetzlaff J., Altman D.G., Group T.P.. **Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement**. *PLoS Med.* (2009) **6**. DOI: 10.1371/journal.pmed.1000097
23. Rahmani S., Asgary S., Askari G., Keshvari M., Hatamipour M., Feizi A., Sahebkar A.. **Treatment of Non-Alcoholic Fatty Liver Disease with Curcumin: A Randomized Placebo-Controlled Trial**. *Phytother. Res.* (2016) **30** 1540-1548. DOI: 10.1002/ptr.5659
24. Kelardeh B.M., Keshavarz S., Karimi M.. **Effects of Nonlinear Resistance Training with Curcumin Supplement on Liver Enzymes in Men with Non- Alcoholic Fatty Liver Disease**. *Rep. Health Care* (2017) **3** 1-9
25. Navekar R., Rafraf M., Ghaffari A., Asghari-Jafarabadi M., Khoshbaten M.. **Turmeric Supplementation Improves Serum Glucose Indices and Leptin Levels in Patients with Nonalcoholic Fatty Liver Diseases**. *J. Am. Coll. Nutr.* (2017) **36** 261-267. DOI: 10.1080/07315724.2016.1267597
26. Chashmniam S., Mirhafez S.R., Dehabeh M., Hariri M., Nezhad M.A., Nobakht M., Gh B.F.. **A Pilot Study of the Effect of Phospholipid Curcumin on Serum Metabolomic Profile in Patients with Non-Alcoholic Fatty Liver Disease: A Randomized, Double-Blind, Placebo-Controlled Trial**. *Eur. J. Clin. Nutr.* (2019) **73** 1224-1235. DOI: 10.1038/s41430-018-0386-5
27. Mirhafez S.R., Farimani A.R., Dehhabe M., Bidkhori M., Hariri M., Ghouchani F.N.M., Abdollahi F.. **Effect of Phytosomal Curcumin on Circulating Levels of Adiponectin and Leptin in Patients with Non-Alcoholic Fatty Liver Disease: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial**. *J. Gastrointestin. Liver Dis.* (2019) **28** 7. DOI: 10.15403/jgld-179
28. Hariri M., Gholami A., Mirhafez S.R., Bidkhori M., Sahebkar A.. **A Pilot Study of the Effect of Curcumin on Epigenetic Changes and DNA Damage among Patients with Non-Alcoholic Fatty Liver Disease—A Randomized, Double-Blind, Placebo-Controlled, Clinical Trial**. *Complement. Ther. Med.* (2020) **51** 102447. DOI: 10.1016/j.ctim.2020.102447
29. Kelardeh B.M., Rahmati-Ahmadabad S., Farzanegi P., Helalizadeh M., Azarbayjani M.-A.. **Effects of Non-Linear Resistance Training and Curcumin Supplementation on the Liver Biochemical Markers Levels and Structure in Older Women with Non-Alcoholic Fatty Liver Disease**. *J. Bodyw. Mov. Ther.* (2020) **24** 154-160. DOI: 10.1016/j.jbmt.2020.02.021
30. Saberi-Karimian M., Keshvari M., Ghayour-Mobarhan M., Salehizadeh L., Rahmani S., Behnam B., Jamialahmadi T., Asgary S., Sahebkar A.. **Effects of Curcuminoids on Inflammatory Status in Patients with Non-Alcoholic Fatty Liver Disease: A Randomized Controlled Trial**. *Complement. Ther. Med.* (2020) **49** 102322. DOI: 10.1016/j.ctim.2020.102322
31. Panahi Y., Kianpour P., Mohtashami R., Jafari R., Simental-Mendía L.E., Sahebkar A.. **Curcumin Lowers Serum Lipids and Uric Acid in Subjects With Nonalcoholic Fatty Liver Disease: A Randomized Controlled Trial**. *J. Cardiovasc. Pharmacol.* (2016) **68** 223-229. DOI: 10.1097/FJC.0000000000000406
32. Panahi Y., Kianpour P., Mohtashami R., Jafari R., Simental-Mendía L., Sahebkar A.. **Efficacy and Safety of Phytosomal Curcumin in Non-Alcoholic Fatty Liver Disease: A Randomized Controlled Trial**. *Drug Res.* (2017) **67** 244-251. DOI: 10.1055/s-0043-100019
33. Jazayeri-Tehrani S.A., Rezayat S.M., Mansouri S., Qorbani M., Alavian S.M., Daneshi-Maskooni M., Hosseinzadeh-Attar M.-J.. **Nano-Curcumin Improves Glucose Indices, Lipids, Inflammation, and Nesfatin in Overweight and Obese Patients with Non-Alcoholic Fatty Liver Disease (NAFLD): A Double-Blind Randomized Placebo-Controlled Clinical Trial**. *Nutr. Metab.* (2019) **16** 8. DOI: 10.1186/s12986-019-0331-1
34. Saadati S., Sadeghi A., Mansour A., Yari Z., Poustchi H., Hedayati M., Hatami B., Hekmatdoost A.. **Curcumin and Inflammation in Non-Alcoholic Fatty Liver Disease: A Randomized, Placebo Controlled Clinical Trial**. *BMC Gastroenterol.* (2019) **19**. DOI: 10.1186/s12876-019-1055-4
35. Saadati S., Hatami B., Yari Z., Shahrbaf M.A., Eghtesad S., Mansour A., Poustchi H., Hedayati M., Aghajanpoor-pasha M., Sadeghi A.. **The Effects of Curcumin Supplementation on Liver Enzymes, Lipid Profile, Glucose Homeostasis, and Hepatic Steatosis and Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease**. *Eur. J. Clin. Nutr.* (2019) **73** 441-449. DOI: 10.1038/s41430-018-0382-9
36. Cicero A.F.G., Sahebkar A., Fogacci F., Bove M., Giovannini M., Borghi C.. **Effects of Phytosomal Curcumin on Anthropometric Parameters, Insulin Resistance, Cortisolemia and Non-Alcoholic Fatty Liver Disease Indices: A Double-Blind, Placebo-Controlled Clinical Trial**. *Eur. J. Nutr.* (2020) **59** 477-483. DOI: 10.1007/s00394-019-01916-7
37. Higgins J.P.T., Altman D.G., Gøtzsche P.C., Jüni P., Moher D., Oxman A.D., Savovic J., Schulz K.F., Weeks L., Sterne J.A.C.. **The Cochrane Collaboration’s Tool for Assessing Risk of Bias in Randomised Trials**. *BMJ* (2011) **343** d5928. DOI: 10.1136/bmj.d5928
38. Easterbrook P.J., Gopalan R., Berlin J.A., Matthews D.R.. **Publication Bias in Clinical Research**. *Lancet* (1991) **337** 867-872. DOI: 10.1016/0140-6736(91)90201-Y
39. Balduzzi S., Rücker G., Schwarzer G.. **How to Perform a Meta-Analysis with R: A Practical Tutorial**. *Evid. Based Ment. Health* (2019) **22** 153-160. DOI: 10.1136/ebmental-2019-300117
40. Viechtbauer W.. **Conducting Meta-Analyses in R with the Metafor Package**. *J. Stat. Softw.* (2010) **36** 1-48. DOI: 10.18637/jss.v036.i03
41. 41.
R Core Team
R: A Language and Environment for Statistical ComputingR Foundation for Statistical ComputingVienna, Austria2019. *R: A Language and Environment for Statistical Computing* (2019)
42. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T.. **Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks**. *Genome Res.* (2003) **13** 2498-2504. DOI: 10.1101/gr.1239303
43. Richardson W.S., Wilson M.C., Nishikawa J., Hayward R.S.. **The Well-Built Clinical Question: A Key to Evidence-Based Decisions**. *ACP J. Club* (1995) **123** A12-A13. DOI: 10.7326/ACPJC-1995-123-3-A12
44. Pulido-Moran M., Moreno-Fernandez J., Ramirez-Tortosa C., Ramirez-Tortosa M.. **Curcumin and Health**. *Molecules* (2016) **21**. DOI: 10.3390/molecules21030264
45. Keating S.E., Adams L.A.. **Exercise in NAFLD: Just Do It**. *J. Hepatol.* (2016) **65** 671-673. DOI: 10.1016/j.jhep.2016.06.022
46. Bull F.C., Al-Ansari S.S., Biddle S., Borodulin K., Buman M.P., Cardon G., Carty C., Chaput J.-P., Chastin S., Chou R.. **World Health Organization 2020 Guidelines on Physical Activity and Sedentary Behaviour**. *Br. J. Sport. Med.* (2020) **54** 1451-1462. DOI: 10.1136/bjsports-2020-102955
47. Machado M., Cortez-Pinto H.. **Diet, Microbiota, Obesity, and NAFLD: A Dangerous Quartet**. *Int. J. Mol. Sci.* (2016) **17**. DOI: 10.3390/ijms17040481
48. Ngu M.H., Norhayati M.N., Rosnani Z., Zulkifli M.M.. **Curcumin as Adjuvant Treatment in Patients with Non-Alcoholic Fatty Liver (NAFLD) Disease: A Systematic Review and Meta-Analysis**. *Complement. Ther. Med.* (2022) **68** 102843. DOI: 10.1016/j.ctim.2022.102843
49. Goodarzi R., Sabzian K., Shishehbor F., Mansoori A.. **Does Turmeric/Curcumin Supplementation Improve Serum Alanine Aminotransferase and Aspartate Aminotransferase Levels in Patients with Nonalcoholic Fatty Liver Disease? A Systematic Review and Meta-Analysis of Randomized Controlled Trials: Turmeric/Curcumin Supplementation and Liver Enzymes**. *Phytother. Res.* (2019) **33** 561-570. DOI: 10.1002/ptr.6270
50. Yang K., Chen J., Zhang T., Yuan X., Ge A., Wang S., Xu H., Zeng L., Ge J.. **Efficacy and Safety of Dietary Polyphenol Supplementation in the Treatment of Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis**. *Front. Immunol.* (2022) **13** 949746. DOI: 10.3389/fimmu.2022.949746
51. Jalali M., Mahmoodi M., Mosallanezhad Z., Jalali R., Imanieh M.H., Moosavian S.P.. **The Effects of Curcumin Supplementation on Liver Function, Metabolic Profile and Body Composition in Patients with Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials**. *Complement. Ther. Med.* (2020) **48** 102283. DOI: 10.1016/j.ctim.2019.102283
52. Wei Z., Liu N., Tantai X., Xing X., Xiao C., Chen L., Wang J.. **The Effects of Curcumin on the Metabolic Parameters of Non-Alcoholic Fatty Liver Disease: A Meta-Analysis of Randomized Controlled Trials**. *Hepatol. Int.* (2019) **13** 302-313. DOI: 10.1007/s12072-018-9910-x
53. Baziar N., Parohan M.. **The Effects of Curcumin Supplementation on Body Mass Index, Body Weight, and Waist Circumference in Patients with Nonalcoholic Fatty Liver Disease: A Systematic Review and Dose–Response Meta-analysis of Randomized Controlled Trials**. *Phytother. Res.* (2020) **34** 464-474. DOI: 10.1002/ptr.6542
|
---
title: 'Sibling Resemblance in Physical Activity Levels: The Peruvian Sibling Study
on Growth and Health'
authors:
- Carla Santos
- José Maia
- Sara Pereira
- Olga Vasconcelos
- Rui Garganta
- J. Timothy Lightfoot
- Go Tani
- Donald Hedeker
- Peter T. Katzmarzyk
- Alcibíades Bustamante
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001479
doi: 10.3390/ijerph20054210
license: CC BY 4.0
---
# Sibling Resemblance in Physical Activity Levels: The Peruvian Sibling Study on Growth and Health
## Abstract
Physical activity is associated with a host of positive health outcomes and is shaped by both genetic and environmental factors. We aim to: [1] estimate sibling resemblance in two physical activity phenotypes [total number of steps∙day−1 and minutes for moderate steps per day (min∙day−1)]; and [2] investigate the joint associations of individual characteristics and shared natural environment with intra-pair sibling similarities in each phenotype. We sampled 247 biological siblings from 110 nuclear families, aged 6–17 years, from three Peruvian regions. Physical activity was measured using pedometers and body mass index was calculated. *In* general, non-significant variations in the intraclass correlation coefficients were found after adjustment for individual characteristics and geographical area for both phenotypes. Further, no significant differences were found between the three sib-ship types. Sister-sister pairs tended to take fewer steps than brother-brother (β = −2908.75 ± 954.31). Older siblings tended to walk fewer steps (β = −81.26 ± 19.83), whereas body mass index was not associated with physical activity. Siblings living at high-altitude and in the Amazon region had higher steps/day (β = 2508.92 ± 737.94; β = 2213.11 ± 776.63, respectively) compared with their peers living at sea-level. *In* general, we found no influence of sib-types, body mass index, and/or environment on the two physical activity phenotypes.
## 1. Introduction
Physical activity (PA) has been associated with a variety of positive health outcomes, generating transitional benefits from childhood through adolescence into adulthood [1,2], including decreases in obesity [3], cardiovascular disease, and diabetes [4], and increases in cognitive function as well as academic achievement [5]. Despite its recognized benefits, updated information on the prevalence and trends in PA [6,7] showed that the majority of children and adolescents worldwide are physically inactive, putting their current and future health at risk, and *Peru is* no exception [8]. For example, a recent global estimate from 146 countries showed that $81\%$ of children and adolescents aged 11–17 years were physically inactive [9], with the prevalence of insufficient physical activity in Peruvian youth increasing from $82.6\%$ in 2001 to $84.7\%$ in 2016. To reverse current trends, it is important to investigate what types of factors can effectively influence daily active play and PA behaviors in childhood and adolescence.
There is considerable variability among children in their level and patterns of PA, and this variability is shaped by a host of genetic [10,11,12] and environmental [13,14] factors. Behaviors such as PA are often influenced by household and family characteristics, as families often share common interests and experiences [15,16,17]. However, different family members also express a degree of autonomy when it comes to lifestyle behaviors, and sometimes variation among related subjects is also remarkable [18].
The study of siblings offers unique insights into their biology and behavior, given their relationships to each other, as well as to other family members. For example, siblings share a substantial fraction of their genes that are transmitted from their parents; in addition, siblings often grow and mature in similar environments including the household, school, and neighborhood contexts. In addition, they also differ in their chronological age, maturity status, sex, body composition, physical fitness, or lifestyle choices [19,20,21].
Genome-wide association studies (GWAS) have provided evidence that variation in PA is associated with polymorphisms in several genes [22,23,24]. However, several reviews of the extant literature do not identify specific genetic factors exclusively responsible for physical activity phenotypes [25]. On the other hand, variation in sibling resemblance depending on the sib-type and the phenotype has also been considered. For example, Pereira, et al. [ 26], using questionnaire data in Portuguese sibling pairs, showed that after adjustments for several covariates (biological, behavioural, familial, and environmental characteristics), sister-sister pairs demonstrated greater resemblance in their PA (ρ = 0.53) than brother-sister (ρ = 0.26) or brother-brother pairs (ρ = 0.18). In contrast, Jacobi, et al. [ 27] found no differences in correlations between siblings (all ρ = 0.28) when using PA data collected with pedometers.
Since PA is a complex and multifaceted trait, it has also been documented that a significant fraction of its variation can be explained by different environmental exposures throughout the lifespan [28], and this is particularly evident in developing countries like Peru. The distinct living settings of Peruvians have been recognized as a kind of “natural laboratory”, a singular territory that offers an opportunity to assess the impact of geographical variation on PA levels by combining settings on the spectrum of both rural-urban developments as well as lowland-highland scenarios. Peruvians are exposed daily to different natural stressors (e.g., altitude, temperature, pollutants), as well as social and economic inequalities (e.g., access to health care, quality of nutrition, access to public recreational infrastructure) in a unique geographical diversity [29], which can influence intrapair similarities in PA levels. To date, there is only one study focusing on physical fitness phenotypes in Peru, which concluded that both individual characteristics and geographical area of residence were significantly related to the magnitude of sibling resemblance as well as the mean levels of physical fitness [21].
Despite this recognition, to date there is no available evidence regarding variation in PA levels among Peruvian siblings, especially embracing the diversity of the three distinct geographical areas. Hence, using sibling data, as well as a multilevel statistical approach [30], we explored resemblance in PA levels among Peruvian siblings conditioned on the additive effects of their individual characteristics and shared natural environment. Specifically, we intend to: [1] estimate sibling resemblance in two PA phenotypes [total number of steps∙day−1 and minutes for moderate steps (min∙day−1)]; and [2] investigate the joint associations of individual characteristics (age and body mass index) as well as a shared natural environment.
## 2.1. Design and Participants
Our sample originates from The Peruvian Sibling Study on Growth and Health [31]. This study probes into sibling resemblance in body composition, physical fitness, physical activity, different facets of their motor development as well as gross motor coordination. A total of 247 biological siblings [(147 females and 100 males from 110 nuclear families ($67.2\%$ two siblings; $32.8\%$ three siblings)] were selected. All are native to three Peruvian geographical areas located at different altitudes: sea-level (Barranco = 58 m), Amazon region (La Merced and San Ramon = 751 m), and high-altitude (Junín = 4107 m). Only families that had two or three children, aged between 6 and 17 years, with complete PA data were considered in the present paper. Parents or legal guardians provided written informed consent. The project was approved by the Ethics Committee of the School of Physical Education and Sports, National University of Education Enrique Guzmán y Valle, Peru (UNE EGyV). Following their approval, all known siblings were invited to participate in the study.
## 2.2.1. Anthropometry
Body measurements were made according to standardized protocols [32]. Height was measured with a portable stadiometer (Sanny, Model ES-2060) holding the child′s head in the Frankfurt plane, to the nearest 0.1 cm; weight was measured with a digital scale (Pesacon, Model IP68), with a precision of 0.1 kg. Body mass index (BMI) was calculated using the standard formula: BMI = [weight(kg)/height(m)2].
## 2.2.2. Physical Activity
In order to objectively measure PA, we used pedometers, a body movement sensor that validly and reliably assesses PA among children and youth [33,34]. Pedometers have been used in different populations from different countries [35], and their validity has been studied [36]. Subjects used the Omron Model Walking style II pedometer (Omron Healthcare, Inc., Muko, Japan) over five consecutive days (three weekdays and two weekend days). These pedometers have a multiday memory function that automatically stores the total number of steps∙day −1 (a proxy measure of the total volume of PA), and the walking time, in minutes (min∙day −1), at a moderate or brisk pace in a day (a proxy measure of moderate-to-vigorous PA—this counts the amount of time spent walking at 3.0 METs or more) [37]. Siblings were instructed in the use of the pedometer, learning to remove it only for bathing and before sleeping at night. The devices were attached to the trouser belt (strap) using a clip, leaving the unit perpendicular to the ground. For the present study, only data from sib-ships with complete information from five consecutive days (Wednesday to Sunday) with an average of 12 h∙day −1 of pedometer use were considered.
## 2.2.3. Shared Environment Characteristics (Natural Environment)
Given the country’s heterogeneity in geographical terms, participants came from the three distinct regions located at different altitudes: sea-level, Amazon region, and high-altitude. Barranco (58 m) was the chosen city at sea-level and this is one of the 43 districts of Lima Province, located on the shore of the Pacific Ocean. The cities of La Merced and San Ramon (751 m) in the Chanchamayo district represented the Amazon region that is the largest in the Peruvian territory and occupies ~$60\%$ of its surface. The Junín district (4107 m) was used to represent the high-altitude location on the southern shore of Lake Junín or Chinchaycocha.
## 2.3. Data Quality Control
Data quality control was enhanced by all assessment team members being systematically trained by the lead researchers of the project to: (i) comply with the correct use of technical body measurement procedures; and (ii) instruct parents and children about the pedometer use protocol and persuade them to follow their regular PA routine. Further, IBM-SPSS v26 software was used to facilitate data entry and to cross-check data elements, employing automatic controls to ensure values were not outside known ranges.
## 2.4. Statistical Procedures
Analysis of the data was conducted in a sequential manner. We first performed data cleaning and initial exploratory analyses to identify outliers and check for normality of distributions. In order to normalize the distribution of the phenotype minutes for moderate steps (min∙day −1), a log transformation was applied and the sum of log-scores was computed. Descriptive statistics for all phenotypes i.e., means and standard deviations, were calculated. Differences between geographical residence areas were examined with analysis of variance (ANOVA). Along with the ANOVA, Tukey HSD tests were used for multiple comparisons. SPSS v26 software was used for all analyses, and the Type-I error rate was set at $5\%$. As sibling data are clustered, and since individuals are nested within their sib-ships (brother-brother BB, sister-sister SS, brother-sister BS), multilevel models were used for statistical analysis [38]. Separates within and between sib-ship variances were first estimated to comply with our first aim. As such, different intraclass correlation coefficients (ρ) with corresponding $95\%$ confidence intervals ($95\%$ CI) for each PA phenotype were computed. Further, based on the likelihood-ratio test, we compared a model that constrained ρ to be equal across sib-ship pairs (Null model) to a model that freely estimated ρ across sib-ship pairs (Model 1). The following models were henceforth estimated with the same or different ρ, depending on the result attained from the likelihood-ratio test.
For the second aim, the model was expanded (Model 2) to include individual variables such as age and BMI, with ρ being re-estimated for each sib-type. Finally, the full model (Model 3) included the geographical area of residence. For model comparison, the likelihood ratio test was used. Given that there are only three regions (sea-level, Amazon region, and high-altitude), as advocated, we did not treat region as a level in the multilevel model [39]. Instead, dummy variables were used to account for differences attributable to region in the fixed part of Model 3, with sea-level as the reference category. Continuous covariates were mean-centered, and sea-level BB pairs served as the reference category. For the multilevel analyses, STATA 14 software was used, with the Type-I error rate set at $5\%$.
## 3. Results
Table 1 shows descriptive statistics for all study variables. On average, no statistically significant differences ($p \leq 0.05$) were found among sib-ship pairs from the three geographical areas for chronological age and height. Further, siblings living in the Amazon region are heavier ($F = 5.55$, $p \leq 0.05$), have a higher BMI ($F = 13.55$, $p \leq 0.05$), and take more steps∙day−1 ($F = 21.52$, $p \leq 0.05$) compared with their peers from the other regions. On the other hand, sib-ships from the Amazon region spent fewer minutes for moderate steps (min∙day −1) compared to those at sea-level ($F = 3.53$, $p \leq 0.05$).
Table 2 provides estimates for the unadjusted and adjusted sibling’s correlations at each PA phenotype. For both phenotypes, Model 1 did not improve model fit relative to the Null model. Thus, there is insufficient evidence to reject the assumption of equal intraclass correlation for the three sib-pairs (BB, SS and BS). From the null model, total number of steps∙day−1 intraclass correlation = 0.44 ($95\%$CI = 0.31–0.58), and minutes for moderate steps (min∙day−1) intraclass correlation = 0.35 ($95\%$CI = 0.22–0.51). *In* general, the inclusion of individual characteristics (Model 2), as well as the different geographical areas, did not significantly influence the size of the intraclass correlations in both phenotypes. Additionally, for the minutes of moderate steps phenotype, the last model (Model 3) was not tested since Model 2 was not better than the previous model (Δ = −157.54, $$p \leq 0.43$$).
Table 3 shows the multilevel analysis results. Model 3 fit the data significantly better than Model 2 only for total number of steps∙day−1. *In* general, PA averages for BB pairs are β = 11,158.63 ± 1001.06; SS pairs tended to take fewer steps compared with BB (β = −2908.75 ± 954.31), while non-significant differences were found between BS and BB pairs ($p \leq 0.05$). Older siblings tended to take fewer steps (β = −81.26 ± 19.83, $p \leq 0.05$), whereas BMI was not statistically significant ($p \leq 0.05$). Further, siblings living at high-altitude and in the Amazon region tended to take more steps (β = 2508.92 ± 737.94, $p \leq 0.05$; β = 2213.11 ± 776.63, $p \leq 0.05$, respectively) compared with those living at sea-level.
## 4. Discussion
The present study is innovative in providing in-country PA data for Peru dedicated to siblings living at different altitudes with their specific socioeconomic characteristics, cultural disparities as well as built and natural environments. Our results showed that differences in Peruvian sib-ships resemblance in two PA phenotypes were mainly influenced, apparently, by genetic factors since non-significant differences were found in the intraclass correlation coefficients after adjustments for individual characteristics and geographical area of residence. Further, no significant differences were found between the three sib-ship types.
The available literature has reported varying results. For example, Jacobi, Caille, Borys, Lommez, Couet, Charles and Oppert [27], using French nuclear family data in conjunction with pedometer PA measurements, reported low correlations (ρ = 0.28) among siblings for the number of steps per day, although adjustments were only made for sex and age. On the other hand, Maia et al. [ 40], using the Baecke questionnaire in Portuguese family data, showed differences in a total PA phenotype between sib-types, with BB resembling more than SS and BS. Pereira, Katzmarzyk, Gomes, Souza, Chaves, Santos, Santos, Bustamante, Barreira, Hedeker and Maia [26], also using Portuguese siblings data and the same PA assessment tool, showed that with increasing levels of covariate adjustments, SS pairs showed stronger resemblance than BS and BB pairs. A similar trend was also found in a recent paper analyzing Peruvian sibling resemblances in physical fitness components. Significant differences across sib-types were only observed for waist circumference and handgrip strength, with BB correlations being higher than the SS or the BS correlations, after adjustments for individual characteristics (including age, height, body mass index, and maturity offset) and geographical area of residence [21]. In summary, we believe that correlation discrepancies between studies may be due to different sampling strategies, diverse covariate adjustments, different statistical techniques used to compute correlations, and the phenotypic expression as well as instruments used.
Some previous genetic studies have attempted to identify specific genes that may regulate PA [22,41]. However, this is not a straightforward task, as heritability estimates for PA have ranged from moderate to very high [10]. For example, in a review by de Vilhena e Santos, Katzmarzyk, Seabra and Maia [12], the authors reported genome-wide linkage data with markers near different PA related genes, while Lightfoot [24] indicated that only 2 candidate genes showed consistent associations in the regulation of PA—dopamine receptor 1 (Drd1) and helixloop helix 2 (Nhlh2). Further, recent GWAS indicated a genetic contribution to PA, with Doherty et al. [ 42] uncovering 14 loci for device-measured PA, while Klimentidis, et al. [ 43] identified multiple variants in habitual PA including CADM2 and APOE. Notwithstanding this progress, results are still unclear, most probably because of specificities in the production of genome maps in genome-wide linkage studies, uses of different methods to estimate PA, or the different ethnic composition of each sample.
Our multilevel model showed that PA averages for BB pairs are 11,159 steps∙day−1, which means that they tend to comply, on average, with the guideline recommendations for children and adolescents [44]. Consistent with our sibling data, chronological age has been negatively associated with PA [26,45]. In our study, for each year increase in sibling age, there was an average reduction of 81 steps∙day−1, whereas Pereira, Katzmarzyk, Gomes, Souza, Chaves, Santos, Santos, Bustamante, Barreira, Hedeker and Maia [26], based on self-reported PA, similarly revealed a decrease among Portuguese siblings. Using non-sibling data, Duncan, et al. [ 46] also showed a decline in the number of steps per day with age among New Zealand children and adolescents (15,284 weekday steps and 12,948 weekend steps at 5–6 years of age to 14,801 weekday steps and 10,656 weekend steps at 11–12 years of age). Using accelerometry, Alvis-Chirinos, et al. [ 47] also reported a decline in moderate-to-vigorous PA with age among Peruvian youth (1.354 min at 6–9 years to 1167 min at 10–13 years).
Our results also indicated dissimilarities in PA among siblings living in the three geographical areas, which potentially reflect the marked regional variations in terms of sociodemographic, economic, and cultural features. For example, in the city of Barranco, children are exposed to several built constraints like compact urban areas, large population centers, and extensive housing developments, with serious consequences in terms of traffic regulation, not to mention increases in public insecurity and environmental problems. Such local constraints can deprive children of playing freely in the community’s streets without parental supervision, as well as restricting access to public recreational and sports services. This may help to explain the likelihood of sibling pairs walking fewer steps compared with their peers from the other regions. In turn, in Chanchamayo and Junín, children tend to take more steps per day, probably because they find plenty of space for leisure and free playing, helping them to develop their abilities, deepen and widen their experiences, acquire further skills, and discover other interests. However, we could not find a published paper that investigated the links between natural environments (sea-level, Amazon region, or high-altitude) and siblings’ PA to make suitable comparisons.
Notwithstanding the importance of the present data, some limitations must be recognized. Firstly, without data indicating otherwise, it is possible that our sample is not representative of the overall Peruvian sibling population. Secondly, given the study design, genetic and environmental influences could not be estimated separately because no twins were involved. Thirdly, we made no adjustments for family socioeconomic status. While limited, this report also has several unique strengths. Firstly, the study involves a relatively large sample of siblings from three unique environmental contexts, although its size may not have sufficient power to detect putative interactions of different sib-types with their varying environments. Secondly, the study covered both childhood and puberty periods, expanding the range of potential influences from biological and environmental factors. Thirdly, the use of standardized and highly reliable objective methods for data collection makes significant contributions to the available literature. Finally, the use of a multilevel analysis model with individual and environmental data allows for approaching their interaction in the development of PA.
## 5. Conclusions
In conclusion, our model-based results revealed that, in general, there are no significant differences in the intraclass correlation coefficients for both PA phenotypes after adjustment for age and BMI as well as the geographical area of residence. Further, non-significant differences were found between the three sib-ship types. SS pairs tended to take fewer steps∙day−1 than BB, while non-significant differences were found between BS and BB pairs. Older siblings tended to walk fewer steps∙day−1, whereas BMI was not associated with PA. Further, siblings living at high-altitude and in the Amazon region tended to walk more steps∙day−1 compared with their peers living at sea-level.
Overall, our results highlight the significant sibling resemblance effects in explaining variance in PA, with genetic factors apparently being the most important legacy to explain dissimilarity, although environmental features must also be considered.
## References
1. van Sluijs E.M.F., Ekelund U., Crochemore-Silva I., Guthold R., Ha A., Lubans D., Oyeyemi A.L., Ding D., Katzmarzyk P.T.. **Physical activity behaviours in adolescence: Current evidence and opportunities for intervention**. *Lancet* (2021.0) **398** 429-442. DOI: 10.1016/S0140-6736(21)01259-9
2. Kallio P., Pahkala K., Heinonen O.J., Tammelin T.H., Pälve K., Hirvensalo M., Juonala M., Loo B.-M., Magnussen C.G., Rovio S.. **Physical inactivity from youth to adulthood and adult cardiometabolic risk profile**. *Prev. Med.* (2021.0) **145** 106433. DOI: 10.1016/j.ypmed.2021.106433
3. Hills A.P., Andersen L.B., Byrne N.. **Physical activity and obesity in children**. *Br. J. Sports Med.* (2011.0) **45** 866-870. DOI: 10.1136/bjsports-2011-090199
4. Wahid A., Manek N., Nichols M., Kelly P., Foster C., Webster P., Kaur A., Smith C.F., Wilkins E., Rayner M.. **Quantifying the Association Between Physical Activity and Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis**. *J. Am. Heart Assoc.* (2016.0) **5** e002495. DOI: 10.1161/JAHA.115.002495
5. Donnelly J.E., Hillman C.H., Castelli D., Etnier J.L., Lee S., Tomporowski P., Lambourne K., Szabo-Reed A.N.. **Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children**. *Med. Sci. Sports Exerc.* (2016.0) **48** 1197-1222. DOI: 10.1249/MSS.0000000000000901
6. Reilly J.J., Barnes J., Gonzalez S., Huang W.Y., Manyanga T., Tanaka C., Tremblay M.S.. **Recent Secular Trends in Child and Adolescent Physical Activity and Sedentary Behavior Internationally: Analyses of Active Healthy Kids Global Alliance Global Matrices 1.0 to 4.0**. *J. Phys. Act. Health* (2022.0) **19** 729-736. DOI: 10.1123/jpah.2022-0312
7. Aubert S., Barnes J.D., Demchenko I., Hawthorne M., Abdeta C., Abi Nader P., Adsuar Sala J.C., Aguilar-Farias N., Aznar S., Bakalár P.. **Global Matrix 4.0 Physical Activity Report Card Grades for Children and Adolescents: Results and Analyses From 57 Countries**. *J. Phys. Act. Health* (2022.0) **19** 700-728. DOI: 10.1123/jpah.2022-0456
8. Marques A., Henriques-Neto D., Peralta M., Martins J., Demetriou Y., Schönbach D.M.I., de Matos M.G.. **Prevalence of Physical Activity among Adolescents from 105 Low, Middle, and High-Income Countries**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17093145
9. Guthold R., Stevens G.A., Riley L.M., Bull F.C.. **Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1·6 million participants**. *Lancet Child Adolesc. Health* (2020.0) **4** 23-35. DOI: 10.1016/S2352-4642(19)30323-2
10. Lightfoot J.T., de Geus E., Booth F.W., Bray M.S., Hoed M.D., Kaprio J., Kelly S.A., Pomp D., Saul M., Thomis M.. **Biological/Genetic Regulation of Physical Activity Level**. *Med. Sci. Sports Exerc.* (2018.0) **50** 863-873. DOI: 10.1249/MSS.0000000000001499
11. Lin X., Eaton C.B., Manson J.E., Liu S.. **The Genetics of Physical Activity**. *Curr. Cardiol. Rep.* (2017.0) **19** 119. DOI: 10.1007/s11886-017-0938-7
12. Santos D.M.D.V.E., Katzmarzyk P.T., Seabra A.F.T., Maia J.A.R.. **Genetics of Physical Activity and Physical Inactivity in Humans**. *Behav. Genet.* (2012.0) **42** 559-578. DOI: 10.1007/s10519-012-9534-1
13. Li L., Moosbrugger M.E.. **Correlations between Physical Activity Participation and the Environment in Children and Adolescents: A Systematic Review and Meta-Analysis Using Ecological Frameworks**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph18179080
14. Weinberg D., Stevens G.W.J.M., Bucksch J., Inchley J., De Looze M.. **Do country-level environmental factors explain cross-national variation in adolescent physical activity? A multilevel study in 29 European countries**. *BMC Public Health* (2019.0) **19**. DOI: 10.1186/s12889-019-6908-9
15. Rhodes R.E., Guerrero M.D., Vanderloo L.M., Barbeau K., Birken C.S., Chaput J.-P., Faulkner G., Janssen I., Madigan S., Mâsse L.. **Development of a consensus statement on the role of the family in the physical activity, sedentary, and sleep behaviours of children and youth**. *Int. J. Behav. Nutr. Phys. Act.* (2020.0) **17** 74. DOI: 10.1186/s12966-020-00973-0
16. Kracht C.L., Sisson S.B.. **Sibling influence on children’s objectively measured physical activity: A meta-analysis and systematic review**. *BMJ Open Sport Exerc. Med.* (2018.0) **4** e000405. DOI: 10.1136/bmjsem-2018-000405
17. Blazo J.A., Smith A.L.. **A systematic review of siblings and physical activity experiences**. *Int. Rev. Sport Exerc. Psychol.* (2018.0) **11** 122-159. DOI: 10.1080/1750984X.2016.1229355
18. Pereira S., Santos C., Katzmarzyk P.T., Maia J.. **Familial Resemblance in Body Shape and Composition, Metabolic Syndrome, Physical Activity and Physical Fitness: A Summary of Research in Portuguese Families and Siblings**. *Twin Res. Hum. Genet.* (2019.0) **22** 651-659. DOI: 10.1017/thg.2019.46
19. Frisell T., Öberg S., Kuja-Halkola R., Sjölander A.. **Sibling Comparison Designs**. *Epidemiology* (2012.0) **23** 713-720. DOI: 10.1097/EDE.0b013e31825fa230
20. Keyes K.M., Smith G.D., Susser E.. **On Sibling Designs**. *Epidemiology* (2013.0) **24** 473-474. DOI: 10.1097/EDE.0b013e31828c7381
21. Santos C., Bustamante A., Vasconcelos O., Pereira S., Garganta R., Lightfoot J.T., Tani G., Hedeker D., Katzmarzyk P.T., Maia J.. **Sibling Resemblances in Physical Fitness in Three Distinct Regions in Peru: The Peruvian Sibling Study on Growth and Health**. *Behav. Genet.* (2022.0) **52** 195-204. DOI: 10.1007/s10519-022-10099-7
22. Abdellaoui A., Dolan C.V., Verweij K.J.H., Nivard M.G.. **Gene–environment correlations across geographic regions affect genome-wide association studies**. *Nat. Genet.* (2022.0) **54** 1345-1354. DOI: 10.1038/s41588-022-01158-0
23. Kim J., Oh S., Min H., Kim Y., Park T.. **Practical issues in genome-wide association studies for physical activity**. *Ann. N. Y. Acad. Sci.* (2011.0) **1229** 38-44. DOI: 10.1111/j.1749-6632.2011.06102.x
24. Lightfoot J.T.. **Current Understanding of the Genetic Basis for Physical Activity**. *J. Nutr.* (2011.0) **141** 526-530. DOI: 10.3945/jn.110.127290
25. de Geus E.J.. **Genetic Pathways Underlying Individual Differences in Regular Physical Activity**. *Exerc. Sport Sci. Rev.* (2023.0) **51** 2-18. DOI: 10.1249/JES.0000000000000305
26. Pereira S., Katzmarzyk P.T., Gomes T.N., Souza M., Chaves R.N., Santos F.K., Santos D., Bustamante A., Barreira T., Hedeker D.. **Resemblance in physical activity levels: The Portuguese sibling study on growth, fitness, lifestyle, and health**. *Am. J. Hum. Biol.* (2018.0) **30** e23061. DOI: 10.1002/ajhb.23061
27. Jacobi D., Caille A., Borys J.-M., Lommez A., Couet C., Charles M.A., Oppert J.-M.. **FLVS Study Group Parent-Offspring Correlations in Pedometer-Assessed Physical Activity**. *PLoS ONE* (2011.0) **6**. DOI: 10.1371/journal.pone.0029195
28. Wild C.P.. **Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology**. *Cancer Epidemiol. Biomark. Prev.* (2005.0) **14** 1847-1850. DOI: 10.1158/1055-9965.EPI-05-0456
29. Gallup J.L., Gaviria A., Lora E.. *Is Geography Destiny? Lessons from Latin America* (2003.0)
30. Hedeker D., Mermelstein R.J., Demirtas H.. **Modeling between-subject and within-subject variances in ecological momentary assessment data using mixed-effects location scale models**. *Stat. Med.* (2012.0) **31** 3328-3336. DOI: 10.1002/sim.5338
31. Bustamante A., Beunen G., Maia J.. *Como Crecen y se Desarrollan los Niños y Adolescentes en La Merced y San Ramón. Al-cances para la Educación Física, el Deporte y la Salud* (2011.0) 174
32. Lohman T.G., Roche A.F., Martorell R.. *Anthropometric Standardization Reference Manual* (1988.0) **Volume 177**
33. Clemes S.A., Biddle S.J.. **The Use of Pedometers for Monitoring Physical Activity in Children and Adolescents: Measurement Considerations**. *J. Phys. Act. Health* (2013.0) **10** 249-262. DOI: 10.1123/jpah.10.2.249
34. Lubans D.R., Morgan P., Tudor-Locke C.. **A systematic review of studies using pedometers to promote physical activity among youth**. *Prev. Med.* (2009.0) **48** 307-315. DOI: 10.1016/j.ypmed.2009.02.014
35. Beets M.W., Bornstein D., Beighle A., Cardinal B.J., Morgan C.F.. **Pedometer-Measured Physical Activity Patterns of Youth: A 13-Country Review**. *Am. J. Prev. Med.* (2010.0) **38** 208-216. DOI: 10.1016/j.amepre.2009.09.045
36. Tudor-Locke C., Williams J.E., Reis J.P., Pluto D.. **Utility of Pedometers for Assessing Physical Activity**. *Sports Med.* (2004.0) **34** 281-291. DOI: 10.2165/00007256-200434050-00001
37. **Omron Instruction Manual Go Smart Tri-Axis Pocket Pedometer Model HJ-303**
38. Hox J.J., Moerbeek M., Van de Schoot R.. *Multilevel Analysis: Techniques and Applications* (2018.0)
39. Snijders T.A.B., Bosker R.J.. *Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling* (2012.0)
40. Maia J., Gomes T.N., Trégouët D.-A., Katzmarzyk P.T.. **Familial resemblance of physical activity levels in the Portuguese population**. *J. Sci. Med. Sport* (2014.0) **17** 381-386. DOI: 10.1016/j.jsams.2013.09.004
41. de Geus E.J., Bartels M., Kaprio J., Lightfoot J.T., Thomis M.. **Genetics of Regular Exercise and Sedentary Behaviors**. *Twin Res. Hum. Genet.* (2014.0) **17** 262-271. DOI: 10.1017/thg.2014.42
42. Doherty A., Smith-Byrne K., Ferreira T., Holmes M.V., Holmes C., Pulit S.L., Lindgren C.M.. **GWAS identifies 14 loci for device-measured physical activity and sleep duration**. *Nat. Commun.* (2018.0) **9** 5257. DOI: 10.1038/s41467-018-07743-4
43. Klimentidis Y.C., Raichlen D.A., Bea J., Garcia D.O., Wineinger N.E., Mandarino L.J., Alexander G.E., Chen Z., Going S.B.. **Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE**. *Int. J. Obes.* (2018.0) **42** 1161-1176. DOI: 10.1038/s41366-018-0120-3
44. Tudor-Locke C., Craig C.L., Beets M.W., Belton S., Cardon G.M., Duncan S., Hatano Y., Lubans D.R., Olds T.S., Raustorp A.. **How many steps/day are enough? for children and adolescents**. *Int. J. Behav. Nutr. Phys. Act.* (2011.0) **8** 78. DOI: 10.1186/1479-5868-8-78
45. Corder K., Sharp S.J., Atkin A.J., Andersen L.B., Cardon G., Page A., Davey R., Grøntved A., Hallal P.C., Janz K.F.. **Age-related patterns of vigorous-intensity physical activity in youth: The International Children’s Accelerometry Database**. *Prev. Med. Rep.* (2016.0) **4** 17-22. DOI: 10.1016/j.pmedr.2016.05.006
46. Duncan J.S., Schofield G., Duncan E.K.. **Pedometer-Determined Physical Activity and Body Composition in New Zealand Children**. *Med. Sci. Sports Exerc.* (2006.0) **38** 1402-1409. DOI: 10.1249/01.mss.0000227535.36046.97
47. Alvis-Chirinos K., Huamán-Espino L., Pillaca J., Aparco J.P.. **Measurement of physical activity by triaxial accelerometers in schoolchildren from three peruvian cities**. *Rev. Peru. Med. Exp. Salud Publica* (2017.0) **34** 28-35. DOI: 10.17843/rpmesp.2017.341.2764
|
---
title: 'Oesophageal Atresia: Prevalence in the Valencian Region (Spain) and Associated
Anomalies'
authors:
- Adriana Agurto-Ramírez
- Laura García-Villodre
- Ana Ruiz-Palacio
- Berta Arribas-Díaz
- Laia Barrachina-Bonet
- Lucía Páramo-Rodríguez
- Óscar Zurriaga
- Clara Cavero-Carbonell
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001480
doi: 10.3390/ijerph20054042
license: CC BY 4.0
---
# Oesophageal Atresia: Prevalence in the Valencian Region (Spain) and Associated Anomalies
## Abstract
The objective was to determine the prevalence of oesophageal atresia (OA) and describe the characteristics of OA cases diagnosed before the first year of life, born between 2007 and 2019, and residents in the Valencian Region (VR), Spain. Live births (LB), stillbirths (SB), and termination of pregnancy for fetal anomaly (TOPFA) diagnosed with OA were selected from the Congenital Anomalies population-based Registry of VR (RPAC-CV). The prevalence of OA per 10,000 births with $95\%$ confidence interval was calculated, and socio-demographic and clinical variables were analyzed. A total of 146 OA cases were identified. The overall prevalence was $\frac{2.4}{10}$,000 births, and prevalence by type of pregnancy ending was 2.3 in LB and 0.03 in both SB and TOPFA. A mortality rate of $\frac{0.03}{1000}$ LB was observed. A relationship was found between case mortality and birth weight (p-value < 0.05). OA was primarily diagnosed at birth ($58.2\%$) and $71.2\%$ of the cases were associated with another congenital anomaly, mainly congenital heart defects. Significant variations in the prevalence of OA in the VR were detected throughout the study period. In conclusion, a lower prevalence in SB and TOPFA was identified compared to EUROCAT data. As several studies have identified, an association between OA cases and birth weight was found.
## 1. Introduction
Oesophageal atresia (OA) is a disorder characterized by an interruption in the continuity of the oesophagus, with or without tracheoesophageal fistula (TEF), which communicates with the trachea [1]. It is the most frequent congenital anomaly (CA) of the oesophagus [2]. CAs are structural or functional abnormalities that are present from birth, although they can manifest at later periods, and constitute a diverse group of conditions of prenatal origin that may be due to single gene defects, chromosomal abnormalities, multifactorial inheritance, environmental teratogens, or lack of micronutrients [3].
Most CAs are considered rare diseases due to their low prevalence (in Europe, less than five cases per ten thousand inhabitants). Rare diseases, including those of genetic origin, are chronically debilitating, disabling, and even life-threatening conditions [4]. The global incidence of OA varies from $\frac{1}{2500}$ to $\frac{1}{4500}$ live births (LB) [2]. The prevalence of OA for the period 2007–2019 in the European network of population registries for the epidemiological surveillance of CA (EUROCAT) is $\frac{2.63}{10}$,000 births, remaining stable during the last decades but with a slight variation between European regions [5].
OA can be present in association with other CAs, generally those included in the VACTERL association, such as vertebral defects, anal atresia, cardiac malformations, TEF, renal anomalies, and limb malformations [6]. The etiology of this disease remains unknown, although it has been linked to genetic and environmental factors. The most associated genetic factors are trisomies, such as Down, Edwards or Patau syndrome, as well as alterations of a single gene, such as CHARGE and Feingold syndromes or Fanconi anemia, among others. Among the environmental factors, maternal exposure to drugs such as alcohol and tobacco, the use of in vitro fertilization techniques and gestational diabetes mellitus stand out [7].
OA is differentiated into types depending on its location and the presence or absence of TEF. The tenth revision of the international classification of diseases with the extension of the British Pediatric Association (ICD10-BPA) used by EUROCAT, classifies OA as OA without mention of a fistula or without other specification (code Q39.0) and OA with TEF (code Q39.1). According to Vogt’s classification [8], in $86\%$ of the cases of type III OA or with distal TEF are detected, in $7\%$, type I or without associated TEF, in $4\%$, type V or with TEF without atresia and, with less frequency, type II OA or with proximal TEF, and type IV or with proximal and distal TEF (<$1\%$) [8]. A comparison of both classifications is presented in Figure 1.
The diagnosis is usually done in the first 24 h of life and may be suspected in the presence of hypersalivation or the inability to swallow saliva. During the prenatal stage, the presence of polyhydramnios or the absence of stomach bubbles, normally observed between 16 and 20 weeks of gestation (GW), can be considered predictive factors. Another warning sign is the dilation of the atretic blind fundus detected during swallowing in the third-trimester ultrasound [9]. Diagnosis confirmation is obtained by a chest and abdominal X-ray demonstrating the abnormality [10].
Case mortality is directly related to low birth weight and major congenital heart defects [1]. Factors such as prematurity can have a negative influence, increasing the mortality of cases; however, the presence of TEF and the existence of other associated anomalies have not been shown to increase mortality [11].
The CA population-based registry of VR (RPAC-CV), which is part of EUROCAT [12], collects information on those diagnosed with OA before the first year of life, and who are residents in the VR. Based on these data, a study was performed to determine the prevalence of OA in the VR and describe the characteristics and distribution of cases with OA born between 2007 and 2019 in the VR.
## 2. Materials and Methods
A cross-sectional study was performed on cases diagnosed with OA before the first year of life, with or without TEF, born during the period 2007 and 2019 in the VR. The VR is one of seventeen regions of Spain, with a population of approximately 5 million and an annual number of births around 45,000.
The RPAC-CV was used as a source of information, from which the cases with a confirmed diagnosis of OA were obtained, coded with the codes Q39.0 and Q39.1 of the ICD10-BPA. The inclusion criteria used were those marked by EUROCAT that consider as cases all those residing in the VR who present at least one major CA [12]. The study subjects were LB, stillbirths (SB), and termination of pregnancy for fetal anomaly (TOPFA), diagnosed prenatally or during the first year of life.
The variables included in the analysis were those related to the case, to the CA, and to the pregnant woman.
Regarding the statistical analysis, firstly, the prevalence per 10,000 births and its $95\%$ confidence intervals ($95\%$CI) were calculated for the whole period and for each year. In addition, the prevalence by the type of pregnancy ending was calculated. The distribution of cases by sex and weight at birth was obtained. Birth weight in LB was divided according to the classification recommended by the World Health Organization (WHO) [13]: very low birth weight (VLBW) if ≤1500 g, ≤2500 g low birth weight (LBW), >2500 g–3999 g normal weight, and ≥4000 g macrosomic [13], and the mean weight of LB cases at birth was obtained.
The frequency of OA was described according to the number of babies at the pregnancy ending, as well as by gestational age, classifying the cases as less than 28 GW, 28–32 GW, 33–36 GW, and 37 GW or more [14]. The mean gestational age at pregnancy ending was obtained for all cases, including SB and TOPFA.
In LB cases who died during the first year of life, the time elapsed from birth to death was calculated, obtaining the median of the days elapsed. The overall crude mortality rate out of 1000 births and groups according to birth weight were obtained. In addition, the frequency of cases that required some surgical procedure during the first year of life was calculated. A Fisher exact test was carried out to study the relationship between birth weight categories and death of the cases (yes/no).
Moreover, the frequency of cases according to the type of OA was determined. Once the RPAC-CV cases coded according to the ICD10-BPA were obtained, they were adapted to the Vogt’s classification [8] using the literal of the diagnosis that includes the location of the TEF (type V was not taken into account in this study because it does not include OA diagnosis). In addition, the CA and syndromes most frequently associated with OA were identified and grouped by subgroups according to EUROCAT [12], and their frequency was analyzed. The frequency of cases was calculated according to the time of diagnosis, as well as the mean gestational age of the first CA in cases with prenatal detection.
The number of cases conceived by assisted conception and the frequency of pregnant women with a history of spontaneous abortions and previous TOPFA were studied, as well as the frequency of maternal diseases before and during pregnancy. The drugs used during the first trimester of pregnancy were classified according to the groups of the anatomical-therapeutic-chemical (ATC) classification [15], and the country of birth of each pregnant woman was determined.
Finally, the distribution of cases and the prevalence according to the mother’s residence by provinces of the VR were analyzed to describe their geographical distribution.
Statistical analysis was performed using the IBM SPSS Statistics 22 software by applying the chi-square statistical test for qualitative variables and Student’s T test for quantitative variables, to detect statistically significant differences.
## 3. Results
A total of 146 OA cases were identified during the period from 2007 to 2019 in the RPAC-CV. The overall period prevalence was $\frac{2.4}{10}$,000 births ($95\%$CI: 2.0–2.8), being 2016 the year with the highest prevalence ($\frac{3.8}{10}$,000 births), and 2014 the one with the lowest ($\frac{1.6}{10}$,000 births). Figure 2 shows the evolution of the annual prevalence for each year of the study period.
Regarding the distribution by type of pregnancy ending, $97.3\%$ (142 cases) of the OA cases were LB, $1.4\%$ (2 cases) were SB, and $1.4\%$ (2 cases) corresponded to TOPFA. The prevalence by type of pregnancy ending was $\frac{2.3}{10}$,000 births ($95\%$CI: 2.0–2.7) for LB and $\frac{0.03}{10}$,000 births ($95\%$CI: 0.0–0.1) for both SB and TOPFA. Table 1 shows the annual prevalence of OA cases, with or without TEF, according to the type of pregnancy ending.
The distribution by sex of the OA cases was $57.5\%$ male and $41.1\%$ female. In $1.4\%$ of the cases, corresponding to TOPFA, this information was unknown.
Regarding the weight of the LB cases, a mean birth weight of 2426 ± 0.963 g was obtained. In addition, $10.3\%$ had VLBW, $41.8\%$ LBW, $43.8\%$ normal weight, and $0.7\%$ were macrosomic. In $0.7\%$ of cases, this information was unknown.
It was observed that $6.8\%$ of the cases corresponded to twin pregnancies and $2.1\%$ to triple gestations. The rest of the cases were single pregnancies ($91.1\%$). The mean gestational age at the time of pregnancy ending was 36.7 ± 6.3 GW, and it was identified that $47.3\%$ of cases were between 33–36 GW, $38.4\%$ 37 GW or more, $10.3\%$ between 28–32 GW and, finally, $3.4\%$ less than 28 GW. The GW at pregnancy ending was unknown in $0.7\%$ of the cases.
Considering only LB cases, $13.7\%$ died before one year of age. The median number of days elapsed between birth and death was 6 days. A crude mortality rate during the first year of life of 0.03 per 1000 births was observed for the period 2007–2019. In relation to the weight at birth, of the 20 cases dead, $85.0\%$ had VLBW or LBW (Table 2). Mortality in LBW cases was higher than in normal weight cases, with a crude mortality rate of 0.01 per 1000 births for VLBW, 0.02 per 1000 births for LBW, and 0.005 per 1000 births for normal weight. The mortality rate during the period is shown in Figure 3. A Fisher’s exact test was performed to study the relationship between the weight categories at birth and the death of cases, obtaining a statistically significant association ($p \leq 0.05$).
In $88.7\%$ of LB cases, at least one surgical procedure was performed during the first year of life, while surgery was not required in $1.4\%$, and in $0.7\%$ surgery was not needed because it was considered too severe for the procedure. In $9.2\%$, this information was unknown.
Concerning the type of OA, according to Vogt’s classification [8] or location of the TEF, a higher frequency of OA with distal TEF or type III was detected, followed by OA without TEF or type I (Table 3).
Of the 146 cases, $71.2\%$ had another CA associated. A total of 334 associated malformations were identified since more than one different associated anomaly was identified in some of the cases studied. Most of these malformations corresponded to congenital heart defects (Table 4). The relationship between congenital heart disease (yes/no) and case mortality (yes/no) was studied using the chi-square test, where no statistically significant relationship was obtained ($p \leq 0.05$).
In addition, OA was detected to be associated with syndromes or associations of malformations in $15.8\%$ of the total cases. Among these, the most frequent was the VACTERL association (in $43.5\%$ of cases), followed by Edwards syndrome ($26.1\%$). In third place, the Polymalformative syndrome ($13.0\%$) was found, and finally, the Patau, Crouzon, Cri-du-chat syndromes, and the CHARGE association, with $4.3\%$ each.
According to the time of diagnosis of the first CA in each case (it can be the OA or another associated CA), in $58.2\%$ of cases it was detected at birth, and in $37.7\%$ it was diagnosed prenatally. In $2.1\%$, it was seen during the first week of life, and in $2.1\%$ of the cases this information was unknown.
In those diagnosed prenatally, ultrasound was the predominant diagnostic technique during the prenatal stage. In $43.6\%$ of cases, the first malformation was detected in the third trimester of pregnancy, $30.9\%$ during the second trimester, and $3.6\%$ during the first trimester (Table 5). In $21.8\%$ of cases, this information was unknown. The mean gestational age at prenatal diagnosis was 27.2 ± 6.5 GW.
The mean age of the pregnant women at the time of pregnancy ending was 32 years (with a range of 18 to 47 years). A total of $13.0\%$ of the pregnancies were conceived by assisted conception. The relationship between assisted conception and the type of OA was studied using the chi-square test, where no statistically significant differences were identified ($p \leq 0.05$). On the other hand, $21.2\%$ of the pregnant women had a history of spontaneous abortions and $11.0\%$ of previous TOPFA.
Endocrine diseases ($11.6\%$), such as hypothyroidism, obesity, and hyperlipidemia, were the most frequently observed medical diseases before pregnancy in the pregnant women, followed by a personal history of CA ($5.5\%$), such as kidney abnormalities disease, congenital heart disease, and pleural abnormalities. Likewise, gynecological pathologies ($4.8\%$), infections ($4.1\%$), hereditary genetic diseases ($3.4\%$), respiratory ($2.0\%$), psychiatric, vascular, digestive, and allergies were found, with a frequency of $1.3\%$ each.
In addition, $37.7\%$ of the pregnant women presented some pathologies during pregnancy. Specifically, a total of 67 diseases were diagnosed, in some cases the pregnant women had more than one disease. Polyhydramnios ($20.0\%$), gestational diabetes mellitus ($18.5\%$), hypothyroidism ($10.8\%$), urinary tract infections ($10.8\%$), and gestational hypertension ($7.7\%$) were more frequently observed. A total of $26.7\%$ of the pregnant women did not have gestational diseases, and there was no information available in $35.6\%$ (Table 6).
A total of $37.0\%$ of the pregnant women took drugs during the first trimester of pregnancy, while in $45.9\%$, this information was not available. The most used drugs were antibiotics, mainly clindamycin in suppositories and ampicillin, followed by antithyroid drugs, vitamin and pregnancy supplements, corticosteroids, and antihypertensives (Table 7).
Regarding the country of birth of the pregnant women, $56.8\%$ were Spanish born and $17.8\%$ were foreigners. Among the most frequent foreign countries of birth were Moroccan, Romanian, and Bolivian origin. The country of origin was unknown in $25.4\%$ of the pregnant women.
Regarding the geographical distribution according to the maternal residence, it was observed that $49.3\%$ of the pregnant women resided in the province of Valencia, $43.8\%$ in the province of Alicante, and $6.8\%$ in the province of Castellón. The prevalence by provinces for the study period was $\frac{2.9}{10}$,000 births ($95\%$CI: 2.2–3.7) in Alicante, $\frac{2.3}{10}$,000 births ($95\%$CI: 1.8–2.8) in Valencia, and $\frac{1.4}{10}$,000 births ($95\%$CI: 0.5–2.2) in Castellón.
## 4. Discussion
The overall prevalence of OA cases obtained in the VR for the period 2007–2019 was more similar than EUROCAT [5]: $\frac{2.3}{10}$,000 births for the same period. It was also more similar than other population-based registries of CA which belong to EUROCAT [5], such as the Basque Country (Spain), whose prevalence was $\frac{2.5}{10}$,000 births. In the case of Norway, a European country whose population is quite comparable to that of VR and is also part of EUROCAT [5], we could find a prevalence only slightly higher ($\frac{2.8}{10}$,000 births) than that in VR [5].
Furthermore, studies such as the one by Nassar et al. [ 16], whose cases, classified using the ICD9-BPA or ICD10-BPA which belonged to members of birth defects surveillance programs in North America, South America, Europe, and Australia, found a global prevalence of OA similar to that obtained in the RPAC-CV: $\frac{2.4}{10}$,000 births during the period 1998–2007 [16].
In VR, significant variations were detected in the annual prevalence of OA cases throughout the study period, with the lowest prevalence being in 2014 and the highest in 2016.
The prevalence by type of pregnancy ending of OA cases identified in EUROCAT [5] during the period 2007–2019 was higher than that obtained in the VR both in SB ($\frac{0.06}{10}$,000 births) and in TOPFA ($\frac{0.13}{10}$,000 births).
Concerning the sex of OA cases in the VR, a slight male predominance was detected, in agreement with what was described in the work of Vara Callau et al. [ 8], in which a ratio of 1.5:1 was found. However, the frequency of twin and triple pregnancies was lower in the VR than that found by these authors [8].
Regarding the time of diagnosis in cases of OA of the VR, $37.7\%$ were detected prenatally, this value being higher than that found by Sfeir et al. [ 11], which described, for the period 1998–2007, a prenatal diagnosis in $30\%$ of cases [11]. It is important to remark that Sfeir describes only prenatal OA diagnosis, and in VR, any first CA diagnosis is included, which is not necessarily OA. Advances in the technique applied to prenatal tests may be the reason for this increase. On the other hand, coincidences have been found in the time elapsed until the moment of diagnosis in the cases detected postnatally, both being within the first 24 h of life [17].
The mean gestational age at the time of pregnancy ending in OA cases in the VR was slightly lower than that described by Vara Callau [8], 36.7 ± 6.3 GW vs. 37.1 ± 2.6 GW, respectively [8]. A total of $61.0\%$ of OA cases of the RPAC-CV ended the pregnancy with less than 37 GW, a much higher frequency than that found in the general population, where a prematurity rate of $8.3\%$ had been described [18]. A total of $52.1\%$ of the cases were low birth weight (including VLBW and LBW), a higher value than that described by Galarreta et al. [ 19], where it was found that $49.6\%$ of the cases were VLBW and LBW at birth [19]. When comparing the frequency of cases with VLBW, $10.3\%$ found in our sample contrasts with $8.6\%$ described in the aforementioned study [19].
Moreover, in OA cases in the VR, a higher percentage of male sex, low weight, and preterm gestational age (≤36SG) at the time of pregnancy ending were identified in comparison to all the cases with AC from the RPAC-CV during the same period of study [20].
According to others studies [1,21], the mortality of OA cases is directly related to birth weight and associated heart defects. In the VR, statistically significant differences in birth weight and mortality in LB cases were found. Congenital heart defects were the most frequent ones associated with OA in VR. However, no statistically significant differences were found between congenital heart disease and OA cases mortality [1].
The association of OA with other CAs has been repeatedly described in different studies [1,22], suggesting the need to look for associated malformations before diagnosing OA. A higher frequency of CA associated with OA was found in VR ($71.2\%$) compared to the $50\%$ described by De Jong et al. [ 7]. Among the associated CA, the author [7] describes a frequency of $10\%$ of cases related to some component of the VACTERL association, a higher frequency than that found in VR ($6.2\%$), although also prevailing over other associated syndromes.
In the OA cases of VR, $15.8\%$ were associated with syndromes or associations of malformations and $6.9\%$ with chromosomal abnormalities, equivalent to that described in the literature [22] and lower than that observed by Galarreta et al. [ 19], which describes $10.2\%$ of cases associated with chromosomal abnormalities. The main chromosomal abnormality related to the RPAC-CV cases was Edwards syndrome ($4.1\%$), with a lower frequency than that described by Felix et al. [ 23] but prevailing over the rest of the chromosomal abnormalities.
In VR, the frequency of cases with type III OA was lower than in similar studies [9]; however, the frequency of cases with OA type I was higher than that found in these studies [9]. This may be due to the fact that we have a high percentage of OA cases with TEF without specifying the location, and, according to the literature [9], it would be expected that they were mainly type III.
In addition, in pregnant women with OA cases from the RPAC-CV, $21.2\%$ of the history of previous spontaneous abortions and $11.0\%$ of the history of previous TOPFA were found, coinciding with the $20\%$ of the history of spontaneous abortions that are described in the literature [24] and with $11.7\%$ of the history of TOPFA in Spanish public hospitals in 2015 [25].
Gestational diabetes mellitus has been associated with the appearance of CA, macrosomia, neonatal complications, and a high percentage of perinatal mortality [17]. In VR, it was found that $8.2\%$ of pregnant women with OA cases developed gestational diabetes during pregnancy, a higher incidence than that described in the literature [17], where an incidence between $1\%$ and $5\%$ of pregnancies was estimated [17]. In addition, a $5.5\%$ personal history of CA was observed in pregnant women, a percentage that coincides with that described by Spitz [26], who also describes a similar proportion of a history of CA in first-degree relatives with one or more components of the VACTERL association [26].
The limitations of the study may be due to the small number of cases intrinsically associated with OA as it is a rare disease, which could only be expanded by studying a more extended period or expanding the study territory. Another limitation is the lack of information found in some of the clinical variables under study, which will foreseeably improve over time since the recent implementation of the electronic medical record in the Spanish health system, which seems to be increasing the quality of health data and its collection [16].
## 5. Conclusions
In conclusion, the global prevalence of OA cases obtained in the RPAC-CV was similar to EUROCAT ($\frac{2.3}{10}$,000 births) for the same period. However, EUROCAT identified a higher prevalence in SB ($\frac{0.06}{10}$,000 births) and TOPFA ($\frac{0.13}{10}$,000) than those obtained in VR. OA is a CA whose mortality is influenced by factors such as birth weight. In many cases, the OA is associated with other CAs, mainly congenital heart defects. The appearance of TEF is quite frequent, being type III OA the one that prevails. Although the prenatal diagnosis of OA has increased over time, detection at birth continues to be more frequent.
## References
1. Pr Lewis S.. **Atresia of the Esophagus ORPHA:1199**. (2015.0)
2. Pinheiro P.F.M., Simões e Silva A.C., Pereira R.M.. **Current knowledge on esophageal atresia**. *World J. Gastroenterol.* (2012.0) **18** 3662-3672. DOI: 10.3748/wjg.v18.i28.3662
3. **Premature Births n.d**. (2018.0)
4. **Health & Consumer Protection Directorate General. Useful Information on Rare Diseases from an EU Perspective**
5. **Prevalence Charts and Tables 2021**
6. van de Putte R., van Rooij I.A.L.M., Marcelis C.L.M., Guo M., Brunner H.G., Addor M.C., Cavero-Carbonell C., Cavero-Carbonell C., Draper E.S., Etxebarriarteun L.. **Spectrum of congenital anomalies among VACTERL cases: A EUROCAT population-based study**. *Pediatr. Res.* (2020.0) **87** 541-549. DOI: 10.1038/s41390-019-0561-y
7. de Jong E.M., Felix J.F., de Klein A., Tibboel D.. **Etiology of esophageal atresia and tracheoesophageal fistula: “mind the gap”**. *Curr. Gastroenterol. Rep.* (2010.0) **12** 215-222. DOI: 10.1007/s11894-010-0108-1
8. Callau M.V., Pérez D.R., Esgueda A.J.G., Torralba L.G., Sanz M.L.R., Montañés N.C., Gracia S.R.. **Esophageal atresia: Descriptive study of a series of 34 patients/ Oesophageal atresia: Descriptive study of a 34 patients series**. *Pediatric. Act. Esp.* (2014.0) **72** 76
9. Scott D.A., Adam M.P., Everman D.B., Mirzaa G.M., Pagon R.A., Wallace S.E., Bean L.J.H., Gripp K.W., Amemiya A.. **Esophageal Atresia/Tracheoesophageal Fistula Overview. 2009**. *GeneReviews [Internet]* (1993.0)
10. Houben C.H., Curry J.I.. **Current status of prenatal diagnosis, operative management, and outcome of esophageal atresia/tracheo -esophageal fistula**. *Prenatal. Diagn* (2008.0) **28** 667-675. DOI: 10.1002/pd.1938
11. Sfeir R., Michaud L., Salleron J., Gottrand F.. **Epidemiology of esophageal atresia**. *Dis. Esophagus* (2013.0) **26** 354-355. DOI: 10.1111/dote.12051
12. 12.
EUROCAT
EUROCAT Guide 1.4: Instruction for the Registration of Congenital Anomalies n.d. EUROCAT Central RegistryUniversity of UlsterColeraine, Northern Ireland2013. *EUROCAT Guide 1.4: Instruction for the Registration of Congenital Anomalies n.d. EUROCAT Central Registry* (2013.0)
13. **Global nutrition targets 2025: Low birth weight policy document (WHO/NMH/NHD/14.5)**. *Birth Weight Policy Brief* (2014.0)
14. **Centers for Disease Control and Prevention (US) & International Clearinghouse for Birth Defects Monitoring Systems**. *Birth Defects Surveillance of Congenital Anomalies: Atlas of Selected Congenital Anomalies* (2014.0)
15. **ATC/DDD Index 2022 2021**
16. Nassar N., Leoncini E., Amar E., Arteaga-Vázquez J., Bakker M.K., Bower C., Canfield M.A., Castilla E.E., Cocchi G., Correa A.. **Prevalence of esophageal atresia among 18 international birth defects surveillance programs**. *Birth Defects Res. Part A Clin. Mol. Teratol.* (2012.0) **94** 893-899. DOI: 10.1002/bdra.23067
17. Eriksson U.J.. **Congenital anomalies in diabetic pregnancy**. *Semin. Fetal Neonatal Med.* (2008.0) **14** 85-93. DOI: 10.1016/j.siny.2008.11.001
18. García-Reymundo M., Demestre X., Calvo M.J., Ginovart G., Jiménez A., Hurtado J.A.. **Late preterm in Spain: Experience of the SEN Group34-36**. *An Pediatr (Barc)* (2018.0) **88** 246-252. DOI: 10.1016/j.anpedi.2017.05.006
19. Galarreta C.I., Vaida F., Bird L.M.. **Patterns of malformation associated with esophageal atresia/tracheoesophageal fistula: A retrospective single center study**. *Am. J. Med. Genet A* (2020.0) **182** 1351-1363. DOI: 10.1002/ajmg.a.61582
20. **Anomalías Congénitas en la Comunitat Valenciana, 2007–2019**
21. Cassina M., Ruol M., Pertile R., Midrio P., Piffer S., Vicenzi V., Saugo M., Stocco C.F., Gamba P., Clementi M.. **Prevalence, characteristics, and survival of children with esophageal atresia: A 32-year population-based study including 1,417,724 consecutive newborns**. *Birth Defects Res Part A Clin. Mol. Teratol.* (2016.0) **106** 542-548. DOI: 10.1002/bdra.23493
22. Spitz L.. **Oesophageal atresia**. *Orphanet J. Rare Dis.* (2007.0) **2** 24. DOI: 10.1186/1750-1172-2-24
23. Felix J.F., de Jong E.M., Torfs C.P., de Klein A., Rottier R.J., Tibboel D.. **Genetic and environmental factors in the etiology of esophageal atresia and/or tracheoesophageal fistula: An overview of the current concepts**. *Birth Defects Res. Part A Clin. Mol. Teratol.* (2009.0) **85** 747-754. DOI: 10.1002/bdra.20592
24. Griebel C.P., Halvorsen J., Golemon T.B., Day A.A.. **Management of spontaneous abortion**. *Am. Acad. Fam. Physicians* (2005.0) **72** 1243-1250
25. **Statistical Data. Voluntary Termination of Pregnancy na**
26. Spitz L.. **Esophageal atresia. Lessons I have learned in a 40-year experience**. *J. Pediatric Surg. Case Rep.* (2006.0) **41** 1635-1640
|
---
title: Acetylsalicylic Acid Effect in Colorectal Cancer Taking into Account the Role
of Tobacco, Alcohol and Excess Weight
authors:
- Didac Florensa
- Jordi Mateo
- Francesc Solsona
- Leonardo Galván
- Miquel Mesas
- Ramon Piñol
- Leonardo Espinosa-Leal
- Pere Godoy
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001481
doi: 10.3390/ijerph20054104
license: CC BY 4.0
---
# Acetylsalicylic Acid Effect in Colorectal Cancer Taking into Account the Role of Tobacco, Alcohol and Excess Weight
## Abstract
Excess weight, smoking and risky drinking are preventable risk factors for colorectal cancer (CRC). However, several studies have reported a protective association between aspirin and the risk of CRC. This article looks deeper into the relationships between risk factors and aspirin use with the risk of developing CRC. We performed a retrospective cohort study of CRC risk factors and aspirin use in persons aged >50 years in Lleida province. The participants were inhabitants with some medication prescribed between 2007 and 2016 that were linked to the Population-Based Cancer Registry to detect CRC diagnosed between 2012 and 2016. Risk factors and aspirin use were studied using the adjusted HR (aHR) with $95\%$ confidence intervals (CI) using a Cox proportional hazard model. We included 154,715 inhabitants of Lleida (Spain) aged >50 years. Of patients with CRC, $62\%$ were male (HR = 1.8; $95\%$ CI: 1.6–2.2), $39.5\%$ were overweight (HR = 2.8; $95\%$ CI: 2.3–3.4) and $47.3\%$ were obese (HR = 3.0; $95\%$ CI: 2.6–3.6). Cox regression showed an association between aspirin and CRC (aHR = 0.7; $95\%$ CI: 0.6–0.8), confirming a protective effect against CRC and an association between the risk of CRC and excess weight (aHR = 1.4; $95\%$ CI: 1.2–1.7), smoking (aHR = 1.4; $95\%$ CI: 1.3–1.7) and risky drinking (aHR = 1.6; $95\%$ CI: 1.2–2.0). Our results show that aspirin use decreased the risk of CRC and corroborate the relationship between overweight, smoking and risky drinking and the risk of CRC.
## 1. Introduction
Colorectal cancer (CRC) is the third leading cause of cancer death globally and the second in Europe, and its incidence is steadily rising in developing nations [1], with nearly 520,000 new cases in Europe in 2020 [2], even though a large proportion of these case are highly preventable [3]. A study in nine European countries found that approximately $20\%$ of CRC cases may be related to overweight, smoking and risky drinking [4]. In contrast, studies have shown that long-term aspirin use may prevent CRC [5,6].
Shaukat et al. found a direct relationship between the body mass index (BMI) and long-term CRC mortality and suggested that BMI modulation may reduce the risk of CRC mortality [7]. A recent study has shown the role of obesity and overweight in early-onset CRC, and concluded that obesity is a strong risk factor [8]. Ghazaleh Dashti et al. found an association between risky drinking and an increased risk of CRC [9]. Likewise, a study has suggested an association between passive smoking and the risk of CRC [10].
Some studies have found a protective effect of aspirin against CRC [11] and various studies have concluded that aspirin reduces the overall risk of CRC recurrence and mortality and colorectal adenomas. Ma et al. recently found that aspirin, including low-dose aspirin, reduced the risk of CRC [12]. A recent study by Zhang et al. on the effect of aspirin use for 5 and 10 years found that the continuous use of aspirin increases the protective effect on CRC [13]. A Danish study also found that the continuous use of low-dose aspirin was associated with a reduced CRC risk [14]. Some studies have shown differing results on the protective effect of aspirin due to the different designs used, the type of follow-up, the recorded aspirin consumption and the size and type of population. Although the data seem compelling, a limitation of these analyses is that they do not take into account risk factors for CRC [15]. These previous studies investigated the association between the use of aspirin and CRC, but they did not study the role played by risk factors such as tobacco smoking, alcohol or excess weight. In this study, we explore how these factors, combined with aspirin use, affect the risk of CRC in a particular society.
The objective of this study was to determine the protective effect of aspirin against CRC, taking into account the effect of other risk factors (overweight/obesity, risky drinking and smoking), in Lleida, a province in Catalonia, Spain, with a large rural population and an agri-food industry that may present specific risk factors [16,17].
## 2.1. Study Population
We conducted a retrospective cohort study of aspirin use and risk factors to analyze the impact of these factors on the risk of CRC. We carried out the study on 154,715 inhabitants of Lleida aged >50 years at the start of the study period, with data available on aspirin use from 1 January 2007 to 31 December 2016 in the Catalan Health Service (CatSalut) system. The reason for selecting this period was to ensure that those CRC cases detected in 2012 had the opportunity to be exposed to aspirin for at least five years. This population was linked to the Lleida Population-based Cancer Registry to detect CRC diagnosed between 2012 and 2016.
Data on aspirin use were obtained from the number of packages dispensed by pharmacies. Catalonia has a public health system in which medicines are dispensed in pharmacies after presenting a doctor’s prescription. Drugs administered to hospitalized patients and those prescribed by private providers are not registered in the CatSalut system, and therefore were not included in this study. The CRC cases in the sample were obtained from the Lleida Population-Based Cancer Registry, and the demographic characteristics of participants, including age and sex, were obtained from the CatSalut system. Figure 1 shows a flowchart of the study population. Initially, the pharmacy database registered 724,070 inhabitants with any prescription, although 346,365 were excluded because they did not reside in the Lleida region. Another exclusion criterion was age. We only included inhabitants aged >50 years at the start of the observed period [2007], resulting in 154,717 inhabitants. We also excluded those inhabitants who did not register the risk factors correctly, although the cases excluded were minimal.
As has been presented before, this study included different databases. To enable this linkage, it was necessary to use a personal identification code called CIP. This code is unique to each inhabitant who resides in Catalonia and permits us to identify them in the Catalan Health Service and its registers (hospitals, pharmacies or primary care centers).
## 2.2. Data Collection
Data on CRC diagnoses were obtained from the Lleida Population-Based Cancer Registry using five consecutive years of incidence data, from 2012 to 2016. This period was chosen as the available years validated by the professionals of the register. Potential CRC cases were validated by checking medical records. We used hospital and pathological anatomy records as the main information sources. Cancers were identified following the rules defined by the International Association of Cancer Registries, the International Association for Research on Cancer and the European Network of Cancer Registries.
The risk factors included were risky drinking, smoking and body mass index. This information was extracted using the eCAP software (V 20.4.3) used by primary care physicians to record all patient information, which registers information from 2001. The values of these variables at the time that this study started were obtained. Body mass index (BMI) was calculated by the weight and height of the patient using the formula BMI=weightkg/height(m)2 and categorized as follows: 18.5–24.9 normal weight, 25–29.9 overweight and >30 obesity [18]. The ICD-10 international criteria identified risky drinking and smoking. The ICD-10 code for risky drinking is F10.2, and those for smoking are F17 (mental and behavioral disorders due to tobacco use) and Z72 (tobacco use). Risky drinking was defined as consumption of >40 g/day of alcohol in men and >24 g/day in women [19]. The Spanish Health Ministry defined these grams per day with the supervision of the WHO [20]. The software also states the date of smoking onset. Smokers were defined as exposure for >5 years before the start of the study. The reason for using a period of five years was due to a previous study that suggested that this period might increase the risk of cancer [21]. Former smokers were considered smokers because the observed points in the dataset were minimal, and adding this new category could have imbalanced the dataset. General characteristics are represented in Table 1.
## 2.3. Exposure
Aspirin was categorized according to the Anatomical Therapeutic Chemical (ATC) classification system as A01AD05 (acetylsalicylic acid) medication. The use of aspirin was evaluated based on the defined daily dose (DDD) and the milligrams (mg) accumulated dose consumed by each patient throughout the study period. The DDD is a technical unit of measurement that corresponds to the daily maintenance dose of a drug for its main indication in adults and a given route of administration. The DDDs of active ingredients are established by the WHO and published on the WHO Collaborating Center for Drug Statistics Methodology website [22,23].
Exposure was determined from computerized pharmacy data and consisted of the total DDD dispensed to an individual during the study period. For instance, if a person consumed aspirin for a while, then stopped using it and later started again, the total DDD consumed during the following period was considered. To be considered as exposed to aspirin, the total number of years of consumption had to be ≥5 years. The number of years was based on previous studies, which suggested this period as the minimum for aspirin to have a protective effect [13,24]. To consider exposure to aspirin, the minimum consumed daily was >75 mg [25,26]. The number of DDD calculated this value in mg.
## 2.4. Statistical Analysis
Descriptive analyses were performed to evaluate the association between characteristics at baseline, exposure and outcomes. Patients’ characteristics, risk factors and aspirin exposure were analyzed to determine the association with the risk of CRC. The incidence rate of CRC was calculated to each factor over a specified period. A bivariate analysis was initially used to estimate the crude hazard ratios for the association between aspirin consumption and the risk of incident CRC.
A Cox proportional hazard model was used to determine the HR and the corresponding $95\%$ CI. The models were adjusted by sex, age, aspirin exposure, BMI, risky drinking and smoking. Subsequently, stratified models were calculated by sex.
The probability values for the statistical tests were two-tailed, and a CI that did not contain 1.0 was regarded as statistically significant. Results with wide CIs should be interpreted cautiously. All statistical analyses were performed using R (R Core Team 2019), an open-source programming language and environment for statistical analysis and graphic representation.
## 3. Results
We analyzed 154,715 inhabitants of Lleida aged >50 years, of whom 1276 ($0.8\%$) had CRC between 2012 and 2016. The mean CRC incidence rate and the total cases by sex and age group for the five study years are shown in Figure 2a,b.
The sociodemographic information and aspirin exposure in patients with CRC (Table 2) were analyzed in the bivariate analysis.
We recorded 485 (0.8 × 1000) females and 791 (1.4 × 1000) males (HR = 1.9; $95\%$ CI; 1.6–2.0) with CRC. Most patients were from the 60–69 years (HR = 1.8; $95\%$ CI; 1.6–2.1) and 70–79 years age groups (HR = 2.0; $95\%$ CI; 1.9–2.6). There were 1138 (1.2 × 1000) CRC cases without aspirin consumption and 138 with aspirin consumption (1.0 × 1000) (HR = 0.9; $95\%$ CI; 0.8–1.1). There were 504 (1.2 × 1000) cases with overweight (HR = 2.5; $95\%$ CI; 2.2–3.1) and 603 (1.3 × 1000) with obesity (HR = 2.7; $95\%$ CI; 2.3–3.3), and there were 56 (2.2 × 1000) cases with risky drinking (HR = 2.1; $95\%$ CI; 1.6–2.7), while 220 (2.0 × 1000) were smokers (HR = 2.0; $95\%$ CI; 1.8–2.4).
Cox regression showed variations in the outcomes (Table 3). Sex, age and aspirin exposure were significantly associated with CRC. The adjusted HR (aHR) for males was 1.8 ($95\%$ CI: 1.6–2.1) and 1.8 ($95\%$ CI: 1.6–2.1) in the 60–69 years age group, 2.3 ($95\%$ CI: 1.9–2.7) in the 70–79 years age, 2.2 ($95\%$ CI: 1.8–2.6) in the 80–89 years age group and 0.2 ($95\%$ CI: 0.1–0.3) in the 90 years age group. Aspirin consumption had an aHR of 0.7 ($95\%$ CI: 0.6–0.8). The BMI also was significant. Overweight had an aHR of 1.4 ($95\%$ CI: 1.2–1.7) and obesity of 1.5 ($95\%$ CI: 1.3–1.8). Risky drinking had a significant aHR of 1.6 ($95\%$ CI: 1.2–2.0) and smoking an aHR of 1.4 ($95\%$ CI: 1.3–1.7). Figure 3 represents the adjusted hazard ratios graphically.
HRs were adjusted by gender, age, aspirin use, BMI, risky drinking and smoking.
Table 4 shows the results of the Cox regression stratified by sex. In the case of males, the results were similar to the general table. In this model, aspirin exposure remained significant (aHR: 0.7; $95\%$ CI: 0.6–0.8), as did the BMI, risky drinking and smoking. In females, aspirin use remained significant (aHR: 0.6; $95\%$ CI: 0.4–0.8), but, of the risk factors, only obesity remained significant (aHR: 1.4; $95\%$ CI: 1.2–1.9). Figure 4 represents the adjusted hazard ratios graphically.
HRs were adjusted by age, aspirin use, BMI, risky drinking and smoking.
## 4. Discussion
Our results confirm the negative association between aspirin consumption and CRC independently of the other risk factors measured. Males may be at a higher risk of CRC than females but aspirin may be slightly more protective in females.
Reports support a delayed effect of aspirin on CRC [27]. A meta-analysis by Rothwell et al. examined the long-term effects of aspirin on CRC outcomes using trials of aspirin [28]. Studies on the impact of aspirin in CRC prevention have been published [6,29], although the effects of risk factors and aspirin use together have not yet been analyzed. Therefore, our findings corroborate the research in the field highlighting the protective effect of aspirin and go beyond comparing this positive effect with the negative effects caused by several risk factors.
Several recent studies have suggested an association between aspirin use and some specific cancers. Ciu et al. concluded that high-dose aspirin reduced the risk of pancreatic cancer [30]. Jacobo et al. analyzed studies on the relationship between aspirin and breast cancer [31] and concluded that aspirin consumption reduced the relative risk of breast cancer. Sieros et al. suggested that aspirin reduced the risk of esophageal cancer [32].
We found significant differences according to sex, suggesting that men have a higher risk of developing CRC. It has been reported that men have higher cumulative levels of smoking than women and a higher alcohol intake, which may explain the higher risk [33].
People aged between 60 and 80 years had a higher risk of CRC and the 80–89 years and 90–99 years age groups had a lower risk [34,35]. Older adults may have a differential mechanism compared with younger people. For example, aging is associated with alterations in DNA methylation, which may affect the susceptibility to cancer. The gut microbiota of older people differs from that of younger adults, which may influence drug metabolism and inflammatory processes. Genetics, underreporting and age-related physiological effects could explain the reduced risk [36].
We found some differences with respect to risk factors, such as overweight/obesity, risky drinking and smoking. Overweight represented $39.5\%$ of total CRC cases and obesity $47.3\%$. Therefore, approximately $85\%$ of patients with CRC presented excess weight, suggesting exposure to a poor diet. These results corroborated previous studies [37,38,39,40]. Excess weight is one of the most important risk factors for CRC. Individuals with a higher BMI have higher levels of chronic inflammation, and obesity may act through the gut microbiome on colorectal tumorigenesis and also promotes colorectal cancer in mice. There were notable differences in risky drinking. Patients with risky drinking had a higher risk of CRC (HR = 2.2). Meta-analyses of case–control and cohort studies suggest that high alcohol consumption might be associated with an increased risk of colorectal cancer. The epidemiological evidence has been complemented by molecular evidence on the mechanisms that could explain this association [17,41]. Similar results were obtained for smoking (HR = 2.0). The crude HR obtained also indicated this association between smoking and CRC [42]. Smoking was more closely associated with colorectal tumors that arose from non-conventional pathways, such as the serrated polyps pathway, and smoking was significantly associated with the risk of advanced serrated polyps in a screening population.
The Cox regression included all the remaining model variables, such as risk factors and aspirin exposure for CRC. Sociodemographic variables such as gender and age confirmed the correlation with CRC. Males were 1.8 times more at risk than females. This may be related to men having excess body weight and higher exposure to alcohol and smoking than women [43]. Regarding the age groups, the results confirmed that the 70–79 years age group had the highest risk, which was 2.3 times greater than the 50–59 years (ref. group) and the 69–69 years and 80–89 years age groups. Other studies found similar outcomes on the incidence and association related to CRC [44,45].
The use of aspirin for ≥5 years was significant in the Cox regression. The analysis suggested that aspirin decreased the risk of CRC. The HR was 0.7 ($95\%$ CI: 0.6–0.8), meaning that it reduced the risk of CRC by $30\%$ [46,47]. Studies have found reductions of 20–$30\%$ [46] and $27\%$ [47] in the risk for CRC. The risk factors were correlated with an increased risk of CRC. Overweight and obesity were significantly associated with a CRC risk 1.4 and 1.5 times higher, respectively. Obesity had a higher risk, although the HR was similar [48]. Risky drinking and smoking also had a significant HR. Risky drinking had a 1.6 times higher risk and smoking a 1.4 times higher risk. Other studies also found these associations [49,50], with a 1.3 and 1.2 times higher risk for risky drinking, respectively, and a 1.2 higher risk for smoking [50].
The Cox regression stratified by sex also obtained significant results. Men and women had similar outcomes according to age. The trends were the same as the non-stratified regression. The risk of CRC was higher in people aged 60–89 years in both sexes. The use of aspirin also maintained the association with a reduced CRC risk. Specifically, in females, aspirin could prevent CRC, in the best case, by up to $40\%$. A similar percentage was obtained by Cook et al. [ 51] in a randomized controlled trial, which showed $42\%$ aspirin protection against CRC risk among women. These results corroborated the fact that aspirin reduces the risk of CRC in both sexes [52]. However, the results related to risk factors were significant in males. Overweight and obesity were associated with a 1.5 and 1.6 times higher risk of CRC, respectively [53]. Risky drinking and smoking were also correlated with the CRC risk. The differences between males and females may be that males more often have a poor diet and drink and smoke more than females [54]. In females, only obesity was significantly associated with an increased risk of CRC. Moreover, as a previous study concluded [55], only excess weight among men was significantly associated with increased CRC risk. In addition, the authors suggested that this risk might be reversed in obese men taking aspirin. Similarly, in our study, in the analysis stratified by normal weight and overweight/obesity, aspirin was protective against CRC in both groups, but it was only statistically significant in overweight/obese patients (Supplementary Table S1) [55]. The remaining risk factors were not related to CRC, although the HR was >1 in all of them. Individual susceptibility and the type of exposure may explain these results. Men probably have a different pattern of consumption than women and are more intensely exposed to alcohol and smoking. In addition, it seems that without the effect of aspirin, these factors are related to CRC (Supplementary Table S2).
The preventive effect of aspirin has been attributed to the inhibition of cyclooxygenase (COX), the enzyme responsible for the synthesis of prostaglandins [56,57]. COX-2 is abnormally expressed in many cancer cell lines and is involved in the processes of carcinogenesis, angiogenesis and tumor growth. Additional mechanisms of aspirin include the induction of apoptosis through COX-independent pathways. Future research should also study the role of aspirin metabolites and the role of the intestinal microbiota in cancer prevention against CRC.
Long-term aspirin is prescribed for patients with a high cardiovascular risk of non-focal continuous pain due to arthritis, and the results of this study may support this indication [58,59].
The study has some limitations. Firstly, some patients could buy aspirin directly in pharmacies without a doctor’s prescription, and this consumption is underreported. Second, some patients may not take the medication, even if they have purchased it at the pharmacy, and, in this case, aspirin use will be overreported. Third, although the Population-Based Cancer *Registry is* exhaustive, it cannot be ruled out that some cases were diagnosed in hospitals in other territories and some cases have not been correctly registered. We were unable to study the dose–response relationship between low-dose aspirin and CRC because more than $90\%$ of aspirin use in this study was at a dose of 100 mg/day, which did not allow us to assess the highest related dose effect. Another limitation is the lack of specification of the types of CRC, such as familial polyposis or familiar cancer genetics, as a possible bias. This information was not taken into account in the register. A limitation that must be considered concerns the CRC cases diagnosed before 2012. These cases were not included because the Cancer Registry started registering cases in 2012. However, CRC cases prior to 2012 would not have had the opportunity to be exposed to risk factors or aspirin for a period of 5 or more years and would not have been recorded as incident cases in this study. Despite this, CRC is a type of cancer that can present another primary cancer a few years later; therefore, some CRC cases may be included. moreover, related to the risk factors, some bias was present due to under-reporting, although the percentage of our cases was similar to the prevalence observed in Catalonia. Finally, the impact of these excluded cases was minimal because they were younger than 50, where cancer may be unrelated to risk factors, or they were cases from other regions, and few patients had to be excluded due to a lack of information on risk factors.
The study’s strengths included the fact that data are presented on risk factors, such as excess weight, smoking and risky drinking. The study was performed with information from clinical practice, with physicians unaware of the study objectives, which avoided investigator bias.
## 5. Conclusions
This retrospective study found an association between aspirin use for ≥5 years and a reduced risk of CRC. The protective effect due to aspirin was higher in women. The results also showed an association between the risk of CRC and risk factors such as overweight, obesity, smoking and risky drinking, specifically in men. Moreover, the risk of CRC in women was significantly associated with obesity. The 70–79 and 80–89 age groups had a higher risk of CRC in men and women. Therefore, despite some limitations, such as the lack of information on food or dietary factors or some bias in the aspirin prescriptions, the results are according to the recently published literature.
*In* general, these results reinforce the need for public health messaging about the harmful effects of smoking, alcohol use and excess weight, and the use of aspirin to prevent CRC under prescription. They also encourage continued research into CRC to find new factors or interactions among them associated with this cancer. They also may help the health system to focus on preventing them and recommend the continuous use of aspirin under medical supervision.
## References
1. Rawla P., Sunkara T., Barsouk A.. **Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors**. *Gastroenterol. Rev. Przegląd Gastroenterol.* (2019.0) **14** 89-103. DOI: 10.5114/pg.2018.81072
2. Ferlay J., Ervik M., Lam F., Colombet M., Mery L., Piñeros M., Znaor A., Soerjomataram I.. *Global Cancer Observatory: Cancer Today* (2020.0)
3. Cardoso R., Guo F., Heisser T., Hackl M., Ihle P., De Schutter H., Van Damme N., Valerianova Z., Atanasov T., Májek O.. **Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: An international population-based study**. *Lancet Oncol.* (2021.0) **22** 1002-1013. DOI: 10.1016/S1470-2045(21)00199-6
4. Aleksandrova K., Pischon T., Jenab M., Bueno-de-Mesquita H.B., Fedirko V., Norat T., Romaguera D., Knüppel S., Boutron-Ruault M.C., Dossus L.. **Combined impact of healthy lifestyle factors on colorectal cancer: A large European cohort study**. *BMC Med.* (2014.0) **12**. DOI: 10.1186/s12916-014-0168-4
5. Burn J., Sheth H., Elliott F., Reed L., Macrae F., Mecklin J.-P., Möslein G., McRonald F.E., Bertario L., Evans D.G.. **Cancer prevention with aspirin in hereditary colorectal cancer (Lynch syndrome), 10-year follow-up and registry-based 20-year data in the CAPP2 study: A double-blind, randomised, placebo-controlled trial**. *Lancet* (2020.0) **395** 1855-1863. DOI: 10.1016/S0140-6736(20)30366-4
6. Serrano D., Patrignani P., Stigliano V., Turchetti D., Sciallero S., Roviello F., D’Arpino A., Grattagliano I., Testa S., Oliani C.. **Aspirin Colorectal Cancer Prevention in Lynch Syndrome: Recommendations in the Era of Precision Medicine**. *Genes* (2022.0) **13**. DOI: 10.3390/genes13030460
7. Shaukat A., Dostal A., Menk J., Church T.R.. **BMI Is a Risk Factor for Colorectal Cancer Mortality**. *Dig. Dis. Sci.* (2017.0) **62** 2511-2517. DOI: 10.1007/s10620-017-4682-z
8. Li H., Boakye D., Chen X., Hoffmeister M., Brenner H.. **Association of Body Mass Index With Risk of Early-Onset Colorectal Cancer: Systematic Review and Meta-Analysis**. *Am. J. Gastroenterol.* (2021.0) **116** 2173-2183. DOI: 10.14309/ajg.0000000000001393
9. Dashti S.G., Buchanan D.D., Jayasekara H., Ouakrim D.A., Clendenning M., Rosty C., Winship I.M., MacRae F.A., Giles G.G., Parry S.. **Alcohol consumption and the risk of colorectal cancer for mismatch repair gene mutation carriers**. *Cancer Epidemiol. Biomarkers Prev.* (2017.0) **26** 366-375. DOI: 10.1158/1055-9965.EPI-16-0496
10. Yang C., Wang X., Huang C.H., Yuan W.J., Chen Z.H.. **Passive Smoking and Risk of Colorectal Cancer: A Meta-analysis of Observational Studies**. *Asia Pac. J. Public Health* (2016.0) **28** 394-403. DOI: 10.1177/1010539516650724
11. Coyle C., Cafferty F.H., Langley R.E.. **Aspirin and Colorectal Cancer Prevention and Treatment: Is It for Everyone?**. *Curr. Colorectal Cancer Rep.* (2016.0) **12** 27-34. DOI: 10.1007/s11888-016-0306-9
12. Ma S., Han T., Sun C., Cheng C., Zhang H., Qu G., Bhan C., Yang H., Guo Z., Yan Y.. **Does aspirin reduce the incidence, recurrence, and mortality of colorectal cancer? A meta-analysis of randomized clinical trials**. *Int. J. Colorectal Dis.* (2021.0) **36** 1653-1666. DOI: 10.1007/s00384-021-03889-8
13. Guo C.G., Ma W., Drew D.A., Cao Y., Nguyen L.H., Joshi A.D., Ng K., Ogino S., Meyerhardt J.A., Song M.. **Aspirin Use and Risk of Colorectal Cancer Among Older Adults**. *JAMA Oncol.* (2021.0) **7** 428-435. DOI: 10.1001/jamaoncol.2020.7338
14. Friis S., Riis A.H., Erichsen R., Baron J.A., Sørensen H.T.. **Low-Dose Aspirin or Nonsteroidal Anti-inflammatory Drug Use and Colorectal Cancer Risk**. *Ann. Intern. Med.* (2015.0) **163** 347-355. DOI: 10.7326/M15-0039
15. Cho M.H., Yoo T.G., Jeong S.M., Shin D.W.. **Association of Aspirin, Metformin, and Statin use with gastric cancer incidence and mortality: A nationwide cohort study**. *Cancer Prev. Res.* (2021.0) **14** 95-104. DOI: 10.1158/1940-6207.CAPR-20-0123
16. Henley S.J., Anderson R.N., Thomas C.C., Massetti G.M., Peaker B., Richardson L.C.. **Invasive cancer incidence, 2004–2013, and deaths, 2006–2015, in nonmetropolitan and metropolitan counties—United States**. *MMWR Surveill. Summ.* (2017.0) **661** 1-13. DOI: 10.15585/mmwr.ss6614a1
17. Florensa D., Godoy P., Mateo J., Solsona F., Pedrol T., Mesas M., Pinol R.. **The Use of Multiple Correspondence Analysis to Explore Associations between Categories of Qualitative Variables and Cancer Incidence**. *IEEE J. Biomed. Health Inform.* (2021.0) **25** 3659-3667. DOI: 10.1109/JBHI.2021.3073605
18. **WHO|World Health Organization**
19. Sierosławski J., Foster J., Moskalewicz J.. **Survey of European drinking surveys. Alcohol survey experiences of 22 European countries**. *Drugs Educ. Prev. Policy* (2013.0) **20** 383-398. DOI: 10.3109/09687637.2013.797381
20. **Update on the Risks Related to Alcohol Consumption Levels, Consumption Patterns and Type of Alcoholic Beverages**
21. Kenfield S.A., Stampfer M.J., Rosner B.A., Colditz G.A.. **Smoking and smoking cessation in relation to mortality in women**. *JAMA* (2008.0) **299** 2037-2047. DOI: 10.1001/jama.299.17.2037
22. **WHOCC—ATC/DDD Index**
23. Torres-Bondia F., Dakterzada F., Galván L., Buti M., Besanson G., Gill E., Buil R., de Batlle J., Piñol-Ripoll G.. **Proton pump inhibitors and the risk of Alzheimer’s disease and non-Alzheimer’s dementias**. *Sci. Rep.* (2020.0) **10** 21046. DOI: 10.1038/s41598-020-78199-0
24. Chan A.T., Giovannucci E.L., Meyerhardt J.A., Schernhammer E.S., Wu K., Fuchs C.S.. **Aspirin Dose and Duration of Use and Risk of Colorectal Cancer in Men**. *Gastroenterology* (2008.0) **134** 21-28. DOI: 10.1053/j.gastro.2007.09.035
25. Hwang I.C., Chang J., Kim K., Park S.M.. **Aspirin Use and Risk of Hepatocellular Carcinoma in a National Cohort Study of Korean Adults**. *Sci. Rep.* (2018.0) **8** 4968. DOI: 10.1038/s41598-018-23343-0
26. Thun M.J., Jacobs E.J., Patrono C.. **The role of aspirin in cancer prevention**. *Nat. Rev. Clin. Oncol.* (2012.0) **9** 259-267. DOI: 10.1038/nrclinonc.2011.199
27. Rothwell P.M., Wilson M., Elwin C.E., Norrving B., Algra A., Warlow C.P., Meade T.W.. **Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials**. *Lancet* (2010.0) **376** 1741-1750. DOI: 10.1016/S0140-6736(10)61543-7
28. Rothwell P.M., Fowkes F.G.R., Belch J.F., Ogawa H., Warlow C.P., Meade T.W.. **Effect of daily aspirin on long-term risk of death due to cancer: Analysis of individual patient data from randomised trials**. *Lancet* (2011.0) **377** 31-41. DOI: 10.1016/S0140-6736(10)62110-1
29. Maniewska J., Jeżewska D.. **Non-Steroidal Anti-Inflammatory Drugs in Colorectal Cancer Chemoprevention**. *Cancers* (2021.0) **13**. DOI: 10.3390/cancers13040594
30. Cui X.J., He Q., Zhang J.M., Fan H.J., Wen Z.F., Qin Y.R.. **High-dose aspirin consumption contributes to decreased risk for pancreatic cancer in a systematic review and meta-analysis**. *Pancreas* (2014.0) **43** 135-140. DOI: 10.1097/MPA.0b013e3182a8d41f
31. Jacobo-Herrera N.J., Pérez-Plasencia C., Camacho-Zavala E., Figueroa González G., López Urrutia E., Garćia-Castillo V., Zentella-Dehesa A.. **Clinical evidence of the relationship between aspirin and breast cancer risk (review)**. *Oncol. Rep.* (2014.0) **32** 451-461. DOI: 10.3892/or.2014.3270
32. Liao L.M., Vaughan T.L., Corley D.A., Cook M.B., Casson A.G., Kamangar F., Abnet C.C., Risch H.A., Giffen C., Freedman N.D.. **Nonsteroidal Anti-inflammatory Drug Use Reduces Risk of Adenocarcinomas of the Esophagus and Esophagogastric Junction in a Pooled Analysis**. *Gastroenterology* (2012.0) **142** 442-452.e5. DOI: 10.1053/j.gastro.2011.11.019
33. Yang Y., Wang G., He J., Ren S., Wu F., Zhang J., Wang F.. **Gender differences in colorectal cancer survival: A meta-analysis**. *Int. J. Cancer* (2017.0) **141** 1942-1949. DOI: 10.1002/ijc.30827
34. Favoriti P., Carbone G., Greco M., Pirozzi F., Pirozzi R.E.M., Corcione F.. **Worldwide burden of colorectal cancer: A review**. *Updat. Surg.* (2016.0) **68** 7-11. DOI: 10.1007/s13304-016-0359-y
35. Permanyer I., Scholl N.. **Global trends in lifespan inequality: 1950–2015**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0215742
36. Nolen S.C., Evans M.A., Fischer A., Corrada M.M., Kawas C.H., Bota D.A.. **Cancer—Incidence, prevalence and mortality in the oldest-old. A comprehensive review**. *Mech. Ageing Dev.* (2017.0) **164** 113-126. DOI: 10.1016/j.mad.2017.05.002
37. Shahjehan F., Merchea A., Cochuyt J.J., Li Z., Colibaseanu D.T., Kasi P.M.. **Body mass index and long-term outcomes in patients with colorectal cancer**. *Front. Oncol.* (2018.0) **8** 620. DOI: 10.3389/fonc.2018.00620
38. Liu P.H., Wu K., Ng K., Zauber A.G., Nguyen L.H., Song M., He X., Fuchs C.S., Ogino S., Willett W.C.. **Association of Obesity With Risk of Early-Onset Colorectal Cancer Among Women**. *JAMA Oncol.* (2019.0) **5** 37-44. DOI: 10.1001/jamaoncol.2018.4280
39. Sanford N.N., Giovannucci E.L., Ahn C., Dee E.C., Mahal B.A.. **Obesity and younger versus older onset colorectal cancer in the United States, 1998–2017**. *J. Gastrointest. Oncol.* (2020.0) **11** 121-126. DOI: 10.21037/jgo.2019.12.07
40. Jaspan V., Lin K., Popov V.. **The impact of anthropometric parameters on colorectal cancer prognosis: A systematic review and meta-analysis**. *Crit. Rev. Oncol. Hematol.* (2021.0) **159** 103232. DOI: 10.1016/j.critrevonc.2021.103232
41. Bradbury K.E., Murphy N., Key T.J.. **Diet and colorectal cancer in UK Biobank: A prospective study**. *Int. J. Epidemiol.* (2020.0) **49** 246-258. DOI: 10.1093/ije/dyz064
42. Akter S., Islam Z., Mizoue T., Sawada N., Ihira H., Tsugane S., Koyanagi Y.N., Ito H., Wang C., Tamakoshi A.. **Smoking and colorectal cancer: A pooled analysis of 10 population-based cohort studies in Japan**. *Int. J. Cancer* (2021.0) **148** 654-664. DOI: 10.1002/ijc.33248
43. Wong M.C.S., Huang J., Lok V., Wang J., Fung F., Ding H., Zheng Z.J.. **Differences in Incidence and Mortality Trends of Colorectal Cancer Worldwide Based on Sex, Age, and Anatomic Location**. *Clin. Gastroenterol. Hepatol.* (2021.0) **19** 955-966.e61. DOI: 10.1016/j.cgh.2020.02.026
44. Rasool S., Kadla S.A., Rasool V., Ganai B.A.. **A comparative overview of general risk factors associated with the incidence of colorectal cancer**. *Tumor Biol.* (2013.0) **34** 2469-2476. DOI: 10.1007/s13277-013-0876-y
45. Siegel R.L., Fedewa S.A., Anderson W.F., Miller K.D., Ma J., Rosenberg P.S., Jemal A.. **Colorectal Cancer Incidence Patterns in the United States, 1974–2013**. *JNCI J. Natl. Cancer Inst.* (2017.0) **109** djw322. DOI: 10.1093/jnci/djw322
46. Rodríguez-Miguel A., García-Rodríguez L.A., Gil M., Montoya H., Rodríguez-Martín S., de Abajo F.J.. **Clopidogrel and Low-Dose Aspirin, Alone or Together, Reduce Risk of Colorectal Cancer**. *Clin. Gastroenterol. Hepatol.* (2019.0) **17** 2024-2033.e2. DOI: 10.1016/j.cgh.2018.12.012
47. Bosetti C., Santucci C., Gallus S., Martinetti M., La Vecchia C.. **Aspirin and the risk of colorectal and other digestive tract cancers: An updated meta-analysis through 2019**. *Ann. Oncol.* (2020.0) **31** 558-568. DOI: 10.1016/j.annonc.2020.02.012
48. Steele C.B., Thomas C.C., Henley S.J., Massetti G.M., Galuska D.A., Agurs-Collins T., Puckett M., Richardson L.C.. **Vital Signs: Trends in Incidence of Cancers Associated with Overweight and Obesity—United States, 2005–2014**. *Morb. Mortal. Wkly. Rep.* (2017.0) **66** 1052. DOI: 10.15585/mmwr.mm6639e1
49. Park S.Y., Wilkens L.R., Setiawan V.W., Monroe K.R., Haiman C.A., Le Marchand L.. **Alcohol Intake and Colorectal Cancer Risk in the Multiethnic Cohort Study**. *Am. J. Epidemiol.* (2019.0) **188** 67-76. DOI: 10.1093/aje/kwy208
50. Choi Y.J., Lee D.H., Han K.D., Kim H.S., Yoon H., Shin C.M., Park Y.S., Kim N.. **The relationship between drinking alcohol and esophageal, gastric or colorectal cancer: A nationwide population-based cohort study of South Korea**. *PLoS ONE* (2017.0) **12**. DOI: 10.1371/journal.pone.0185778
51. Cook N.R., Lee I.M., Zhang S.M., Moorthy M.V., Buring J.E.. **Alternate-day, low-dose aspirin and cancer risk: Long-term observational follow-up of a randomized trial**. *Ann. Intern. Med.* (2013.0) **159** 77-85. DOI: 10.7326/0003-4819-159-2-201307160-00002
52. Brasky T.M., Potter J.D., Kristal A.R., Patterson R.E., Peters U., Asgari M.M., Thornquist M.D., White E.. **Non-steroidal anti-inflammatory drugs and cancer incidence by sex in the VITamins and Lifestyle (VITAL) cohort**. *Cancer Causes Control* (2012.0) **23** 431-444. DOI: 10.1007/s10552-011-9891-8
53. Kim H., Giovannucci E.L.. **Sex differences in the association of obesity and colorectal cancer risk**. *Cancer Causes Control* (2016.0) **28** 1-4. DOI: 10.1007/s10552-016-0831-5
54. Bentham J., Di Cesare M., Bilano V., Bixby H., Zhou B., Stevens G.A., Riley L.M., Taddei C., Hajifathalian K., Lu Y.. **Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults**. *Lancet* (2017.0) **390** 2627-2642. DOI: 10.1016/S0140-6736(17)32129-3
55. Movahedi M., Bishop D.T., Macrae F., Mecklin J.P., Moeslein G., Olschwang S., Eccles D., Evans D.G., Maher E.R., Bertario L.. **Obesity, aspirin, and risk of colorectal cancer in carriers of hereditary colorectal cancer: A prospective investigation in the CAPP2 study**. *J. Clin. Oncol.* (2015.0) **33** 3591-3597. DOI: 10.1200/JCO.2014.58.9952
56. Kuo C.N., Pan J.J., Huang Y.W., Tsai H.J., Chang W.C.. **Association between nonsteroidal anti-inflammatory drugs and colorectal cancer: A population-based case-control study**. *Cancer Epidemiol. Biomark. Prev.* (2018.0) **27** 737-745. DOI: 10.1158/1055-9965.EPI-17-0876
57. Sankaranarayanan R., Kumar D.R., Altinoz M.A., Bhat G.J.. **Mechanisms of Colorectal Cancer Prevention by Aspirin—A Literature Review and Perspective on the Role of COX-Dependent and -Independent Pathways**. *Int. J. Mol. Sci.* (2020.0) **21**. DOI: 10.3390/ijms21239018
58. Gu Q., Dillon C.F., Eberhardt M.S., Wright J.D., Burt V.L.. **Preventive aspirin and other antiplatelet medication use among U.S. adults aged ≥ 40 years: Data from the national health and nutrition examination survey, 2011–2012**. *Public Health Rep.* (2015.0) **130** 643-654. DOI: 10.1177/003335491513000614
59. Duffy D., Kelly E., Trang A., Whellan D., Mills G.. **Aspirin for Cardioprotection and Strategies to Improve Patient Adherence**. *Postgrad. Med.* (2015.0) **126** 18-28. DOI: 10.3810/pgm.2014.01.2721
|
---
title: 'Building a City with Low Noise Pollution: Exploring the Mental Health Effect
Thresholds of Spatiotemporal Environmental Noise Exposure and Urban Planning Solution'
authors:
- Xue Zhang
- Suhong Zhou
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001488
doi: 10.3390/ijerph20054222
license: CC BY 4.0
---
# Building a City with Low Noise Pollution: Exploring the Mental Health Effect Thresholds of Spatiotemporal Environmental Noise Exposure and Urban Planning Solution
## Abstract
Urban noise pollution and health hazards have become serious social problems and challenges. Noise prevention and control is the most cost-effective health strategy. However, in urban planning and noise control, reliable evidence is still lacking on individual spatiotemporal environmental noise exposure and its mental health effects. This study used real-time noise exposure data and GPS trackers from 142 volunteers aged 18 to 60 years in Guangzhou, and further analyzed the differences in environmental noise exposure and its mental health impact thresholds under individual spatiotemporal behavior. The results showed that the noise exposure of residents under daily activities has obvious differences in time, space and place. Regarding the threshold relationship between noise exposure and mental health, noise exposure at night, work, personal affairs, travel and sleep activities, as well as at home and work had a threshold effect on residents’ mental health. Noise thresholds were 60 dB, 60 dB, and about 34 dB at night, during work or at a workplace, and while sleeping, respectively. The optimal sound environment for personal affairs, traveling, and at home was around 50 dB, 55–70 dB, and 45 dB, respectively. The environmental noise exposure assessment and mental health impact threshold analysis based on the spatial and temporal activities of individuals will provide important reference for government management departments in planning and policy formulation.
## 1. Introduction
Rapid urban social and economic development has triggered serious environmental noise pollution and increased the risk of mental disorders. In 2011, the World Health Organization and the European Union jointly stated that noise has become the second major public health hazard after air pollution [1]. In Europe, more than $30\%$ of the population was exposed to road traffic noise of over 55 decibels at night, and lead to serious sleep disturbances and adverse health effects [2]. In China, the report noted that in 2020, urban departments of ecology and environment, public security, housing and urban and rural development received over 2 million complaints about environmental noise, involving domestic noise, construction noise, industrial noise and traffic noise [3]. *Noise* generated in urban construction and daily life has seriously affected human behavior, well-being, productivity and health [4]. Excessive exposure to noise pollution not only made people irritable and worsen sleep, but also increased the prevalence of hypertension and cardiovascular and cerebrovascular diseases, endangering people’s physical and mental health [5,6,7,8,9].
The effect of environmental noise on human mental health became a major concern in terms of geography and public health, with the increasing prominence of mental health disorders and becoming one of the biggest challenges in public health problems. Studies have shown a dose–response relationship between noise exposure and mental health in residents [10,11]. Large amounts of noise studies derived from road traffic and airports have shown that the risk of poor mental health increases with increased exposure to traffic noise [7,12,13,14,15,16].
Residents’ health is closely related to their daily life. Environmental exposure and residents’ mental health from the perspective of individual activity and movement has become a new direction to examine [17,18,19]. Since 1996, some scholars have carried out relevant research on individual noise exposure surveys. In the process of investigation, the questionnaire was used to understand the time arrangement of main activities, personal living, working environment and basic information of individuals. The individual’s exposure to noise was also collected throughout the day and night [20]. In recent years, with the development of GPS and GIS technology and the application of portable environmental monitoring equipment, more and more scholars have realized the limitations of static geographic noise exposure assessment based on an individuals’ place of residence or workplace [21,22,23]. Real-time noise monitoring and assessment methods that consider individual daily activities and dynamic changes in the time and location of noise can more accurately capture the real noise environment of residents [9,24,25,26]. However, studies on the relationship between residents’ mental health and spatiotemporal environmental noise exposure are still very scarce.
In the face of severe environmental noise and health problems in cities, noise control is the most economical and effective health strategy. Determining accurate environmental health impact thresholds is essential for urban policy makers and residents to understand environmental health risks and respond to environmental hazards [27]. In the study of the relationship between environmental noise and mental health, the essence of the noise threshold is that a certain limit of noise pollution does little harm to the health of residents. However, when the environmental noise pollution or noise exposure exceeds a certain limited value, it will greatly aggravate the health risks and cause serious harm to the residents’ health. The World Health Organization reported that noise over 70 dB can cause severe hearing damage [28]. In the threshold approach of noise response problems, the usual criterion used is to define the points where $10\%$ of the population is severely or severely affected by noise as excessive noise [29]. However, there is no clear threshold level of noise present for resident health effects, due to the effects of many characteristics of the noise itself, environmental factors, and extensive differences in individual sensitivity to noise. Therefore, it is particularly critical to study the nonlinear relationship between noise and mental health research through machine learning methods to explore the threshold problem of the effect of noise on mental health [24,30,31].
The purpose of this study is to investigate the effect threshold of residents’ noise exposure and mental health in different time periods, places and activities. It particularly focuses on [1] spatiotemporal activity-travel characteristics of resident and noise exposure differences; [2] threshold for the effect of environmental noise exposure on mental health under different time periods, places, and activities. The contribution is that it truly reflects the characteristics of residents’ noise exposure in their daily activities, and for the first time proposes and answers the threshold of the effect of noise on mental health under different time, place and activity background. The findings of this study will help urban planners to develop more precise environmental noise control standards for specific space and provide reference for individual active noise prevention and control.
## 2.1. Study Area and Survey
The data used in this study come from a survey on residents’ daily activity and environmental exposure in Guangzhou from November 2018 to January 2019. The survey covers 7 communities in Tangxia Sub-district, Tianhe District, and Guangzhou, with a total of around 2.2 km2. Tangxia Sub-district is a representative large-scale comprehensive community integrating commercial housing, affordable housing, public rental housing and rental housing of “urban village”. Using Tangxia Sub-district as a case study, we can examine the differences in activity–travel patterns and noise exposure of residents from various socioeconomic backgrounds, and further explore the relationship between noise exposure and residents’ mental health at different time periods, activities and places.
All volunteers were asked to carry a portable noise sensor (SLM-25 Sound Level Meters) for 48 h covering a workday and a weekend day (from 3 a.m. on Sunday to 3 a.m. on Tuesday) to record real-time noise exposure data, as well as to complete the daily activity–travel and environmental health questionnaire. A total of 156 volunteers participated in the survey, among which 10 people participated in the preliminary survey, and 4 people experienced serious data loss due to equipment failure or personal reasons during the survey stage. Finally, there were 142 volunteers with complete data sets. The specific survey procedures and equipment information used were introduced in detail in Zhang’s research [24].
The data mainly used in this research include two parts: daily activity-travel survey and individual noise exposure assessment. Among them, the volunteers’ basic information and family information, including gender, age, education, income, housing, time spent in green space and neighborhood, were collected using the residents’ daily activity survey, the results of which are detailed in Table 1. In addition, the volunteers were asked to recall information on all travel and activities of the survey day, and record in detail the types and starting time of activities, the types of activity location, travel mode, starting and ending points of travel, and starting time of travel throughout the day. All travel and activities should be recorded in temporal continuity. In this study, the main activity places of residents were divided into home or community, workplace, school, shopping malls or supermarkets, restaurants, parks, hospitals or clinics, relatives’ homes, sports fields and 10 other types. The main activity types of residents were divided into work or business, personal affairs, family affairs, shopping, entertainment and leisure, social activities and 7 other types.
Self-reported mental health data were also obtained from the questionnaires. The World Health Organization’s Five Well-Being Index (WHO-5) detailing feeling “cheerful and in good spirits”, “calm and relaxed”, “active and vigorous”, “fresh and rested”, and “daily life has been filled with things that interest me” during the past two weeks was used to assess the subjective mental health [32,33,34]. The responses were quantified on a 5-point Likert scale ranging from 1 (none of the time) to 5 (all of the time). The total scores of the WHO-5 range from 5 to 25, and the higher the score, the better the psychological condition.
The GPS-equipped mobile phones and the activity–travel diaries were used to record the trajectory of residents’ daily behavior. Through the GPS-equipped mobile phones, it was possible to record the space–time trajectory of residents in real time and reflect the real activity paths and locations of residents. The activity–travel trajectory data points obtained by the two survey methods can be verified and integrated with each other, which further ensures the reliability of the data. The data of the questionnaire, real-time noise levels, activity travel diary, and GPS track points were integrated based on participants’ unique identifiers.
## 2.2. Personal Noise Exposure Assessment
The real-time minute-by-minute noise data recorded by the portable noise sensors were further calculated as the A-weighted equivalent sound pressure level of residents at different times, activities and places. The A-weighted equivalent sound pressure level is a general method adopted by International Organization for Standardization (ISO) to measure the noise exposure of an individual [35]. It refers to the average value of the A sound level according to energy for a certain period of time. Personal noise exposure measured by the A-weighted equivalent sound pressure level (LAeq,T) over T hour was calculated according to the following Formula [1]:[1]LAeq,$T = 10$lg(1T∑$$n = 1$$T100.1Leq,Tn)dB(A) where T represents cumulative time T minutes; LAeq,T is the A-weighted equivalent sound level over a total of T minutes; Leq,*Tn is* the A-weighted equivalent sound level at the n minute, which is the reading of the every-minute sound level collected by the portable noise sensors.
The A-weighted equivalent sound level within 48 h on a workday and a weekend day (Leaq,48h), A-weighted equivalent sound level within 24 h on a workday (Leaq,W) and on a weekend day (Leaq,R), A-weighted equivalent sound level between 6:00 and 22:00 on a workday and a weekend day (Leaq,D) and A-weighted equivalent sound level between 22:00 and 6:00 on a workday and a weekend day (Leaq,N) were calculated. In addition, the A-weighted equivalent sound levels of residents in different activities and places during the two days of the survey were calculated, which are detailed in Table 1.
## 2.3. Model and Methodology
The random forest method was applied to disentangle the complex relationships between the noise exposure and mental health under different spatio-temporal and activity dimensions. The random forest method is an ensemble learning method that explores finer associations between results and explanatory variables, rather than assuming a priori a particular relationship that fits the data [36]. It works by combining decision trees from multiple individuals to optimize model fitting and prediction, and finally make the loss function reach a minimum value or maintain stability. The random forest method has been widely used in environmental health research to study the non-linear threshold relationship [24,25,37,38]. The permutation importance measure introduced by Breiman [2001] can quantify the relative importance of explanatory variables in the prediction results and increase the interpretability of the model [36]. The importance for variable xi is formulated as:[2]VIxi=1n∑t(QMSEt−QMSE,pit) where VIxi is the variable importance of variable xi; n is the total number of decision trees in the forest; QMSEt represents the mean square error before the tree t arrangement; QMSE,pit represents the mean squared error after the arrangement of variable xi.
In addition, the partial dependence graph generated by random forests enables the visualization of the relationship between the results and the explanatory variables [39]. In this study, it describes the marginal effect of noise exposure on mental health, while controlling for the average effect of all other explanatory variables in a given model. The partial dependence of fs^(xs) on xs is formulated as follows:[3] fs^(xs)=1N∑$i = 1$Nf^(xs, xic) where x1c, x2c, …, xic are the values of the other variables xc in the dataset, and N is the number of instances.
## 3.1. Trajectories of Residents’ Daily Activities
A total of 142 residents’ activity–travel records were obtained during a workday and a weekend day. There were 2189 trips and activities during workdays, with an average of 15.42 per person. A total of 2050 trips and activities were recorded on weekend days, with an average of 14.44 per person. Using the ArcGIS tool, the real spatial and temporal trajectory of residents on workdays and weekend days were depicted, respectively, as shown in Figure 1. The range of residents’ daily activity was within the main urban area. On workdays, the trajectory of residents’ activities were similar, with the main activity sites in Yuexiu District, Tianhe District and Haizhu District, whereas only some residents’ activity tracks extend to Huangpu District and Liwan District. On weekend days, residents’ places of activity were scattered, mainly in Yuexiu and Tianhe, followed by Liwan District, Haizhu District, Huangpu District, Panyu District and Nansha District. The activity trajectories of residents on workdays and weekends indicated that residents have been involved in different activity spaces in their daily life. The environmental noise exposure assessment based on fixed places of residence is biased from the real environment. Therefore, it is necessary to pay attention to the noise exposure and mental health effects of residents from the perspective of dynamic activities.
## 3.2. Temporal and Spatial Distribution Characteristics of Residents’ Daily Activities
Figure 2 showed the distribution characteristics of residents’ activity place and time on workday and weekend. On workdays, the main place of residents’ activities was home (community), and the average time spent at home (community) accounts for $72\%$ of the total time, about 17.3 h, followed by work place ($20.9\%$ of the total time, about 5 h), restaurant ($1.5\%$ of the total time, about 0.36 h), school ($1.1\%$ of the total time, about 0.26 h) and other ($1.5\%$ of the total time, about 0.36 h) (Figure 2a). From the change in residents’ activity locations on workdays, the peak hours for arriving and leaving the workplace were 7:00–9:00 am and 17:00–19:00 pm, respectively. Between 11:00 and 14:00, the number of people in offices decreased while the number of people in restaurants and restaurants increased. In addition, the main time for residents to appear and leave in the school was 7:30 am to 8:30 am and 16:00 pm to 17:30 pm, which corresponds to the time for students to travel to and from school.
On weekends, the average time of residents at home (community) accounted for $80.9\%$ of the total time, about 19.4 h, followed by workplace ($5.3\%$ of the total time, about 1.27 h), family and friends’ homes ($2.9\%$ of the total time, about 0.7 h), restaurants ($2.3\%$ of the total time, about 0.55 h) and others ($3.3\%$ of the total time, about 0.79 h). The time when residents are not at home and in the community on weekends was mainly between 8:00 am and 18:30 pm, and it decreased significantly after 18:30 pm. Compared with workdays, residents spend continuous amounts of time in malls or supermarkets, parks and relatives’ homes on weekends, which reflected that residents have more lasting and flexible time in leisure and entertainment places on weekends.
The relationship between activity type and time spent of residents on workdays and weekends was further analyzed (Table 2). On workdays, the average time spent on personal affairs was 12.3 h, accounting for $51.2\%$ of the total time, followed by work or business (4.5 h, accounting for $18.9\%$), family affairs (2.5 h, accounting for $10.4\%$), entertainment and leisure (2.44 h, accounting for $10.2\%$), and travel (1.6 h, accounting for $6.8\%$). Compared with workdays, the average working time of residents on weekends decreased by 3 h, whereas the time for personal affairs, family affairs and recreation increased by 0.5 h, 0.9 h and 1.16 h, respectively.
This part of the analysis showed the basic characteristics of residents’ activity spaces, types and time allocation. It also provided a basis for studying noise exposure and mental health effects based on different spaces, activities and time.
## 3.3. Characteristics of Residents Noise Exposure
We analysed personal noise exposure levels from different time, space and activities in people’s daily life. In terms of time, the characteristics of noise exposure on workday and weekend, in the daytime and nighttime, and the variation characteristics of noise exposure during 24 h were analyzed. Spatially, the noise exposure of residents was calculated according to the main activity places of home or community, workplace, school, mall or supermarkets, restaurant, park, hospital or clinic, relatives’ home, playground and other. Finally, the noise exposure levels of residents in nine main types of activities, including work or business, personal affairs, family affairs, shop, recreation, social, sleep, travel and other were analyzed by descriptive statistics.
## 3.3.1. Temporal Characteristics of Residents Noise Exposure
Figure 3 illustrated that the average LAeq,24h of resident noise exposure on workdays and weekends was 62.03 dB and 61.98 dB, respectively. The proportion of residents with LAeq,24h below 55 dB was $16.8\%$ for both days. The proportions of residents with LAeq,24h at 55–60 dB, 60–65 dB and over 65 dB were $21.8\%$, $27.4\%$ and $33.7\%$ on workdays, and $19\%$, $32.3\%$ and $31.6\%$ on weekends. By comparison, it can be found that there were certain differences in noise exposure levels between residents on workdays and weekends. For $4.2\%$, $11.3\%$ and $37.3\%$ of the residents, the LAeq,24h difference on workdays and weekends was greater than 15 dB, 10 dB, 5 dB, respectively. Only $16.2\%$ of the residents’ differences in noise exposure over two days was less than 1 dB.
Figure 4 and Figure 5 showed the equivalent sound level of noise exposure in the day and night on workday and weekend. Residents had high noise exposure in the day which the LAeq,D was 61.19 dB and 61.71 dB on workdays and weekends. Moreover, over eighty and sixty percent of the residents were exposed to LAeq,D exceeding 55 dB and 60 dB. In contrast, seven percent more residents on weekends than on workdays were exposed to a high-noise environment in which the LAeq,D was 65 dB. From the residents’ noise exposure at night, the LAeq,N were 49.28 dB and 47.18 dB on workdays and weekends, respectively. $42.3\%$ and $31.7\%$ of the residents’ average noise exposure value at night exceeded 50 dB, $24.6\%$ and $19.0\%$ of the residents’ average noise exposure value at night exceeded 55 dB. In contrast, the residents’ noise exposure at night on weekends was generally lower than that on workdays.
Clearly, there is substantial variation in noise exposure among individuals. From the average equivalent sound level value of noise exposure in the day and night, residents may be at a higher risk of noise exposure. According to environmental noise guidelines for the European Region, the 24 h equivalent sound level value of recreational noise sources should be within 70 dB, and for road traffic, aircraft and railway noise sources, the equivalent sound level should be lower than 53 dB, 45 dB and 54 dB [38]. Moreover, noise levels starting at 40 dB are mentioned in the WHO night noise guidelines as having adverse health effects. The WHO night noise guidelines (WHO, 2009) mentioned that noise levels at night from 40 dB have adverse health effects [40].
Figure 6 showed the equivalent sound level of residents’ noise exposure per ten minutes at various time periods on workday and weekend. It can be found that the proportion of residents exposed to different noise levels on workdays and weekends had the same time rhythm, with two clear stationary periods and two changing periods in time. The period from 0:00 to 7:00 was a stable period in which the residents’ noise exposure on workdays and weekends was at a low level, and $70\%$ of the residents’ noise exposure was less than 45 dB. At 8:00–20:00 on workdays and 9:00–21:00 on weekends, it was another stable period when the residents’ noise exposure was at a high level, with nearly half of the residents’ noise exposure values exceeding 55 dB. The noise exposure level of most residents increased from 7:00 to 8:00 on workdays and from 7:00 to 9:00 on weekends, and decreased from 20:00 to 0:00 on workdays and from 21:00 to 0:00 on weekends. The time rhythm of residents’ noise exposure levels may be directly related to the activity arrangement on workdays and weekends. The noise exposure of different activities and activity spaces needs to be further studied.
## 3.3.2. Characteristics of Residents’ Noise Exposure under Different Activity Types
Figure 7 showed the equivalent sound level of residents’ noise exposure under different activity types on workday and weekend. *In* general, there were great difference of residents’ noise exposure under different activities. Noise exposure was high during the shopping, the travel, the social, work or business, exceeding 50 dB on workday and weekend. While noise exposure during sleep was low, with less than 45 dB on both days. In addition, the noise exposure in personal affairs, family affairs and recreational activities was about 48–49 dB, respectively. The noise exposure was roughly similar under different activity types on workdays and weekends, and varied greatly only during shopping, other activities and social activities; the noise exposure on weekends was higher than that on workdays. Residents’ noise exposure was different under different activity types; the noise tolerance values (A dose that causes discomfort) of residents may also be different under different activity types. Further research on the relationship between noise and mental health should be conducted in specific activity scenarios.
## 3.3.3. Characteristics of Residents’ Noise Exposure under Different Activity Places
Figure 8 showed the equivalent sound level of residents’ noise exposure in different activity places on workday and weekend. Residents in shopping malls, supermarkets, restaurants, hospitals and other activity places were exposed to high noise which exceeded 50 dB on workdays and weekends. On the other hand, the noise exposure was low at home and at relatives’ homes, both between 45 and 50 dB. In addition, the residents’ noise exposure in schools and parks were about 48–55 dB. Compared with the residents’ noise exposure in different activity places on workdays and weekends, the noise exposure was roughly the same for two days only in sports halls, hospitals, clinics, homes, workplaces and other activity locations. Noise exposure was significantly higher on weekends than on workdays in relatives’ homes, parks, restaurants, shopping malls and supermarkets. This may be related to the flow of people and bustle of different activity places on workdays and weekends. Similarly, further research on the relationship between noise and mental health should be conducted in a specific activity space.
## 3.4. Self-Reported Mental Health Characteristics of Residents
Figure 9 showed that the mean value of residents’ self-reported mental health was 15.6. According to the World Health Organization’s Five Well-Being Indexes (WHO-5) [32], a score of less than 13 indicates the person’s mental status is poor. In total, $25.4\%$ of the residents had psychological problems, and their self-rated mental health value was lower than 13.
## 3.5. The Mental Health Effect Thresholds of Spatiotemporal Environmental Noise Exposure
We further analyzed the relationship between noise exposure and residents’ mental health at different times, activities and places by random forest model. It should be noted that the sample size of residents during shopping, social and other activities was too small to be analyzed. Similarly, the relationship between noise exposure and mental health in different activity places was analyzed only at home and in the workplace. Individual attribute variables were taken as control variables in all models, and the relative contribution of noise exposure to mental health is shown in Table 3.
Residents’ noise exposure during sleep (Leaq,Sl, Model 11) was an important independent variable for mental health, with a $6.06\%$ contribution. Noise exposure at workspace (Leaq,WS, Model 13), noise exposure during work (Leaq,Work, Model 6), noise exposure at home or in community (Leaq,Home, Model 12), noise exposure at travel (Leaq,Tra, Model 10), noise exposure in personal affairs (Leaq,PA, Model 7), and noise exposure at night (Leaq,N, Model 3) had $4.42\%$, $3.86\%$, $3.63\%$, $3.54\%$, $3.08\%$ and $1.20\%$ contribution towards mental health, respectively. However, the noise exposure over two days (Leaq,48h, Model 1), noise exposure during daytime (Leaq,D, Model 2), noise exposure on workdays and weekends (Leaq,W, Model 4 and Leaq,R, Model 5), noise exposure during family affairs (Leaq,FA, Model 8) and recreational activities (Leaq,Re, Model 9) had no significant effect on mental health, and their relative importance was negative.
Figure 10 showed the partial dependence between seven noise exposure variables and mental health. The partial dependence plot gives a graphical description of the marginal effect of the variable on the response variable after taking into account the average effect of all other variables in the model.
The difference in self-reported mental health among residents exposed to different levels of noise at night was 1.5 points. When the noise value at night was between 35 and 60 dB, its impact on the mental health level of residents is not clear. In this range, the mental health level fluctuated irregularly with the change of the noise value of about 0.5 points. When the noise value reached about 60 dB, the mental health level decreased significantly, and then decreased slowly until it remained at the lowest level.
The threshold for mental health effects of noise exposure at work was 60 dB. When the noise value was lower than 60 dB, its impact on mental health was very small, and the corresponding mental health value was about 14.5 to 15.5 points. When the noise level reached 60 dB, the mental health level dropped sharply, from 15 points to 11 points. According to the WHO (Five) Well-Being Index (1998 version), a score below 13 indicates poor health. We concluded that the tolerance threshold of noise at work was 60 dB; exceeding this level will lead to mental health problems in residents.
For the three models of noise exposure during personal business, noise exposure during travel, and noise exposure at home, it does not seem that the lower the noise level, the better the mental health. Considering the changes in mental health levels, the optimal sound range was found for personal affairs, travel and staying at home. Among them, when the noise value of personal affairs was around 40 dB, the mental health level was the lowest. However, when the noise value was in the range of 42–50 dB, the mental health level increased with the increase in the noise value. However, when the noise value exceeded 52 dB, the mental health level dropped sharply. When the noise value reached 55 dB, the mental health level dropped to about 15.5 and then stayed basically unchanged. It can be seen that around 50 dB (45–52 dB) seems to be the most comfortable sound level for personal affairs. Similarly, when the noise value was 43–53 dB, the mental health level was at the lowest level. When the noise value was lower than 43 dB or higher than 70 dB, the mental health level was at the middle level. Thus, the noise value of 55–70 dB was the best range of mental health level during travel. The lowest level of mental health was found at home with noise levels lower than 42 dB or higher than 52 dB, whereas the optimal range for mental health was 44–52 dB.
The mental health of residents was most sensitive to noise during sleep, and the difference in the mental health value was 3.4 points. When the noise level increased from 34 dB to 37 dB, the mental health score dropped sharply by about 2.2 points. Subsequently, the mental health score gradually decreased with the increase in the noise value, until the noise value reached 64 dB, the mental health score also fell to the lowest level and basically did not change.
Similar to the model of noise exposure at work, the mental health score dropped significantly from 15 (good health) to 12 (poor health) when noise exposure levels exceeded 60 dB at the workplace. The difference was that when the noise level was between 32 dB and 45 dB, the mental health score decreased slightly with the increase in the noise level. In the noise range of 48–52 dB, the mental health level was the best.
## 4.1. Comparison of the Research Findings with Current Environmental Noise Standards
In this study, the noise threshold identified were 60 dB at night, 60 dB at work, and about 34 dB during sleep, about 50 dB for personal business, 55–70 dB for travel, and 45 dB at home. The optimal sound environment for personal affairs, travel and at home were about 50 dB, 55–70 dB and 45 dB, respectively. Compared with the existing standards for environmental noise, the WHO recommend that the average level of road traffic noise, rail traffic noise and air noise at night should be controlled below 45 dB, 44 dB and 40 dB, respectively [41]. Additionally, according to the Acoustic Environmental Quality Standards (GB 3096-2008), the nighttime noise limits of class 0 acoustic environment functional area (referring to areas that require special quiet such as convalescent area) and class 1 acoustic environment functional area (referring to the areas whose main functions are residential, medical and health care, culture and education, scientific research and design, administrative office, and need to keep quiet) were 40 dB and 45 dB, respectively [42]. The nighttime noise threshold of 60 dB obtained in this study was much higher than the WHO recommendations and the existing nighttime noise standards in China, which may be related to the failure to distinguish noise sources in this study. However, the sleeping noise threshold identified in this study was 45 dB which lower than the WHO recommended nighttime exposure level. It indicated that the key period of noise control at night is during residents’ sleep period, whereas residents may have a higher tolerance to noise derived from activities related to life and entertainment (such as cooking, electrical appliances, talking, TV, mobile phones) during non-sleep period. For example, previous studies showed that the association between road traffic noise and the probability of a high level of sleep disturbances was OR: 2.13 ($95\%$ CI: 1.82–2.48) per 10 dB increase in noise [43], but when the noise source was not specified, the probability was OR: 1.09 ($95\%$ CI: 0.94–1.27) per 10 dB increase and was no longer statistically significant [44,45,46,47]. It can be verified that lower sound levels are not always better at home and in personal affairs, and the optimal ambient sound values for both were around 45 dB and 50 dB, respectively. According to the noise threshold value of 60 dB during the working period or in the working place, it was higher than the daytime limit value of 55 dB given in the Sound Environment Quality Standard.
Traffic noise has recognized as the main noise source in urban environment, and has been widely proved to have adverse effects on residents’ health [48,49,50,51]. In this study, the optimal sound environment for travel was determined to be 55–70 dB, which was well below the WHO guideline for protecting human hearing (70 dB) (World Health Organization, 1980 [28]) and the day-time limit standard 70 dB for class 4a functional areas (expressways, primary highways, secondary highways, urban expressways, urban trunk roads, urban secondary trunk roads, urban rail transit (surface section), and areas on both sides of inland waterways) in Sound Environment Quality Standards. Some studies have pointed out that the noise threshold of bus passengers’ instantaneous emotional influence is 65–79 dB, and suggested that the noise level should be controlled at 65 dB [31]. It can be found that residents tend to be exposed to a high noise environment when traveling (average value is 55.5 dB), which may lead to residents gradually adapting to a high noise level in the process of traveling, and only when the noise value exceeds the maximum to bring physiological (hearing) impact on them will become sensitive.
## 4.2. Policy Implications
In order to further promote the prevention and control of urban noise pollution and improve residents’ adverse health problems caused by noise, it is necessary to put forward more accurate noise prevention and control measures for urban areas, and encourage residents to take more active noise prevention and control measures. Therefore, the following measures were outlined in this paper as policy basis for urban management departments, ecological environment departments and reference for individual noise prevention and control. More research on noise and health effects is required to develop in the future to form a more reliable policy support and basis.
First of all, it is necessary to strictly control noise levels in key places (such as residential areas) and sensitive time periods (such as while sleeping at night). Soundproof windows and soundproof doors can be installed in residential buildings and office buildings where noise sources are close to each other in living and working areas.
Secondly, differentiated noise control standards should be formulated for different places, and an early warning mechanism should be adopted for important public places with environmental noise. For shopping malls, supermarkets, restaurants and other places, the noise impact threshold of residents can be combined to provide a more comfortable and pleasant reference standard for environmental noise. In stations, squares, buses and other places with high human flow density, especially in important occasions where people gather for major holidays, the early warning mechanism for people with environmental noise should be developed according to the residents’ emotional response to environmental noise exposure.
Finally, it is necessary to strengthen the education around noise hazards and self-prevention and control for residents. There are some differences in noise sensitivity and health hazard awareness among residents. Government management departments should further use social media, neighborhood committees and other media and means to guide residents to avoid and protect themselves against high noise risk, so as to reduce the effect of urban noise on residents’ health.
## 4.3. Strengths and Limitations
This study pays more attention to the threshold effects of environmental noise exposure in their daily activities on mental health. Our research findings highlight the difference in noise threshold in different time, space and activity backgrounds. The result can provide theoretical reference for the formulation of differentiated noise control measures in specific activity places, as well as resident active noise prevention and control and noise health risk intervention.
However, this research still has several limitations. For example, in the threshold study of noise from residents’ daily activities on mental health, the relationship between noise and mental health is complex, which is affected by acoustic and non-acoustic factors. In this study, only individual attributes were considered but some acoustic factors (such as type of noise source and noise frequency) were not considered, which may lead to some changes in the threshold. Secondly, the study only analyzed the threshold relationship between objective environmental noise exposure and mental health. In the future, the objective noise exposure and subjective noise perception of individuals can be further combined for analysis. Thirdly, it ignores the cumulative effects of environmental noise exposures on residents’ mental health over prolonged periods. In the future, the noise threshold on mental health can be comprehensively evaluated by combining the effect of exposure time. Fourthly, home and community have been considered to be the same type in the activity location type. However, noise levels between activities at home and outdoor in the community may be pretty different, and may have differentiated mental health effects.
Finally, limited by the difficulty of data collection, high cost and long-time consumption, this paper only had more than 100 samples in the dynamic environmental exposure monitoring and investigation of residents’ daily activities. It failed to cover more activity types and activity places in the analysis of noise exposure in daily activities and mental health. In future studies, targeted noise threshold research can be conducted according to different activity types, places and times.
## 5. Conclusions
This paper mainly studied the threshold effect of environmental noise exposure on residents’ mental health in their daily activities. The results showed that the noise exposure of residents under daily activities has obvious differences in time, space and place. Noise exposure at night, work, during personal affairs, travel and sleep activities, as well as at home and at the workplace, had a threshold effect on residents’ mental health. Noise thresholds were 60 dB, 60 dB, and about 34 dB at night, during work or at work, and while sleeping, respectively. The optimal sound environment for personal business, travel, and at home were around 50 dB, 55–70 dB, and 45 dB, respectively. The noise thresholds for each time, activity and place determined in this study were different from the existing recommendations and standards. More research is needed to provide a basis for more accurate noise control standards. This study makes an important contribution to explore the noise threshold and optimal acoustic environment level for different times, activities and places. It can also be a policy basis for urban management departments, ecological and environmental departments, and a reference for individual noise prevention and control.
## References
1. 1.
World Health Organization
Burden of Disease from Environmental Noise: Quantifcation of Healthy Life Years Lost in EuropeWHOGeneva, Switzerland2011126. *Burden of Disease from Environmental Noise: Quantifcation of Healthy Life Years Lost in Europe* (2011.0) 126
2. Pirrera S., De Valck E., Cluydts R.. **Nighttime road traffic noise: A review on its assessment and consequences on sleep and health**. *Environ. Int.* (2010.0) **36** 492-498. DOI: 10.1016/j.envint.2010.03.007
3. **China Environment Noise Prevention and Control Annual Report**. (2021.0)
4. 4.
European Commission
The Green Paper on Future Noise PolicyEuropean CommissionMaastricht, The Netherlands1996. *The Green Paper on Future Noise Policy* (1996.0)
5. Lercher P., Brink M., Rudisser J., Van Renterghem T., Botteldooren D., Baulac M., Baulac J.. **The effects of railway noise on sleep medication intake: Results from the ALPNAP-study**. *Noise Health* (2010.0) **12** 110-119. DOI: 10.4103/1463-1741.63211
6. Sørensen M., Hvidberg M., Hoffmann B., Andersen Z.J., Nordsborg R.B., Lillelund K.G., Jakobsen J., Tjønneland A., Overvad K., Raaschou-Nielsen O.. **Exposure to road traffic and railway noise and associations with blood pressure and self-reported hypertension: A cohort study**. *Environ. Int.* (2011.0) **10** 92. DOI: 10.1186/1476-069X-10-92
7. Sygna K., Aasvang G.M., Aamodt G., Oftedal B., Krog N.H.. **Road traffic noise, sleep and mental health**. *Environ. Res.* (2014.0) **131** 17-24. DOI: 10.1016/j.envres.2014.02.010
8. Dzhambov A., Tilov B., Markevych I., Dimitrova D.. **Residential road traffic noise and general mental health in youth: The role of noise annoyance, neighborhood restorative quality, physical activity, and social cohesion as potential mediators**. *Environ. Int.* (2017.0) **109** 1-9. DOI: 10.1016/j.envint.2017.09.009
9. Ma J., Li C., Kwan M.-P., Kou L., Chai Y.. **Assessing personal noise exposure and its relationship with mental health in Beijing based on individuals’ space-time behavior**. *Environ. Int.* (2020.0) **139** 105737. DOI: 10.1016/j.envint.2020.105737
10. Halpern D.. *Mental Health and the Built Environment: More than Bricks and Mortar?* (1995.0)
11. Lercher P.. **Environmental noise and health: An integrated research perspective**. *Environ. Int.* (1996.0) **22** 117-129. DOI: 10.1016/0160-4120(95)00109-3
12. Seidler A., Hegewald J., Seidler A.L., Schubert M., Wagner M., Dröge P., Haufe E., Schmitt J., Swart E.. **Association between aircraft, road and railway traffic noise and depression in a large case-control study based on secondary data**. *Environ. Res.* (2017.0) **152** 263-271. DOI: 10.1016/j.envres.2016.10.017
13. Halonen J.I., Lanki T., Yli-Tuomi T., Turunen A.W., Peniti J., Kivimäki M., Vahtera J.. **Associations of traffic noise with self-rated health and psychotropic medication use**. *Scand. J. Work Environ. Health* (2014.0) **40** 235-243. DOI: 10.5271/sjweh.3408
14. Orban E., McDonald K., Sutcliffe R., Hoffmann B., Fuks K.B., Dragano N., Viehmann A., Erbel R., Jöckel K.-H., Pundt N.. **Residential road traffic noise and high depressive symptoms after five years of follow-up: Results from the Heinz Nixdorf recall study**. *Environ. Health Perspect.* (2016.0) **124** 578-585. DOI: 10.1289/ehp.1409400
15. Bocquier A., Cortaredona S., Boutin C., David A., Bigot A., Sciortino V., Nauleau S., Gaudart J., Giorgi R., Verger P.. **Is exposure to night-time traffic noise a risk factor for purchase of anxiolytic–hypnotic medication? A cohort study**. *Eur. J. Public Health* (2013.0) **24** 298-303. DOI: 10.1093/eurpub/ckt117
16. Kim Y.. **Impacts of the perception of physical environments and the actual physical environments on self-rated health**. *Int. J. Urban Sci.* (2016.0) **20** 73-87. DOI: 10.1080/12265934.2015.1127178
17. Ma J., Chai Y.W., Fu T.T.. **Progress of research on the health impact of people’s space-time behavior and environmental pollution exposure**. *Prog. Geogr.* (2017.0) **36** 1260-1269
18. Zhou S.H., Zhang L., Lin R.P.. **Progress and prospect of the research on geographical environment exposure and public health**. *Sci. Technol. Rev.* (2020.0) **38** 43-52
19. Kwan M.P.. **The uncertain geographic context problem**. *Ann. Assoc. Am. Geogr.* (2012.0) **102** 958-968. DOI: 10.1080/00045608.2012.687349
20. Zheng D., Cai X., Song H., Chen T.. **Study on personal noise exposure in China**. *Appl. Acoust.* (1996.0) **48** 59-70. DOI: 10.1016/0003-682X(95)00061-D
21. Jensen H.A., Rasmussen B., Ekholm O.. **Neighbour and traffic noise annoyance: A nationwide study of associated mental health and perceived stress**. *Eur. J. Public Health* (2018.0) **28** 1050-1055. DOI: 10.1093/eurpub/cky091
22. Kim J., Kwan M.P.. **How neighborhood effect averaging may affect assessment of individual exposures to air pollution: A study of ozone exposures in Los Angeles**. *Ann. Assoc. Am. Geogr.* (2020.0) **111** 121-140. DOI: 10.1080/24694452.2020.1756208
23. Kwan M.P., Schwanen T.. **Geographies of mobility**. *Ann. Assoc. Am. Geogr.* (2016.0) **106** 243-256. DOI: 10.1080/24694452.2015.1123067
24. Zhang X., Zhou S., Kwan M.-P., Su L., Lu J.. **Geographic Ecological Momentary Assessment (GEMA) of environmental noise annoyance: The influence of activity context and the daily acoustic environment**. *Int. J. Health Geogr.* (2020.0) **19** 50. DOI: 10.1186/s12942-020-00246-w
25. Tao Y., Chai Y., Kou L., Kwan M.P.. **Understanding noise exposure, noise annoyance, and psychological stress: Incorporating individual mobility and the temporality of the exposure-effect relationship**. *Appl. Geogr.* (2020.0) **125** 102283. DOI: 10.1016/j.apgeog.2020.102283
26. Kou L., Tao Y., Kwan M.-P., Chai Y.. **Understanding the relationships among individual-based momentary measured noise, perceived noise, and psychological stress: A geographic ecological momentary assessment (GEMA) approach**. *Health Place* (2020.0) **64** 102285. DOI: 10.1016/j.healthplace.2020.102285
27. Srebotnjak T., Polzin C., Giljum S., Herbert S., Lutter S.. *Establishing Environmental Sustainability Thresholds and Indicators Final Report* (2010.0)
28. **International classification of impairments, disabilities, and handicaps: A manual of classification relating to the consequences of disease, published in accordance with resolution WHA29**. *Proceedings of the Twenty-Ninth World Health Assembly*
29. Schultz T.J.. **Synthesis of social surveys on noise annoyance**. *J. Acoust. Soc. Am.* (1978.0) **64** 377-405. DOI: 10.1121/1.382013
30. Babisch W.. **Updated exposure-response relationship between road traffic noise and coronary heart diseases: A meta-analysis**. *Noise Health* (2014.0) **16** 1-9. DOI: 10.4103/1463-1741.127847
31. Zhang L., Zhou S.H., Kwan M.P.. **A comparative analysis of the impacts of objective versus subjective neighborhood environment on physical, mental, and social health**. *Health Place* (2019.0) **59** 102170. DOI: 10.1016/j.healthplace.2019.102170
32. Bech P., Olsen L.R., Kjoller M., Rasmussen N.K.. **Measuring well-being rather than the absence of distress symptoms: A comparison of the SF-36 mental health subscale and the WHO-five well-being scale**. *Int. J. Meth. Psych. Res.* (2003.0) **12** 85-91. DOI: 10.1002/mpr.145
33. Awata S., Bech P., Yoshida S., Hirai M., Suzuki S., Yamashita M., Ohara A., Hinokio Y., Matsuoka H., Oka Y.. **Reliability and validity of the Japanese version of the World Health Organization-five well-being index in the context of detecting depression in diabetic patients**. *Psychiat. Clin. Neuros.* (2007.0) **61** 112-119. DOI: 10.1111/j.1440-1819.2007.01619.x
34. Krieger T., Zimmermann J., Huffziger S., Ubl B., Diener C., Kuehner C., Holtforth M.G.. **Measuring depression with a well-being index: Further evidence for the validity of the WHO well-being index (WHO-5) as a measure of the severity of depression**. *J. Affect. Disord.* (2014.0) **156** 240-244. DOI: 10.1016/j.jad.2013.12.015
35. **Directive 2002/49/EC of the European parliament and the Council of 25 June 2002 relating to the assessment and management of environmental noise**. *Off. J. Eur. Comm.* (2002.0) **189** 12-26
36. Breiman L.. **Random forests**. *Mach. Learn.* (2001.0) **45** 5-32. DOI: 10.1023/A:1010933404324
37. Zhang L., Zhou S., Qi L., Deng Y.. **Nonlinear effects of the neighborhood environments on residents’ mental health**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph192416602
38. Zang P., Qiu H., Xian F., Yang L., Qiu Y., Guo H.. **Nonlinear effects of the built environment on light physical activity among older adults: The case of Lanzhou, China**. *Int. J. Environ. Res. Public Health* (2022.0) **19**. DOI: 10.3390/ijerph19148848
39. Hastie T., Tibshirani R., Friedman J.. *The Elements of Statistical Learning: Data Mining, Inference, and Prediction* (2009.0)
40. 40.
WHO Regional Office for Europe
Night Noise Guidelines for EuropeWHO Regional Office for EuropeCopenhagen, Denmark2009Available online: http://www.euro.who.int/en/health-topics/environment-andhealth/noise/publications/2009/night-noise-guidelines-for-europe(accessed on 2 November 2022). *Night Noise Guidelines for Europe* (2009.0)
41. 41.
World Health Organization
Environmental Noise Guidelines for the European RegionWHO Regional Office for EuropeCopenhagen, Denmark2018Available online: https://www.quotidianosanita.it/allegati/allegato226054.pdf(accessed on 30 November 2022). *Environmental Noise Guidelines for the European Region* (2018.0)
42. **National Standards of the People’s Republic of China, GB3096: 2008. Environmental Quality Standard for Noise**
43. Basner M., McGuire S.. **WHO environmental noise guidelines for the European Region: A systematic review on environmental noise and effects on sleep**. *Int. J. Environ. Res. Public Health* (2018.0) **15**. DOI: 10.3390/ijerph15030519
44. Bodin T., Björk J., Ardö J., Albin M.. **Annoyance, sleep and concentration problems due to combined traffic noise and the benefit of quiet side**. *Int. J. Environ. Res. Public Health* (2015.0) **12** 1612-1628. DOI: 10.3390/ijerph120201612
45. Brink M.. **Parameters of well-being and subjective health and their relationship with residential traffic noise exposure—A representative evaluation in Switzerland**. *Environ. Int.* (2011.0) **37** 723-733. DOI: 10.1016/j.envint.2011.02.011
46. Frei P., Mohler E., Roeoesli M.. **Effect of nocturnal road traffic noise exposure and annoyance on objective and subjective sleep quality**. *Int. J. Hyg. Environ. Health* (2014.0) **217** 188-195. DOI: 10.1016/j.ijheh.2013.04.003
47. Halonen J.I., Vahtera J., Stansfeld S., Yli-Tuomi T., Salo P., Pentti J., Kivimäki M., Lanki T.. **Associations between nighttime traffic noise and sleep: The Finnish public sector study**. *Environ. Health Perspect.* (2012.0) **120** 1391-1396. DOI: 10.1289/ehp.1205026
48. van Kempen E., Casas M., Pershagen G., Foraster M.. **WHO environmental noise guidelines for the European region: A systematic review on environmental noise and cardiovascular and metabolic effects: A Summary**. *Int. J. Environ. Res. Public Health* (2018.0) **15**. DOI: 10.3390/ijerph15020379
49. Héritier H., Vienneau D., Foraster M., Eze I.C., Schaffner E., Thiesse L., Brink M.. **Transportation noise exposure and cardiovascular mortality: A nationwide cohort study from Switzerland**. *Eur. J. Epidemiol.* (2017.0) **32** 307-315. DOI: 10.1007/s10654-017-0234-2
50. Halonen J.I., Hansell A., Gulliver J., Morley D., Blangiardo M., Fecht D., Toledano M.B., Beevers S., Anderson H.R., Kelly F.J.. **Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London**. *Eur. Heart J.* (2015.0) **36** 2653-2661. DOI: 10.1093/eurheartj/ehv216
51. Singh D., Kumari N., Sharma P.. **A review of adverse eeffcts of road traffic noise on human health**. *Fluct. Noise Lett.* (2018.0) **17** 1830001. DOI: 10.1142/S021947751830001X
|
---
title: How Can a Bundled Payment Model Incentivize the Transition from Single-Disease
Management to Person-Centred and Integrated Care for Chronic Diseases in the Netherlands?
authors:
- Sterre S. Bour
- Lena H. A. Raaijmakers
- Erik W. M. A. Bischoff
- Lucas M. A. Goossens
- Maureen P. M. H. Rutten-van Mölken
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001506
doi: 10.3390/ijerph20053857
license: CC BY 4.0
---
# How Can a Bundled Payment Model Incentivize the Transition from Single-Disease Management to Person-Centred and Integrated Care for Chronic Diseases in the Netherlands?
## Abstract
To stimulate the integration of chronic care across disciplines, the Netherlands has implemented single-disease management programmes (SDMPs) in primary care since 2010; for example, for COPD, type 2 diabetes mellitus, and cardiovascular diseases. These disease-specific chronic care programmes are funded by bundled payments. For chronically ill patients with multimorbidity or with problems in other domains of health, this approach was shown to be less fit for purpose. As a result, we are currently witnessing several initiatives to broaden the scope of these programmes, aiming to provide truly person-centred integrated care (PC-IC). This raises the question if it is possible to design a payment model that would support this transition. We present an alternative payment model that combines a person-centred bundled payment with a shared savings model and pay-for-performance elements. Based on theoretical reasoning and results of previous evaluation studies, we expect the proposed payment model to stimulate integration of person-centred care between primary healthcare providers, secondary healthcare providers, and the social care domain. We also expect it to incentivise cost-conscious provider-behaviour, while safeguarding the quality of care, provided that adequate risk-mitigating actions, such as case-mix adjustment and cost-capping, are taken.
## 1. Introduction
In many countries, the prevalence of chronic diseases, and in particular people with multimorbidity, i.e., two or more chronic diseases, is increasing [1]. Two thirds of people over 45 will develop multimorbidity in their remaining lifetime [2]. To address their needs, many countries are now implementing different models of integrated care [3]. As the Netherlands were among the first countries to do so on a very large scale, there are lessons to be learned for other countries from how this evolved in the Netherlands, in particular with regards to possible incentives for truly person-centred and integrated care.
Historically, the Dutch healthcare system has had a strong primary care sector, in which general practitioners (GPs) act as gatekeepers to secondary care (i.e., patients need a referral by the GP) [4]. To improve the quality of care to people with chronic diseases, single-disease management programmes (SDMPs) have been introduced in Dutch primary care since 2010, for diabetes type-2 (DM2) [5], cardiovascular risk management (CVR) [6], and chronic obstructive pulmonary disease (COPD) [7]. Therefore, the GP is the main caregiver for many patients with chronic diseases in the Netherlands. These SDMPs were based on chronic care standards, which are essentially clinical guidelines for providing high quality, multidisciplinary, integrated care.
To coordinate the implementation of the SDMPs in a region, a new organisational entity, the primary care cooperative (care group), was introduced. Today, there are 130 primary care cooperatives in the Netherlands based on the collaborations of general practices [8]. For the daily execution of the SDMP’s and to reduce the workload of GPs, a new professional role was introduced in the GP practice, namely that of the nurse practitioner. The nurse practitioner regularly monitors symptoms and physiological parameters of patients with the chronic diseases mentioned above, and provides lifestyle and coping advice [9].
To further incentivise the integration of multidisciplinary care, the implementation of the SDMPs was supported by a bundled payment model [10]. The bundled payment covers the costs of coordination, the costs of regular check-ups by the nurse practitioner or the GP, three hours with the dietician for people with DM2, the foot therapist for patients with DM2, the physiotherapist for patients with more severe COPD, and a single (tele)consultation with a medical specialist when necessary. Health insurers contract primary care cooperatives, which in turn subcontract GPs and other healthcare providers for providing the services in the bundle [11]. The fee of the bundled payment results from the negotiation between the primary care cooperative and the health insurer about the content and price of the services in the bundle, which thus varies between primary care cooperatives.
Compared to other countries, the scope of the bundled payment in the *Netherlands is* limited, both in terms of target population and services included in the bundle. For instance, in the United States, accountable care organisations are generally responsible for all healthcare expenditures of a delineated patient population [12,13]. In the Gesundes Kinzigtal programme in Germany, the target population includes a group of 33,000 patients from Baden-Württemberg, who are insured by two public health insurers. Key characteristics focus on prevention, self-management, reduction of polypharmacy, patient-centred care, and shared decision-making. The programme is funded by a capitation-based payment combined with a shared savings model [14,15]. In the United Kingdom (UK), general practices receive a lump sum for all GP-care, some specialist care, and generic medication [16,17]. In the UK, integrated care organisations are introduced to stimulate integration between primary care physicians and specialists. The integrated care organisations are responsible for a case-mix corrected budget per capita [18].
As a result of the introduction of the SDMPs in the Netherlands, the vast majority of patients with DM2, CVR, and COPD are now treated in primary care. The quality of chronic care is monitored by InEeN, a primary care interest organisation, which annually publishes process- and outcome-indicators at the care-group level [19]. These indicators were found to improve over time [20], but the clinical relevance and long-term impact of these improvements are uncertain [14,20,21]. Improvements in the work experience of GPs were also reported [9,14,20].
However, the SDMPs have several limitations. First, the chronic care programmes focus on a single chronic disease, rather than adopting a holistic approach that considers the social context of the chronically ill patient (e.g., family, living environment, financial resources, and the work situation) [21,22]. The programmes mainly aim to improve clinical disease-specific indicators, and there is less attention paid to psychological and social aspects. This does not match well with how our perspective on disease and health has evolved. In the Netherlands, many primary care cooperatives have recently embraced the new concept of so-called positive health (‘health as the ability to adapt and to self-manage, in the face of social, physical, and emotional challenges’) that was introduced in 2011 by Huber et al. [ 23,24]. Second, the scope of the services included in the current bundles is limited. The bundled payment does not cover care that transcends the chronic disease [25,26]. The bundled payment does not include all primary healthcare, no secondary care, no mental health care, and no social services. It might stimulate collaboration between healthcare providers in primary care (e.g., between the GP and the dietician), but less so between the GP and the specialist or between the GP and the social worker.
The introduction of the SDMP and the bundled payments were expected to improve the efficiency of care delivery and reduce healthcare expenditures or the growth thereof [27]. However, there is evidence that they increased the total costs of healthcare, especially in patients with multimorbidity [14,28]. This cost increase probably results from a combination of the detection of unmet needs in patients with multimorbidity, double declarations, and an incentive to refer the more complex patients to secondary care to avoid costs exceeding the bundled payment [28]. The currently used SDMPs and bundled payments are not suitable for patients with multiple chronic diseases.
As a result, we are currently witnessing several initiatives to broaden the scope of the SDMPs aiming to provide person-centred and integrated care (PC-IC) [28,29]. This raises the question of which payment model would best support this transition [29]. As a first step, InEeN proposed merging the current bundled payments for people with multiple of the respective chronic diseases to remove duplication [30]. However, that proposal would still not fully incentivise PC-IC. This paper aims to present an alternative payment model that incentivises the integrated nature of a PC-IC programme for people with chronic diseases. It is based on a targeted literature review of (incentives in) traditional and more recent payment models in different countries and inspired by a specific PC-IC initiative in the Netherlands.
## 2.1. Case Example: OPTIMA FORMA
The proposed payment model was specifically designed to match with one of the initiatives to move towards PC-IC in the Netherlands, i.e., the project, OPTIMA FORMA—*Towards a* patient-centred multimorbidity approach for chronic disease management in primary care. In this project, healthcare providers, patients, GP experts with a special interest in DM2, COPD, or CVD, primary care cooperatives coordinators, and researchers developed a new integrated care programme that goes beyond the disease-specific clinical domain. The new care plan has a quadruple aim: [1] enhancing patient experience, [2] improving population health, [3] reducing costs, and [4] improving the work life of health care providers [31].
In the PC-IC programme, a holistic assessment of the health status is performed, personal goals are set, and interventions to achieve these goals are put in place [32,33]. The first step in this programme is assessing the integral health status of the patient (health across multiple domains—Figure 1), using a (preferably digital) questionnaire at home and physical measurements (i.e., blood pressure, weight, and glucose levels). The second step is an appointment in which the results are discussed with the patient in a semi-structured way. The case manager asks if the patient recognizes himself in the results of the assessment, if there are other issues that the patient would like to discuss, and the priorities of the patient. Personal goals are formulated in the third step, which can range from purely medical goals to social goals. In the fourth step, the healthcare provider and patient will together choose the right interventions to achieve these goals, based on the experience of the healthcare provider, the ideas of the patient, and a list of regional options. Different methods can be used to achieve these goals (i.e., through self-management, with e-health, with coaching from a non-medical care provider, with coaching from a healthcare provider within the GP practice, or with coaching from a healthcare provider outside the GP practice). The goals and interventions are documented in a personal healthcare plan, which is preferably digitally available to all relevant healthcare providers and the patient. Then, referrals are made if necessary, and the treatment is started. An evaluation is planned and carried out, if necessary multiple times. If a treatment goal is reached or another treatment goal is more urgent, the cycle can be repeated. The development of this PC-IC approach is described elsewhere in this issue [34].
## 2.2. Incentives in Payment Models
To design a payment model that would match the PC-IC programme of OPTIMA FORMA, we first studied the incentives for providers and other stakeholders that are present in the current Dutch healthcare system for all types of healthcare services used by patients with chronic disease. We classified these payment models according to the typology of Quinn [2015] [35] and identified the incentives related to these payment methods. Quinn [2015] [35] classifies eight basic payment methods in health care: [1] Per time period (budget/salary), [2] Per beneficiary (capitation), [3] Per recipient (contact capitation), [4] Per episode (case rates/per stay/bundled payments), [5] Per day (per diem/per visit), [6] Per service (fee for service (FFS)), [7] Per dollar of costs (cost reimbursement), and [8] Per dollar of charges (percentage of charges).
Secondly, we studied incentives for stakeholders in innovative payment models. These innovative payment models were identified through the alternative payment model (APM) framework described by the Health Care Payment Learning and Action Network (HCP-LAN) [36]. The identified alternative payment models were: [1] pay for performance, [2] shared savings models, and [3] (sub)population-based bundled payment. We combined elements of these models to design an alternative payment model to stimulate PC-IC care for people with chronic diseases.
## 2.3. Design of an Alternative Payment Model
In the next step, we selected three alternative payment models and explicitly focused on the distinctive elements in their design. Since we aimed to propose an alternative payment model for the Dutch setting, the selection was based on two criteria, namely comprehensiveness and origin in the Dutch setting. The selection included:a population-based bundled payment model with an explicit incentive for quality of care of Cattel and Eijkenaar [37].a shared savings model of Hayen et al. [ 38].the alternative payment model of Steenhuis et al. [ 39].
We combined the design elements and design choices that were mentioned by these models into Table 1. Table 1 was used to guide the design of an alternative payment model that would fit the PC-IC programme OPTIMA FORMA. The design choices made were primarily informed by theory on provider-incentives and results from previous evaluation studies of the identified innovative payment models: [1] pay-for-performance [40,41], [2] shared-savings models [13,15,42,43], and [3] (sub)population—based bundled payments [37,44,45].
## 2.4. Expected Impact on Integration of Care
In the last step, we projected the expected impact of the innovative payment model on the integration of care, using the spider-web linked to the typology of Stokes et al [46]. This typology classifies the level of integrated care on eight domains: [1] Target population, [2] Time, [3] Sectors, [4] Provider coverage, [5] Financial pooling/sharing, [6] Income, [7] Multiple disease/needs focus, and [8] Quality measurements [46]. The higher the number, the higher the level of integration (1 = integration is poorly stimulated, 2 = integration is mediately stimulated, and 3 = integration is highly stimulated).
## 3.1. Incentives Induced by Different Payment Models
In Table 2, we provide a summary of current and alternative payment models to fund care for patients with chronic diseases in the Netherlands.
Table 2 provides insight into the incentives induced by each payment model. None of the presented payment models above fully incentivises PC-IC. The SDMPs are currently funded by a fixed annual fee, which is paid in three monthly instalments (chronic care episode). The bundle primarily includes the GP, practice nurses, and a few paramedics working in the primary care sector. Hence, it stimulates collaboration between these service providers, but not beyond. It is likely to improve the quality and efficiency in primary care, but it also creates an incentive for adverse selection and referral of complex patients to secondary care. This is present, even though the fixed fee is based on a weighted average of resources used by patients with different severities. It also stimulates so-called ‘over-bundling’, referring to the incentive to enrol more patients than necessary. These undesired incentives can be mitigated by carefully combining elements of different payment models [37]. From a theoretical perspective, a bundled payment with a broader scope in terms of target population and services, in combination with a shared savings model and a pay-for-performance model seems promising [37].
## 3.2. Proposed Payment Model for Person-Centred and Integrated Care
Figure 2 shows the proposed payment model for all patients with one or more chronic conditions, starting with those that are currently included in the existing bundles for DM2, CVR, and COPD. The patient population is delineated by diagnosed chronic disease (at least DM2, CVR, or COPD), insurance (the patient has to be insured at one of the participating health insurers), and GP-practice (GP-practice has to collaborate with one of the participating primary care cooperatives). The payment model consists of three parts: [1] a person-centred bundled payment, [2] a shared savings model that pertains to all healthcare costs, and [3] a pay-for-performance part.
Part one is a person-centred bundled payment that will be prospectively paid to the primary care cooperatives. For each patient, a personal healthcare plan is designed within the OPTIMA FORMA project (Figure 1). The services that can be included in the personal healthcare plan are shown in Figure 3. The bundled payment is based on the weighted average sum of all included services. The weighting is based on the number of patients that use a service and the costs of the service. The primary care cooperative is responsible for the coordination, organization, and financing of all subcontracted participating providers since the primary care cooperative is the main contractor.
Part two is a virtual budget that contains all expected (healthcare) costs of these patients (the contracted bundled payment and the contracted expenditures outside the bundled payment). The case-mix adjusted weighted virtual budget will be compared to the realised expenditures to estimate the savings or losses. It is important to cap the expenditures, so the primary care cooperative does not bear the risk for patients with extreme high (unexpected) expenditures. One could start with a one-sided shared savings model, meaning that only the savings and not the losses will be shared between the health insurer and the primary care cooperative in the region, to mitigate risks for the primary care cooperative and avoid adverse behaviour. The savings will be distributed in a prespecified ratio between the primary care cooperative and the health insurer.
In part three, the prespecified ratio to share the savings depends on the quality of the delivered care. This pay-for-performance part depends on the measured performance of the monitored quality indicators. It is important to avoid time-consuming checklists and process indicators and adopt a small set of key outcome indicators. This requires trust from the health insurers and leads to more flexibility for providers to only provide services that are applicable for a patient instead of ticking boxes to show that they followed the correct process. Quality indicators are measured at primary care cooperative level.
More details are provided in Appendix A.
The contract between the insurer and the primary care cooperatives should be signed for multiple years, preferably for three to five years. This provides the opportunity to explore the potentials of the alternative payment model (stimulate integration of care, improve quality of care, and reduce overall healthcare costs) and to gain mutual trust between the different stakeholders [39,43,75]. When the contract is renewed, changes can be made accordingly. For instance, after three or five years the one-sided shared savings model could be transformed into a two-sided shared savings model (the primary care cooperative also shares in the potential losses). A two-sided shared savings model stimulates cost-conscious behaviour better, but also increases the financial risk for the primary care cooperative [12,13,76].
## 3.3. Consequences of the Proposed Payment Model
The suggested alternative payment model is expected to be associated with incentives presented in Table 3. Each of the three parts of the proposed payment model has desirable and undesirable consequences, and the latter can be mitigated by the other part(s).
As the range of services that can be included in the individual care plan (Figure 3) is much wider than in the current bundle for SDMP, the person-centred bundled payment is expected to stimulate the holistic approach that is aimed for by the PC-IC programme. The primary care cooperative and its associated care providers will have an incentive to improve efficiency by better coordination and collaboration because the budget extends over a wider range of services. This increases mutual responsibility. One of the perverse incentives of a bundled payment that may not cover the full care path of a patient, is that patients are referred to services outside the bundle [50]. The shared savings model mitigates this perverse incentive because the comparison of the actual and the expected expenditure (i.e., the virtual budget) pertains to the total healthcare expenditure. This could result in cost-conscious behaviour [38,77]. The current bundles for SDMPs do not incorporate a shared savings model. If the shared savings model stimulates cost savings through increased efforts to slow down the progression of disease and prevent acute hospital admissions, it also improves health outcomes. However, to mitigate financial risks for the primary care cooperative, a one-sided shared savings model is preferred over a two-sided shared savings model to avoid adverse behaviour of the primary care cooperative, especially at the beginning [78]. A perverse incentive of the person-centred bundled payment model and a shared savings model is cutting costs on necessary care. The pay-for-performance part of the model aims to reduce this risk by stimulating a high quality of care.
Like all payment methods, this alternative payment model still induces some undesirable consequences which are hard to eliminate by one of the three parts of the payment model. The risk of reducing costs by cutting necessary care might be there to some extent. Furthermore, the threshold can be lowered to include patients in the person-centred bundled payment for whom one may expect little cost. However, adequate case-mix adjustment and capping costs could reduce these risks. To some extent, the person-centred bundled payment also reduces the choice of the patient because certain care providers are contracted, and others might not. As the personal healthcare plan is based on the needs, capabilities, and wishes of the patients, it is important that the contracted providers are able to provide the services shown in Figure 3 [39]. Another undesired consequence is that the primary care cooperative bears too much risk because all expenditures are included in the virtual budget. The primary care cooperative might not be able to control all of these expenditures. The incentives of providers outside the person-centred bundled payment are not well aligned because these physicians are mostly paid FFS. It is important that these providers feel motivated to collaborate. This might be achieved by investing part of the savings in joint quality improvement and innovation plans, which are attractive to these providers as well. Every pay-for-performance model introduces a risk for gaming behaviour, but the size of that risk depends on the proportion of a provider’s income that comes from the quality-payment. The challenge is to strike a balance between a sufficiently large proportion to incentivize quality improvement and a sufficiently small proportion to avoid gaming [77].
## 3.4. Impact on Integration
Figure 4 shows the degree of integration of the proposed payment model and the currently used bundled payments for the SDMPs on the eight dimensions of the framework based on Stokes et al [46]. Table 4 explains the levels that were expected for each domain.
## 4. Discussion
The aim of this paper was to design a bundled payment model that incentivises the transition from single-disease management to PC-IC for patients with chronic diseases. Based on a targeted literature review, we identified the incentives which are (theoretically) generated by the eight basic payment methods classified by Quinn [35] and the alternative payment models identified through the APM framework [36]. Based on the identified incentives, we designed an alternative payment model for PC-IC that consists of three main elements, i.e., [1] a person-centred bundled payment, [2] shared savings, and [3] pay-for-performance. The combination of these elements is expected to provide well-aligned, desired incentives towards multi-disciplinary collaboration to meet a patient’s needs, capabilities, and preferences. Each element is necessary to mitigate the undesired incentives of other elements. Furthermore, adequate risk-adjustment and cost-capping are prerequisites to mitigate large risks for providers and to mitigate adverse behaviour.
The implementation of this alternative payment model comes with certain challenges. The first challenge pertains to the investment of resources needed for implementation, which mainly include financial investments (e.g., transition costs to the alternative payment model) and time investments (e.g., to expand collaborations) [39]. To manage the alternative payment model, the software in place should be adapted to monitor the costs and quality of care over time [39]. Administrative costs of monitoring quality of care and negotiating about the conditions of the contract may increase, but this may be offset by a reduction in administrative costs when the services no longer have to be separately claimed [39].
The second challenge is to define the patient population that will be included in the person-centred bundled payment. The population of patients with DM2, CVR, and/or COPD is very heterogenous in terms of patient-characteristics, disease-severity, and co-existing morbidity patterns. For an adequate estimation of the expected expenditures, necessary to determine the savings or losses, clear inclusion and exclusion criteria need to be defined.
The third challenge is to estimate an appropriate budget for the person-centred bundle. The budget will be estimated by a weighted sum of the costs of all (health)care modules provided in the bundle. The weighting will be carried out by predicting the number of patients that would use the various modules. As time after implementation progresses, figures regarding the relative use of the modules will become more reliable. Specifically, for OPTIMA FORMA, a clinical and economic evaluation study is planned that will provide the first estimates of the utilization of specific services. Micro-costing studies are necessary to determine the costs per module.
For an appropriate comparison of expected and actual expenditures and to avoid extreme savings or catastrophic losses [79], adequate adjustment for differences in case-mix is important. Many countries with a multiple payer system (e.g., multiple social health insurers) like in the Netherlands, apply some form of risk equalization to distribute [part of] the budget among the payers. Whether variables included in the risk equalization formula of the health insurance system can also be used to adjust for differences in the case-mix of providers remains to be investigated. It is obvious that variables that are influenced by the PC-IC programme cannot be used in the case-mix adjustment because that would diminish/eliminate the estimated effects [38].
Another challenge when designing the alternative payment model is to determine the quality indicators for the pay-for-performance part of the alternative payment model. It is important to select quality indicators that are sensitive to improvements by the PC-IC programme and the alternative payment model. Based on a systematic literature review, specific design features that contribute to the desired effect of pay-for-performance are: [1] using outcome measures that are very specific and easy to track; [2] targeting individuals or small teams; [3] using absolute rather than relative targets; [4] frequently paying with little delay after delivery; and [5] involving providers from the start in the design [40]. Primary care cooperatives are reluctant to accept financial responsibility for indicators they cannot influence [80]. Conceptually, one would like to have one or more indicators for each of the four aims of PC-IC, but the challenge is to find the right balance between registration burden [79] and information need.
To increase the chances of the successful implementation of PC-IC, several requirements need to be met. In their paper on the successful implementation of integrated care for people with multimorbidity, Looman et al [29] stressed the importance of ten mechanisms, of which one is securing long-term funding and adopting an innovative payment model that overcomes fragmentation. However, most important is constructive alignment, meaning that simultaneous measures at the micro, meso, and macro levels are needed to support the implementation of PC-IC [29]. With respect to the payment model, this implies that the incentives for all participating healthcare providers, as well as with existing financial streams, have to be aligned [39,80].
A more fundamental question that arises is whether a population-based payment model that would extend to the entire population in a geographically defined area (e.g., a region) and all care providers within that area would not be a more appropriate alternative compared to the alternative payment model proposed here. Especially, because that would stimulate prevention of disease and network care for the entire population in the catchment area, all of which is paid for from one bundled budget [50]. On one hand, it could fit the integrated nature of the PC-IC programme, but on the other hand, the step from the currently used bundled payments to a population-based payment might be too big. As it currently stands, the PC-IC programme OPTIMA FORMA focusses on people with the mentioned chronic diseases. If the population of interest were defined as the entire population of insured people in a region, the effect of the PC-IC programme could easily be diluted. That does not alter the fact the PC-IC programmes would benefit from economies of scale, which could reduce the financial risks for primary care cooperatives.
## 5. Conclusions
To conclude, we designed a payment model with well-aligned incentives to support the adoption of PC-IC. This model consists of: [1] a person-centred bundled payment; [2] a shared savings model; and [3] a pay-for-performance part in which the sharing ratio between insurer and provider is conditional on the performance of the provider. This alternative model is likely to be an adequate alternative for the relatively limited bundled payment model that is currently used to fund the SDMPs in the Netherlands.
## References
1. Chen Y.H., Karimi M., Rutten-Van Mölken M.P.M.H.. **The disease burden of multimorbidity and its interaction with educational level**. *PLoS ONE* (2020.0) **15**. DOI: 10.1371/journal.pone.0243275
2. Velek P., Luik A.I., Brusselle G.G.O., Stricker B.C., Bindels P.J.E., Kavousi M., Kieboom B.C.T., Voortman T., Ruiter R., Ikram M.A.. **Sex-specific patterns and lifetime risk of multimorbidity in the general population: A 23-year prospective cohort study**. *BMC Med.* (2022.0) **20** 304. DOI: 10.1186/s12916-022-02487-x
3. Struckmann V., Leijten F.R., van Ginneken E., Kraus M., Reiss M., Spranger A., Boland M.R., Czypionka T., Busse R., Mölken M.R.-V.. **Relevant models and elements of integrated care for multi-morbidity: Results of a scoping review**. *Health Policy* (2017.0) **122** 23-35. DOI: 10.1016/j.healthpol.2017.08.008
4. Macinko J., Starfield B., Shi L.. **The Contribution of Primary Care Systems to Health Outcomes within Organization for Economic Cooperation and Development (OECD) Countries, 1970–1998**. *Health Serv. Res.* (2003.0) **38** 831-865. DOI: 10.1111/1475-6773.00149
5. **NHG-Standaard Diabetes Mellitus Type 2 (M01)**. (2021.0)
6. **Zorgstandaard Vasculair Risicomanagement Deel I (Voor Zorgverleners). 2009. pp. 1–27**
7. **Zorgstandaard COPD. 1991. pp. 1–30**
8. Out K.E.M., de Jong J.D.. **Het Perspectief van Zorggroepen en Gezondheidscentra op Onderhandelingen en Contracten Met Zorgverzekeraars. Nivel**. (2016.0)
9. van den Berg M., de Bakker D.. **Meta-Analyse Introductie: Introductie Praktijkondersteuning op HBO-Niveau in de Huisartsenpraktijk in Nederland. Nivel**. (2003.0)
10. de Bakker D., Raams J., Schut E., Vrijhoef B., de Wildt J.. **Eindrapport van de Evaluatiecommissie—Integrale Bekostiging Integrale bekostiging van zorg: Werk in uitvoering. Nivel**. (2012.0)
11. Tsiachristas A., Hipple-Walters B., Lemmens K.M., Nieboer A.P., Rutten-van Mölken M.P.. **Towards integrated care for chronic conditions: Dutch policy developments to overcome the (financial) barriers**. *Health Policy* (2011.0) **101** 122-132. DOI: 10.1016/j.healthpol.2010.10.013
12. McWilliams J.M., Chernew M.E., Landon B.E., Schwartz A.L.. **Performance Differences in Year 1 of Pioneer Accountable Care Organizations**. *N. Engl. J. Med.* (2015.0) **372** 1927-1936. DOI: 10.1056/NEJMsa1414929
13. Song Z., Ji Y., Safran D.G., Chernew M.E.. **Health Care Spending, Utilization, and Quality 8 Years into Global Payment**. *N. Engl. J. Med.* (2019.0) **381** 252-263. DOI: 10.1056/NEJMsa1813621
14. Busse R., Stahl J.. **Integrated Care Experiences And Outcomes In Germany, The Netherlands, and England**. *Health Aff.* (2014.0) **33** 1549-1558. DOI: 10.1377/hlthaff.2014.0419
15. Hildebrandt H., Hermann C., Knittel R., Richter-Reichhelm M., Siegel A., Witzenrath W.. **Gesundes Kinzigtal Integrated Care: Improving population health by a shared health gain approach and a shared savings contract**. *Int. J. Integr. Care* (2010.0) **10** e046. DOI: 10.5334/ijic.539
16. Coulter A.. **Evaluating general practice fundholding in the United Kingdom**. *Eur. J. Public Health* (1995.0) **5** 233-239. DOI: 10.1093/eurpub/5.4.233
17. Gosden T., Torgerson D.J.. **The effect of fundholding on prescribing and referral costs: A review of the evidence**. *Health Policy* (1997.0) **40** 103-114. DOI: 10.1016/S0168-8510(96)00888-3
18. **Where next for Integrated Care Organisations in the English NHS?**
19. **Transparante Ketenzorg. 2021. pp. 4–7**
20. Struijs J.N., de Jong-van Til J.T., Lemmens L.C., Drewes H.W., de Bruin S.R., Baan C.A.. **Drie Jaar Integrale Bekostiging van Diabeteszorg. Effecten Op Zorgproces En Kwaliteit van Zorg. RIVM**. (2017.0)
21. Murtagh S., McCombe G., Broughan J., Carroll Á., Casey M., Harrold Á., Dennehy T., Fawsitt R., Cullen W.. **Integrating Primary and Secondary Care to Enhance Chronic Disease Management: A Scoping Review**. *Int. J. Integr. Care* (2021.0) **21** 4. DOI: 10.5334/ijic.5508
22. Smith S.M., Soubhi H., Fortin M., Hudon C., O’Dowd T.. **Managing patients with multimorbidity: Systematic review of interventions in primary care and community settings**. *BMJ* (2012.0) **345** e5205. DOI: 10.1136/bmj.e5205
23. Huber M., Knottnerus J.A., Green L., van der Horst H., Jadad A.R., Kromhout D., Leonard B., Lorig K., Loureiro M.I., van der Meer J.W.M.. **How should we define health?**. *BMJ* (2011.0) **343** 1-3. DOI: 10.1136/bmj.d4163
24. Huber M., van Vliet M., Giezenberg M., Winkens B., Heerkens Y., Dagnelie P.C., Knottnerus J.A.. **Towards a ‘patient-centred’ operationalisation of the new dynamic concept of health: A mixed methods study**. *BMJ Open* (2016.0) **6** e010091. DOI: 10.1136/bmjopen-2015-010091
25. Guthrie B., Payne K., Alderson P., McMurdo M.E.T., Mercer S.. **Adapting clinical guidelines to take account of multimorbidity**. *BMJ* (2012.0) **345** e6341. DOI: 10.1136/bmj.e6341
26. Wallace E., Salisbury C., Guthrie B., Lewis C., Fahey T., Smith S.M.. **Managing patients with multimorbidity in primary care**. *BMJ* (2015.0) **350** 6-11. DOI: 10.1136/bmj.h176
27. Tsiachristas A., Dikkers C., Boland M.R., Mölken M.P.R.-V.. **Exploring payment schemes used to promote integrated chronic care in Europe**. *Health Policy* (2013.0) **113** 296-304. DOI: 10.1016/j.healthpol.2013.07.007
28. Karimi M., Tsiachristas A., Looman W., Stokes J., van Galen M., Rutten-van Mölken M.. **Bundled payments for chronic diseases increased health care expenditure in the Netherlands, especially for multimorbid patients**. *Health Policy* (2021.0) **125** 751-759. DOI: 10.1016/j.healthpol.2021.04.004
29. Looman W., Struckmann V., Köppen J., Baltaxe E., Czypionka T., Huic M., Pitter J., Ruths S., Stokes J., Bal R.. **Drivers of successful implementation of integrated care for multi-morbidity: Mechanisms identified in 17 case studies from 8 European countries**. *Soc. Sci. Med.* (2021.0) **277** 113728. DOI: 10.1016/j.socscimed.2021.113728
30. **Denkraam Integratie Zorgprogramma’s Voor Chronische Aandoeningen. 2019. pp. 1–17**
31. Bodenheimer T., Sinsky C.. **From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider**. *Ann. Fam. Med.* (2014.0) **12** 573-576. DOI: 10.1370/afm.1713
32. Vercoulen J.H.. **A simple method to enable patient-tailored treatment and to motivate the patient to change behaviour**. *Chronic Respir. Dis.* (2012.0) **9** 259-268. DOI: 10.1177/1479972312459974
33. Bischoff E., Vercoulen J., Elbers L., Behr R., Schermer T.. **De NCSI-Methode: Maatwerk Voor COPD-Zorg**. *Huisarts-en Wet.* (2016.0) **59** 242-247. DOI: 10.1007/s12445-016-0151-8
34. Raaijmakers L.H.A., Schermer T.R., Wijnen M., Van Bommel H.E., Michielsen L., Boone F., Vercoulen J.H., Bischoff E.W.M.A.. **Development of a person-centred integrated care approach for chronic disease management in Dutch primary care: A mixed-method study**. *Int. J. Environ. Res. Public Health* (2023.0)
35. Quinn K.. **The 8 Basic Payment Methods in Health Care**. *Ann. Intern. Med.* (2015.0) **163** 300-306. DOI: 10.7326/M14-2784
36. **Alternative Payment Model: APM Framework Refreshed for 2017**
37. Cattel D., Eijkenaar F.. **Value-Based Provider Payment Initiatives Combining Global Payments With Explicit Quality Incentives: A Systematic Review**. *Med Care Res. Rev.* (2019.0) **77** 511-537. DOI: 10.1177/1077558719856775
38. Hayen A.P., van den Berg M.J., Meijboom B.R., Struijs J.N., Westert G.P.. **Incorporating shared savings programs into primary care: From theory to practice**. *BMC Health Serv. Res.* (2015.0) **15** 580. DOI: 10.1186/s12913-015-1250-0
39. Steenhuis S., Struijs J., Koolman X., Ket J., van der Hijden E.. **Unraveling the Complexity in the Design and Implementation of Bundled Payments: A Scoping Review of Key Elements From a Payer’s Perspective**. *Milbank Q.* (2020.0) **98** 197-222. DOI: 10.1111/1468-0009.12438
40. Eijkenaar F., Emmert M., Scheppach M., Schöffski O.. **Effects of pay for performance in health care: A systematic review of systematic reviews**. *Health Policy* (2013.0) **110** 115-130. DOI: 10.1016/j.healthpol.2013.01.008
41. Mendelson A., Kondo K., Damberg C., Low A., Motúapuaka M., Freeman M., O’Neil M., Relevo R., Kansagara D.. **The Effects of Pay-for-Performance Programs on Health, Health Care Use, and Processes of Care: A systematic review**. *Ann. Intern. Med.* (2017.0) **166** 341-353. DOI: 10.7326/M16-1881
42. Hayen A.. **Shared Savings and Patient Cost Sharing in the Dutch Health Care System**. *Ph.D. Thesis* (2018.0)
43. McWilliams J.M., Hatfield L.A., Landon B.E., Hamed P., Chernew M.E.. **Medicare Spending after 3 Years of the Medicare Shared Savings Program**. *N. Engl. J. Med.* (2018.0) **379** 1139-1149. DOI: 10.1056/NEJMsa1803388
44. Agarwal R., Liao J.M., Gupta A., Navathe A.S.. **The Impact Of Bundled Payment On Health Care Spending, Utilization, And Quality: A Systematic Review**. *Health Aff.* (2020.0) **39** 50-57. DOI: 10.1377/hlthaff.2019.00784
45. Struijs J.N., de Vries E.F., van Dorst H.D.C.A., Over E.A.B., Baan C.A.. **Geboortezorg in Beeld—Een Nulmeting En Eerste Ervaringen Met Het Werken Met Integrale Bekostiging. RIVM**. (2018.0)
46. Stokes J., Struckmann V., Kristensen S.R., Fuchs S., van Ginneken E., Tsiachristas A., van Mölken M.R., Sutton M.. **Towards incentivising integration: A typology of payments for integrated care**. *Health Policy* (2018.0) **122** 963-969. DOI: 10.1016/j.healthpol.2018.07.003
47. Kralj B., Kantarevic J.. **Quality and quantity in primary care mixed-payment models: Evidence from family health organizations in Ontario**. *Can. J. Econ./Rev. Can. D’économique* (2013.0) **46** 208-238. DOI: 10.1111/caje.12003
48. Krasnik A., Groenewegen P.P., Pedersen P.A., von Scholten P., Mooney G., Gottschau A., Flierman H.A., Damsgaard M.T.. **Changing remuneration systems: Effects on activity in general practice**. *BMJ* (1990.0) **300** 1698-1701. DOI: 10.1136/bmj.300.6741.1698
49. Iversen T., Luras H.. **The effect of capitation on GPs’ referral decisions**. *Health Econ.* (2000.0) **9** 199-210. DOI: 10.1002/(SICI)1099-1050(200004)9:3<199::AID-HEC514>3.0.CO;2-2
50. van der Hijden E., Steenhuis S., Hofstra G., van der Wolk J., Bijlsma W., Struijs J., Koolman X.. **Ontwikkelingen in zorginkoop: Van inkoop van verrichtingen naar inkoop van zorgbundels**. *Maandbl. Voor Account. en Bedrijfsecon.* (2019.0) **93** 223-239. DOI: 10.5117/mab.93.33441
51. Tsiachristas A.. **Payment and economic evaluation of integrated care**. *Int. J. Integr. Care* (2015.0) **15** e013. DOI: 10.5334/ijic.2009
52. Gosden T., Forland F., Kristiansen I., Sutton M., Leese B., Giuffrida A., Sergison M., Pedersen L.. **Capitation, salary, fee-for-service and mixed systems of payment: Effects on the behaviour of primary care physicians**. *Cochrane Database Syst. Rev.* (2000.0) **2000** CD002215. DOI: 10.1002/14651858.CD002215
53. Gosden T., Forland F., Kristiansen I.S., Sutton M., Leese B., Giuffrida A., Sergison M., Pedersen L.. **Impact of payment method on behaviour of primary care physicians: A systematic review**. *J. Health Serv. Res. Policy* (2001.0) **6** 44-55. DOI: 10.1258/1355819011927198
54. Simoens S., Giuffrida A.. **The Impact of Physician Payment Methods on Raising the Efficiency of the Healthcare System: An international comparison**. *Appl. Health Econ. Health Policy* (2004.0) **3** 39-46. DOI: 10.2165/00148365-200403010-00008
55. Barros P.P.. **Cream-skimming, incentives for efficiency and payment system**. *J. Health Econ.* (2003.0) **22** 419-443. DOI: 10.1016/S0167-6296(02)00119-4
56. Robinson J.C.. **Theory and Practice in the Design of Physician Payment Incentives**. *Milbank Q.* (2001.0) **79** 149-177. DOI: 10.1111/1468-0009.00202
57. Andrawis J.P., Koenig K.M., Bozic K.J.. **Bundled payment care initiative: How this all started**. *Semin. Arthroplast. JSES* (2016.0) **27** 188-192. DOI: 10.1053/j.sart.2016.10.008
58. Cutler D.M., Ghosh K.. **The Potential for Cost Savings through Bundled Episode Payments**. *N. Engl. J. Med.* (2012.0) **366** 1075-1077. DOI: 10.1056/NEJMp1113361
59. Weeks W.B., Rauh S.S., Wadsworth E.B., Weinstein J.N.. **The Unintended Consequences of Bundled Payments**. *Ann. Intern. Med.* (2013.0) **158** 62-64. DOI: 10.7326/0003-4819-158-1-201301010-00012
60. Struijs J.N., Mohnen S.M., Molema C.C.M., de Jong-van Til J.T., Baan C.A.. **Effect van Integrale Bekostiging Op Curatieve. RIVM**. (2010.0)
61. **De Invloed van Financiele Prikkels op de Behandeltijd in de GGZ. Me Judice**. (2015.0)
62. Advies Zorgprestatiemodel Ggz en fz.. **Advies Zorgprestatiemodel Ggz en fz. 2019. pp. 1–60**
63. **Belonen van Zorg Die Waarde Toevoegt Inhoud**. (2018.0)
64. **Invoering Abonnementstarief in de Wmo per 2020 Uitvoeringstoets naar de Gemeenten**. (2018.0)
65. **Doorontwikkeling Bekostiging Wlz**. (2017.0)
66. Eijkenaar F., Schut E.. **Uitkomstbekostiging in de Zorg: Een (on) Begaanbare Weg? 2015. pp. 1–126**
67. Holmstrom B., Milgrom P.. **Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design**. *J. Law, Econ. Organ.* (1991.0) **7** 24-52. DOI: 10.1093/jleo/7.special_issue.24
68. Smith P.C., York N.. **Quality incentives: The case of U.K. general practitioners—An ambitious U.K. quality improvement initiative offers the potential for enormous gains in the quality of primary health care**. *Health Aff.* (2004.0) **23** 112-118. DOI: 10.1377/hlthaff.23.3.112
69. Kirschner K., Braspenning J., Akkermans R.P., Jacobs J.E.A., Grol R.. **Assessment of a pay-for-performance program in primary care designed by target users**. *Fam. Pract* (2013.0) **30** 161-171. DOI: 10.1093/fampra/cms055
70. Campbell S.M., Reeves D., Kontopantelis E., Sibbald B., Roland M.. **Effects of Pay for Performance on the Quality of Primary Care in England**. *N. Engl. J. Med.* (2009.0) **361** 368-378. DOI: 10.1056/NEJMsa0807651
71. **Longzorg Gestuurd op Uitkomsten. 2018. pp. 24–25**
72. **LongZorg Nijkerk Doelstelling**. (2018.0)
73. McWilliams J.M., Hatfield L.A., Chernew M.E., Landon B.E., Schwartz A.L.. **Early Performance of Accountable Care Organizations in Medicare**. *N. Engl. J. Med.* (2016.0) **374** 2357-2366. DOI: 10.1056/NEJMsa1600142
74. Song Z., Rose S., Safran D.G., Landon B.E., Day M.P., Chernew M.E.. **Changes in Health Care Spending and Quality 4 Years into Global Payment**. *N. Engl. J. Med.* (2014.0) **371** 1704-1714. DOI: 10.1056/NEJMsa1404026
75. Ouayogodé M.H., Colla C.H., Lewis V.A.. **Determinants of success in Shared Savings Programs: An analysis of ACO and market characteristics**. *Healthcare* (2016.0) **5** 53-61. DOI: 10.1016/j.hjdsi.2016.08.002
76. Nyweide D.J., Lee W., Cuerdon T.T., Pham H.H., Cox M., Rajkumar R., Conway P.H.. **Association of Pioneer Accountable Care Organizations vs Traditional Medicare Fee for Service With Spending, Utilization, and Patient Experience**. *JAMA—J. Am. Med. Assoc.* (2015.0) **313** 2152-2161. DOI: 10.1001/jama.2015.4930
77. Cattel D., Eijkenaar F., Schut F.T.. **Value-based provider payment: Towards a theoretically preferred design**. *Health Econ. Policy Law* (2018.0) **15** 94-112. DOI: 10.1017/S1744133118000397
78. Berwick D.M.. **Launching Accountable Care Organizations—The Proposed Rule for the Medicare Shared Savings Program**. *N. Engl. J. Med.* (2011.0) **364** e32. DOI: 10.1056/NEJMp1103602
79. **Overheveling van Zorg? Of Overheveling van Problemen? 2020. pp. 34–36**
80. de Vries E.F., Drewes H.W., Struijs J.N., Heijink R., Baan C.A.. **Barriers to payment reform: Experiences from nine Dutch population health management sites**. *Health Policy* (2019.0) **123** 1100-1107. DOI: 10.1016/j.healthpol.2019.09.006
|
---
title: 'Systemic Lupus Erythematosus and Risk of Dry Eye Disease and Corneal Surface
Damage: A Population-Based Cohort Study'
authors:
- Ching-Han Tseng
- Ying-Hsuan Tai
- Chien-Tai Hong
- Ying-Xiu Dai
- Tzeng-Ji Chen
- Yih-Giun Cherng
- Shih-Chung Lai
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001508
doi: 10.3390/ijerph20053776
license: CC BY 4.0
---
# Systemic Lupus Erythematosus and Risk of Dry Eye Disease and Corneal Surface Damage: A Population-Based Cohort Study
## Abstract
Systemic lupus erythematosus (SLE) potentially involves multiple parts of the ocular system, including the lacrimal glands and the cornea. The present study sought to assess the risk of aqueous-deficient dry eye disease (DED) and corneal surface damage in patients with SLE. We conducted a population-based cohort study using Taiwan’s National Health Insurance research database to compare the risks of DED and corneal surface damage between subjects with and without SLE. Proportional hazard regression analyses were used to calculate the adjusted hazard ratio (aHR) and $95\%$ confidence interval (CI) for the study outcomes. The propensity score matching procedure generated 5083 matched pairs with 78,817 person-years of follow-up for analyses. The incidence of DED was 31.90 and 7.66 per 1000 person-years in patients with and without SLE, respectively. After adjusting for covariates, SLE was significantly associated with DED (aHR: 3.30, $95\%$ CI: 2.88–3.78, $p \leq 0.0001$) and secondary Sjögren’s syndrome (aHR: 9.03, $95\%$ CI: 6.86–11.88, $p \leq 0.0001$). Subgroup analyses demonstrated that the increased risk of DED was augmented among patients with age < 65 years and female sex. In addition, patients with SLE had a higher risk of corneal surface damage (aHR: 1.81, $95\%$ CI: 1.35–2.41, $p \leq 0.0001$) compared to control subjects, including recurrent corneal erosion (aHR: 2.98, $95\%$ CI: 1.63–5.46, $$p \leq 0.0004$$) and corneal scar (aHR: 2.23, $95\%$ CI: 1.08–4.61, $$p \leq 0.0302$$). In this 12-year nationwide cohort study, we found that SLE was associated with increased risks of DED and corneal surface damage. Regular ophthalmology surveillance should be considered to prevent sight-threatening sequelae among patients with SLE.
## 1. Introduction
Dry eye disease (DED) is a multifactorial disorder that is characterized by the disruption of tear film homeostasis [1]. Tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles in developing ocular symptoms, including punctate epithelial keratitis, filamentary keratitis, superior limbic keratoconjunctivitis, lid parallel conjunctival folds, and lid wiper epitheliopathy [2]. The prevalence of DED varies globally, ranging from 5 to $50\%$ across different countries and regions [3]. In Taiwan, the prevalence rate of DED was reported to be $5\%$ to $34\%$, with females and the elderly in the majority [4,5,6,7]. It should be noted that patients with DED have a significantly higher risk of corneal surface damage due to progressive ocular surface inflammation and disruption [3]. Recurrent corneal erosion, corneal ulcers, and corneal scars represent common findings among patients with severe corneal surface damage. Previous studies have revealed several risk factors for DED-associated corneal surface damage, including younger age, female sex, diabetes mellitus, and autoimmune diseases (e.g., rheumatoid arthritis) [8]. Importantly, DED symptoms have an adverse impact on patients’ visual functions, daily activities, work productivity, and vision-related quality of life [9].
Systemic lupus erythematosus (SLE) is a chronic, complex, and multifaceted autoimmune disorder, while its etiology remains largely unclear [10]. SLE predominantly affects females, especially in their 20 s and 30 s [10]. The prevalence of SLE varies across different countries [11]. In Taiwan, the prevalence rate was reported to be 97.5 per 100,000 population [12]. Approximately one-third of SLE patients suffer from ocular involvement, of which keratoconjunctivitis sicca represents the most common manifestation [13,14,15,16]. In a previous report, the risks of DED, cataracts, and glaucoma were significantly higher in patients with SLE [17]. However, there is limited population-based data demonstrating the association between SLE and DED or serious corneal surface damage. The relationship between SLE and DED is not completely clarified due to some methodological drawbacks of previous studies, including small sample size (n < 1000) [14,16,17], insufficient adjustment for confounders [14,16,17], and restriction to single institutions [14,16] or specific populations (children) [14]. In addition, few studies have evaluated the potential impact of SLE on the development of corneal surface damage, and relevant risk factors remain largely unknown [14,16,17]. In this population-based cohort study, we used Taiwan’s National Health Insurance (NHI) research database to evaluate the temporal relationship between SLE and DED or corneal surface damage. Based on the current literature [13,14,15,16,17], we hypothesized that SLE was associated with both DED and corneal surface damage in this 12-year nationwide cohort.
## 2.1. Data Source
This study obtained ethical approval from the Taipei Medical University-Joint Institutional Review Board (approval no. TMU-JIRB-N202210011; date of approval on 6 October 2022). Written informed consent was waived by the Institutional Review Board due to the retrospective nature of this research. All methods were performed following the Declaration of Helsinki 2013 and relevant study guidelines [18]. Taiwan’s National Health Insurance program was launched in March 1995 and offered insurance to more than $99\%$ of 23.3 million Taiwanese residents. The NHI research database contains comprehensive claims data of the insured beneficiaries, including demographic characteristics (e.g., date of birth and sex), medical diagnoses, prescription drugs, and medical expenditures. The NHI research database has been widely used for public health statistics and risk assessment [19,20,21]. In the present study, we included subjects from the three Longitudinal Health Insurance Databases (LHID2000, LHID2005, and LHID2010), which contains original claims data of 1 million randomly sampled beneficiaries from the original NHI research database in the years 2000, 2005, and 2010, respectively [22].
## 2.2. Inclusion and Exclusion Criteria
Patients who had at least 2 rheumatology clinic visits with the diagnoses of SLE between 1 January 2002 and 30 June 2013 were included consecutively. We utilized the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to ascertain the diagnoses of SLE, coexisting diseases, and ocular disorders (Supplementary Table S1). The index date was defined as the date of the first SLE diagnosis. Patients were excluded due to the following conditions: any previous diagnoses of DED, corneal ulcers, recurrent corneal erosion, corneal scars, interstitial and deep keratitis, corneal neovascularization, ocular burns, or open globe injury in the ophthalmology service before the index date. Subjects were also excluded if they had been prescribed eye lubricants before the index date or died in the follow-up period.
## 2.3. Outcome Assessment
The primary outcome was DED, which was defined as the diagnosis made twice by certified ophthalmologists with the prescriptions of cyclosporine ophthalmic emulsion in the ophthalmology care service (Supplementary Table S1). In the reimbursement regulations of Taiwan’s National Health Insurance, cyclosporine ophthalmic emulsion treatment can be used when patients’ Schirmer test scores are less than 5 mm in 5 min [5,8]. The secondary outcomes included secondary Sjögren’s syndrome (SS) and severe forms of corneal surface damage, which were defined as any diagnosis of corneal ulcers, recurrent corneal erosion, or corneal scars made twice by certified ophthalmologists.
## 2.4. Covariates for Model Adjustment
Insurance premium was classified into $0–$500, $501–$800, and >$800 United States dollars per month. The ICD-9-CM codes of physicians’ diagnoses within 24 months before the index date were employed to determine the following comorbidities, chosen based on data availability and existing literature: hypertension, diabetes mellitus, coronary artery disease, chronic obstructive pulmonary disease, chronic liver disease, chronic kidney disease, cerebrovascular disease, thyroid disease, major depressive disorder, anxiety disorder, sleeping disorder, and cancer (Supplementary Table S1) [23]. The Charlson comorbidity index score was calculated to evaluate the comorbidity level of included subjects [24]. We also evaluated the concurrent prescription of systemic corticosteroids within 6 months after the index date. The numbers of hospitalizations and emergency visits within 24 months before the index date were analyzed to assess the level of medical resource utilization of the studied patients.
## 2.5. Statistical Analysis
A non-parsimonious multivariable logistic regression model was used to calculate a propensity score for SLE and non-SLE subjects. Each SLE subject was matched to a non-SLE control using the nearest neighbor matching algorithm within a tolerance limit of 0.05 and without replacement to balance the distributions of age, sex, and monthly insurance premium between the two groups [25]. Baseline patient characteristics were compared between matched pairs using the absolute standardized mean difference [26]. We used multivariable Cox proportional hazards regression models to calculate the adjusted hazard ratio (aHR) and $95\%$ confidence interval (CI) for the study outcomes. The multivariable models adjusted for the variables of age, sex, monthly insurance premium, coexisting diseases, Charlson comorbidity index score, use of systemic corticosteroids, number of hospitalizations, and number of emergency room visits. The Kaplan-Meier curves and log-rank tests were used to compare the cumulative incidence of ophthalmological outcomes between the two groups. Stratified analyses were also conducted by age≥ or <65 years, male or female, different Charlson comorbidity index scores, and use of systemic corticosteroids or not to examine the risk of DED within these strata. A two-sided p-value of <0.05 was considered statistically significant. All the statistical analyses were conducted using Statistics Analysis System (SAS), Version 9.4 (SAS Institute Inc., Cary, NC, USA).
## 3.1. Baseline Patient Characteristics
The matching procedure generated 5083 matched pairs with 78,817 person-years of follow-up for analyses (Supplementary Figure S1). The baseline distributions of demographic and patient characteristics are shown in Table 1. Noticeably, patients with SLE were more likely to have more comorbidities, higher Charlson comorbidity index scores, prescriptions of systemic corticosteroids, and greater numbers of hospitalizations and emergency room visits.
## 3.2. Dry Eye Disease
The incidence of DED was 31.90 and 7.66 per 1000 person-years in the SLE and non-SLE groups, respectively (Table 2). The interval between enrollment and DED diagnosis was median 2.6 (interquartile range: 0.6–5.6) years in the SLE patients and 5.1 (2.3–7.5) years in the non-SLE controls ($p \leq 0.0001$). The results of univariate and multivariable proportional hazards regression analyses for DED were shown in Table 3. After adjusting for covariates, SLE was significantly associated with increased DED compared to non-SLE controls (aHR: 3.30, $95\%$ CI: 2.88–3.78, $p \leq 0.0001$). Figure 1A demonstrates the cumulative incidence of DED in the two groups. SLE was also linked to secondary SS (aHR: 9.03, $95\%$ CI: 6.86–11.88, $p \leq 0.0001$). Other independent factors for DED were age (aHR: 1.03), female sex (aHR: 2.56), monthly insurance premium (501–800 vs. 0–500 USD, aHR: 0.92; ≥801 vs. 0–500 USD, aHR: 1.20), hypertension (aHR: 0.78), cerebrovascular disease (aHR: 0.68), sleeping disorder (aHR: 1.24), Charlson comorbidity index (1 vs. 0, aHR: 1.45; 2 vs. 0, aHR: 1.35; ≥3 vs. 0, aHR: 0.74), and use of systemic corticosteroids (aHR: 1.41). Subgroup analyses showed that the aHR for DED was higher in patients with age < 65 years (aHR: 3.48) and female sex (aHR: 3.47) compared to those with age ≥ 65 years (aHR: 1.99) and male sex (aHR: 2.20), respectively (Table 4).
## 3.3. Corneal Surface Damage
The incidence of corneal surface damage was 3.93 and 2.12 per 1000 person-years in the SLE and non-SLE groups, respectively (Table 2). The time to corneal surface damage was median 4.3 years (interquartile range: 1.7–7.8) in the SLE patients and 3.8 years (interquartile range: 2.0–7.4) in the non-SLE controls ($$p \leq 0.9610$$). The multivariable model showed that SLE was significantly associated with increased corneal surface damage (aHR: 1.81, $95\%$ CI: 1.35–2.41, $p \leq 0.0001$; Table 5 and Figure 1B). Further analyses showed that SLE was significantly associated with higher risks of recurrent corneal erosion (aHR: 2.98, $95\%$ CI: 1.63–5.46, $$p \leq 0.0004$$) and corneal scar (aHR: 2.23, $95\%$ CI: 1.08–4.61, $$p \leq 0.0302$$). Another independent factor for corneal surface damage was female sex (aHR: 1.94, $95\%$ CI: 1.28–2.94, $$p \leq 0.0017$$).
## 4. Discussion
The present study demonstrated that patients with SLE exhibited significantly greater risks of DED and corneal surface damage, especially for recurrent corneal erosion compared with age, sex and insurance premium-matched controls. Subgroup analyses further revealed that the higher SLE-associated risk of DED was observed in subjects of males and females, age ≥ 65 and <65 years old, and those with or without systemic corticosteroid treatment. Considering the devastating impact of DED and corneal surface damage on visual functions, patients with SLE should be alerted on these corneal disorders.
SLE is the third most common autoimmune disorder in Taiwan [27]. The prevalence rate of SLE remarkably increased during the 21st century [28]. Although the ocular symptoms are not included into the 11 diagnostic criteria of SLE, they are not uncommon and about one-third of patients are suffered [29]. Keratoconjunctivitis sicca is the most ocular manifestation of SLE [30], while all the parts of eye, including sclera, uvea, retina, and optic nerve, are possibly involved [31]. There are several reasons accounting for the association between SLE and DED. One is the comorbidity of Sjögren’s syndrome, which causes the reduction of tear. On the other hand, the infiltration of immune cells and immune complex into the epithelial basement membrane are also evident [32], and the increase in proinflammatory cytokine, i.e., interleukin-17, is detected in the tear film of SLE patients [33,34]. The present study also found a significant association between SLE with secondary SS in Taiwanese patients. However, the adjusted risk of DED was similar between SLE patients with and without systemic corticosteroid compared with controls, which may indicate the consequence of an ocular-specific inflammatory response in SLE patients, and topical immunosuppressants are more suitable for the management of DED [35,36].
Corneal ulcer is defined as the lesion of the corneal epithelium, which is a major threat of vision [37]. Without the proper treatment, patients may only rely on the corneal transplant for regaining their vision [38]. Most of the corneal ulcers result from the infection, including bacteria, virus, fungus, and protozoa [38]. On the other hand, non-infectious corneal ulcers, usually present as peripheral ulcerative keratitis (PUK), are highly associated with autoimmune diseases [39]. The fibrocyte and macrophage infiltration in the corneal matrix triggers the inflammatory response, and the accumulation of immune complex is found in the capillary network of cornea in PUK [40,41]. In the present study, the overall risk of corneal surface damage was significantly higher in patients with SLE. Despite that there was only a trend toward increased corneal ulcers in SLE patients, referring to recurrent corneal erosion and corneal scar, more severe types of corneal damage, there were significantly increased risks in SLE patients. The lack of significance in the corneal ulcer among SLE patients may result from other types of PUK, such as infectious or contact lens-related keratitis.
The strength of the present study was the delineation of the association between SLE and DED or corneal surface damage. Meanwhile, the population-based study provided reliable epidemiological evidence and good generalizability about the risk assessment. DED causes substantial discomfort for the SLE patients, and the management of these ocular manifestations of SLE should be emphasized. In addition, despite that SLE is not the major contributor of PUK, it did increase the risk of recurrent corneal ulcer and corneal scar, which may exert a devastating effect on eyesight. However, there were some limitations to the present study. First, since the NHI research database was diagnosis and treatment-based, the laboratory data was unavailable. Therefore, the severity and progression of SLE, DED and corneal surface damage could not be further evaluated. Second, the lack of some social habit information (e.g., alcohol and tobacco consumption) and physical examination data (e.g., body mass index, blood pressure, and visual acuity) might also introduce a bias to the analytical results. Third, we only matched the variables of age, sex and monthly insurance premium between the two groups in the propensity-score matching process in order to increase the sample size and statistical power of matched datasets. Given the fact that the incidence of corneal surface injury was relatively low (approximately $2\%$ to $5\%$ in the 12-year follow-up), a large patient sample is essential to detect a potential risk difference between SLE and non-SLE populations. Fourth, because the use of corticosteroids is a known risk factor for DED and corneal surface damage, the imbalance in the distribution of corticosteroid prescriptions might bias the study results. Further studies are needed to evaluate the potential impact of corticosteroids and immunosuppressants on corneal diseases among SLE patients. Finally, our cohort was only followed up until the 31 December 2013, due to the regulations of the NHI research database.
## 5. Conclusions
The present study demonstrated a higher risk of DED and severe forms of corneal surface damage in patients with SLE. Considering the increasing prevalence of SLE, the vision issues, which affect the quality of life substantially, should be empathized with the rheumatologists and ophthalmologists. Prophylactic and therapeutic management should be further developed for this susceptible population.
## References
1. Craig J.P., Nichols K.K., Akpek E.K., Caffery B., Dua H.S., Joo C.K., Liu Z., Nelson J.D., Nichols J.J., Tsubota K.. **TFOS DEWS II Definition and Classification Report**. *Ocul. Surf.* (2017.0) **15** 276-283. DOI: 10.1016/j.jtos.2017.05.008
2. Yu L., Yu C., Dong H., Mu Y., Zhang R., Zhang Q., Liang W., Li W., Wang X., Zhang L.. **Recent developments about the pathogenesis of dry eye disease: Based on immune inflammatory mechanisms**. *Front. Pharmacol.* (2021.0) **12** 732887. DOI: 10.3389/fphar.2021.732887
3. Stapleton F., Alves M., Bunya V.Y., Jalbert I., Lekhanont K., Malet F., Na K.S., Schaumberg D., Uchino M., Vehof J.. **TFOS DEWS II Epidemiology Report**. *Ocul. Surf.* (2017.0) **15** 334-365. DOI: 10.1016/j.jtos.2017.05.003
4. Lin P.Y., Tsai S.Y., Cheng C.Y., Liu J.H., Chou P., Hsu W.M.. **Prevalence of dry eye among an elderly Chinese population in Taiwan: The Shihpai Eye Study**. *Ophthalmology* (2003.0) **110** 1096-1101. DOI: 10.1016/S0161-6420(03)00262-8
5. Kuo Y.K., Lin I.C., Chien L.N., Lin T.Y., How Y.T., Chen K.H., Dusting G.J., Tseng C.L.. **Dry eye disease: A review of epidemiology in Taiwan, and its clinical treatment and merits**. *J. Clin. Med.* (2019.0) **8**. DOI: 10.3390/jcm8081227
6. Pan L.Y., Kuo Y.K., Chen T.H., Sun C.C.. **Dry eye disease in patients with type II diabetes mellitus: A retrospective, population-based cohort study in Taiwan**. *Front. Med.* (2022.0) **9** 980714. DOI: 10.3389/fmed.2022.980714
7. Lin I.C., Kuo Y.K., Liu H.Y., Chien L.N.. **Trends in diagnosed dry eye disease incidence, 2001 to 2015: A nationwide population-based study in Taiwan**. *Cornea* (2022.0) **41** 1372-1377. DOI: 10.1097/ICO.0000000000002987
8. Hung N., Kang E.Y., Lee T.W., Chen T.H., Shyu Y.C., Sun C.C.. **The risks of corneal surface damage in aqueous-deficient dry eye disease: A 17-year population-based study in Taiwan**. *Am. J. Ophthalmol.* (2021.0) **227** 231-239. DOI: 10.1016/j.ajo.2021.03.013
9. Hossain P., Siffel C., Joseph C., Meunier J., Markowitz J.T., Dana R.. **Patient-reported burden of dry eye disease in the UK: A cross-sectional web-based survey**. *BMJ Open* (2021.0) **11** e039209. DOI: 10.1136/bmjopen-2020-039209
10. Fortuna G., Brennan M.T.. **Systemic lupus erythematosus: Epidemiology, pathophysiology, manifestations, and management**. *Dent. Clin. N. Am.* (2013.0) **57** 631-655. DOI: 10.1016/j.cden.2013.06.003
11. Barber M.R.W., Drenkard C., Falasinnu T., Hoi A., Mak A., Kow N.Y., Svenungsson E., Peterson J., Clarke A.E., Ramsey-Goldman R.. **Global epidemiology of systemic lupus erythematosus**. *Nat. Rev. Rheumatol.* (2021.0) **17** 515-532. DOI: 10.1038/s41584-021-00668-1
12. Yeh K.W., Yu C.H., Chan P.C., Horng J.T., Huang J.L.. **Burden of systemic lupus erythematosus in Taiwan: A population-based survey**. *Rheumatol. Int.* (2013.0) **33** 1805-1811. DOI: 10.1007/s00296-012-2643-6
13. Palejwala N.V., Walia H.S., Yeh S.. **Ocular manifestations of systemic lupus erythematosus: A review of the literature**. *Autoimmune Dis.* (2012.0) **2012** 290898. DOI: 10.1155/2012/290898
14. Ong Tone S., Elbaz U., Silverman E., Levy D., Williams S., Mireskandari K., Ali A.. **Evaluation of dry eye disease in children with systemic lupus erythematosus and healthy controls**. *Cornea* (2019.0) **38** 581-586. DOI: 10.1097/ICO.0000000000001902
15. Kemeny-Beke A., Szodoray P.. **Ocular manifestations of rheumatic diseases**. *Int. Ophthalmol.* (2020.0) **40** 503-510. DOI: 10.1007/s10792-019-01183-9
16. Sitaula R., Shah D.N., Singh D.. **The spectrum of ocular involvement in systemic lupus erythematosus in a tertiary eye care center in Nepal**. *Ocul. Immunol. Inflamm.* (2011.0) **19** 422-425. DOI: 10.3109/09273948.2011.610023
17. Hsu C.S., Hsu C.W., Lu M.C., Koo M.. **Risks of ophthalmic disorders in patients with systemic lupus erythematosus: A secondary cohort analysis of population-based claims data**. *BMC Ophthalmol.* (2020.0) **20**. DOI: 10.1186/s12886-020-01360-w
18. von Elm E., Altman D.G., Egger M., Pocock S.J., Gøtzsche P.C., Vandenbroucke J.P.. **Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies**. *BMJ* (2007.0) **335** 806-808. DOI: 10.1136/bmj.39335.541782.AD
19. Ting H.C., Ma S.H., Tai Y.H., Dai Y.X., Chang Y.T., Chen T.J., Chen M.H.. **Association between alopecia areata and retinal diseases: A nationwide population-based cohort study**. *J. Am. Acad. Dermatol.* (2022.0) **87** 771-778. DOI: 10.1016/j.jaad.2021.10.045
20. Tai C.Y., Liu H.Y., Cata J.P., Dai Y.X., Chen M.H., Chen J.T., Chen T.J., Wu H.L., Cherng Y.G., Li C.C.. **The association between general anesthesia and new postoperative uses of sedative-hypnotics: A nationwide matched cohort study**. *J. Clin. Med.* (2022.0) **11**. DOI: 10.3390/jcm11123360
21. Lai S.C., Wang C.W., Wu Y.M., Dai Y.X., Chen T.J., Wu H.L., Cherng Y.G., Tai Y.H.. **Rheumatoid arthritis associated with dry eye disease and corneal surface damage: A nationwide matched cohort study**. *Int. J. Environ. Res. Public Health* (2023.0) **20**. DOI: 10.3390/ijerph20021584
22. **Data Subsets**
23. Shanti Y., Shehada R., Bakkar M.M., Qaddumi J.. **Prevalence and associated risk factors of dry eye disease in 16 northern West bank towns in Palestine: A cross-sectional study**. *BMC Ophthalmol.* (2020.0) **20**. DOI: 10.1186/s12886-019-1290-z
24. Li B., Evans D., Faris P., Dean S., Quan H.. **Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases**. *BMC Health Serv. Res.* (2008.0) **8**. DOI: 10.1186/1472-6963-8-12
25. Austin P.C.. **A comparison of 12 algorithms for matching on the propensity score**. *Stat. Med.* (2014.0) **33** 1057-1069. DOI: 10.1002/sim.6004
26. Austin P.C.. **Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples**. *Stat. Med.* (2009.0) **28** 3083-3107. DOI: 10.1002/sim.3697
27. See L.C., Kuo C.F., Chou I.J., Chiou M.J., Yu K.H.. **Sex- and age-specific incidence of autoimmune rheumatic diseases in the Chinese population: A Taiwan population-based study**. *Semin. Arthritis Rheum.* (2013.0) **43** 381-386. DOI: 10.1016/j.semarthrit.2013.06.001
28. Leong P.Y., Huang J.Y., Chiou J.Y., Bai Y.C., Wei J.C.. **The prevalence and incidence of systemic lupus erythematosus in Taiwan: A nationwide population-based study**. *Sci. Rep.* (2021.0) **11** 5631. DOI: 10.1038/s41598-021-84957-5
29. Tan E.M., Cohen A.S., Fries J.F., Masi A.T., McShane D.J., Rothfield N.F., Schaller J.G., Talal N., Winchester R.J.. **The 1982 revised criteria for the classification of systemic lupus erythematosus**. *Arthritis Rheum.* (1982.0) **25** 1271-1277. DOI: 10.1002/art.1780251101
30. Jensen J.L., Bergem H.O., Gilboe I.M., Husby G., Axéll T.. **Oral and ocular sicca symptoms and findings are prevalent in systemic lupus erythematosus**. *J. Oral Pathol. Med.* (1999.0) **28** 317-322. DOI: 10.1111/j.1600-0714.1999.tb02047.x
31. Read R.W.. **Clinical mini-review: Systemic lupus erythematosus and the eye**. *Ocul. Immunol. Inflamm.* (2004.0) **12** 87-99. DOI: 10.1080/09273940490895308
32. Heiligenhaus A., Dutt J.E., Foster C.S.. **Histology and immunopathology of systemic lupus erythematosus affecting the conjunctiva**. *Eye* (1996.0) **10** 425-432. DOI: 10.1038/eye.1996.94
33. Oh J.Y., Kim M.K., Choi H.J., Ko J.H., Kang E.J., Lee H.J., Wee W.R., Lee J.H.. **Investigating the relationship between serum interleukin-17 levels and systemic immune-mediated disease in patients with dry eye syndrome**. *Korean J. Ophthalmol.* (2011.0) **25** 73-76. DOI: 10.3341/kjo.2011.25.2.73
34. Frith P., Burge S.M., Millard P.R., Wojnarowska F.. **External ocular findings in lupus erythematosus: A clinical and immunopathological study**. *Br. J. Ophthalmol.* (1990.0) **74** 163-167. DOI: 10.1136/bjo.74.3.163
35. Jung H.H., Ji Y.S., Sung M.S., Kim K.K., Yoon K.C.. **Long-term outcome of treatment with topical corticosteroids for severe dry eye associated with Sjögren's syndrome**. *Chonnam Med. J.* (2015.0) **51** 26-32. DOI: 10.4068/cmj.2015.51.1.26
36. Prinz J., Maffulli N., Fuest M., Walter P., Bell A., Migliorini F.. **Efficacy of topical administration of corticosteroids for the management of dry eye disease: Systematic review and meta-analysis**. *Life* (2022.0) **12**. DOI: 10.3390/life12111932
37. Ahmed F., House R.J., Feldman B.H.. **Corneal abrasions and corneal foreign bodies**. *Prim. Care* (2015.0) **42** 363-375. DOI: 10.1016/j.pop.2015.05.004
38. Sharma S.. **Keratitis**. *Biosci. Rep.* (2001.0) **21** 419-444. DOI: 10.1023/A:1017939725776
39. Cao Y., Zhang W., Wu J., Zhang H., Zhou H.. **Peripheral ulcerative keratitis associated with autoimmune disease: Pathogenesis and treatment**. *J. Ophthalmol.* (2017.0) **2017** 7298026. DOI: 10.1155/2017/7298026
40. Riley G.P., Harrall R.L., Watson P.G., Cawston T.E., Hazleman B.L.. **Collagenase (MMP-1) and TIMP-1 in destructive corneal disease associated with rheumatoid arthritis**. *Eye* (1995.0) **9** 703-718. DOI: 10.1038/eye.1995.182
41. Watanabe R., Ishii T., Yoshida M., Takada N., Yokokura S., Shirota Y., Fujii H., Harigae H.. **Ulcerative keratitis in patients with rheumatoid arthritis in the modern biologic era: A series of eight cases and literature review**. *Int. J. Rheum. Dis.* (2017.0) **20** 225-230. DOI: 10.1111/1756-185X.12688
|
---
title: 'Duration and Influencing Factors of Postoperative Urinary Incontinence after
Robot-Assisted Radical Prostatectomy in a Japanese Community Hospital: A Single-Center
Retrospective Cohort Study'
authors:
- Tadashi Kasai
- Taro Banno
- Kazutaka Nakamura
- Yukiko Kouchi
- Haruki Shigeta
- Fumio Suzuki
- Yudai Kaneda
- Divya Bhandari
- Anju Murayama
- Katumori Takamatsu
- Naomi Kobayashi
- Toyoaki Sawano
- Yoshitaka Nishikawa
- Hiroyuki Sato
- Akihiko Ozaki
- Tomohiro Kurokawa
- Norio Kanzaki
- Hiroaki Shimmura
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001515
doi: 10.3390/ijerph20054085
license: CC BY 4.0
---
# Duration and Influencing Factors of Postoperative Urinary Incontinence after Robot-Assisted Radical Prostatectomy in a Japanese Community Hospital: A Single-Center Retrospective Cohort Study
## Abstract
Objectives: Post-operative urinary incontinence (PUI) after robotic-assisted radical prostatectomy (RARP) is an important complication; PUI occurs immediately after postoperative urethral catheter removal, and, although approximately $90\%$ of patients improve within one year after surgery, it can significantly worsen their quality of life. However, information is lacking on its nature in community hospital settings, particularly in Asian countries. The purposes of this study were to investigate the time required to recover from PUI after RARP and to identify its associated factors in a Japanese community hospital. Methods: Data were extracted from the medical records of 214 men with prostate cancer who underwent RARP from 2019 to 2021. We then calculated the number of days elapsed from the surgery to the initial outpatient visit confirming PUI recovery among the patients. We estimated the PUI recovery rate using the Kaplan–Meier product limit method and evaluated associated factors using the multivariable Cox proportional hazards model. Results: The PUI recovery rates were $5.7\%$, $23.4\%$, $64.6\%$, and $93.3\%$ at 30, 90, 180, and 365 days following RARP, respectively. After an adjustment, those with preoperative urinary incontinence experienced significantly slower PUI recovery than their counterparts, while those with bilateral nerve sparing experienced recovery significantly sooner than those with no nerve sparing. Conclusion: Most PUI improved within one year, but a proportion of those experiencing recovery before 90 days was smaller than previously reported.
## 1. Introduction
Prostate cancer is the third most common cancer globally, with 1,414,259 diagnosed cases in 2020 [1]. Among various treatment methods for prostate cancer, the main treatment measure has been surgery, namely radical prostatectomy. Robot-assisted radical prostatectomy (RARP) in particular has become the standard procedure, accounting for the majority of cases undergoing radical prostatectomy [2].
One of the most important postoperative complications of radical prostatectomy is PUI, which transiently but surely jeopardizes the postoperative quality of life (QOL) of patients [3,4]. While PUI is reportedly milder with RARP compared with open and laparoscopic radical prostatectomy, it is still an important complication that would exacerbate the QOL of prostate cancer patients; thereby, its management holds significant clinical implications [5].
PUI after radical prostatectomy occurs immediately after removal of the urethral catheter post-surgery. Recovery occurs over time, with approximately $90\%$ of patients improving within 1 year after surgery [6]. It has been reported that various preoperative factors are associated with PUI, such as old age, high obesity, the presence of complications, preoperative erectile dysfunction, a short membranous urethral length, urethral volume, urethral morphology, and bladder factors including the presence of preoperative voiding muscle overactivity and poor bladder compliance [7,8,9,10]. For surgical methods, urethral sphincter-sparing and nerve-sparing as well as newly invented hood techniques are effective in preventing PUI [11]. In addition, it has been indicated that pelvic floor muscle exercises performed preoperatively stimulate early recovery from PUI [12].
In Japan, the number of prostate cancer diagnoses has been on the rise as the aging population increases. In 2018, prostate cancer had the highest number of patients among males, with 92,021 annual cases [13]. Moreover, the number of deaths has been increasing, with 12,759 in 2020, recording the highest number [14]. An improved prognosis for prostate cancer has further emphasized the clinical significance of proper PUI management following RARP, the predominant surgical procedure for prostate cancer. However, previous evidence of PUIs has been mostly based on research performed in Europe and the United States, with only limited reports available from Asia. In addition, Japanese clinical research on the duration of PUI has progressed mostly in university hospitals, and information is lacking on PUI management in general community hospitals. This is an important perspective given that RARP has been widely preformed outside of university hospitals, at least in Japan. Therefore, we aimed to investigate the duration of PUI after RARP at Jyoban Hospital, a community hospital that has conducted a large number of RARP procedures, and its associated factors.
## 2.1. Setting and Participants
This investigation was conducted at Jyoban Hospital of Tokiwa Foundation in Iwaki City, Hamadori Region of Fukushima Prefecture. The population of Iwaki City was approximately 320,000 as of October 2019. It has traditionally been regarded as a remote area, suffering from a physician undersupply in the long term: specifically, its number of medical doctors was 167 per 100,000 population in 2018, compared to the Japanese national average of 247 per 100,000 in the same year; and the average age of medical doctors was 56.4 years old in 2018, compared to the national average of 49.9 years old in the same year. In these difficult circumstances, the Tokiwa Foundation took over the operation of Jyoban Hospital in 2010 from Iwaki City, and the hospital has developed over time during the last decade. During this process, its urology department has taken the lead, expanding into one of the largest community-based urological departments in Japan at present. Indeed, the Department of Urology now has the latest version of the Da Vinci operation system, Da Vinci Xi (Intuitive Surgical Inc., Sunnyvale, CA, USA), and conducts various types of robot-assisted laparoscopic surgery, including RARP. In 2018, the department saw the hospitalization of 638 patients with prostate cancer, the fourth highest number in the country for prostate cancer treatment, with 113 RARPs performed in 2019.
In this study, we considered the patients who underwent RARP from 1 April 2019 to 31 March 2021. When considering the detailed procedure, an indication for nerve sparing was made separately for the left and right sides. Nerve sparing was performed for low- and intermediate-risk patients, according to D’Amico’s classification, on the side where no cancer was detected on biopsy or MRI. Along with this principle, we took the patient’s wishes into consideration when making a comprehensive decision on whether nerve sparing could be performed. Further, lymph node dissection (LND) was not performed in most of our patients. This was because there is little evidence that lymph node dissection in prostate cancer could provide additional benefits to the patient receiving surgery, and it would rather increase the risk of lower-extremity edema. In this sense, while lymph node dissection would lead to accurate staging, the direct therapeutic benefit is unknown as it is associated with poor perioperative outcomes [15]. In our institution, the procedure was performed by multiple surgeons, i.e., a primary surgeon and two or three assistants (according to chart data), and it may be performed by an experienced surgeon or by residents under the guidance of experienced surgeons.
## 2.2. Data Extraction
From the medical records of Jyoban Hospital, we extracted data on the following: the dates of RARP and initial outpatient visits confirming RARP recovery; age; presence or absence of type 2 diabetes; history of alcohol consumption and smoking; presence or absence of transurethral prostatic surgery for benign prostatic hyperplasia; presence or absence of preoperative radiotherapy; presence or absence of preoperative urinary incontinence; systolic and diastolic blood pressure; height; weight at surgery; body mass index (BMI); obesity, defined as body mass index of 25 or above; albumin level; initial prostate-specific antigen (PSA); preoperative Gleason score; D’Amico’s classification; pathological T stage; presence or absence of lymph node dissection; main operator; presence or absence of nerve sparing; postoperative complication of inguinal hernia and intestinal obstruction; presence or absence of continued pelvic floor muscle exercises.
The duration of PUI was defined as the number of days elapsed from the date when the RARP was conducted to the date of the shortest outpatient visit when the physician in charge confirmed the recovery from PUI among those considered. We defined PUI recovery as when the two following conditions were met: [1] the patient was aware that their PUI had improved and [2] they changed their urinary incontinence pads less than or equal to 1 pad per day [3]. The patients who used incontinence pads but did not change them were considered to have improved their PUI because they may have used them as precautionary measures. If the records of the degree of PUI diverged between a doctor and a nurse, the one with the lower grade was selected. Patients for whom the date of the outpatient visit could not be verified and patients for whom both the number of urinary incontinence pad changes and the number of PUI could not be verified were excluded.
## 2.3. Analysis Method
We conducted two analyses in this study. First, we estimated the rate of PUI recovery following the RARP using the Kaplan–Meier product limit method. Then, we constructed a Cox proportional hazard regression model for PUI recovery to evaluate its associated factors. We considered all the sociodemographic and clinical variables as covariates, using the backward stepwise variable selection method (inclusion criteria, $p \leq 0.1$). The covariates with a small number of participants were re-grouped, as necessary. As a sensitivity analysis, we employed a multiple imputation method to fill in missing values for all the covariates. Based on an assumption of missing at random, we constructed the model 10 times using a Markov chain Monte Carlo method and integrated the results. All the data were analyzed with Stata version 15.0 (College Station, TX, USA).
## 3. Results
A total of 214 patients underwent RARP, and the analysis was performed on 209 patients after excluding five patients with missing values in the outcome (i.e., the time interval between surgery and urinary continence).
Sociodemographic and clinical patient information is shown in Table 1. The median age of the patients was 71 years (interquartile range 67–76), $11.5\%$ ($$n = 24$$) had diabetes, $9.6\%$ ($$n = 20$$) had preoperative urinary incontinence, and their median BMI was 24.4 (interquartile range 22.2–26.2), with 85 patients ($40.7\%$) being obese. The median value of albumin and initial PSA were 3.9 g/dL (interquartile range 3.7–4.1) and 8.9 ng/mL (interquartile range 5.9–16.0). The most common preoperative Gleason score was 7, with a proportion of $45.0\%$ ($$n = 94$$); $48.8\%$ of the patients were diagnosed as high risk according to D’Amico’s classification before RARP, and $84.7\%$ of the patients were diagnosed as pathological T2 after RARP. Further, $64.1\%$ ($$n = 134$$) of the patients were operated on by experienced doctors, and unilateral and bilateral nerve sparing was achieved in $45.7\%$ ($$n = 91$$) and $5.5\%$ ($$n = 11$$) of the patients, respectively. Postoperative complications of inguinal hernia and intestinal obstructions occurred in $7.7\%$ ($$n = 16$$) and $2.4\%$ ($$n = 5$$) of the patients. Lastly, pelvic muscle exercise was performed in $93.8\%$ ($$n = 195$$) of the patients.
Figure 1 shows the results of the Kaplan–Meier survival analysis curve. The rates of urinary continence were evaluated at 30 days (4 weeks/1 month), 90 days (12 weeks/3 months), 180 days (24 weeks/6 months), and 365 days (48 weeks/12 months), with recovery rates of $5.7\%$, $23.4\%$, $64.6\%$, and $93.3\%$, respectively.
Table 2 shows the results of univariable and multivariable Cox proportional hazards regression analysis. After an adjustment, those with preoperative urinary incontinence experienced significantly slower PUI recovery than those without preoperative urinary incontinence (hazard ratio 0.28, $95\%$ confidence interval 0.14–0.57). Patients with high albumin levels had slower recovery from PUI than those with low albumin levels (hazard ratio 0.54, $95\%$ confidence interval 0.35–0.81). In addition, recovery from PUI was slower after surgery performed by residents compared to surgery performed by experienced physicians (hazard ratio 0.61, $95\%$ confidence interval 0.44–0.86). In contrast, those with bilateral nerve sparing experienced PUI recovery significantly sooner than those with no nerve sparing (hazard ratio 2.87, $95\%$ confidence interval 1.43–5.77), while those with unilateral nerve sparing also tended to experience PUI recovery sooner than those with no nerve sparing (hazard ratio 1.35, $95\%$ confidence interval 0.97–1.87). The sensitivity analysis using the multiple imputation method did not converge and we could not obtain reasonable findings.
## 4. Discussion
In this study investigating PUI recovery following RARP in a community hospital in a remote area suffering from a physician undersupply in Japan, we primarily found that rates of PUI recovery at 90 days (12 weeks/3 months) and 365 days (48 weeks/12 months) were $23.4\%$ and $93.3\%$. We also found that preoperative urinary incontinence, higher albumin levels and surgery performed by unexperienced surgeons were associated with delayed PUI recovery, while nerve sparing was significantly associated with early recovery. This study predominantly presents important knowledge to medical institutions in a similar rural community setting in Japan, but, in the meantime, we believe that its implications could be valuable beyond this setting.
With regard to PUI recovery at 365 days, the obtained findings were no worse than the previous findings reported in the systematic review by Ficarra et al., where the proportion of PUI recovery at 12 months ranged from 89 to $92\%$ [3]. We may have overestimated the proportion of PUI recovery in this study, given that our definition of PUI recovery was liberal, allowing the use of one pad per day, while some previous studies implemented a definition of no pads per day as the definition [3]. Nonetheless, it is notable that our observation was superior to that of all previous studies implementing a definition of no more than one pad per day for PUI recovery in the same systematic review [3]. In this respect, it is reasonable to say that at least we may have achieved outcomes comparable to the previous studies at 365 days.
In contrast, our findings of PUI recovery at 90 days ($23.4\%$) were inferior to the figures reported in previous studies. Indeed, Ficarra et al. reported in their systematic review that the proportion of patients experiencing three-month PUI recovery was $65\%$ [3]. It is difficult to conclusively determine the underlying mechanism of the disparity in the findings of ours and the previous studies. However, our observations about nerve sparing could provide important clues to understand this phenomenon. In our study, nerve sparing was associated with early PUI recovery. However, the proportion of patients with bilateral nerve sparing was relatively low at only $5.5\%$, primarily due to the high-risk profile of the patients, with only $11.0\%$ classified as low risk according to D’Amico’s classification. A contrasting phenomenon was observed in a previous study by Kim et al., which showed that $53.9\%$ ($\frac{285}{529}$) of the patients experienced bilateral nerve sparing and that $60\%$ of them experienced PUI recovery by 12 weeks [16]. Further, given that the effect of nerve sparing appeared to have been strong during the earlier phase of PUI recovery [16], the low proportion of bilateral nerve sparing may have been a primary contributor to the delayed PUI recovery.
In addition, the presence of preoperative symptoms of urinary incontinence may have delayed the PUI recovery in this study. Ficarra et al. reported that preoperative lower urinary tract symptoms delayed the recovery from PUI [3]. However, given that only a limited proportion of the patients experienced preoperative urinary incontinence in this study, its contribution to delayed PUI recovery may have been limited.
In our study, surgery performed by inexperienced surgeons led to delayed PUI recovery, which is in line with multiple previous studies. While the acceptance of young doctors and their on-the-job training is a critical factor allowing hospitals located in rural settings to sustain their workforce and provide necessary care for local residents, it is also important to minimize the drawbacks resulting from this process. Jyoban Hospital recently implemented the dual console of the Da Vinci surgical system, which allows a senior doctor to provide real-time supervision of an operation conducted by younger surgeons.
Moreover, the patients with higher albumin levels had a longer recovery time from PUI, which has not been reported in the previous literature [17]. Furthermore, this finding differs from what one would intuitively expect from surgical findings regarding wound healing and requires further investigation.
It is also important to note any potential implications of PUI recovery following RARP in rural community settings. Indeed, our finding at 12 months was rather superior to that observed in a Japanese elite university hospital: Hakozaki et al. reported that only $85.0\%$ of their patients experienced PUI recovery one year after RARP [18]. This means that rather than the status and ranking of hospitals, the experience and proficiency of surgeons affects the outcomes of patients, which is a fact explained above.
However, rural community hospitals face clear disadvantages compared to university hospitals, such as the challenge of organizing a comprehensive support framework for patients that involves multiple hospital staff. In this study, almost all patients performed preoperative pelvic floor muscle exercises, but no early improvement was seen. One reason for this is that we could not assign a single instructor to each patient but rather had three (or more) instructors working on rotation; therefore, the instructions for the exercises were not consistent. In addition, the instructions were mainly oral explanations; thus, the patients’ pelvic floor muscular contraction during the exercise was not accurately confirmed. Furthermore, in elderly patients, their understanding of the exercise could have been insufficient. These factors may have contributed to the inadequate effectiveness of the exercise, and we believe that a lack of manpower prevented us from providing better training to PUI. However, it has been demonstrated that pelvic floor muscle exercises are clinically significant [19,20,21], particularly in the early phase after surgery [12]. Thus, it is desirable to improve the way in which we teach pelvic floor muscle exercises so that every patient can enjoy their benefits. For example, the use of pelvic floor muscle exercise pamphlets could be useful in explaining the purposes of standardizing and unifying the content of instruction by physical therapists.
## 5. Limitations
There were several limitations in this study. First, this was a single-institution study with only a limited number of patients. This could have limited the generalizability of the observed findings and resulted in the omission of some important factors in the regression analysis, such as obesity [22], but this is the first study investigating PUI recovery after RARP in a Japanese community setting, which is an important novelty of the study. Second, we did not evaluate various confounding factors, such as anatomical ones (prostate size, preoperative urethral length, and maximum urethral closure pressure) and surgical procedure methods. As a result, the findings of the regression analysis may have been limited. Third, the definition of PUI recovery relying on the self-reported count of the urine pads may be affected by various biases. For example, the usage of urine pads may have not been standardized among the patients, which may have affected the counts of the pads used among the patients.
## 6. Conclusions
In this study, which examined the recovery from PUI following RARP in a Japanese community setting, we found that PUI was eliminated in $93.3\%$ of the patients at 365 days, which was comparable to previous reports. However, recovery at 90 days was observed in only $23.4\%$ of the patients, which was slower than reported in previous studies. Our analysis revealed that preoperative urinary incontinence, higher albumin levels and surgery performed by inexperienced surgeons were associated with a delayed recovery from PUI. On the other hand, nerve sparing was significantly associated with an earlier recovery from PUI.
## References
1. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F.. **Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries**. *CA Cancer J. Clin.* (2021.0) **71** 209-249. DOI: 10.3322/caac.21660
2. Trinh Q.D., Sammon J., Sun M., Ravi P., Ghani K.R., Bianchi M., Jeong W., Shariat S.F., Hansen J., Schmitges J.. **Perioperative outcomes of robot-assisted radical prostatectomy compared with open radical prostatectomy: Results from the nationwide inpatient sample**. *Eur. Urol.* (2012.0) **61** 679-685. DOI: 10.1016/j.eururo.2011.12.027
3. Ficarra V., Novara G., Rosen R.C., Artibani W., Carroll P.R., Costello A., Menon M., Montorsi F., Patel V.R., Stolzenburg J.U.. **Systematic review and meta-analysis of studies reporting urinary continence recovery after robot-assisted radical prostatectomy**. *Eur. Urol.* (2012.0) **62** 405-417. DOI: 10.1016/j.eururo.2012.05.045
4. Amano K., Suzuki K., Ito Y.. **Changes in quality of life and lower urinary tract symptoms over time in cancer patients after a total prostatectomy: Systematic review and meta-analysis**. *Support. Care Cancer* (2022.0) **30** 2959-2970. DOI: 10.1007/s00520-021-06595-x
5. Du Y., Long Q., Guan B., Mu L., Tian J., Jiang Y., Bai X., Wu D.. **Robot-Assisted Radical Prostatectomy Is More Beneficial for Prostate Cancer Patients: A System Review and Meta-Analysis**. *Med. Sci. Monit.* (2018.0) **24** 272-287. DOI: 10.12659/MSM.907092
6. Novara G., Ficarra V., D’Elia C., Secco S., Cioffi A., Cavalleri S., Artibani W.. **Evaluating urinary continence and preoperative predictors of urinary continence after robot assisted laparoscopic radical prostatectomy**. *J. Urol.* (2010.0) **184** 1028-1033. DOI: 10.1016/j.juro.2010.04.069
7. von Bodman C., Matsushita K., Savage C., Matikainen M.P., Eastham J.A., Scardino P.T., Rabbani F., Akin O., Sandhu J.S.. **Recovery of urinary function after radical prostatectomy: Predictors of urinary function on preoperative prostate magnetic resonance imaging**. *J. Urol.* (2012.0) **187** 945-950. DOI: 10.1016/j.juro.2011.10.143
8. Haga N., Ogawa S., Yabe M., Akaihata H., Hata J., Sato Y., Ishibashi K., Hasegawa O., Kikuchi K., Shishido F.. **Association between postoperative pelvic anatomic features on magnetic resonance imaging and lower tract urinary symptoms after radical prostatectomy**. *Urology* (2014.0) **84** 642-649. DOI: 10.1016/j.urology.2014.04.044
9. Paparel P., Akin O., Sandhu J.S., Otero J.R., Serio A.M., Scardino P.T., Hricak H., Guillonneau B.. **Recovery of urinary continence after radical prostatectomy: Association with urethral length and urethral fibrosis measured by preoperative and postoperative endorectal magnetic resonance imaging**. *Eur. Urol.* (2009.0) **55** 629-637. DOI: 10.1016/j.eururo.2008.08.057
10. Honda M., Kawamoto B., Morizane S., Hikita K., Muraoka K., Sejima T., Takenaka A.. **A prognostic model for predicting urinary incontinence after robot-assisted radical prostatectomy**. *Int. J. Med. Robot.* (2017.0) **13** e1780. DOI: 10.1002/rcs.1780
11. Schlomm T., Heinzer H., Steuber T., Salomon G., Engel O., Michl U., Haese A., Graefen M., Huland H.. **Full functional-length urethral sphincter preservation during radical prostatectomy**. *Eur. Urol.* (2011.0) **60** 320-329. DOI: 10.1016/j.eururo.2011.02.040
12. Chang J.I., Lam V., Patel M.I.. **Preoperative Pelvic Floor Muscle Exercise and Postprostatectomy Incontinence: A Systematic Review and Meta-analysis**. *Eur. Urol.* (2016.0) **69** 460-467. DOI: 10.1016/j.eururo.2015.11.004
13. **National Cancer Registry, Ministry of Health, Labour and Welfare. Cancer Statistics**
14. **Vital Statistics of Japan, Ministry of Health, Labour and Welfare. Cancer Statistics. Cancer Information Service**
15. Fossati N., Willemse P.M., Van den Broeck T., van den Bergh R.C.N., Yuan C.Y., Briers E., Bellmunt J., Bolla M., Cornford P., De Santis M.. **The Benefits and Harms of Different Extents of Lymph Node Dissection During Radical Prostatectomy for Prostate Cancer: A Systematic Review**. *Eur. Urol.* (2017.0) **72** 84-109. DOI: 10.1016/j.eururo.2016.12.003
16. Kim M., Park M., Pak S., Choi S.K., Shim M., Song C., Ahn H.. **Integrity of the Urethral Sphincter Complex, Nerve-sparing, and Long-term Continence Status after Robotic-assisted Radical Prostatectomy**. *Eur. Urol. Focus* (2019.0) **5** 823-830. DOI: 10.1016/j.euf.2018.04.021
17. Shao I.H., Chang Y.H., Hou C.M., Lin Z.F., Wu C.T.. **Predictors of short-term and long-term incontinence after robot-assisted radical prostatectomy**. *J. Int. Med. Res.* (2018.0) **46** 421-429. DOI: 10.1177/0300060517715396
18. Hakozaki K., Takeda T., Yasumizu Y., Tanaka N., Matsumoto K., Morita S., Kosaka T., Mizuno R., Asanuma H., Oya M.. **Predictors of urinary function recovery after laparoscopic and robot-assisted radical prostatectomy**. *Int. Braz. J. Urol.* (2023.0) **49** 50-60. DOI: 10.1590/s1677-5538.ibju.2022.0362
19. Mottet N., van den Bergh R.C.N., Briers E., Van den Broeck T., Cumberbatch M.G., De Santis M., Fanti S., Fossati N., Gandaglia G., Gillessen S.. **EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent**. *Eur. Urol.* (2021.0) **79** 243-262. DOI: 10.1016/j.eururo.2020.09.042
20. Heidenreich A., Aus G., Bolla M., Joniau S., Matveev V.B., Schmid H.P., Zattoni F.. **EAU guidelines on prostate cancer**. *Eur. Urol.* (2008.0) **53** 68-80. DOI: 10.1016/j.eururo.2007.09.002
21. Baumann F.T., Reimer N., Gockeln T., Reike A., Hallek M., Ricci C., Zopf E.M., Schmid D., Taaffe D., Newton R.U.. **Supervised pelvic floor muscle exercise is more effective than unsupervised pelvic floor muscle exercise at improving urinary incontinence in prostate cancer patients following radical prostatectomy—A systematic review and meta-analysis**. *Disabil. Rehabil.* (2021.0) **44** 5374-5385. DOI: 10.1080/09638288.2021.1937717
22. Sarychev S., Witt J.H., Wagner C., Oelke M., Schuette A., Liakos N., Karagiotis T., Mendrek M., Kachanov M., Graefen M.. **Impact of obesity on perioperative, functional and oncological outcomes after robotic-assisted radical prostatectomy in a high-volume center**. *World J. Urol.* (2022.0) **40** 1419-1425. DOI: 10.1007/s00345-022-03989-2
|
---
title: Evaluation of Satisfaction with the Built Environment of University Buildings
under the Epidemic and Its Impact on Student Anxiety
authors:
- Qiang Wen
- Haiqiang Liu
- Jinyuan Chen
- Huiyao Ye
- Zeyu Pan
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001516
doi: 10.3390/ijerph20054183
license: CC BY 4.0
---
# Evaluation of Satisfaction with the Built Environment of University Buildings under the Epidemic and Its Impact on Student Anxiety
## Abstract
Anxiety on college campuses has increased due to the COVID-19 epidemic’s profound effects on society. Much research has been conducted on how the built environment influences mental health; however, little has been undertaken on how it affects student mental health in the context of the epidemic from the architectural scale perspective of academic buildings. Based on online survey data, this study develops multiple linear regression and binary logistic regression models to investigate students’ satisfaction ratings of the academic buildings’ physical environments during the epidemic and how these satisfaction ratings affect students’ anxiety tendencies. According to the study’s findings regarding the natural exposure perspective, students who perceived the academic building’s poor semi-open space view ($$p \leq 0.004$$, OR = 3.22) as unsatisfactory factors were more likely to show anxiety tendencies. In terms of the physical conditions, students who were dissatisfied with the noise level in the classroom ($$p \leq 0.038$$, OR = 0.616) and the summer heat in semi-open spaces ($$p \leq 0.031$$, OR = 2.38) were more likely to exhibit anxiety tendencies. Additionally, even after controlling for confusing distractions, the general satisfaction rating of the academic building’s physical environments ($$p \leq 0.047$$, OR = 0.572) was still able to significantly and negatively affect students’ anxiety tendencies. The study’s findings can be used in the architectural and environmental planning of academic buildings focusing on mental health.
## 1. Introduction
COVID-19 outbreaks are linked to poor mental health outcomes, including anxiety and depression symptoms [1]. In 2020, over 41 million students were enrolled in higher educational institutions [2], and college students were more likely to experience anxiety and depression, with $15\%$ of Chinese college students suffering anxiety symptoms [3]. As the outbreak was effectively contained, students’ mental health improved slightly [4], but the closed management of most campuses also contributed to mental health issues among college students.
The built environment is the physical environment constructed for human life and activities, which has an impact on people’s physical health [5,6,7] as well as their psychological health (e.g., anxiety and depression) [8,9,10,11]. In recent years, extensive research has been conducted on the relationship between the built environment and mental health [12,13,14,15], including residential floor levels [16], green spaces [17,18], and exposure to indoor air pollutants [19]. The built environment can impact people’s mental health by influencing their connection to nature, personal control, and indoor air quality [20]. On the other hand, some studies do not conclusively demonstrate a connection between noise, building form, or a green environment and mental health [21].
Crowding, poor indoor air circulation, and ambient air pollution are risk factors for COVID-19 transmission in the built environment [22]. Higher ambient levels of delicate particulate matter and nitrogen oxides are associated with an increased risk of COVID-19 morbidity, severity, and mortality [22]. On the other hand, the frequency of use of green space and green window views in the home was related to higher levels of self-worth, life satisfaction, and subjective well-being during the COVID-19 epidemic, as well as lower levels of depression, anxiety, and loneliness [23]. During an isolation lockdown, staying in touch with nature (blue-green space) lowered the risk of developing depressive and anxiety symptoms [24] and was found to mitigate the adverse effects of social isolation on mental health [25,26].
There are several related studies on the campus’ built environment’s effect on students’ mental health during an epidemic. Students’ perceptions of the built environment, attitudes, and walking patterns on campus during an epidemic may impact their physical and mental well-being [27]. In an isolated shelter area, preferred interior colors, indoor plants, artwork, and high-quality green and sky views can all lower the risk of anxiety and depression [28]. The campus environment during the epidemic had the greatest psychological recovery effect for blue spaces, followed by green spaces, sports fields, while it was least pronounced in gray spaces [29].
According to existing research, aside from the stress induced by academic pressure, the epidemic’s development exacerbated students’ anxiety [3], and the campus’s built-up environment also impacted students’ mental health and anxiety [28,29]. The majority of research on the relationship between the built environment and mental health has been completed on large-scale built environments [12,21] or small-scale residential buildings [9,16]; fewer studies have been conducted at the architectural scale of academic buildings, particularly in the context of epidemics. Furthermore, current research focuses on the independent effects of specific factors on mental health, such as green space [17,24], air pollution [19], and traffic noise [30]. While these factors are usually spatially correlated as built environment features, separately evaluating them would ignore the potential confounding effects between them.
The closed campus management, which forced students to spend more time in the academic building in the context of the epidemic, caused students to have harmful psychological emotions. The academic building’s physical environment can have a more substantial effect on students’ mental health. However, the built environment is rarely created with design aspects deliberately intended to enhance mental health [31]. At the scale of academic buildings, the built environment has three dimensions: the interior environment, the semi-open space environment, and the external perimeter environment, which includes both physical conditions and natural exposure. This article aims to examine how satisfied students are with each aspect of the academic building’s constructed environment in the context of the epidemic, as well as how these ratings affect students’ tendency for anxiety. In order to prevent the interference of confounding factors, a multi-factor holistic model was created for the study. With the aim of reducing student anxiety and enhancing mental health through improvements to the built environment of academic buildings, the study’s findings will aid in relevant decision-making and serve as a reference for the design and renovation of the architecture and environment of academic buildings.
The rest of the document is organized as follows. Section 2 covers the relevant theory and the formulation of hypotheses; Section 3 covers the study design, variable factors, and statistical methods; Section 4 presents the statistical results; Section 5 provides an analytical discussion of the results, including the study’s limitations and future work; and Section 6 summarizes the key findings and conclusions.
## 2.1. Natural Exposure and Mental Health
Studies have shown that exposure to nature has a positive effect on improving physical health, mental health [32], and cognitive functioning [33]; that reduced exposure has a negative impact on mental health outcomes [20]; and that individuals who live in areas with a lack of green space and who are physically unhealthy are more likely to experience mental health issues [34]. During the COVID-19 epidemic, frequent use of green space and the presence of a view from a home’s windows were linked to higher levels of self-worth, life satisfaction, and subjective well-being, as well as lower levels of sadness, anxiety, and loneliness [23]. Staying in touch with nature (blue-green space) can lessen the risk of experiencing symptoms of sadness and anxiety [24] and can buffer or lessen the negative impacts of social isolation on mental health [25,26]. According to studies conducted from a campus perspective, campus greenery significantly enhances students’ physical health. It lowers physical stress [35], and exposure to natural settings can have a positive effect on academic performance [36,37,38]. Good window views can positively affect students’ mental health and mood, as well as their ability to concentrate [39,40]. In conclusion, natural exposure to the built environment has an enormously beneficial impact on mental health. We tested the hypothesis that students unsatisfied with the natural exposure component in the built environment would be more prone to anxiety.
## 2.2. Physical Conditions and Mental Health
From the perspective of ventilation and mental health, studies have shown that air pollution caused by poor ventilation is associated with adverse mental health outcomes [20,41,42,43] and that indoor air pollutants cause adverse health effects, ranging from respiratory diseases [44] to cognitive effects [45,46,47]. Fine airborne particles (PM2.5) formed by poor ventilation have been linked to symptoms of depression and anxiety [45], which play an essential role in depression and psychotic disorders [48,49]. Low ventilation rates increase indoor carbon dioxide concentrations, a potential health risk [50]. In addition, air pollution can deter participation in outdoor activities, which can affect mental health [51,52]. During the outbreak, higher levels of ambient delicate particulate matter and nitrogen oxides were connected to an elevated risk of COVID-19 morbidity, severity, and fatality [22].
According to research on the thermal environment and mental health associations, cold temperatures reduce negative mental health outcomes while hot temperatures increase them [53]. High temperatures have been associated with depression, anxiety, mood disorders, aggression [54], a decrease in positive mood, an increase in negative mood, and fatigue [55]. They may also exacerbate psychiatric conditions and impact mental health [56], as well as increase the risk of suicide and hospitalization for mental illness [57]. In addition, studies have found a relationship between the thermal environment of the classroom and student learning efficiency [58].
In terms of the relationship between noise and mental health, several studies have shown that noise has a detrimental effect on mental health [30,59,60], that annoyance brought on by noise exposure is positively associated with anxiety and depression [59], that urban and traffic noise is related to adverse mental health outcomes, and that neighborhood noise disturbance increases the likelihood of having poor mental health [30].
According to research, daylight and illumination are related to reduced weariness, relief from melancholy, reduced depressive symptoms, and many other health advantages [61], with inadequate lighting increasing the likelihood of developing depression by $60\%$ [31]. Quality lighting has been linked to decreased stress, anxiety, and improved mood [62]. Furthermore, studies show that lighting that produces visual comfort increases health, well-being, and satisfaction, improving learning and visual performance [63].
In summary, physical conditions in the built environment have a significant impact on mental health. Many studies have shown that air pollution, heat and noise exposure, and poor illumination negatively impact mental health. Therefore, we tested the following hypothesis: students dissatisfied with physical conditions will show a greater tendency to be anxious.
## 3.1. Study Design
This study first compiled the built environment factors of the academic building based on existing literature, then designed questionnaires and collected data based on the study’s purpose, modeled the research data, conducted the statistical analysis, and discussed it with existing studies to reach its conclusions. This paper investigated student satisfaction ratings for each aspect of the academic building’s built environment in the context of the epidemic and the relationship between these satisfaction ratings and student anxiety tendencies. Based on the earlier analysis and the model’s streamlining through variable screening, 16 built environment factors were used as independent variables. General satisfaction and anxiety tendencies were used as dependent variables to create multiple linear regression and binary logistic models, respectively, to cut out the interference of confounding factors and investigate the independent influence.
On the “Questionnaire Star” platform, a self-administered computerized survey was used to collect the data. During the COVID-19 epidemic in April 2022, college campuses routinely established a strictly controlled system of access to the campus. They prohibited all students from being on campus in order to control the spread of the new coronavirus outbreak. We recruited college students from 139 universities in various provinces to participate in the questionnaire study to ensure the data collection’s representativeness and value. After the screening, 241 valid research questionnaires were found out of 279 that had been gathered. The reliability tests showed that the scale questions were consistent and that the study data were highly reliable (Cronbach’s Alpha = 0.928). There is some association between the topics, and the validity of the study data is good, according to the findings of the validity test in the factor analysis (KMO value = 0.923, estimated chi-square = 1605.185, $p \leq 0.05$).
## 3.2. Built Environment Factors
Table 1 depicts the constructed environment at the scale of the academic building analyzed in this work, which includes both natural exposure and physical conditions, as well as three dimensions of the inside, semi-open area, and outside perimeter of the teaching structure. Watching nature, being with nature, and seeking physical connection with nature are three components of nature exposure that have a positive impact on mental health [64], so natural exposure at the scale of a teaching building had five components: the indoor landscape view of classrooms, the number of semi-open spaces, semi-open space landscape view, surrounding landscape and leisure facilities, and surrounding green vegetation. An approximate 50-m radius was used to outline the academic building’s surrounding boundary. After field research, we found that students often closed the curtains to shelter themselves from direct sunlight when using the classroom and that the classroom landscape view was only accessible from the window seats, the study of the classroom internal landscape view was discarded. Additionally, the physical conditions included four dimensions: ventilation, thermal environment, classroom noise, and classroom lighting, with ventilation including three aspects, namely indoor ventilation, semi-open space ventilation, and ventilation around the teaching building. The thermal environment likewise included three aspects: the indoor thermal environment, the semi-open space thermal environment, and the outdoor thermal environment around the teaching building.
## 3.3. Questionnaire Composition and Variables
The questionnaire included five parts, as shown in Table 2, the first of which asked about personal traits and background. Four independent factors were included in this section: gender, grade, climate zone, and inner or outer corridor. According to the “*What is* your school?” survey, the variable “climate zone” was discovered. The data was divided into four categories: “severe cold regions”, “cold regions”, “hot summer and cold winter regions”, and “hot summer and warm winter regions.” The second part concerned students’ overall satisfaction with the built environment and whether they tended to be anxious. Anxiety symptoms were measured using the Generalized Anxiety Inventory (GAD-7) [65]. The third, fourth, and fifth sections, with a total of 14 independent variables, focused on students’ satisfaction evaluations of each aspect of the built environment of the academic building, including the indoor environment, outdoor environment, and semi-open space environment. The indoor environment consisted of four items: classroom ventilation, lighting, noise, and thermal comfort, while the outdoor environment also consisted of four items: landscape and leisure facilities, green vegetation, wind environment, and thermal environment around the academic building. The semi-open space environment consisted of six items: quantity, wind environment, landscape view, winter cold, summer heat, and semi-open space satisfaction. A Likert scale was used for indoor and outdoor environmental factors and semi-open space satisfaction, indicating students’ satisfaction with each factor of the built environment of the academic building on a 5-point scale from “very dissatisfied” to “very satisfied.” The sub-elements of the semi-open space environment of the academic building are dichotomous variables, expressing “whether they are unsatisfactory factors”.
## 3.4. Statistical Methods
The data were statistically analyzed using SPSS 26.0 software. First, descriptive statistics were run, which included demographic factors, background characteristics, students’ general satisfaction with the built environment, whether they were anxious, and satisfaction with each built environment factor. All variables were then subjected to a correlation analysis matrix and co-linearity test with a t-test and a chi-square test to exclude those variables with a large autocorrelation and no significant effect on the dependent variable. A tolerance greater than 0.1 or a variance inflation factor less than 5 indicates no covariance. A correlation coefficient below 0.8 indicates no strong correlation between the variables. The model was simplified to obtain the 17 variables used in the subsequent modeling.
In order to investigate which factors of the built environment significantly affect students’ evaluation of overall satisfaction with the built environment, a multivariate linear regression model was used with “general satisfaction” as the dependent variable and 16 other variables as independent variables. A binary logistic regression model was developed to investigate the built environment factors that may influence college students’ anxiety tendencies, with “anxiety tendencies” as the dependent variable and the satisfaction rating of each built environment factor of the academic building as the independent variable.
## 4.1. Descriptive Statistics
The samples were primarily concentrated in hot summer and cold winter regions ($57.7\%$) and hot summer and warm winter regions ($27.4\%$), with the remainder located in severe cold regions ($2.9\%$) and cold regions ($12\%$). There were significantly more inner corridors ($76.6\%$) than outer corridors ($22.4\%$) in the academic building. Juniors ($21.2\%$) and seniors ($44.8\%$) constituted most of the sample grades. The Generalized Anxiety Scale (GAD-7) scores range from 0 to 21 on a scale of normal (0 to 4), mild anxiety (5 to 9), moderate anxiety (10 to 14), and severe anxiety (15 to 21). With a cut-off score of 5, respondents were divided into two groups: those without anxiety tendencies (GAD-7 score <5) and those with anxiety tendencies (GAD-7 score ≥ 5) [65]. By the statistics, as shown in Table 3, there were significantly more students without anxiety tendencies ($77.6\%$ of the whole sample) than students with anxiety tendencies ($22.4\%$ of the entire sample). Male students had an anxiety rate of $22.7\%$, while female students had an anxiety rate of $22.1\%$.
*The* general satisfaction rating ($M = 3.49$, SD = 0.91) of students with the built environment of the academic building was ordinary. As shown in Figure 1, among all built environment factors, students were least satisfied with the classroom noise environment ($M = 3.29$) and most satisfied with the surrounding green vegetation ($M = 3.73$). Students’ satisfaction with the surrounding landscape facilities (SD = 1.04) had the greatest variability in their opinion ratings, and their satisfaction with the surrounding thermal environment (SD = 0.89) had the least variability in their opinion ratings.
As shown in Table 4, college students were more satisfied with the outdoor environment around the academic building ($M = 3.52$), the internal environment of the classroom was average ($M = 3.425$), and the semi-open space environment of the academic building was rated the lowest ($M = 3.39$). In the indoor classroom environment, students had the highest satisfaction rating for lighting ($M = 3.62$, SD = 0.90) and the lowest satisfaction rating for noise ($M = 3.29$, SD = 1.00). During the epidemic, students’ range of activities was restricted, and their sensitivity to noise increased. Noises from the surrounding traffic environment, adjacent classrooms, and corridors may be the main sources of noise disturbance. Additionally, the indoor thermal environment ($M = 3.37$, SD = 1.02) and the ventilation environment ($M = 3.42$, SD = 0.96) received average satisfaction ratings. In the outdoor environment surrounding the academic building, students rated the highest level of satisfaction with the green vegetation environment ($M = 3.73$, SD = 0.93) and the lowest with the landscape and leisure facilities ($M = 3.33$, SD = 1.04), indicating that students have a higher demand for the surrounding landscape and leisure facilities. Concerning physical conditions, the wind environment surrounding the academic building was assessed as good ($M = 3.61$, SD = 0.89), while satisfaction with the thermal environment was rated as average ($M = 3.41$, SD = 0.89).
The factor “low number” ($37.8\%$) was the most frequently chosen in the questionnaire survey on respondents’ dissatisfaction with the semi-open space environment in the academic building, followed by “cold in winter” ($32.4\%$), “hot in summer” ($27.0\%$), “poor view of the landscape” ($22.0\%$), and “high wind speed” ($19.5\%$), as shown in Figure 2.
## 4.2.1. Co-Linearity Test
A co-linearity test is required to bring the dependent and independent variables of the logistic regression directly into the linear regression model to obtain the tolerance and variance inflation factors and avoid the covariance problem between variables, which affects the correct estimation of the regression model. Table 5 displays the results; the variance inflation factors are all less than 5, with the largest value being “Surrounding wind environment” (3.702), and the model has no multicollinearity issues.
## 4.2.2. Correlation Check
In order to eliminate the covariance problem, a Pearson correlation matrix analysis was undertaken on the independent variables of all scale questions to screen out the significantly more correlated independent variables. Table 6 shows that the correlation coefficients between each independent variable were less than 0.8, with the “surrounding wind environment” and “surrounding green vegetation” having the strongest correlation ($r = 0.765$). Additionally, it can also be noted that the connection between each built environment aspect and general satisfaction with the built environment of the academic building ranged from 0.532 to 0.618, with the “surrounding wind environment” having the lowest correlation ($r = 0.532$).
## 4.2.3. One-Factor Test
In order to remove independent variables that were not substantially different in the binary logistic regression model, a chi-square cross-tabulation test was performed between the categorical independent variables and the dependent variable (anxiety tendencies). As demonstrated in Table 7, “semi-open space cold in winter” ($$p \leq 0.863$$) and “high wind speed in semi-open space” ($$p \leq 0.855$$) were not significantly different from “anxiety tendencies” among the semi-open space dissatisfaction variables. In terms of the demographic background variables, “gender” ($$p \leq 0.913$$), “grade” ($$p \leq 0.36$$), “climate zone” ($$p \leq 0.102$$), and “inner or outside corridor” ($$p \leq 0.684$$) did not differ significantly for “anxiety tendencies” and were therefore included as control variables and not further analyzed. On the other hand, the continuous independent variable of the scale questions was analyzed using an independent samples t-test with “anxiety tendencies”. According to Table 8, “surrounding wind environment” ($t = 1.073$, $$p \leq 0.284$$) was the least significant independent variable with the dependent variable.
A chi-squared cross-tabulation was conducted between the categorical independent factors and the dependent variable (general satisfaction) of the multiple linear regression model. As shown in Table 9, among the demographic background variables, “climate zone” significantly differed from “general satisfaction” ($$p \leq 0.011$$). In contrast, “gender”, “grade”, and “inner or outside corridor” did not ($$p \leq 0.533$$, $$p \leq 0.781$$, and $$p \leq 0.684$$, respectively). “ high wind speed in semi-open space” ($$p \leq 0.813$$), “semi-open space cold in winter” ($$p \leq 0.268$$), and “semi-open space hot in summer” ($$p \leq 0.142$$) were not significantly different from “general satisfaction” among the semi-open space dissatisfaction factors.
## 4.2.4. Summary of Independent Variable Screening
To simplify the model with a small sample of cases, the co-linearity test, correlation test, and one-way analyses (chi-square test, t-test) were used to examine the relationships between all variables. Furthermore, independent variables with higher covariance and those that did not significantly differ from the dependent variable might be eliminated. Combined with the comprehensive analysis from the professional point of view, the excluded independent variables were “surrounding wind environment”, “high wind speed in semi-open space”, and “semi-open space cold in winter”. Table 10 shows the remaining 16 variables and models developed, with demographic background variables (gender, grade, climate zone, inner or outer corridor) included as control variables not to be analyzed in the study.
## 4.3. Multiple Linear Regression Analysis
A multivariate linear regression model was developed with “general satisfaction” as the dependent variable and 16 other variables as independent variables. The stepwise method was used in building the multiple linear regression model. The regression equation was statistically significant ($F = 42.265$, $p \leq 0.05$), and the coefficient of determination of the model (R2 = 0.593) suggested that the eight significant independent variables explained $59.3\%$ of the general satisfaction with the built environment. The Durbin-Watson test value of 1.906 indicates that the samples are independent.
Table 11 shows the coefficient table for the multiple linear regression model. The results show that the factors that significantly and positively affected the general satisfaction of the built environment were “classroom ventilation” (beta = 0.194, $t = 3.252$, $$p \leq 0.001$$), “classroom thermal environment” (beta = 0.176, $t = 2.810$, $$p \leq 0.005$$), “classroom noise environment” (beta = 0.138, $t = 2.365$, $$p \leq 0.019$$), “surrounding landscape and leisure facilities” (beta = 0.290, $t = 5.307$, $p \leq 0.001$), and “surrounding thermal environment” (beta = 0.150, $t = 2.397$, $$p \leq 0.017$$). The overall satisfaction rating of the built environment was not significantly impacted by students’ satisfaction with “classroom lighting”, “poor view of semi-open space”, “semi-open space hot in summer”, “low number of semi-open spaces”, or “surrounding green vegetation”.
According to the size of the standardized coefficient, “surrounding landscape and leisure facilities” (beta = 0.290), “classroom ventilation” (beta = 0.194), “classroom thermal environment” (beta = 0.176), “surrounding thermal environment” (beta = 0.150), and “classroom noise environment” (beta = 0.138) played the most significant role in influencing general satisfaction with the built environment. In the context of the epidemic, the factors influencing students’ overall satisfaction evaluation of the built environment are primarily the surrounding landscape and leisure facilities, classroom ventilation, classroom thermal and acoustic settings, and so on.
## 4.4. Binary Logistic Regression Analysis
In order to evaluate the influence of each built environment satisfaction factor on students’ anxiety tendencies, a binary logistic regression model was designed with “anxiety tendencies” as the dependent variable and 16 additional built environment factors as independent variables. Since in the previous study, as shown in Section 4.2, we screened and streamlined the independent variables, we used the entry method in building the binary logistic regression model to comprehensively study each independent variable’s effect on the dependent variable. As shown in Table 12, the Omnibus tests revealed that the model was statistically significant ($p \leq 0.05$, $F = 63.66$). The Hosmer-Lemeshow test revealed that the model was well-fitted ($$p \leq 0.17$$ > 0.05), and the −2 log-likelihood value was 184.14, which quantitatively evaluated the model fit. The model successfully classified $79.9\%$ of the observed samples, $31.4\%$ of which were predicted to have anxiety tendencies and $93.1\%$ of which were predicted to have no anxiety tendencies.
The absolute magnitude of the regression coefficients, as shown in Table 13, indicates the degree of influence of each influencing factor on students’ anxiety tendencies; the positive and negative signs indicate the direction of influence; and the OR values indicate the probability of occurrence of anxiety tendencies relative to the reference group. Statistical results showed the independent variables that significantly influenced students’ anxiety tendencies were: “poor view of semi-open space” ($B = 1.169$, $$p \leq 0.004$$, OR = 3.220), “semi-open space hot in summer” ($B = 0.867$, $$p \leq 0.031$$, Exp(B) = 2.380), “classroom noise environment” (B = −0.485, $$p \leq 0.038$$, OR = 0.616), and “general satisfaction” (B = −0.559, $$p \leq 0.047$$, OR = 0.572). On the other hand, “classroom ventilation”, “classroom lighting”, “classroom thermal environment”, “surrounding landscape and leisure facilities”, “surrounding green vegetation”, “surrounding thermal environment”, “semi-open space satisfaction”, and “the low number of semi-open spaces” had no significant effect on the development of students’ anxiety tendencies.
The poor view of semi-open space had the most significant effect on the probability of anxiety occurrence among students, followed by the semi-open space hot in summer, both of which positively affected the probability of anxiety tendencies among students, as shown in Figure 3. *The* general satisfaction with the built environment of the academic building and the satisfaction with the classroom noise environment negatively affected the probability of student anxiety. Among dissatisfaction factors with semi-open space, “poor view of semi-open space” could significantly and positively affect students’ anxiety tendencies (OR = 3.220, $$p \leq 0.004$$). Students who perceived a poor view of semi-open space as a dissatisfactory factor had a 2.22-fold increased risk of anxiety. “ Semi-open space hot in summer” influenced students’ anxiety significantly and positively (OR = 2.380, $$p \leq 0.031$$). Students who perceived summer heat in semi-open spaces as a dissatisfaction factor were 1.38 times more likely to have anxiety.
Even after controlling for the confounding effects of other variables, “general satisfaction with the built environment” showed an independently significant, negative influence on students’ anxiety (OR = 0.572, $$p \leq 0.047$$). With each level of improvement from “very dissatisfied” to “very satisfied” about the students’ overall satisfaction with the built environment, their anxiety tendencies declined by 0.43 times. The “classroom noise environment” could significantly and negatively affect the probability of students’ anxiety tendencies (OR = 0.616, $$p \leq 0.038$$). As students’ satisfaction with the classroom noise environment increased by one level from “very dissatisfied” to “very satisfied”, the probability of having anxiety decreased by 0.38 times.
## 5.1. Main Findings
This study discovered that the semi-open space design and the classroom noise environment are the most critical characteristics that can significantly influence students’ anxiety tendencies in the context of the epidemic and at the scale of the academic building. Semi-open space landscape views and summer heat are all characteristics of semi-open space that can significantly affect student anxiety. The evaluation of the semi-open space landscape view, in particular, played the most important role in determining whether students were anxious or not. The semi-open space view variable represents the method of natural contact. The summer hot in semi-open spaces reflects the physical conditions of semi-open spaces. Other natural exposure factors, such as “surrounding green vegetation”, “surrounding landscape and leisure facilities”, and “low number of semi-open spaces”, did not significantly affect students’ anxiety tendencies. Regarding other physical conditions, only “classroom noise environment” and “semi-open space hot in summer” significantly influenced the probability of students’ anxiety tendencies. “ Classroom ventilation”, “classroom lighting”, and “classroom thermal environment”; “surrounding thermal environment”, “surrounding wind environment”; “high wind speed in semi-open space”, “semi-open space cold in winter”; all did not affect students’ tendency to anxiety. It is noteworthy that general satisfaction with the built environment, excluding the confounding effect of other variables, can still significantly affect the probability of students’ anxiety tendencies.
Regarding physical conditions, the factors that can significantly influence students’ general satisfaction evaluation of the built environment include “classroom ventilation”, “classroom thermal environment”, “classroom noise environment”, and “surrounding thermal environment”. “ Classroom lighting”, “surrounding wind environment”, “surrounding thermal environment”, and “high wind speed in semi-open space” did not significantly affect students’ general satisfaction evaluation of the built environment. In terms of natural contact, the landscape and leisure facilities around the academic building significantly affected the overall satisfaction evaluation of students with the built environment. In contrast, surrounding green vegetation, the low number of semi-open spaces, and the poor view of the semi-open space had no significant effect on the overall satisfaction evaluation.
## 5.2. Natural Exposure and Tendency to Anxiety
At the scale of the academic building, nature contact includes four aspects. These are the surrounding landscape and leisure facilities, surrounding green vegetation, the number of semi-open spaces, and landscape views of semi-open spaces, reflecting different levels of contact with nature [64]: observing nature, being with nature, and seeking physical interaction with nature. However, only the view of the semi-open space landscape significantly affects the probability of generating students’ anxiety tendencies.
Consistent with previous research, this study found that students who were satisfied with the view of the semi-open space were less likely to develop anxiety. Observing nature reduces stress via psychophysiological pathways and is beneficial to mental health [66], and there is a positive correlation between natural window views and improved mental health [67,68,69,70]. Semi-open spaces can also provide multisensory experiences, such as landscape views, bird sounds, and flower fragrances [71], which can improve mental health through many senses [72]. Semi-open spaces allow people to interact with nature during an epidemic, verifying and supplementing existing research on the advantages for mental health of exposure to nature [32].
In contrast to the proposed hypothesis, the surrounding landscape, leisure facilities, and green vegetation did not significantly affect the students’ tendency to be anxious. That differs from previous research, which found that plants provide psychological recovery benefits [73] and that engagement with nature is also beneficial for psychological well-being [74,75]. That could be due to the outbreak’s particular situation and the issue of natural exposure accessibility. In the context of the epidemic, concentrated outdoor activities for students were discouraged, and the leisure facilities of the surrounding landscape and green vegetation had relatively poor accessibility for students in academic buildings, raising the expense of students’ exposure to nature. Therefore, regardless of the students’ satisfaction with the leisure facilities in the surrounding landscape and green vegetation, their tendency to be anxious was unaffected. For pupils studying in the classroom, on the other hand, the semi-open space had better accessibility and convenience and offered a natural shelter from wind and rainy weather. In a context where public activities are discouraged, the natural contact afforded by semi-open spaces is critical for students’ emotional regulation. On the other hand, maintaining social distance measures in the context of the epidemic is an option for the government to help reduce transmission. A lack of socialization may harm students’ mental health [76,77]. Semi-open spaces provide a certain level of social interaction [22], which benefits mental health.
For students’ mental health in the context of the epidemic, built and environmental design and renovation based on mental health should pay more attention to the accessibility and convenience of natural contact facilities, reach a certain amount, have a good landscape view and thermal environment, and provide a certain amount of space for social interaction. Additionally, the outdoor landscape and recreational facilities should be optimized to provide students with total satisfaction with the built environment.
## 5.3. Physical Conditions and Tendency to Anxiety
In terms of physical conditions, only the classroom noise environment and the hot semi-open summer space significantly affected students’ anxiety. In contrast to the previous section’s hypothesis, “classroom ventilation”, “classroom lighting”, “classroom thermal environment”, “surrounding thermal environment”, “surrounding wind environment”, “high wind speed in semi-open space”, and “semi-open space cold in winter”, did not affect students’ anxiety. Additionally, “classroom ventilation”, “classroom thermal environment”, “classroom noise environment”, and “surrounding thermal environment” can significantly affect students’ overall satisfaction ratings of the built environment.
From the perspective of the wind environment, the satisfaction ratings of three aspects, the classroom ventilation, the surrounding wind environment, and high wind speed in semi-open space, had no significant influence on the students’ tendency to anxiety. That is different from previous research, which shows that inadequate ventilation results in air pollution and lowers indoor air quality, which can impact mental health [20]. Air pollution has been linked to poor mental health [41,42,43] as well as behavioral determinants of mental health [51,52], and PM2.5 in air pollution has been associated with anxiety and depression symptoms [43]. The difference in the results of this article could be due to students’ selective behavior to improve ventilation or avoid being in an unfavorable wind environment when they are unsatisfied with the wind environment. Students can improve ventilation by employing mechanical exhausts or opening windows, and in response to poor external wind conditions, students may choose to remain indoors. Studies have shown that poor ventilation can impact mental health. However, students are also less likely to suffer from anxiety when ventilation can be controlled and improved, or when avoidance is an option.
From the thermal environment perspective, students who perceived “semi-open space hot in summer” as a factor of dissatisfaction were more likely to develop anxiety tendencies. That is consistent with existing research that suggests summer heat waves may trigger anxiety due to emotional and physical discomfort [78]. Additionally, studies have shown that heat exposure harms mental health, with elevated body temperature linked to depression and anxiety [54]. However, students’ satisfaction ratings of “classroom thermal environment”, “surrounding thermal environment”, and “semi-open space cold in winter” do not affect their anxiety tendencies. It could be because students can improve the thermal environment indoors by using air conditioning and ventilation or choose to be inside when thermal comfort is poor. They can regulate by adding clothes in semi-open spaces where it is cold in the winter. As a result, students’ satisfaction with the classroom thermal environment and surrounding thermal environment, as well as their attitude toward “semi-open space cold in winter”, do not affect their anxiety. On the other hand, “semi-open spaces hot in summer” is more challenging to regulate artificially. The semi-open spaces are convenient and necessary natural contact spaces for students in the context of the epidemic. Thus, students who are dissatisfied with the heat in the semi-open spaces in the summer are more likely to develop anxiety.
From the perspective of the noise environment, students’ satisfaction ratings of the classroom noise environment were found to have a significant inverse effect on their anxiety tendencies. That is consistent with current research findings corroborating the correlation of noise with depression and anxiety [79] and the negative impact on mental health [59,60]. When students are dissatisfied with the noisy environment but unable to change or control it, they develop anxiety. Students’ satisfaction with the noisy environment is lowest on university campuses in cities, which are typically more bothered by city and traffic noise. Because urban and traffic noise is linked to poor mental health outcomes [30], students who are dissatisfied with their noisy surroundings are more likely to feel anxious. The study’s findings also reveal flaws in the present campus’s noise prevention plan. Treatment for noise prevention should be considered throughout planning, building design, and material construction.
From the perspective of the lighting environment, students’ satisfaction ratings of the classroom lighting had no significant effect on the development of anxiety tendencies, and most students were satisfied with classroom lighting. Even with poor lighting, students can control and improve it by turning on the lights or pulling the curtains. Although increasing exposure to natural light has antidepressant effects [80] and human vision is usually better in daylight than in electric lighting [61], non-sunlit areas do not always cause visual discomfort [63]. As a result, students dissatisfied with the illumination are less likely to be anxious.
This paper’s findings on the relationship between students’ satisfaction ratings of physical conditions and anxiety tendencies corroborate previous studies. Mental health outcomes are related to an individual’s ability to control their physical self and surroundings [81,82,83,84]. Control over the built environment can alter an individual’s mental health through direct pathways [20]. Thus, whether student satisfaction ratings of physical conditions affect anxiety tendencies may depends on whether improvements in physical conditions are controlled or can be avoided without loss. That explains why unsatisfactory ratings of the lighting, wind conditions and thermal environments do not produce student anxiety. However, “classroom noise” and “summer heat in semi-open spaces” are difficult to improve or avoid, so students’ dissatisfaction can easily lead to anxiety. As a result, there is an urgent need for targeted design and modification of classroom noise and summer heat protection in semi-open areas to improve students’ mental health and minimize anxiety. Moreover, students’ ratings of classroom ventilation, the thermal environment, the noise environment, and the surrounding thermal environment all significantly impact their overall satisfaction with the built environment. Consequently, there is a need to improve them in the design and renovation of the built environment.
## 5.4. General Satisfaction with the Built Environment and Anxiety Tendencies
After controlling for the confounding effects of other built environment factors, general satisfaction with the built environment can negatively and significantly influence students’ anxiety tendencies, confirming previous research on the crucial role of the built environment on mental health (e.g., anxiety and depression) [8,9,10,11]. It also shows that, in addition to the built environment factors summarized in this study, the built environment, as a physical environment created for human life and activity, contains other factors that may influence students’ tendency to anxiety. For example, preferred interior colors, indoor plants, and artwork with windows can reduce the risk of anxiety and depression [28]. On the other hand, students’ anxiety has an inverse effect on overall satisfaction evaluation of the built environment. However, the magnitude of the effect is small, indicating that students’ anxiety can have some influence on the evaluation and judgment of satisfaction.
## 5.5. Limitations and Future Work
This study has certain limitations. We collected the data through an online questionnaire. Students were exposed to various irrelevant elements when filling out the questionnaire, which may have resulted in a discrepancy with the students’ actual psychological state. As mentioned above, other factors of the built environment deserve further research, such as interior layout, color, vignettes, different decorative settings, cultural shaping, and behavioral thinking, that could be included in future research efforts as variables affecting mental health. Additionally, with the normalization of epidemic prevention, whether there is a change in the satisfaction rating of academic buildings on the impact of student anxiety, further research based on this study can be conducted in the future to accurately analyze the impact of the built environment on mental health and provide a reference for a healthier campus design.
## 6. Conclusions
In this study, the built environment is used as a research perspective at the scale of academic buildings. Students’ satisfaction ratings of various features of the built environment of university teaching buildings in the context of the epidemic and the impact of these satisfaction ratings on students’ anxiety tendencies were counted and analyzed using online questionnaire data. The built environment factors studied included three dimensions of the academic building’s indoor, semi-open space, and outdoor perimeter, including both natural contact and physical conditions, as well as demographic background variables, for a total of 16 variables included in the final multiple linear regression and binary logistic regression models, respectively. Additionally, the study developed a multi-factor, holistic model to avoid the interference of confounding factors.
From the perspective of nature exposure, the results of this study showed that students who were dissatisfied with the poor view of semi-open spaces were more at risk of anxiety. At the same time, satisfaction ratings of the surrounding landscape and leisure facilities, the surrounding green vegetation, and the low number of semi-open spaces had no significant effect on students’ anxiety tendencies. From the viewpoint of physical conditions, the satisfaction rating of the classroom noise environment significantly and negatively impacted students’ anxiety tendencies. Students dissatisfied with the hot semi-open space in summer were more at risk of anxiety. In contrast, the satisfaction rating of the classroom ventilation, lighting, thermal environment, and the surrounding thermal environment had no significant impact on students’ anxiety tendencies. In addition, the evaluation of the general satisfaction with the built environment also significantly negatively impacted students’ anxiety.
This study also found that the following factors significantly influenced students’ general satisfaction with the academic building’s built environment: classroom ventilation, noise environment, thermal environment, surrounding thermal environment, and the landscape and leisure facilities.
In the context of the epidemic, the architectural design and renovation of academic buildings based on mental health should prioritize the design of semi-open spaces. Designs should have a good view of the landscape and a better thermal environment in summer, in addition to improving the design of classrooms against noise. Additionally, improving classroom ventilation, the thermal environment, the noise environment, the surrounding thermal environment, and surrounding landscape facilities are all essential to enhance students’ overall satisfaction with the built environment.
Students often suffer from unfavorable psychological emotions in the context of the epidemic and the campus’s local management. The study’s findings can guide the architectural design and renovation of related academic buildings to increase students’ satisfaction with the built environment, improve psychological health, and alleviate anxiety.
## References
1. Rajkumar R.P.. **COVID-19 and mental health: A review of the existing literature**. *Asian J. Psychiatry* (2020) **52** 102066. DOI: 10.1016/j.ajp.2020.102066
2. Dong Z., Zhao K., Ren M., Ge J., Chan I.Y.. **The impact of space design on occupants’ satisfaction with indoor environment in university dormitories**. *Build. Environ.* (2022) **218** 109143. DOI: 10.1016/j.buildenv.2022.109143
3. Chang J.-J., Ji Y., Li Y.-H., Pan H.-F., Su P.-Y.. **Prevalence of anxiety symptom and depressive symptom among college students during COVID-19 pandemic: A meta-analysis**. *J. Affect. Disord.* (2021) **292** 242-254. DOI: 10.1016/j.jad.2021.05.109
4. Liu C., Tang J., Shen C., Zhan X., Bu E., Shen B., Huang W.. **Research of the Changes in the Psychological Status of Chinese University Students and the Influencing Factors During the COVID-19 Pandemic**. *Front. Psychol.* (2022) **13** 891778. DOI: 10.3389/fpsyg.2022.891778
5. Chen Y., Chen B.. **Modeling of effect of residential indoor environment on health based on a questionnaire survey of selected China cities**. *Build. Environ.* (2018) **148** 173-184. DOI: 10.1016/j.buildenv.2018.10.056
6. Zhu N., Chong D.. **Evaluation and improvement of human heat tolerance in built environments: A review**. *Sustain. Cities Soc.* (2019) **51** 101797. DOI: 10.1016/j.scs.2019.101797
7. Dendup T., Feng X., O’Shaughnessy P., Astell-Burt T.. **Perceived built environment and type 2 diabetes incidence: Exploring potential mediating pathways through physical and mental health, and behavioural factors in a longitudinal study**. *Diabetes Res. Clin. Pract.* (2021) **176** 108841. DOI: 10.1016/j.diabres.2021.108841
8. Buttazzoni A., Parker A., Minaker L.. **Investigating the mental health implications of urban environments with neuroscientific methods and mobile technologies: A systematic literature review**. *Health Place* (2021) **70** 102597. DOI: 10.1016/j.healthplace.2021.102597
9. Guzman V., Garrido-Cumbrera M., Braçe O., Hewlett D., Foley R.. **Associations of the natural and built environment with mental health and wellbeing during COVID-19: Irish perspectives from the Green COVID study**. *Lancet Glob. Health* (2021) **9** S20. DOI: 10.1016/S2214-109X(21)00128-5
10. Liddicoat S., Badcock P., Killackey E.. **Principles for designing the built environment of mental health services**. *Lancet Psychiatry* (2020) **7** 915-920. DOI: 10.1016/S2215-0366(20)30038-9
11. Love P.E.D., Edwards D.J., Irani Z.. **Work Stress, Support, and Mental Health in Construction**. *J. Constr. Eng. Manag.* (2010) **136** 650-658. DOI: 10.1061/(ASCE)CO.1943-7862.0000165
12. Galea S., Ahern J., Rudenstine S., Wallace Z., Vlahov D.. **Urban built environment and depression: A multilevel analysis**. *J. Epidemiol. Community Health* (2005) **59** 822-827. DOI: 10.1136/jech.2005.033084
13. Howden-Chapman P.L., Chandola T., Stafford M., Marmot M.. **The effect of housing on the mental health of older people: The impact of lifetime housing history in Whitehall II**. *BMC Public Health* (2011) **11** 682. DOI: 10.1186/1471-2458-11-682
14. Vieira R.T., Caixeta L., Machado S., Silva A.C., Nardi A.E., Arias-Carrion O., Carta M.G.. **Epidemiology of early-onset dementia: A review of the literature**. *Clin. Pract. Epidemiol. Ment. Health* (2013) **9** 88-95. DOI: 10.2174/1745017901309010088
15. Latkin C.A., Curry A.D.. **Stressful Neighborhoods and Depression: A Prospective Study of the Impact of Neighborhood Disorder**. *J. Health Soc. Behav.* (2003) **44** 34. DOI: 10.2307/1519814
16. Gifford R.. **The Consequences of Living in High-Rise Buildings**. *Arch. Sci. Rev.* (2007) **50** 2-17. DOI: 10.3763/asre.2007.5002
17. Beyer K.M.M., Kaltenbach A., Szabo A., Bogar S., Nieto F.J., Malecki K.M.. **Exposure to Neighborhood Green Space and Mental Health: Evidence from the Survey of the Health of Wisconsin**. *Int. J. Environ. Res. Public Health* (2014) **11** 3453-3472. DOI: 10.3390/ijerph110303453
18. Gascon M., Triguero-Mas M., Martínez D., Dadvand P., Forns J., Plasència A., Nieuwenhuijsen M.J.. **Mental Health Benefits of Long-Term Exposure to Residential Green and Blue Spaces: A Systematic Review**. *Int. J. Environ. Res. Public Health* (2015) **12** 4354-4379. DOI: 10.3390/ijerph120404354
19. Morrow L.A., Stein L., Bagovich G.R., Condray R., Scott A.. **Neuropsychological Assessment, Depression, and Past Exposure to Organic Solvents**. *Appl. Neuropsychol.* (2001) **8** 65-73. DOI: 10.1207/S15324826AN0802_1
20. Beemer C.J., Stearns-Yoder K.A., Schuldt S.J., Kinney K.A., Lowry C.A., Postolache T.T., Brenner L.A., Hoisington A.J.. **A brief review on the mental health for select elements of the built environment**. *Indoor Built Environ.* (2019) **30** 152-165. DOI: 10.1177/1420326X19889653
21. Pelgrims I., Devleesschauwer B., Guyot M., Keune H., Nawrot T.S., Remmen R., Saenen N.D., Trabelsi S., Thomas I., Aerts R.. **Association between urban environment and mental health in Brussels, Belgium**. *BMC Public Health* (2021) **21**. DOI: 10.1186/s12889-021-10557-7
22. Frumkin H.. **COVID-19, the Built Environment, and Health**. *Environ. Health Persp.* (2021) **129** 75001. DOI: 10.1289/EHP8888
23. Soga M., Evans M.J., Tsuchiya K., Fukano Y.. **A room with a green view: The importance of nearby nature for mental health during the COVID-19 pandemic**. *Ecol. Appl.* (2020) **31** e2248. DOI: 10.1002/eap.2248
24. Pouso S., Borja Á., Fleming L.E., Gómez-Baggethun E., White M.P., Uyarra M.C.. **Contact with blue-green spaces during the COVID-19 pandemic lockdown beneficial for mental health**. *Sci. Total Environ.* (2021) **756** 143984. DOI: 10.1016/j.scitotenv.2020.143984
25. Yang Y., Wang L., Passmore H.A., Zhang J., Zhu L., Cai H.. **Viewing nature scenes reduces the pain of social ostracism**. *J. Soc. Psychol.* (2021) **161** 197-215. DOI: 10.1080/00224545.2020.1784826
26. Cartwright B.D.S., White M.P., Clitherow T.J.. **Nearby Nature ‘Buffers’ the Effect of Low Social Connectedness on Adult Subjective Wellbeing over the Last 7 Days**. *Int. J. Environ. Res. Public Health* (2018) **15**. DOI: 10.3390/ijerph15061238
27. Liu M., Zhao S., Li J.. **Associations among perceived built environment, attitudes, walking behavior, and physical and mental state of college students during COVID-19**. *Travel Behav. Soc.* (2022) **28** 170-180. DOI: 10.1016/j.tbs.2022.04.003
28. Asim F., Chani P., Shree V.. **Impact of COVID-19 containment zone built-environments on students’ mental health and their coping mechanisms**. *Build. Environ.* (2021) **203** 108107. DOI: 10.1016/j.buildenv.2021.108107
29. Sun S., Chen Y., Mu S., Jiang B., Lin Y., Gao T., Qiu L.. **The Psychological Restorative Effects of Campus Environments on College Students in the Context of the COVID-19 Pandemic: A Case Study at Northwest A&F University, Shaanxi, China**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18168731
30. Jensen H.A.R., Rasmussen B., Ekholm O.. **Neighbour and traffic noise annoyance: A nationwide study of associated mental health and perceived stress**. *Eur. J. Public Health* (2018) **28** 1050-1055. DOI: 10.1093/eurpub/cky091
31. Hoisington A.J., Stearns-Yoder K.A., Schuldt S.J., Beemer C.J., Maestre J.P., Kinney K.A., Postolache T.T., Lowry C.A., Brenner L.A.. **Ten questions concerning the built environment and mental health**. *Build. Environ.* (2019) **155** 58-69. DOI: 10.1016/j.buildenv.2019.03.036
32. Triguero-Mas M., Dadvand P., Cirach M., Martínez D., Medina A., Mompart A., Basagaña X., Gražulevičiene R., Nieuwenhuijsen M.J.. **Natural outdoor environments and mental and physical health: Relationships and mechanisms**. *Environ. Int.* (2015) **77** 35-41. DOI: 10.1016/j.envint.2015.01.012
33. Bratman G.N., Hamilton J.P., Daily G.C.. **The impacts of nature experience on human cognitive function and mental health**. *Ann. N. Y. Acad. Sci.* (2012) **1249** 118-136. DOI: 10.1111/j.1749-6632.2011.06400.x
34. Wang L., Zhou Y., Wang F., Ding L., Love P.E.D., Li S.. **The Influence of the Built Environment on People’s Mental Health: An Empirical Classification of Causal Factors**. *Sustain. Cities Soc.* (2021) **74** 103185. DOI: 10.1016/j.scs.2021.103185
35. Kelz C., Evans G.W., Röderer K.. **The Restorative Effects of Redesigning the Schoolyard: A Multi-Methodological, Quasi-Experimental Study in Rural Austrian Middle Schools**. *Environ. Behav.* (2015) **47** 119-139. DOI: 10.1177/0013916513510528
36. Kuo M., Barnes M., Jordan C.. **Do Experiences With Nature Promote Learning? Converging Evidence of a Cause-and-Effect Relationship**. *Front. Psychol.* (2019) **10** 305. DOI: 10.3389/fpsyg.2019.00305
37. Williams D.R., Dixon P.S.. **Impact of Garden-Based Learning on Academic Outcomes in Schools: Synthesis of Research Between 1990 and 2010**. *Rev. Educ. Res.* (2013) **83** 211-235. DOI: 10.3102/0034654313475824
38. Matsuoka R.H.. **Student performance and high school landscapes: Examining the links**. *Landsc. Urban Plan.* (2010) **97** 273-282. DOI: 10.1016/j.landurbplan.2010.06.011
39. Aries M.B., Veitch J.A., Newsham G.R.. **Windows, view, and office characteristics predict physical and psychological discomfort**. *J. Environ. Psychol.* (2010) **30** 533-541. DOI: 10.1016/j.jenvp.2009.12.004
40. Kaplan R.. **The Nature of the View from Home: Psychological Benefits**. *Environ. Behav.* (2001) **33** 507-542. DOI: 10.1177/00139160121973115
41. Power M.C., Kioumourtzoglou M.-A., Hart J.E., Okereke O.I., Laden F., Weisskopf M.G.. **The relation between past exposure to fine particulate air pollution and prevalent anxiety: Observational cohort study**. *BMJ* (2015) **350** h1111. DOI: 10.1136/bmj.h1111
42. Bakian A.V., Huber R.S., Coon H., Gray D., Wilson P., McMahon W.M., Renshaw P.F.. **Acute Air Pollution Exposure and Risk of Suicide Completion**. *Am. J. Epidemiol.* (2015) **181** 295-303. DOI: 10.1093/aje/kwu341
43. Pun V.C., Manjourides J., Suh H.. **Association of Ambient Air Pollution with Depressive and Anxiety Symptoms in Older Adults: Results from the NSHAP Study**. *Environ. Health Perspect.* (2017) **125** 342-348. DOI: 10.1289/EHP494
44. Madureira J., Paciência I., Rufo J., Ramos E., Barros H., Teixeira J.P., de Oliveira Fernandes E.. **Indoor air quality in schools and its relationship with children’s respiratory symptoms**. *Atmos. Environ.* (2015) **118** 145-156. DOI: 10.1016/j.atmosenv.2015.07.028
45. Ranft U., Schikowski T., Sugiri D., Krutmann J., Krämer U.. **Long-term exposure to traffic-related particulate matter impairs cognitive function in the elderly**. *Environ. Res.* (2009) **109** 1004-1011. DOI: 10.1016/j.envres.2009.08.003
46. Cory-Slechta D., Allen J., Conrad K., Marvin E., Sobolewski M.. **Developmental exposure to low level ambient ultrafine particle air pollution and cognitive dysfunction**. *Neurotoxicology* (2017) **69** 217-231. DOI: 10.1016/j.neuro.2017.12.003
47. Chen J.-C., Schwartz J.. **Neurobehavioral effects of ambient air pollution on cognitive performance in US adults**. *Neurotoxicology* (2009) **30** 231-239. DOI: 10.1016/j.neuro.2008.12.011
48. Zeng Y., Lin R., Liu L., Liu Y., Li Y.. **Ambient air pollution exposure and risk of depression: A systematic review and meta-analysis of observational studies**. *Psychiatry Res.* (2019) **276** 69-78. DOI: 10.1016/j.psychres.2019.04.019
49. Attademo L., Bernardini F., Garinella R., Compton M.T.. **Environmental pollution and risk of psychotic disorders: A review of the science to date**. *Schizophr. Res.* (2017) **181** 55-59. DOI: 10.1016/j.schres.2016.10.003
50. Allen J.G., MacNaughton P., Satish U., Santanam S., Vallarino J., Spengler J.D.. **Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments**. *Environ. Health Perspect.* (2016) **124** 805-812. DOI: 10.1289/ehp.1510037
51. An R., Zhang S., Ji M., Guan C.. **Impact of ambient air pollution on physical activity among adults: A systematic review and meta-analysis**. *Perspect. Public Health* (2017) **138** 111-121. DOI: 10.1177/1757913917726567
52. von Lindern E., Hartig T., Lercher P.. **Traffic-related exposures, constrained restoration, and health in the residential context**. *Health Place.* (2016) **39** 92-100. DOI: 10.1016/j.healthplace.2015.12.003
53. Mullins J.T., White C.. **Temperature and mental health: Evidence from the spectrum of mental health outcomes**. *J. Health Econ.* (2019) **68** 102240. DOI: 10.1016/j.jhealeco.2019.102240
54. Wong L.P., Alias H., Aghamohammadi N., Aghazadeh S., Sulaiman N.M.N.. **Physical, Psychological, and Social Health Impact of Temperature Rise Due to Urban Heat Island Phenomenon and Its Associated Factors**. *Biomed. Environ. Sci.* (2018) **31** 545-550. PMID: 30145991
55. Noelke C., McGovern M., Corsi D.J., Jimenez M.P., Stern A., Wing I.S., Berkman L.. **Increasing ambient temperature reduces emotional well-being**. *Environ. Res.* (2016) **151** 124-129. DOI: 10.1016/j.envres.2016.06.045
56. Woodruff R.E., McMichael T., Butler C., Hales S.. **Action on climate change: The health risks of procrastinating**. *Aust. N. Z. J. Public Health* (2006) **30** 567-571. DOI: 10.1111/j.1467-842X.2006.tb00788.x
57. Thompson R., Hornigold R., Page L., Waite T.. **Associations between high ambient temperatures and heat waves with mental health outcomes: A systematic review**. *Public Health* (2018) **161** 171-191. DOI: 10.1016/j.puhe.2018.06.008
58. Liu H., Ma X., Zhang Z., Cheng X., Chen Y., Kojima S.. **Study on the Relationship between Thermal Comfort and Learning Efficiency of Different Classroom-Types in Transitional Seasons in the Hot Summer and Cold Winter Zone of China**. *Energies* (2021) **14**. DOI: 10.3390/en14196338
59. Beutel M., Jünger C., Klein E., Wild P., Lackner K., Blettner M., Binder H., Michal M., Wiltink J., Brähler E.. **Noise Annoyance is Associated With Depression and Anxiety in the General Population- the Contribution of Aircraft Noise**. *J. Psychosom. Res.* (2016) **85** 56-57. DOI: 10.1016/j.jpsychores.2016.03.138
60. Hammersen F., Niemann H., Hoebel J.. **Environmental Noise Annoyance and Mental Health in Adults: Findings from the Cross-Sectional German Health Update (GEDA) Study 2012**. *Int. J. Environ. Res. Public Health* (2016) **13**. DOI: 10.3390/ijerph13100954
61. Aries M.B.C., Aarts M.P.J., van Hoof J.. **Daylight and health: A review of the evidence and consequences for the built environment**. *Lighting Res. Technol.* (2015) **47** 6-27. DOI: 10.1177/1477153513509258
62. Ulrich R.S.. **Natural Versus Urban Scenes: Some Psychophysiological Effects**. *Environ. Behav.* (1981) **13** 523-556. DOI: 10.1177/0013916581135001
63. Korsavi S.S., Zomorodian Z.S., Tahsildoost M.. **Visual comfort assessment of daylit and sunlit areas: A longitudinal field survey in classrooms in Kashan, Iran**. *Energy Build.* (2016) **128** 305-318. DOI: 10.1016/j.enbuild.2016.06.091
64. Pretty J.. **How nature contributes to mental and physical health**. *Spirit. Health Int.* (2004) **5** 68-78. DOI: 10.1002/shi.220
65. Spitzer R.L., Kroenke K., Williams J.B.W., Löwe B.. **A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7**. *Arch. Intern. Med.* (2006) **166** 1092-1097. DOI: 10.1001/archinte.166.10.1092
66. Chang C.-Y., Chen P.-K.. **Human Response to Window Views and Indoor Plants in the Workplace**. *Hortscience* (2005) **40** 1354-1359. DOI: 10.21273/HORTSCI.40.5.1354
67. Li D., Sullivan W.C.. **Impact of views to school landscapes on recovery from stress and mental fatigue**. *Landsc. Urban Plan.* (2016) **148** 149-158. DOI: 10.1016/j.landurbplan.2015.12.015
68. Chang C.-C., Oh R.R.Y., Le Nghiem T.P., Zhang Y., Tan C.L., Lin B.B., Gaston K.J., Fuller R.A., Carrasco L.R.. **Life satisfaction linked to the diversity of nature experiences and nature views from the window**. *Landsc. Urban Plan.* (2020) **202** 103874. DOI: 10.1016/j.landurbplan.2020.103874
69. Gilchrist K., Brown C., Montarzino A.. **Workplace settings and wellbeing: Greenspace use and views contribute to employee wellbeing at peri-urban business sites**. *Landsc. Urban Plan.* (2015) **138** 32-40. DOI: 10.1016/j.landurbplan.2015.02.004
70. Dravigne A., Waliczek T.M., Lineberger R.D., Zajicek J.M.. **The Effect of Live Plants and Window Views of Green Spaces on Employee Perceptions of Job Satisfaction**. *Hort. Sci.* (2008) **43** 183-187. DOI: 10.21273/HORTSCI.43.1.183
71. Soga M., Gaston K.J.. **The ecology of human–nature interactions**. *Proc. R. Soc. B Boil. Sci.* (2020) **287** 20191882. DOI: 10.1098/rspb.2019.1882
72. Franco L.S., Shanahan D.F., Fuller R.A.. **A Review of the Benefits of Nature Experiences: More Than Meets the Eye**. *Int. J. Environ. Res. Public Health* (2017) **14**. DOI: 10.3390/ijerph14080864
73. Nordh H., Alalouch C., Hartig T.. **Assessing restorative components of small urban parks using conjoint methodology**. *Urban For. Urban Green.* (2011) **10** 95-103. DOI: 10.1016/j.ufug.2010.12.003
74. Bratman G.N., Anderson C.B., Berman M.G., Cochran B., de Vries S., Flanders J., Folke C., Frumkin H., Gross J.J., Hartig T.. **Nature and mental health: An ecosystem service perspective**. *Sci. Adv.* (2019) **5** eaax0903. DOI: 10.1126/sciadv.aax0903
75. Keniger L.E., Gaston K.J., Irvine K.N., Fuller R.A.. **What are the benefits of interacting with nature?**. *Int. J. Environ. Res. Public Health* (2013) **10** 913-935. DOI: 10.3390/ijerph10030913
76. Thoits P.A.. **Mechanisms Linking Social Ties and Support to Physical and Mental Health**. *J. Health Soc. Behav.* (2011) **52** 145-161. DOI: 10.1177/0022146510395592
77. Kawachi I., Berkman L.F.. **Social ties and mental health**. *J. Urban Health* (2001) **78** 458-467. DOI: 10.1093/jurban/78.3.458
78. Akompab D.A., Bi P., Williams S., Grant J., Walker I.A., Augoustinos M.. **Awareness of and Attitudes towards Heat Waves within the Context of Climate Change among a Cohort of Residents in Adelaide, Australia**. *Int. J. Environ. Res. Public Health* (2012) **10** 1-17. DOI: 10.3390/ijerph10010001
79. Stansfeld S.A., Haines M.M., Burr M., Berry B., Lercher P.. **A review of environmental noise and mental health**. *Noise Health* (2000) **2** 1
80. Wirz-Justice A., Graw P., Kräuchi K., Sarrafzadeh A., English J., Arendt J., Sand L.. **‘Natural’ light treatment of seasonal affective disorder**. *J. Affect. Disord.* (1996) **37** 109-120. DOI: 10.1016/0165-0327(95)00081-X
81. Miller S.M.. **Controllability and human stress: Method, evidence and theory**. *Behav. Res. Ther.* (1979) **17** 287-304. DOI: 10.1016/0005-7967(79)90001-9
82. Rodin J.. **Aging and Health: Effects of the Sense of Control**. *Science* (1986) **233** 1271-1276. DOI: 10.1126/science.3749877
83. Shapiro D.J., Schwartz C.E., Astin J.A.. **Controlling ourselves, controlling our world. Psychology’s role in understanding positive and negative consequences of seeking and gaining control**. *Am. Psychol.* (1996) **51** 1213-1230. DOI: 10.1037/0003-066X.51.12.1213
84. Seligman M., Maier S.F., Solomon R.L., Brush F.R.. **CHAPTER 6—Unpredictable and Uncontrollable Aversive Events**. *Aversive Conditioning and Learning* (1971) 347-400
|
---
title: 'Analysis and Evaluation of Dental Caries in a Mexican Population: A Descriptive
Transversal Study'
authors:
- Alejandro Moreno-Barrera
- Pedro Morales-Ruiz
- David Ribas Pérez
- Javier Flores-Fraile
- Antonio Castaño-Seiquer
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001530
doi: 10.3390/ijerph20053873
license: CC BY 4.0
---
# Analysis and Evaluation of Dental Caries in a Mexican Population: A Descriptive Transversal Study
## Abstract
Oral diseases are an important public health problem owing to their high prevalence and strong impact on people, particularly in disadvantaged populations. There is a strong relationship between the socioeconomic situation and the prevalence and severity of these diseases. Mexico is among the countries with a higher frequency range in oral diseases, highlighting dental caries, which affect more than $90\%$ of the Mexican population. Materials and method: A cross-sectional, descriptive, and observational study was carried out in 552 individuals who underwent a complete cariogenic clinical examination in different populations of the state of Yucatan. All individuals were evaluated after providing informed consent and with the consent of their legal guardians for those under legal age. We used the caries measurement methods described by the World Health Organization (WHO). Prevalence of caries, DMFT, and dft indexes were measured. Other aspects were also studied, such as oral habits and the use of public or private dental services. Results: The prevalence of caries in permanent dentition was $84\%$. Moreover, it was found to be statistically related to the following variables: place of residence, socioeconomic level, gender, and level of education ($p \leq 0.05$). For primary teeth, the prevalence was $64\%$ and there was no statistical relation with any of the variables studied ($p \leq 0.05$). Regarding the other aspects studied, more than $50\%$ of the sample used private dental services. Conclusions: *There is* a high need for dental treatment in the population studied. It is necessary to develop prevention and treatment strategies considering the particularities of each population, driving collaborative projects to promote better oral health conditions in disadvantaged populations.
## 1. Introduction
Oral diseases constitute a significant public health problem because of their high prevalence and strong impact on people and society in terms of pain, social, and functional disability [1]. Currently, nine out of ten people in the world are at risk of suffering from an oral disease [2,3,4,5].
Mexico is among the countries with a high frequency range in oral diseases. The prevalence of caries affects more than $90\%$ of the Mexican population [6]. According to the Universal Catalogue of Health Services (CAUSES), the Mexican state offers medical coverage that also includes dental specialties [7].
If we focus on the Yucatan region (which belongs to the south-eastern area of Mexico), according to data from the General Direction of Epidemiology of the Ministry of Health of the Government of Mexico, in 2019, the region had rates of oral diseases comparable to the rest of the country and markedly inferior to other North American (United States of America) or European (United Kingdom or Sweden) countries [8].
Thus, we find an exaggerated high proportion of children with ECC receiving health services (31.8 vs. $6\%$ in the USA) and a caries index (DMFT) at 12 years of age of 2.6 vs. 1.2 in the USA or 0.8 in Sweden. Although in adults aged 35–44 years, the data are similar in terms of DMFT, the percentage of fillings (the so-called restoration index) is markedly higher in the countries mentioned above compared with the Yucatan region ($20\%$ compared with $52\%$ in the United Kingdom or $63\%$ in the USA). Even greater is the difference in terms of edentulism or lack of functional occlusion in adults aged 65–74 years ($55.8\%$ of non-functional occlusion in the southeast region of Mexico compared with $38.2\%$ in the USA) [9,10,11].
Worldwide, the incidence of oral diseases, particularly in disadvantaged populations, remains high [12,13]. Among the main ones, we highlight decayed teeth as the most prevalent, followed by periodontal conditions, malocclusions, and oral trauma, which affect the quality of life of those who suffer from it [2].
Dental caries is defined as a multifactorial chronic disease, which develops under the following conditions; a susceptible host; a cariogenic oral flora; and an appropriate substrate that must be present for a specified period of time and that, in turn, will be influenced by the community, family, and individual predisposition [14,15,16,17].
Caries experience is the number of teeth/surfaces that have caries lesions (at a specified threshold), restorations, and/or are missing owing to caries, accumulated by an individual up to a designated point in time. Though new models or indexes are being explored internationally, the majority of studies measure the caries experience by means of DMFT/S (dft/s) at varying detection levels [18].
Peres and Cos [2009] stated that there is a very strong and persistent relationship between socioeconomic status and the prevalence and severity of oral diseases [3,12]. This is usually linked to a cariogenic diet and poor oral hygiene, as well as the consumption of tobacco and alcohol and low accessibility to oral health services. In addition to other factors such as dental malposition, parental education and associated systemic diseases usually coexist with a lack of oral hygiene [12,19,20,21].
Concerning caries, prevention through proper oral hygiene, a non-cariogenic diet, and topical fluoride is the most effective method to decrease its development. Early detection would also avoid severe complications such as advanced caries, pulpitis, endodontic treatments, and loss of teeth [22]. Caries prevention has traditionally meant inhibition of caries initiation, otherwise called primary prevention. Primary, together with secondary and tertiary prevention, comprise non-operative and operative treatments for caries management [18].
The main objective of this research was to analyse the prevalence and index of dental caries in primary and permanent dentition defined by type of population, rural or urban, among populations of the state of Yucatan, Mexico.
## 2.1. Study Type and Settings
An observational, cross-sectional, and descriptive study carried out as part of the “Yucatán International Cooperation Project” was developed in Temax, Hunucmá, Umán, and Mérida.
The study sample consisted of 552 individuals between 5 and 64 years old who requested dental care. All participants signed an informed consent form and filled out an individual survey on oral health, oral hygiene habits, and quality of life. The information from underage patients was collected by their parents or legal guardians once the consent was signed. In addition, each individual underwent a complete clinical dental examination focused on cariogenic pathology.
The World Health Organization (WHO) criteria for dental caries and care needs related to the condition of the teeth were applied [23]. All of the participants in the study were examined in natural light and a no. 5 flat mirror was used. The participants brushed their teeth before the examination and the teeth were not dried prior to the inspection.
All patients had the same clinical examiner (A.M.), with the same methodology used on all of them them to avoid bias. It was decided to carry out the scanning for data collection through the work from a single examiner (a dentist with lots of experience on caries assessment). With the aim to measure the consistency of the observations, the examiner was subjected to a so-called intra-observer calibration, obtaining the ratio of agreement with a Kappa test (0.85).
## 2.2. Study Variables
The variables analysed were age, sex, place of residence (urban or rural area), socioeconomic level (low rural, low urban, or urban environment), and highest level of studies achieved.
The variables obtained from the questionnaire on oral health attitudes and habits and the use of dental health services were also studied. For the clinical variables of caries indexes, the prevalence of caries and the DMFT index for permanent dentition and dft for primary dentition were studied according to WHO criteria of caries [23].
## 2.3. Statistical Analysis
Statistical analysis was performed using STATA V15 (College Station, TX, USA). Continuous variables were summarized through means and standard deviations (SDs). The categorical variables are presented through the frequency distribution and the simple and cumulative frequencies are reported in percentages.
Associations between prevalence and cavity rates were studied with age, sex, area of origin, socioeconomic level, and education. ANOVA was used for continuous variables and the chi-square test was performed for categorical variables. The critical value to identify statistically significant differences was $p \leq 0.05.$
## 3.1. Sociodemographic Data
The mean age of the population was 28.8 ± 16.2. Four age groups were categorized: children 6–12 years, adolescents 12–19 years, young adults 20–34 years, and older adults 35–64 years. Among the age groups chosen for the study, the so-called “older adults”, made up of subjects between 35 and 64 years of age, represented the largest group, with $37.32\%$. In terms of gender, women accounted for $60.51\%$ of the sample, while men accounted for $39.49\%$.
Concerning other socio-demogaphic data, fifty-six percent of the population studied was rural, with a low socioeconomic level. Twelve percent of the sample reported having no education. Of the $92\%$ who had completed studies, $83\%$ had completed primary, secondary, or high school and only $17\%$ had reached university level. All these data are summarised in Table 1.
## 3.2. Oral Health Attitudes and Practices
The following are some of the most significant results from the oral health attitudes and practices survey of the study participants.
More than half of the surveyed population is very concerned about their oral health ($58\%$), while $7\%$ of them acknowledge having little concern for their oral health (Table 2).
Fifty-four percent of individuals reported brushing three times a day, $34.8\%$ twice a day, and $7\%$ only once a day. In the group of women, $55.9\%$ reported brushing three times a day and this percentage was lower in the group of men (38.5), ($p \leq 0.001$) (Table 3).
Furthermore, $90.45\%$ of the study population used a manual toothbrush. Only three subjects used an electric toothbrush. Sixty-four percent of the population used a toothpaste as the main complementary product for toothbrushing. Almost one in five subjects used mouthwash, while dental floss was only used by $12\%$ of the population (Table 4).
## 3.3. Use of Dental Services
With regard to the frequency of dental check-ups, it can be seen that the most frequently answered response by the population to the question regarding when they should visit their dentist was “when they have a problem” (Table 5).
Moreover, $8.15\%$ of the population acknowledged that they had never visited a dentist, while $20.29\%$ had done so less than six months ago. The rest of the population under study had visited their dentist more than one year ago (Table 6).
More than half of the population used private services, while $35.59\%$ used the public dental services made available by the state. Ten percent stated that they were not aware of the difference between the two types of care (Table 7).
## 3.4. Dentition Status
For primary dentition, the prevalence of caries was $64\%$, with a dft value of 2.8 ± 3.19. More specifically, for children aged 5 and 6 years old, the prevalence obtained was $55\%$, obtaining a dft value = 2.45 ± (3.21). Analyzing the relationship between the dft index and the socio-demographic variables, it is observed that there is no statistical significance ($p \leq 0.05$ in all cases) (Table 8 and Table 9).
Regarding permanent dentition, $94\%$ of the population ($$n = 520$$) had at least one permanent tooth in the mouth. The prevalence of caries was $84\%$, with a DMFT value of 6.3 ± 0.24. The prevalence of caries in individuals aged 12 years was also calculated, which was $54\%$, with a DMFT value of 1.1 (±1.11). Regarding the relationship between the DMFT index and socio-demographic variables, it is observed that there is a statistically significant relationship between DMFT and age, type of residence, socioeconomic level, and educational level ($p \leq 0.05$) (Table 10 and Table 11).
## 4. Discussion
According to the WHO, dental caries is the most prevalent disease in the world, affecting more than $80\%$ of the world’s population, in addition to being considered the most prevalent pathology in the child population [24,25].
For the selection of our simple, and despite having studied a relatively large number of patients, we took a number of patients who came to the Yucatan International Cooperation Project requiring dental care. This could be a limitation of our study, as they were people with a perceived need for treatment and thus would not be representative of the whole population, which is a limitation to be taken into account when interpreting the results. Nevertheless, the authors believe that it serves to show a snapshot of the oral health of the Yucatecan population.
## 4.1. Primary Dentition
The prevalence of carious lesions in primary dentition in the Yucatecan population studied was $64\%$, a figure similar to that in the work carried out by the Mexican group of Montero and Cols. [ 26]. According to the results of Martínez-Pérez and Cols. and Serrano-Piña and Cols., 5 out of 10 children present caries in primary dentition, while for Villalobos and Cols. or García Pérez and Cols., up to 9 out of 10 children have dental caries [27,28,29,30].
In the present investigation, no statistically significant differences ($p \leq 0.05$) were found, but a notable association between a higher prevalence of decayed teeth and the rural environment was found, matching with previous studies [30]. Regarding gender, a higher prevalence of caries was found in the female sex and, with respect to the socioeconomic level, $81\%$ of individuals who presented decayed primary teeth belonged to the “low rural” level; in this case, no statistically significant differences were found with the prevalence of caries, like those confirmed by Frencken et al. in their study [31].
The total value of dft for the studied population was 2.8 ± 3.19. The carious component was 2.69 ± 3.08 and the filled component was 0.11 ± 0.11, which shows a high need for treatment, finding more than two untreated caries in each individual. The value of our results with respect to dft in rural areas is similar to those of Medina-Solís and Cols. They obtained a dft of 2.86 in a sample with children from 6 to 12 years old in a non-urban area in Campeche, a state adjacent to Yucatán. In relation to the urban areas, we obtained a value of 2.7 for dft, compared with the value of 2.4 obtained for the dft index in the above-mentioned study. It is important to note that, if teeth lost as a result of caries had been considered, the dft value could have increased and could resemble the results of Romo and Cols. or Villalobos and Cols. [ 29,32,33].
Although no statistically significant differences were found with the socio-demographic variables, the authors observed that the value of the dft index was higher in men (3.1) than in women (2.3); that the “low rural” socioeconomic group obtained better dft index values than the “low urban”; and, in terms of schooling, individuals without schooling obtained higher levels of dft than those who had studied primary school, at 3.6 and 2.3, respectively.
Following the WHO instructions, 5- and 6-year old children were specifically studied. Although the sample volume is low ($$n = 22$$), given that the data collection was carried out in a random sample, the prevalence was $55\%$, presenting a dft of 2.45 ± 3.21. The results of the present research in terms of prevalence are similar to those of the National Dental Caries and Fluorosis Surveys in Mexico [34]. In the case of dft, the difference is more evident, as it was greater in our study than in the results of the survey (1.5 vs. 2.45). Regarding the decayed component, the value obtained in our research was 2.36 ± 3.23, while the that of the national survey was 1.3. This could be justified because the target population of the project was the one with the least economic resources. The sealed component was 0.09 ± 0.29, highlighting the existing treatment needs in this sector.
## 4.2. Permanent Dentition
On the other hand, the prevalence of caries in permanent dentition was $84\%$, a result similar to that of other studies with the same methodology carried out in other regions of Mexico, Romo and Cols. in Nezahualcóyotl, Aamodt and Cols. in Chiapas, and Islas-Granillo and Cols. in Hidalgo [30,32,35,36].
The results led to a greater presence of decayed teeth in urban populations compared with rural ones ($$p \leq 0.027$$), a fact that coincides with the results published by Ortega-Maldonado et al. [ 37]. Regarding the socioeconomic level, there was a significant association between this variable ($$p \leq 0.031$$) and the prevalence of caries; the lower income level coincides with a greater presence of this pathology, which agrees with other studies published in Mexico by Villalobos-Rodelo and Cols., in addition to the one published by Vega-Lizama and Cols. [ 12,38,39,40].
In relation to schooling, a statistically significant relationship was found with the prevalence in permanent dentition ($$p \leq 0.001$$), as well as with gender ($p \leq 0.05$), as it was more prevalent in the female sex; these results coincide with other international studies [33].
The value of the DMFT index obtained in our population was 6.3 ± 0.24. Regarding this, the decayed component was 4.1 ± 3.91, the absent one was 1.3 ± 2.78, and the obturated one was 0.9 ± 2.16. According to our study, only one in six decayed teeth are filled in the population of our study, showing once again that the need for dental care in our sample is high. Other results obtained by Mexican researchers such as Aamot and Cols. only corroborate the high demand for care that exists in this population [35,39].
The DMFT value was higher in women, a fact that coincides with other studies such as that of Romo and Cols. In addition, statistically significant differences ($p \leq 0.05$) were found with the age variable, with a clear tendency for the DMFT value to increase over time, coinciding with other results published in international literature [32,37]. Regarding the variable place of residence, the urban population obtained higher DMFT levels than the rural population ($p \leq 0.05$). Regarding the socioeconomic level, the population of the low urban group obtained the highest DMFT value ($p \leq 0.05$). Both statistically significant associations may be due to the greater access, by the urban population, to products with large amounts of refined sugars and the high intake of carbonated beverages that occurs in this sector [38,40].
Following the WHO instructions concerning age analysis, 12 year olds were also specifically studied and, although the sample size ($$n = 26$$) was low because of the random selection of the population, the prevalence of caries was $46\%$, with a DMFT value of 1.1 ± 1.11. The decayed component was 1.0 ± 1.75, that of missing teeth was 0.38 ± 0.19, and that of filled teeth was 0.03 ± 0.19. The data obtained in the last National Dental Caries and Fluorosis Survey in Mexico for the 11–12-year-old group showed a $47\%$ prevalence of caries in permanent dentition and a DMFT of 1.5, coinciding almost exactly with the results obtained in the present study [34].
## 4.3. Oral Health Habits and Use of Dental Health Services
Oral health is a determinant of quality of life and the acquisition of preventive habits such as toothbrushing can reduce a large number of oral problems. This adoption of preventive habits clearly increases the likelihood of being in optimal health and has been shown to be significantly influenced by socioeconomic and demographic factors [4,5].
With this initial premise, basic issues such as the frequency of tooth-brushing three times a day and the use of fluoride toothpaste are widespread among the population. However, there is still a sector of the population (the so-called fourth world) that, for different socio-economic and/or cultural reasons, has markedly lower rates of oral hygiene habits than the rest of the population [4,5].
In our study, we have seen a clear relationship between the level of education and the caries index, with a statistically significant difference between both aspects ($p \leq 0.001$), which is in agreement with similar studies in other areas of Latin America or the rest of the world [41,42,43].
It is, therefore, socio-cultural issues that mark the acquisition of health habits in general and oral health in particular. In our sample, around $90\%$ of the population brushed their teeth more than twice a day. This percentage is very similar to that of populations with a higher socioeconomic level, such as the Swedish [43] or Spanish [41] population. It seems that these habits are widely acquired by the Yucatecan population.
The use of topical fluorides in the form of toothpaste or mouthwash has been shown to be a major advance in caries control and their widespread use has lowered caries rates worldwide. The percentage of people using fluoride products in our sample was around $80\%$, somewhat lower than in the countries mentioned above [41,42,43].
With regard to the use of dental health services, the study population reported using mostly private clinics, having visited a dentist in the last two years at a rate of around $60\%$. The use of these health services is markedly different in other countries. In the USA, almost the entire population uses private dental services, while in European countries, this percentage varies according to the portfolio of services offered by the different countries [41,42,43].
## 5. Conclusions
The present investigation reflects a high need for treatment in the served area of Yucatan, finding more than two untreated caries per individual, with a significantly higher prevalence of decayed teeth in rural areas, among those with low-income levels, and in women. It is crucial to generate a prevention and treatment strategy considering the particularities of each population, improving collaborative projects to promote better oral health conditions not only in the Mexican population, but also in the international arena.
## References
1. Gil P.. *Medicina Preventiva y Salud Publica* (2015.0)
2. 2.
Organización Mundial de la Salud
Normas para la Identificacion y Definicion de Problemas DentalesInforme de un Comité de Expertos en Higiene DentalGinebra, Switzerland1962. *Normas para la Identificacion y Definicion de Problemas Dentales* (1962.0)
3. **Salud Bucodental**
4. Seirawan H., Faust S., Mulligan R.. **The impact of oral health on the academic performance of disadvantaged children**. *Am. J. Public Health* (2012.0) **102** 1729-1734. DOI: 10.2105/AJPH.2011.300478
5. Castaño A., Ribas D., Castaño A., Ribas D.. **Odontología Preventiva. Conceptualización y generalidades**. *Odontología Preventiva y Comunitaria La Odontología Social. Un Deber, una Necesidad, un Reto* (2012.0) 31-37
6. 6.
Committee WHO
Avances Recientes en Salud Bucodental: Informe de un Comité de Expertos de la OMSOMS, Serie de Informes técnicos826Organizacion Mundial de la SaludGinebra, Switzerland1992DC.HQ. *Avances Recientes en Salud Bucodental: Informe de un Comité de Expertos de la OMS* (1992.0) DC.HQ
7. **Catálogo Universal de Servicios de Salud de México 2018**. (2018.0)
8. **Secretaría de Salud del Gobierno de México. Resultados del Sistema de Vigilancia Epidemiológica de Patologías Bucales. SIVEPAB 2019**
9. **Oral Health and Function—A Report from the Adult Dental Health Survey 2009. United Kingdom March 2011**
10. Dye B.A., Weatherspoon D.J., Lopez Mitnik G.. **Tooth loss among older adults according to poverty status in the United States from 1999 through 2004 and 2009 through 2014**. *J. Am. Dent. Assoc.* (2019.0) **150** 9-23. DOI: 10.1016/j.adaj.2018.09.010
11. **Nationella Planeringsstödet 2013—Tillgång och Efterfrågan på visa Personalgrupper inom Hälso- och Sjukvård samt Tandvård**
12. Petersen P.E., Bourgeois D., Ogawa H., Estupinan-Day S., Ndiaye C.. **The global burden of oral diseases and risks to oral health**. *Bull. World Health Organ.* (2005.0) **83** 661-669. PMID: 16211157
13. Peres M.A., Macpherson L.M., Weyant R.J., Daly B., Venturelli R., Mathur M.R., Listl S., Celeste R.K., Guarnizo-Herreño C.C., Kearns C.. **Oral diseases: A global public health challenge**. *Lancet* (2019.0) **394** 249-260. DOI: 10.1016/S0140-6736(19)31146-8
14. **FDI El Desafio de las Enfermedades Bucodentales—Una Llamada de la Acción Global**. (2015.0)
15. Núñez D.P., García Bacallao L.. **Bioquímica de la caries dental**. *Rev. Habanera De Cienc. Médicas* (2010.0) **9** 156-166
16. Gato-Fuentes I., Duque-de-Estrada-Riverón J., Pérez-Quiñones J.. **La caries dental. Algunos de los factores relacionados con su formación en niños**. *Rev. Cubana Estomatol.* (2008.0) **45** 1-12
17. Duque-de-Estrada-Riverón J., Pérez-Quiñonez J., Hidalgo-Gato-Fuentes I.. **Caries dental y ecología bucal, aspectos importantes a considerar**. *Rev. Cubana Estomatol.* (2006.0) **43** 1-8
18. Machiulskiene V., Campus G., Carvalho J.C., Dige I., Ekstrand K.R., Jablonski-Momeni A., Maltz M., Manton D.J., Martignon S., Martinez-Mier E.A.. **Terminology of Dental Caries and Dental Caries Management: Consensus Report of a Workshop Organized by ORCA and Cariology Research Group of IADR**. *Caries Res.* (2020.0) **54** 7-14. DOI: 10.1159/000503309
19. Mota-Sanhua V., Ortega-Maldonado M.L.-V.J.. **Factores familiares asociados con el estado de nutricion y la salud oral en adolescentes**. *Rev. Med. Inst. Mex. Seguro Soc.* (2008.0) **46** 253-260. PMID: 19133201
20. Oropeza-Oropeza A., Molina-Frechero N., Castañeda-Castaneira E., Zaragoza-Rosado Y., Leyva D.C.. **Dental caries in the permanent first molars of schoolchildren in the borough of Tláhuac**. *Rev. Mex. Pediatría* (2012.0) **69** 63-68
21. García-Cortés J.O., Mejia-Cruz J.A., Medina-Cerda E., Orozco-De la Torre G., Medina-Solís C.E., Márquez-Rodríguez S., de Jesús Navarrete-Hernández J., Islas-Granillo H.. **Experiencia, prevalencia, severidad, necesidades de tratamiento para caries dental e índice de cuidados en adolescentes y adultos Jóvenes mexicanos**. *Rev. Investig. Clin.* (2014.0) **66** 505-511. PMID: 25729867
22. Soria-Hernández M.A.. **Pasado y presente de la caries dental**. *Acta Pediátrica México* (2010.0) **31** 195-196
23. 23.
WHO
Oral Health Surveys: Basic Methods4th ed.World Health OrganizationGeneva, Switzerland1997. *Oral Health Surveys: Basic Methods* (1997.0)
24. Patricia O., Sylvia P., Mariana M., Susana L., Ramón Á.. **Caries dental. La enfermedad oral más prevalente: Primer Estudio poblacional en jóvenes y adultos uruguayos del interior del país**. *Odontoestomatologia* (2013.0) **15** 26-34
25. Cubero A., Lorido I., González A., Ferrer A., Zapata D., Ambel J.. **Prevalencia de caries dental en escolares de educación infantil de una zona de salud con nivel socioeconómico bajo**. *Rev. Pediatr. Aten. Primaria* (2019.0) **21** 47-59
26. Canseco D.M., Morales P.L., Pérez R.C.C.. **Prevalence of early childhood caries and socioeconomical level**. *Rev. Odontológica Mex.* (2011.0) **15** 96-102
27. Serrano-Pina R., Aguilar-Ayala F.J., Scougall-Vilchis R.J., Trujillo-Guiza M.L., Mendieta-Zeron H.. **Prevalence of Obesity in Elementary School Children and its Association with Dental Caries**. *Oral Health Prev. Dent.* (2020.0) **18** 35-42. PMID: 32051969
28. Martínez-Pérez K.M., Monjarás-Ávila A.J., Patiño-Marín N., Loyola-Rodríguez J.P., Mandeville P.B., Medina-Solís C.E., Islas-Márquez A.J.. **Estudio epidemiológico sobre caries dental y necesidades de tratamiento en escolares de 6 a 12 años de edad de San Luis Potosí**. *Rev. Investig. Clin.* (2010.0) **62** 206-213. PMID: 20815125
29. Villalobos Rodelo J.J., Medina Solís C.E., Molina Frechero N., Vallejos Sánchez A.A., Pontigo Loyola A.P., Espinoza Beltrán J.L.. **Caries dental en escolares de 6 a 12 añios de edad en Navolato, Sinaloa, México: Experiencia, prevalencia, gravedad y necesidades de tratamiento**. *Biomédica* (2006.0) **26** 224-233. DOI: 10.7705/biomedica.v26i2.1412
30. Garcia-Perez A., Irigoyen-Camacho M.E., Borges-Yanez S.A., Zepeda-Zepeda M.A., Bolona-Gallardo I., Maupome G.. **Impact of caries and dental fluorosis on oral health-related quality of life: A cross-sectional study in schoolchildren receiving water naturally fluoridated at above-optimal levels**. *Clin. Oral Investig.* (2017.0) **21** 2771-2780. DOI: 10.1007/s00784-017-2079-1
31. Frencken J.E., Sharma P., Stenhouse L., Green D., Laverty D., Dietrich T.. **Global epidemiology of dental caries and severe periodontitis—A comprehensive review**. *J. Clin. Periodontol.* (2017.0) **44** S94-S105. DOI: 10.1111/jcpe.12677
32. Romo-Pinales M.R., de Jesús Herrera M.I., Bribiesca-García M.E., Rubio-Cisneros J., Hernández-Zavala M.S., Murrieta-Pruneda J.F.. **Caries dental y algunos factores sociales en escolares de Cd. Nezahualcóyotl**. *Bol. Méd. Hosp. Infant. México* (2005.0) **62** 124-135
33. Medina-Solís C.E., Maupomé G., Pelcastre-Villafuerte B., Avila-Burgos L., Vallejos-Sánchez A.A., Casanova-Rosado A.J.. **Desigualdades socioeconómicas en salud bucal: Caries dental en niños de seis a 12 años de edad**. *Rev. Investig. Clin.* (2006.0) **58** 296-304. PMID: 17146941
34. **Encuesta Nacional de Caries y Fluorosis Dental 2011–2014**
35. Aamodt K., Reyna-Blanco O., Sosa R., Hsieh R., De la Garza Ramos M., Garcia Martinez M., Orellana M.F.. **Prevalence of caries and malocclusion in an indigenous population in Chiapas, Mexico**. *Int. Dent. J.* (2015.0) **65** 249-255. DOI: 10.1111/idj.12177
36. Islas-Granillo H., Borges-Yañez S.A., Navarrete-Hernández J.D.J., Veras-Hernández M.A., Casanova-Rosado J.F., Minaya-Sánchez M., Casanova-Rosado A.J., Fernández-Barrera M.Á., Medina-Solís C.E.. **Indicators of oral health in older adults with and without the presence of multimorbidity: A cross-sectional study**. *Clin. Interv. Aging* (2019.0) **14** 219-224. DOI: 10.2147/CIA.S170470
37. Ortega-Maldonado M., Mota-Sanhua V., Lopez-Vivanco J.C.. **Oral health status of adolescents in Mexico City**. *Rev. Salud Publica* (2007.0) **9** 380-387. DOI: 10.1590/S0124-00642007000300006
38. Vega Lizama E.M., Cucina A.. **Maize dependence or market integration? Caries prevalence among indigenous Maya communities with maize-based versus globalized economies**. *Am. J. Phys. Anthropol.* (2014.0) **153** 190-202. DOI: 10.1002/ajpa.22418
39. Aguilar-Zinser V., Irigoyen M.E., Rivera G., Maupome G., Sanchez-Perez L., Velazquez C.. **Cigarette smoking and dental caries among professional truck drivers in Mexico**. *Caries Res.* (2008.0) **42** 255-262. DOI: 10.1159/000135670
40. Villalobos-Rodelo J.J., Medina-Solis C.E., Maupome G., Pontigo-Loyola A.P., Lau-Rojo L., Verdugo-Barraza L.. **Dental caries in schoolchildren from a northwestern community of Mexico with mixed dentition, and some associated clinical, socioeconomic and socio-demographic variables**. *Rev. Investig. Clin.* (2007.0) **59** 256-267. PMID: 18019598
41. Varela-Centelles P., Bugarín-González R., Blanco-Hortas A., Varela-Centelles A., Seoane-Romero J.M., Romero-Méndez A.. **Oral hygiene habits. Results of a population-based study**. *An. Sist. Sanit. Navar.* (2020.0) **43** 217-223. DOI: 10.23938/ASSN.0869
42. Santos J., Antunes L., Namorado S., Kislaya I., João Santos A., Rodrigues A.P., Braz P., Gaio V., Barreto M., Lyshol H.. **Oral hygiene habits in Portugal: Results from the first Health Examination Survey (INSEF 2015)**. *Acta Odontol. Scand.* (2019.0) **77** 334-339. DOI: 10.1080/00016357.2018.1564839
43. Hugoson A., Koch G., Göthberg C., Helkimo A.N., Lundin S.A., Norderyd O., Sjödin B., Sondell K.. **Oral health of individuals aged 3–80 years in Jönköping, Sweden during 30 years (1973–2003). I. Review of findings on dental care habits and knowledge of oral health**. *Swed. Dent. J.* (2005.0) **29** 125-138. PMID: 16463569
|
---
title: Personality Determinants of Exercise-Related Nutritional Behaviours among Polish
Team Sport Athletes
authors:
- Maria Gacek
- Agnieszka Wojtowicz
- Adam Popek
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001531
doi: 10.3390/ijerph20054025
license: CC BY 4.0
---
# Personality Determinants of Exercise-Related Nutritional Behaviours among Polish Team Sport Athletes
## Abstract
A proper diet increases the effectiveness of training and accelerates post-workout regeneration. One of the factors determining eating behaviour are personality traits, including those included in the Big Five model, i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness. The aim of this study was to analyse the personality determinants of peri-exercise nutritional behaviours among an elite group of Polish athletes practicing team sports. The study was conducted in a group of 213 athletes, using the author’s validated questionnaire of exercise-related nutrition behaviours and the NEO-PI-R (Neuroticism Extraversion Openness-Personality Inventory-Revised). A statistical analysis was performed using Pearson’s linear correlation and Spearman’s rank correlation coefficients as well as a multiple regression analysis, assuming a significance level of α = 0.05. It has been shown that the level of the overall index regarding normal peri-exercise eating behaviours decreased with increasing neuroticism (r = −0.18) and agreeableness (r = −0.18). An analysis of the relationship between the personality traits (sub-scales) of the Big Five model demonstrated that the overall index of proper peri-exercise nutrition decreased with the intensification of three neuroticism traits, i.e., hostility/anger (R = −0.20), impulsiveness/immoderation (R = −0.18), and vulnerability to stress/learned helplessness (R = −0.19), and four traits of agreeableness, i.e., straightforwardness/morality (R = −0.17), compliance/cooperation (R = −0.19), modesty (R = −0.14), and tendermindedness/sympathy (R = −0.15) ($p \leq 0.05$). A multiple regression analysis exhibited that the full model consisting of all the analysed personality traits explained $99\%$ of the variance concerning the level of the proper peri-exercise nutrition index. In conclusion, the index of proper nutrition under conditions of physical effort decreases along with the intensification of neuroticism and agreeableness among Polish athletes professionally practicing team sports.
## 1. Introduction
Proper nutrition is an important factor determining exercise capacity and the effectiveness of post-exercise restitution processes [1,2,3,4,5]. They concern the time, quantity, and type of meals, snacks, and liquids consumed before, during, and after physical exercise, taking the specificity of the discipline and individual pre-dispositions as well as food preferences of the competitor into account [5]. Nutrition before training or a competition should be focused on adequate hydration and nutrition, during exercise, on replenishing fluids and energy losses, and after exercise, on accelerating post-exercise regeneration. A greatly significant aspect of peri-exercise nutrition is proper hydration, which is achieved by consuming water and isotonic drinks [1,2,3,4,5,6,7,8,9]. Pre-workout meals should be rich in carbohydrates (with different glycaemic indices) and low-fat protein products, as well as vitamins and mineral salts [5]. Before prolonged exercise (> 60 min), an additional energy reservoir may come from a carbohydrate snack [10]. Nutrition during post-exercise recovery should help restore disturbed homeostasis, optimise water and electrolyte balance, and aid the resynthesis of muscle and liver glycogen, while managing the acid-based balance and replenishment of cellular protein losses [1,2,3,4,5,11]. Indicators of nutrition and hydration status are among the significant biomarkers related to the health, performance, and post-exercise regeneration of athletes [12].
Meanwhile, in research among athletes, numerous quantitative and qualitative nutritional irregularities have been indicated. In this regard, a low supply of carbohydrates, vitamins (including antioxidants), and mineral salts (including potassium and calcium) has been found [13,14,15,16,17]. These are ingredients that play a key role in the energy of physical exercise, skeletal muscle contraction activity, and reduction of oxidative stress [1,10,18,19]. The described nutritional deficiencies may be associated with the insufficient consumption of products that have a high nutritional value, which has been indicated among athletes at research centres in different countries [20,21,22,23,24,25,26].
In the past few years, the health and nutritional behaviour of various population groups, including athletes, have been negatively affected by the COVID-19 pandemic [27,28,29,30]. At the same time, health training and a varied, balanced diet, rich in, among others, vegetables, fruits, and fish, containing immunostimulating ingredients (e.g., vitamins C and D and omega 3 PUFAs), can support the immune system and reduce health risks [29,31]. In endurance athletes, the relationship has been described between a rational diet, physical activity, and an improvement of physical capacity as well as body composition after a mild COVID-19 infection [32].
The nutritional behaviour of athletes is dynamic and conditioned by numerous factors, including personality [33,34,35]. Personality is one of the important aspects of human functioning in personal and social dimensions, related to, among others, cognitive, emotional processes, motivation, undertaken tasks, and achieving success. Personality determines the consistency of predispositions, mental functions, and the behaviour of individuals [36,37]. One of the dominant personality models in the psychology of traits is the Big Five model created by Costa and McCrae, which includes five main personality dimensions (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), and their sub-categories [38,39]. Neuroticism describes a person’s level of emotional stability and resilience. People who score high in this dimension are sensitive and more frequently experience negative emotions, such as fear, anger or sadness, while people with low neuroticism are self-confident and emotionally stable. Extraversion refers to a person’s level of sociability, enthusiasm, and assertiveness. People who score high in this dimension tend to be outgoing, talkative, and energetic, while low scorers tend to be more reserved and introverted. Openness to experience refers to a person’s level of curiosity, creativity, and willingness to experiment. High scorers in this dimension are creative, open-minded, and interested in new experiences, while low scorers are more conventional and practical. Agreeableness is related to a person’s level of kindness, compromise, and empathy. People who score high on this dimension tend to be friendly, compassionate, and cooperative, while people who score low in this dimension tend to be more competitive and suspicious. Conscientiousness refers to a person’s level of self-discipline and responsibility. People who score high in this dimension tend to be responsible, effective, and goal-focused, while people with low scores tend to be more easy-going and less organised. In this way, the Big Five personality model allows for multi-faceted personality characteristics and explains socially and culturally significant behaviours that depend on the configuration of several personality traits at the same time [36,37]. Due to the significance of personality for success in sports, the personality assessment of athletes is an important area of sport psychology [40]. The level of neuroticism, extraversion, agreeableness, and conscientiousness can affect the results of competition in individual sports, although there is no single universal personality profile of athletes [40]. In studies among athletes, low neuroticism has mostly been noted, especially in high-level athletes [41,42,43,44].
Previous Polish research on the relationships between personality traits of the Big Five model and nutritional behaviours among people performing increased physical activity primarily concerned diet health quality among physical education students [45] and diet quality as well as nutritional behaviours among team sports athletes [46,47]. In the cited studies, the authors indicated relationships between the personality dimensions of the Big Five model and indicators of a healthy and unhealthy diet, implementing the qualitative recommendations of the Swiss nutrition pyramid for male athletes practicing team sports. The results of the above-mentioned studies mostly indicate the positive predictive significance of extraversion and conscientiousness, as well as the negative significance of neuroticism for the quality of athletes’ diets [46,47]. Relationships between personality traits and eating behaviours as well as nutritional status have also been the subject of research in population groups other than athletes [48,49,50,51].
To the authors’ knowledge, there is no research on the personality determinants of specific nutritional behaviours among athletes in conditions of physical exertion and post-exercise recovery. Therefore, due to the importance of diet in the peri-exercise period for the capacity of regeneration processes and the effectiveness of these processes, assuming the complexity regarding determinants of nutritional behaviours, a study was carried out on the personality determinants of athletes’ peri-exercise nutritional behaviours. The aim of this research was to analyse the personality determinants of peri-exercise nutritional behaviour among an elite group of Polish athletes professionally training in team sports.
The following research questions were posed: [1] How are athletes’ peri-exercise nutritional behaviours shaped? [ 2] What are the relationships between personality traits and athletes’ peri-exercise nutritional behaviours?
Referring to the results of previous research [45,46,47] and the characteristics regarding the personality dimensions of the Big Five model (including neuroticism, associated with emotional liability, extraversion, regarding positive emotionality, conscientiousness, associated with the ability to control stimulus and being focused on achieving specific goals, and agreeableness, connected with less involvement in performed tasks) [36], a research hypothesis was formulated. It was assumed that personality traits are related to peri-exercise eating behaviours. Along with an increase in the level of extraversion and conscientiousness, the scale of correct eating behaviours also increases, and with the intensification of neuroticism and agreeableness, it experienced a decrease.
## 2.1. Participants
The research was carried out among a group of 213 Polish athletes (males) professionally practicing team sports, including basketball ($$n = 54$$), volleyball ($$n = 53$$), football ($$n = 53$$), and handball ($$n = 53$$). The basic criterion for selection into the study group was practicing sports at a professional level—at the level of the highest league in Poland, and for at least 3 years. The basic criteria for exclusion were belonging to the lower league class and/or failure to meet the criterion of minimum sports experience (3 years). The studied athletes, in relation to the current classification of the level of activity and sports abilities [52], can be assigned to Tier 3 (highly trained/national level). The age of the examined athletes was between 18 and 38 ($M = 26.1$; SD = 4.5), with the sports experience ranging from 3 to 20 years ($M = 8.2$; SD = 4.5). The median number of training sessions per week was 7, and the volume of a single training unit was 90 min. The study was performed in accordance with the principles of the Declaration of Helsinki, after obtaining informed consent from the participants. The research protocol was approved by the Bioethics Committee at the District Medical Chamber in Kraków (No. 105/KBL/OIL/2021).
## 2.2.1. Evaluation of Athletes’ Peri-Exercise Nutritional Behaviour
An original questionnaire regarding qualitative recommendations for peri-exercise nutrition was used to assess the nutritional behaviour of athletes. The questionnaire consists of 15 statements (items) concerning eating behaviours during the peri-exercise period. The responses were evaluated on the 5-point Likert scale (from 1 to 5, from “definitely no”, “rather no”, “hard to say”, and “rather yes” to “definitely yes”). The items included in the questionnaire concerned eating behaviours that are particularly important for post-exercise nutrition strategies, which increase the ability to exercise and the pace of regeneration processes, indicated by the authors of scientific papers in the field of nutritional recommendations for athletes [2,4,5]. The questionnaire enquiries concerned the following: intake of isotonic drinks during exercise, type of meal consumed before and after training, consumption of snacks and the type as well as amount of beverage intake before and after training, including drinks containing carbohydrates and electrolytes, as well as consumption of carbohydrate and protein products after training/competition. The subject of assessment was the athletes’ peri-exercise eating habits (during the previous 6 months). Based on the results of the questionnaire, the degree of implementing individual nutrition recommendations and the overall index of rational nutrition behaviours during the peri-exercise period were assessed (on a scale of 1–75 points, assuming that the higher the index, the more intense the rational peri-exercise eating behaviours). The questionnaire was validated. Test validity was assessed by repeated testing ($$n = 32$$). The value of the linear correlation coefficient was calculated and the H0 null hypothesis was tested: $r = 0$, via the Student’s t-test, obtaining a result confirming reliability of the scale ($r = 0.378$; $$p \leq 0.035$$). Good internal consistency of the scale was also confirmed (Cronbach’s α coefficient was 0.77).
## 2.2.2. Evaluation of Athletes’ Personality Traits
The NEO-PI-R (Neuroticism Extraversion Openness-Personality Inventory-Revised) by P.T. Costa and R.R. McCrae [39] was used in the Polish adaptation by J. Siuta [53]. Characteristics of the NEO-PI-R personality inventory, according to the authors of the original tool and its Polish adaptation [39,53], have been presented in our previous publication [46]. Similarly, the personality traits of the examined group of athletes have already been the subject of our other publication [46]; therefore, they will not be presented in this work.
## 2.3. Statistical Analysis
The collected numerical material was subjected to statistical analysis using the Statistica 13.3 package. Statistical analysis was performed using Pearson’s linear correlation and Spearman’s rank correlation coefficients (depending on the nature of the variables). Multiple regression analysis was also carried out to check which of the variables could explain the level of proper peri-exercise index of nutrition. The stepwise progressive regression procedure (without intercept) was used in the calculations. The analysis also included the calculation of the multivariate determination coefficient (R2) and the standard error of estimation (sy), as well as the values of standardised partial regression coefficients b*, which are a measure of the relative significance regarding individual personality traits (independent variables X) in the model. The analyses were conducted assuming the significance level of α = 0.05.
## 3.1. Athletes’ Peri-Exercise Nutritional Behaviour
With regard to implementing the recommendations of peri-exercise nutrition, it was found that almost all athletes (approx. $98\%$) consumed 200–250 mL of isotonic drinks after training. A high percentage (over $80\%$) consumed fruit and vegetables in their meals before and after training. At the same time, over $70\%$ of the athletes consumed complex carbohydrates in the meal prior to training, 500–600 mL of fluids 2–3 h before training and carbohydrate products after exercise. More than half of the athletes declared the consumption of 1 litre of fluids per 1 h of training and a carbohydrate snack before long-duration training. To a lesser extent (about one-third of the group), the athletes consumed a snack at least 40 min pre-training, a meal at least 2 h before training, 200–600 mL of fluids immediately before training, and complete protein in their pre-exercise meals (Table 1).
The assessment of the peri-exercise eating behaviours (according to median) confirms that, to a high degree, the athletes consumed at least 1 litre of fluids per hour of training (Me = 4), complex carbohydrates in the pre-training meal, vegetables and fruits before training, 500–600 mL of fluids 2–3 h before training, a snack before training lasting more than 2 h, a meal within 30–60 min after training, and carbohydrates in the post-workout meal. Other assessed nutritional recommendations were implemented to a lesser extent and at a similar level (Me = 3.00). The overall index of proper peri-exercise nutrition was 51.9 points (out of 75 max) (Table 2).
## 3.2. Personality Traits and Peri-Exercise Nutritional Behaviour of Athletes
An analysis of the relationship between personality traits and the implementation of peri-exercise nutrition recommendations among athletes showed that the level of the overall index regarding correct eating behaviours (consistent with the recommendations of post-exercise nutrition strategies) decreased with increasing neuroticism (r = −0.18) and agreeableness (r = −0.18). In terms of particular aspects of peri-exercise nutrition, it was shown that with the intensification of neuroticism, the consumption of complex carbohydrates in the pre-workout meal (R = −0.15), snacks before more than 2 h training (R = −0.21), and complete protein consumption (R = −0.20) as well as complex carbohydrates in the post-workout meal decreased (R = −0.14). At the same time, with the intensification of extraversion, the consumption of at least 1 litre of water/isotonic drink for each hour of training decreased (R = −0.17) as well as the consumption of a meal within 30–60 min after ending training (R = −0.15), while the consumption of carbohydrates in the post-workout meal increased ($R = 0.17$). There was also a positive correlation between openness to experience and eating a snack before long-duration training ($R = 0.17$). Simultaneously, along with the intensification of agreeableness, the scale of consuming vegetables and fruits in the pre-training meal (R = −0.17), drinking 500–600 mL of fluids 2–3 h before training (R = −0.14), consuming carbohydrates in the post-workout meal (R = −0.21), and the intake of an isotonic drink in the amount of 200–250 mL every 15–20 min after training experienced a decrease (R = −0.14) (Table 3).
An analysis of the correlations between the personality traits (sub-scales) of the Big Five model showed that the overall index of proper peri-exercise nutrition decreased with the intensification of three neuroticism traits, i.e., hostility/anger (R = −0.20), impulsiveness/immoderation (R = −0.18), and vulnerability to stress/fear/learned helplessness (R = −0.19) and four traits of agreeableness, i.e., straightforwardness/morality (R = −0.17), compliance/cooperation (R = −0.19), modesty (R = −0.14), and tendermindedness/sympathy (R = −0.15) ($p \leq 0.05$) (Table 4).
A multiple regression analysis (dependent variable: overall index of proper peri-exercise nutrition; predictors: personality traits of the Big Five model) indicated that the full model consisting of all analysed personality traits explained $99\%$ of the variance in the level of the index regarding appropriate peri-exercise nutrition, with agreeableness, extraversion, conscientiousness, and openness. The variable with the highest importance was agreeableness (b* = 0.437). The described correlations were directly proportional (Table 5).
## 4. Discussion
In the discussed research, limited implementation has been shown regarding qualitative recommendations for peri-exercise nutrition and significant correlations between some dimensions of personality and peri-exercise nutritional behaviours among elite Polish athletes practicing team sports.
When discussing peri-exercise nutrition, the average level of correct behaviours in this area should be highlighted (51.9 out of 75 points, i.e., $68.5\%$) and the varied level of implementing individual recommendations, including the highest (more than $70\%$ of the group) regarding fluid replenishment before and after exercise, as well as vegetables, fruits, and complex carbohydrates in the pre-workout and post-workout meal. Among the recommendations of peri-exercise nutrition, special importance should be emphasized for supplementing water and electrolytes as well as vegetables and fruits (alkalinizing products, which are, among others, a source of antioxidants, B vitamins, magnesium, potassium, and carbohydrates). This is also true for other carbohydrate products in restoring homeostasis and the optimisation of post-exercise restitution processes, that is, restoring the water–electrolyte and acid-base balance and rebuilding carbohydrate losses (accelerating muscle and liver glycogen resynthesis processes) [1,2,3,4,5]. Consuming appropriate amounts of vegetables and fruits (rich in antioxidant substances, including polyphenols, vitamin C, and carotenoids) contributes to the reduction of oxidative stress indices, i.e., reducing the health risks associated with its level [1,54,55,56,57]. In a situation of high exposure to oxidative stress, in conditions of vigorous physical exercise, a diet rich in dietary antioxidants (including vegetables and fruits) is an important aspect of rational nutrition for athletes [55,58,59,60].
The significance of proper fluid replenishment (and prevention of dehydration in sports) is emphasized by numerous authors [8,9,61,62]. In the research on the subject, the use of isotonic drinks has also been indicated in the effective replenishment of water and electrolyte loss among athletes, including those training volleyball, American football, and rowing [63,64,65]. The subject of numerous studies in the field of sports dietetics was also the assessment of carbohydrate supply as the basic energy substrate in the diet of athletes. In recent meta-analytical studies, the prevalence of low energy and carbohydrate intake among team sports athletes has been confirmed [17]. In other trials, the occurrence has been noted with regard to qualitative nutritional irregularities among athletes, including those practicing team sports [20,21,22,23,25].
Before discussing the relationships between personality traits and exercise-related eating behaviours, it is necessary to point out the basic personality characteristics of the athletes under study. In this regard, it was found that the athletes obtained high scores for extraversion ($M = 121.8$), openness ($M = 115.0$), agreeableness ($M = 123.2$), and conscientiousness ($M = 128.5$), while low scores were observed for neuroticism ($M = 72.1$) [46]. A low level of neuroticism among athletes has also been described in other studies on professional athletes, including those practicing team sports [41,42], also among those from Poland [43], and especially among master-class athletes [44].
The discussed research allowed us to note statistically significant correlations between the personality traits of the Big Five model and their sub-scales and the quality of peri-exercise nutrition among athletes. A negative predictive value regarding neuroticism and agreeableness was found in the overall index of proper peri-exercise nutrition. The correlations found between extraversion and exercise-related eating behaviours were not unambiguous, while within the dimension of openness, a positive relationship was described with one of the aspects of nutrition, i.e., the snack before long-duration training. Conscientiousness and its sub-scales were not related to the quality of peri-exercise nutrition among the studied athletes. The obtained results confirm the difficulties in an unambiguous assessment and interpretation of the relationship between the personality and nutritional behaviours of athletes.
The discussed study *Is a* continuation of our earlier research that was carried out among Polish team sports athletes, which concerned correlations between personality traits and the health quality of the diet (associated with the frequency of consuming products with potentially beneficial and potentially adverse health effects) and with the implementation of the quality recommendations from the Swiss pyramid for athletes [46,47]. The discussed results, indicating the negative predictive significance of neuroticism (and its sub-scales) for normal exercise-related nutritional behaviours, refer to the relationship between neuroticism and a lower health quality of athletes’ diets [46]. Furthermore, among physical education students, the relationship between lower neuroticism and more rational food choices in terms of consuming sea fish was described [45]. In other studies conducted among the general population, it was shown that neuroticism, through the mechanism of emotional eating, promoted the consumption of non-recommended products, including confectionery [48]. The discussed studies on athletes, indicating the negative predictive significance of agreeableness (and its sub-scales), correspond to studies among university students in Ghana, in which a relationship was demonstrated between high agreeableness and irregular eating habits [49]. Agreeableness reduces commitment to performed activities. The described ambiguous relationships between extraversion and some particular exercise-related eating behaviours of athletes training team sports correspond to the positive predictive significance of extraversion as an indicator of a healthy diet of athletes shown in our previous research [46], but also as an indicator of healthy and unhealthy diets among students of physical education [45]. On the one hand, higher extraversion favoured the consumption of vegetables among athletes [46], but also, confectionery products among physical education students [45]. Relationships between conscientiousness and the quality of peri-exercise nutrition were not described, unlike among students of physical education (in them, along with the increase in conscientiousness, the pro-health quality of the diet, expressed by the pro-healthy diet index, pHDI-14, increased) [45]. Positive relationships between conscientiousness and a healthy diet were also noted by other authors in various population groups other than athletes [48,50,51].
It can be concluded that various studies on the personality determinants of eating behaviour in different population groups sometimes provide varied and ambiguous results. Further interdisciplinary research is needed to explain the mechanisms of the observed relationships, which is also pointed out by other authors [51]. Nutritional irregularities found among athletes justify the need to monitor diet and carry out nutritional education, considering the individualisation of influences promoting a healthy way of eating, also in conditions of physical exercise and post-exercise recovery. Learning about the relationships between personality and eating behaviours may be conducive to the personalisation of interactions in the field of nutrition education and diet modification, taking the personality traits of athletes into account. By understanding how personality traits are related to nutrition, we can better identify individuals who may be at a higher risk of poor health outcomes and consequently develop targeted interventions to promote healthy eating. As we learn more about the interplay between personality and nutrition, we may be able to develop more personalised approaches to nutrition. For example, individuals who demonstrate a high level of neuroticism may benefit more from different types of dietary interventions than individuals who exhibit high extraversion. By tailoring our recommendations to an individual’s personality, we may be able to achieve better outcomes.
The limitations of this work are primarily related to the failure to include demographic and sports variables (see training, competition practice, and discipline), as well as one selected nutritional area (peri-exercise nutrition behaviours) and the self-descriptive nature of the applied research tools. The limitations of the study also concern the failure to consider the training loads that determine the nutritional needs of athletes. The limitations indicated as well as others may set the directions for further research, the aim and subject of which should be to achieve a comprehensive assessment of personality determinants in various areas of sports nutrition, taking gender, sports experience, sports level, and type of discipline into account. Further research could concern personality determinants regarding the quantitative aspects of athletes’ diets (e.g., energy consumption, macronutrients, vitamins, and mineral salts), which would contribute to a comprehensive assessment of athletes’ nutrition.
## References
1. Thomas D.T., Erdman K.A., Burke L.M.. **American college of sports medicine joint position statement. Nutrition and athletic performance**. *Med. Sci. Sports Exerc.* (2016) **48** 269-276
2. Kerksick C.M., Arent S., Schoenfeld B.J., Stout J.R., Campbell B., Wilborn C.D., Taylor L., Kalman D., Smith-Ryan A.E., Kreider R.B.. **International society of sports nutrition position stand: Nutrient timing**. *J. Int. Soc. Sport. Nutr.* (2017) **14** 33. DOI: 10.1186/s12970-017-0189-4
3. Kerksick C.M., Wilborn C.D., Roberts M.D., Smith-Ryan A., Kleiner S.M., Jager R., Collins R., Cooke M., Davis J.N., Galvan E.. **ISSN exercise & sports nutrition review uptade: Research & recommendations**. *J. Int. Soc. Sport. Nutr.* (2018) **15** 38
4. Ormsbee M.J., Bach C.W., Baur D.A.. **Pre-exercise nutrition; the role of macronutrients, modified starches and supplements on metabolism and endurance performance**. *Nutrients* (2014) **6** 1782-1788. DOI: 10.3390/nu6051782
5. Thomas D.T., Erdman K.A., Burke L.M.. **Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance**. *J. Acad. Nutr. Diet.* (2016) **116** 501-528. DOI: 10.1016/j.jand.2015.12.006
6. Nuccio R.P., Barnes K.A., Carter J.M., Baker L.B.. **Fluid Balance in Team Sport Athletes and the Effect of Hypohydration on Cognitive, Technical, and Physical Performance**. *Sport. Med.* (2017) **47** 1951-1982. DOI: 10.1007/s40279-017-0738-7
7. Belval L.N., Hosokawa Y., Casa D.J., Adams W.M., Armstrong L.E., Baker L.B., Burke L., Cheuvront S., Chiampas G., González-Alonso J.. **Practical Hydration Solutions for Sports**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11071550
8. Batista M.C.C., dos Santos M.A.P.. **Impact of Hydration on Exercise Performance and Physiological Responses**. *Curr. Nutr. Food Sci.* (2020) **16** 1346-1352. DOI: 10.2174/1573401316666200309113907
9. Rowlands D.S., Kopetschny B.H., Badenhorst C.E.. **The Hydrating Effects of Hypertonic, Isotonic and Hypotonic Sports Drinks and Waters on Central Hydration During Continuous Exercise: A Systematic Meta-Analysis and Perspective**. *Sport. Med.* (2021) **52** 349-375. DOI: 10.1007/s40279-021-01558-y
10. Jeukendrup A.E.. **Periodized Nutrition for Athletes**. *Sport. Med.* (2017) **47** 51-63. DOI: 10.1007/s40279-017-0694-2
11. Baranauskas M., Jablonskienė V., Abaravičius J.A., Samsonienė L., Stukas R.. **Dietary Acid-Base Balance in High-Performance Athletes**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17155332
12. Lee E.C., Fragala M.S., Kavouras S.A., Queen R.M., Pryor J.L., Casa D.J.. **Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes**. *J. Strenght. Cond. Res.* (2017) **31** 2920-2937. DOI: 10.1519/JSC.0000000000002122
13. Steffl M., Kinkorova I., Kokstejn J., Petr M.. **Macronutrient Intake in Soccer Players-A Meta-Analysis**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11061305
14. Lohman R., Carr A., Condo D.. **Nutritional Intake in Australian Football Players: Sports Nutrition Knowledge and Macronutrient and Micronutrient Intake**. *Int. J. Sport Nutr. Exerc. Metab.* (2019) **29** 289-296. DOI: 10.1123/ijsnem.2018-0031
15. Jenner S.L., Trakman G., Coutts A., Kempton T., Ryan S., Forsyth A., Belski R.. **Dietary intake of professional Australian football athletes surrounding body composition assessment**. *J. Int. Soc. Sports Nutr.* (2018) **15** 43. DOI: 10.1186/s12970-018-0248-5
16. Gacek M.. **Sense of self-efficacy and the content of energy and nutrients in the diet of elite Polish basketball players**. *Rocz. Panstw. Zakl. Hig.* (2022) **73** 183-189. PMID: 35748538
17. Castillo M., Lozano-Casanova M., Sospedra I., Norte A., Gutiérrez-Hervás A., Martínez-Sanz J.M.. **Energy and Macronutrients Intake in Indoor Sport Team Athletes: Systematic Review**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14224755
18. Williams C., Rollo I.. **Carbohydrate Nutrition and Team Sport Performance**. *Sports Med.* (2015) **45** 13-22. DOI: 10.1007/s40279-015-0399-3
19. Higgins M.R., Izadi A., Kaviani M.. **Antioxidants and Exercise Performance: With a Focus on Vitamin E and C Supplementation**. *Int. J. Environ. Res. Public Health* (2020) **17**. DOI: 10.3390/ijerph17228452
20. Frączek B., Gacek M., Pięta A., Tyrała F., Mazur-Kurach P., Karpęcka E.. **Dietary mistakes of Polish athletes in relations to the frequency of consuming foods recommended in the Swiss food pyramid for active people**. *Rocz. Panstw. Zakl. Hig.* (2020) **71** 97-104. PMID: 32227788
21. Gacek M., Wojtowicz A.. **Personal Resources and the Nutritional Behaviour of Polish Basketball Players**. *J. Phys. Edu. Sport* (2021) **21** 130-139
22. Croteau K., Eduljee N., Murphy L., Ahearn L., Volpe S.L.. **Health and Lifestyle Behaviors of U.S. Masters World Cup Field Hockey Players**. *Sport J.* (2019) **22** 313-334
23. Deborah R.S., Jones B., Sutton L., King R.F.G.J., Duckworth L.C.. **Dietary Intakes of Elite 14- to 19-Year Old English Academy Rugby Players During a Pre-Season Training Period**. *Int. J. Sport Nutr. Exerc. Metab.* (2016) **26** 506-515. PMID: 27096473
24. Guldemir H.H., Bayraktaroglu E.. **Adolesan amator futbolcularin beslenme durumunun degerlendirilmesi**. *J. Phys. Edu. Sport Sci.* (2020) **18** 42-51
25. Vázquez-Espino K., Rodas-Font G., Farran-Codina A.. **Sport Nutrition Knowledge, Attitudes, Sources of Information, and Dietary Habits of Sport-Team Athletes**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14071345
26. Klein D.J., Eck K.M., Walker A.J., Pellegrino J.K., Freidenreich D.J.. **Assessment of Sport Nutrition Knowledge, Dietary Practices, and Sources of Nutrition Information in NCAA Division III Collegiate Athletes**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13092962
27. Ammar A., Brach M., Trabelsi K., Chtourou H., Boukhris O., Masmoudi L., Bouaziz B., Bentlage E., How D., Ahmed M.. **Effects of COVID-1 9 Home Confinement on Eating Behaviour and Physical Activity: Results of the ECLB-COVID 19 International Online Survey**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12061583
28. Huber B.C., Steffen J., Schlichtiger J., Brunner S.. **Altered nutrition behaviour during COVID-19 pandemic lockdown in young adults**. *Eur. J. Nutr.* (2021) **60** 2593-2602. DOI: 10.1007/s00394-020-02435-6
29. Clemente-Suarez V.J., Ramos-Campo D.J., Mielgo-Ayuso J., Dalamitros A.A., Nikolaidis P.A., Hormeno-Holgado A., Tornero-Aguilera J.F.. **Nutrition in the Actual COVID-19 Pandemic. A Narrative Review**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13061924
30. Śliż D., Wiecha S., Ulaszewska K., Gąsior J.S., Lewandowski M., Kasiak P.S., Mamcarz A.. **COVID-19 and athletes: Endurance sport and activity resilience study-CESAR Study**. *Front. Physiol.* (2022) **13** 1078763. DOI: 10.3389/fphys.2022.1078763
31. Khoramipour K., Basereh A., Hekmatikar A.A., Castell L., Ruhee R.T., Suzuki K.. **Physical activity and nutrition guidelines to help with the fight against COVID-19**. *J. Sport. Sci.* (2021) **39** 101-107. DOI: 10.1080/02640414.2020.1807089
32. Śliż D., Wiecha S., Gąsior J.S., Kasiak P.S., Ulaszewska K., Postuła M., Małek Ł.A., Mamcarz A.. **The Influence of Nutrition and Physical Activity on Exercise Performance after Mild COVID-19 Infection in Endurance Athletes—CESAR Study**. *Nutrients* (2022) **14**. DOI: 10.3390/nu14245381
33. Birkenhead K.L., Slater G.. **A Review of Factors Influencing Athletes’ Food Choices**. *Sport. Med.* (2015) **45** 1511-1522. DOI: 10.1007/s40279-015-0372-1
34. Malsagova K.A., Kopylov A.T., Sinitsyna A.A., Stepanov A.A., Izotov A.A., Butkova T.V., Chingin K., Klyuchnikov M.S., Kaysheva A.L.. **Sports Nutrition: Diets, Selection Factors, Recommendations**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13113771
35. Pelly F.E., Thurecht R.L., Slater G.. **Determinants of Food Choice in Athletes: A Systematic Scoping Review**. *Sport. Med. Open* (2022) **8** 77. DOI: 10.1186/s40798-022-00461-8
36. McCrae R.R., Sutin A.R.. **A Five-Factor Theory Perspective on Causal Analysis**. *Eur. J. Pers.* (2018) **32** 151-166. DOI: 10.1002/per.2134
37. Zell E., Lesick T.L.. **Big five personality traits and performance: A quantitative synthesis of 50+ meta-analyses**. *J. Pers.* (2022) **90** 559-573. DOI: 10.1111/jopy.12683
38. Vassend O., Skrondal A.. **The NEO personality inventory revised (NEO-PI-R): Exploring the measurement structure and variants of the five-factor model**. *Pers. Individ. Differ.* (2011) **50** 1300-1304. DOI: 10.1016/j.paid.2011.03.002
39. Costa P.T., McCrae R.R.. *Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEOFFI) Professional Manual* (1992)
40. Piepiora P., Piepiora Z., Bagińska J.. **Personality and Sport Experience of 20-29-Year-Old Polish Male Professional Athletes**. *Front. Psychol.* (2022) **13** 854804. DOI: 10.3389/fpsyg.2022.854804
41. Trninić V., Trninić M., Penezić Z.. **Personality Differences Between The Players Regarding The Type of Sport and Age**. *Acta Kinesiol.* (2016) **10** 69-74
42. Cutuk S., Kacay Z., Cutuk Z.A.. **The Relationship Between Prosocial and Antisocial Behaviors and Personality Traits in Team Athletes**. *Sakarya Univ. J. Edu.* (2021) **11** 182-194. DOI: 10.19126/suje.840070
43. Lipowski M., Bieleninik Ł.. **Personality superfactors and healthy behaviors of professional athletes**. *Curr. Issues Personal. Psychol.* (2014) **2** 57-67. DOI: 10.5114/cipp.2014.44302
44. Piepiora P.. **Assessment of Personality Traits Influencing the Performance of Men in Team Sports in Terms of the Big Five**. *Front. Psychol.* (2021) **12** 1-8. DOI: 10.3389/fpsyg.2021.679724
45. Gacek M., Kosiba G., Wojtowicz A.. **Personality Determinants of Diet Quality among Polish and Spanish Physical Education Students**. *Int. J. Environ. Res. Public Health* (2021) **18**. DOI: 10.3390/ijerph18020466
46. Gacek M., Wojtowicz A., Popek A.. **Personality Determinants of Diet Health Quality Among an Elite Group of Polish Team Athletes**. *Int. J. Environ. Res. Public Health* (2022) **19**. DOI: 10.3390/ijerph192416598
47. Gacek M., Wojtowicz A., Popek A.. **Personality Determinants of Eating Behaviours Among an Elite Group Of Polish Athletes Training Team Sports**. *Nutrients* (2023) **15**. DOI: 10.3390/nu15010039
48. Keller C., Siegrist M.. **Does personality influence eating styles and food choices? Direct and indirect effects**. *Appetite* (2015) **84** 128-138. DOI: 10.1016/j.appet.2014.10.003
49. Intiful F.D., Oddam E.G., Kretchy I., Quampah J.. **Exploring the relationship between the big five personality characteristics and dietary habits among students in a Ghanian University**. *BMC Psychol.* (2019) **7**. DOI: 10.1186/s40359-019-0286-z
50. Pfeiler T.M., Egloff B.. **Personality and eating habits revisited: Associations between the big five, food choices, and Body Mass Index in a representative Australian sample**. *Appetite* (2020) **149** 104607. DOI: 10.1016/j.appet.2020.104607
51. Pristyna G., Mahmudiono T., Rifqi M.A., Indriani D.. **The relationship between Big Five Personality Traits, eating habits, physical activity, and obesity in Indonesia based on analysis of the 5th wave Indonesia Family Life Survey (2014)**. *Front. Psychol.* (2022) **13** 881436. DOI: 10.3389/fpsyg.2022.881436
52. McKay A.K.A., Stellingwerff T., Smith E.S., Martin D.T., Mujika I., Goosey-Tolfrey V.L., Sheppard J., Burke L.M.. **Defining Training and Performance Caliber: A Participant Classification Framework**. *Int. J. Sport. Physiol. Perform.* (2022) **17** 317-331. DOI: 10.1123/ijspp.2021-0451
53. Siuta J.. *Inwentarz Osobowości NEO-PI-R Paula T. Costy Jr i Roberta R. McCrae: Adaptacja Polska: Podręcznik [NEO-PI-R Personality Inventory by Paul T. Costa Jr and Robert R. McCrae: Polish Adaptation: Textbook]* (2006)
54. Yavari A., Javadi M., Mirmiran P., Bahadoran Z.. **Exercise-Induced Oxidative Stress and Dietary Antioxidants**. *Asian J. Sport. Med.* (2015) **6** e24898. DOI: 10.5812/asjsm.24898
55. Barnard N.D., Goldman D.M., Loomis J.F., Kahleova H., Levin S.M., Neabore S., Batts T.C.. **Plant-Based Diets for Cardiovascular Safety and Performance in Endurance Sports**. *Nutrients* (2019) **11**. DOI: 10.3390/nu11010130
56. Gantenbein K.V., Kanaka-Gantenbein C.. **Mediterranean Diet as an Antioxidant: The Impact on Metabolic Health and Overall Wellbeing**. *Nutrients* (2021) **13**. DOI: 10.3390/nu13061951
57. Barber T.M., Kabisch S., Pfeiffer A.F.H., Weickert M.O.. **The Health Benefits of Dietary Fibre**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12103209
58. Braakhuis A.J., Hopkins W.G.. **Impact of Dietary Antioxidants on Sport Performance: A Review**. *Sport. Med.* (2015) **45** 939-955. DOI: 10.1007/s40279-015-0323-x
59. Margaritelis N.V., Paschalis V., Theodorou A.A., Kyparos A., Nikolaidis N.G.. **Antioxidants in Personalized Nutrition and Exercise**. *Adv. Nutr.* (2018) **9** 813-823. DOI: 10.1093/advances/nmy052
60. Frączek B., Morawska M., Gacek M., Pogoń K.. **Antioxidant activity as well as vitamin C and polyphenol content in the diet for athletes**. *Ital. J. Food Sci.* (2019) **31** 617-630
61. Trangmar S.J., González-Alonso J.. **Heat, Hydration and the Human Brain, Heart and Skeletal Muscles**. *Sport. Med.* (2019) **49** 69-85. DOI: 10.1007/s40279-018-1033-y
62. McDermott B.P., Anderson S.A., Armstrong L.E., Casa D.J., Cheuvront S.N., Cooper L., Kenney W.L., O’Connor F.G., Roberts W.O.. **National Athletic Trainers’ Association Position Statement: Fluid Replacement for the Physically Active**. *J. Athl. Train.* (2017) **52** 877-895. DOI: 10.4085/1062-6050-52.9.02
63. Zapolska J.. **Assessment of Nutrition, Supplementation and Body Composition Parameters on the Example of Professional Volleyball Players**. *Rocz. Panstw. Zakl. Hig.* (2014) **65** 235-242. PMID: 25247804
64. Gacek M.. **Association between general self-efficacy level and use of dietary supplements in the group of American football players**. *Rocz. Panstw. Zakl. Hig.* (2016) **67** 31-36. PMID: 26953579
65. Dominguez R., Lopez-Dominguez R., Lopez-Samanes A., Gene P., Gonzales-Jurado J.A., Sanchez-Oliver A.J.. **Analysis of Sport Supplement Consumption and Body Composition in Spanish Elite Rowers**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12123871
|
---
title: Test–Retest Reliability and Internal Consistency of a Newly Developed Questionnaire
to Assess Explanatory Variables of 24-h Movement Behaviors in Adults
authors:
- Iris Willems
- Vera Verbestel
- Patrick Calders
- Bruno Lapauw
- Marieke De Craemer
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001532
doi: 10.3390/ijerph20054407
license: CC BY 4.0
---
# Test–Retest Reliability and Internal Consistency of a Newly Developed Questionnaire to Assess Explanatory Variables of 24-h Movement Behaviors in Adults
## Abstract
A questionnaire on explanatory variables for each behavior of the 24-h movement behaviors (i.e., physical activity, sedentary behavior, sleep) was developed based on three levels of the socio-ecological model, i.e., the intrapersonal level, interpersonal level and the physical environmental level. Within these levels, different constructs were questioned, i.e., autonomous motivation, attitude, facilitators, internal behavioral control, self-efficacy, barriers, subjective norm, social modeling, social support, home environment, neighborhood, and work environment. The questionnaire was tested for test–retest reliability (i.e., intraclass correlation (ICC)) for each item and internal consistency for each construct (i.e., Cronbach’s Alpha Coefficient) among a group of 35 healthy adults with a mean age of 42.9 (±16.1) years. The total questionnaire contained 266 items, consisting of 14 items on general information, 70 items on physical activity, 102 items on sedentary behavior, 45 items on sleep and 35 items on the physical environment. Seventy-one percent of the explanatory items showed moderate to excellent reliability (ICC between 0.50 and 0.90) and a majority of constructs had a good homogeneity among items (Cronbach’s Alpha Coefficient ≥ 0.70). This newly developed and comprehensive questionnaire might be used as a tool to understand adults’ 24-h movement behaviors.
## 1. Introduction
Healthy lifestyle behaviors (e.g., being physically active, interrupting sitting periods, optimal sleep pattern) have proven beneficial health effects in the prevention of numerous medical conditions such as diabetes, obesity, and cardiovascular diseases [1,2,3,4]. For a long time, these beneficial health effects have been investigated in health promotion research by focusing on one lifestyle behavior in isolation, e.g., moderate to vigorous physical activity (MVPA) [4,5]. However, a shift in research emphasizes the importance of considering all movement behaviors throughout a day, including PA, SB and sleep, as they are interconnected and mutually exclusive parts of a 24-h day [6]. This 24-h movement behavior paradigm recognizes that any change in time in one behavior inevitably leads to a change in time in one of the other behaviors [6,7,8]. While PA and SB are represented by their duration, achieving an optimal sleep pattern involves more than just duration, such as sleep timing and consistency [9].
Research on this 24-h movement behavior paradigm is rapidly expanding. A systematic review by Janssen and colleagues [2020] revealed associations between less optimal 24-h movement behavior compositions (i.e., high SB levels, low PA levels, non-optimal sleep duration) and all-cause mortality, cardiometabolic risk factors and adiposity among a general adult population [8]. Additionally, studies exploring associations between 24-h compositions and cardiometabolic health among healthy and clinical populations (e.g., diabetes) showed beneficial health effects when replacing small amounts of time (e.g., 10 min) from SB into more PA while preserving sleep duration. [ 10,11].
Therefore, a behavior change intervention to promote optimal 24-h movement behavior compositions holds great potential to improve health of general as well as clinical populations [7,8]. In order to obtain effective behavior change, it is fundamental to investigate all factors that determine and explain these behaviors (e.g., self-efficacy, attitude) [12]. An accurate assessment of these factors, also known as explanatory variables, allows for the development of tailored interventions targeting 24-h movement behaviors [12,13]. Questionnaires on explanatory variables of lifestyle behaviors in isolation already exist (e.g., Determinants of Physical Activity Questionnaire (DPAQ), the Sedentary Behavior Change Questionnaire). Until now, there has been no questionnaire conceptualized from a 24-h movement behavior perspective, including comparable explanatory variables for PA, SB, and complying with an optimal sleeping pattern [12,14,15]. Moreover, these existing questionnaires often lack reliability and do not incorporate a theoretical behavior change framework (e.g., Theory of Planned Behavior (TPB), Self-Determination Theory (SDT)) [14]. The main advantage of incorporating a theoretical framework is the potential to use this framework as an explorative as well as an evaluative tool for the development of health promotion interventions [16,17,18,19]. Additionally, incorporating a behavioral change framework might be more effective in promoting behavior change compared to approaches lacking a theoretical foundation [16].
Theoretical frameworks that have been used in behavior change interventions to promote PA, SB and/or sleep are mainly focused on psychological factors centered within the person (e.g., attitude, self-efficacy) [20,21,22,23]. Nevertheless, intrapersonal characteristics of behavior include only one level, whereas a socio-ecological model broadens this perspective to a multilevel analysis of behavior, including physical and socio-cultural surroundings [24,25]. An example of such a multilevel analysis model of behavior is the socio-ecological model of Bronfenbrenner [1977], consisting of four levels [26]. The intrapersonal level includes demographic characteristics as well as other personal psychological factors such as beliefs, barriers, attitudes [26]. The interpersonal level collects information on the social environment such as support of family and friends [26]. The physical environmental level explores the perceived physical environment of a person [26]. The fourth level is the policy level, which includes all laws and rules on a certain behavior [26]. Additionally, Sallis and colleagues [2006] added an extra dimension of different active living domains in which it is possible to be physically active, i.e., household, leisure time, transport and work [25]. Furthermore, existing research identified associations between socio-ecological levels and lifestyle behaviors. Therefore, combining personal and environmental characteristics might be key to gain a broader perspective of explanatory variables of adults’ 24-h movement behaviors [27,28,29].
In summary, literature dealing with the 24-h movement behavior paradigm is rapidly growing and promotes the interrelatedness between daily behaviors which brings new challenges for health promotion research. Therefore, it is valuable to develop a questionnaire to assess the underlying characteristics of the overall 24-h day focusing on PA, SB and complying with and optimal sleeping pattern. The aim of this study was to investigate the test–retest reliability and internal consistency of a newly developed questionnaire on explanatory variables of 24-h movement behaviors among adults, based on a socio-ecological model.
## 2.1. Participants
A sample size of 40 adults with a minimum age of 18 years old was recruited in Flanders, Belgium [30,31,32]. The sample size was calculated for test–retest reliability to detect at least an intraclass correlation coefficient (ICC) of 0.5 (cut-off for moderate reliability) and Cronbach’s Alpha Coefficient (α) of 0.7 (cut-off for sufficient homogeneity) [30,31,32]. A minimum of 30 participants for ICC and a minimum of 24 participants for Cronbach’s Alpha Coefficient was recommended: [1] ICC: observations = 2, R0 = 0, min. ICC = 0.5, power = $90\%$, alpha = 0.05; [2] Cronbach’s Alpha Coefficient: max. items/construct = 20, R0 = 0, min. α =0.7, power = $90\%$, alpha = 0.05. Due to the comprehensiveness of the questionnaire, a drop-out rate of 25 percent was included, which created a total sample size of 40 adults [30,31,32]. Participants were included when meeting the following criteria: [1] minimum age of 18 years; [2] working for at least 50 percent; [3] not having physical (e.g., amputations, paralysis)/cognitive (e.g., dementia)/medical (e.g., heart failure, chronical obstructive pulmonary diseases) conditions that affect daily functioning. By including participants working for at least 50 percent, the working adult population is covered and full-time students and retired adults were automatically excluded.
Participants were recruited by using convenience sampling within the researchers’ network. The study was approved by the Ethics Committee of Ghent University (BC-08622). Prior to the start of the study, informed consent was obtained, which was explained to and signed by all participants.
## 2.2. Questionnaire Development
The questionnaire was developed based on the socio-ecological model of Bronfenbrenner [1977] and the active living domains of Sallis et al. [ 2006] [25,26]. For each 24-h movement behavior, factors within three out of the four levels of the socio-ecological model were assessed, i.e., the intrapersonal, interpersonal and physical environmental level [33]. Questions on PA contained both questions on light PA (LPA) and MVPA. Questions on SB took into account periods of long and uninterrupted SB as well as breaks in SB. Sleep was questioned as the compliance with an optimal sleeping pattern which was defined as a sleep duration ranging from 7 to 9 h and consistent wake-up and go to bed times [7]. Within each of these behaviors, different constructs were questioned, e.g., attitude, social modeling. Items within these constructs were evaluated on a 5-point Likert scale (i.e., [1] strongly disagree–strongly agree, [2] never–always, or [3] less time–a lot of time), except for two constructs—attitude and electronic devices at home. Answers on the attitude construct scale were formulated on a slider Visual Analog Scale (0–100) with five different options: annoying–nice; frustrating–satisfying; unhealthy–healthy; unimportant–important; difficult–easy. The number of electronic devices at home was quantified. Figure 1 provides an overview of the different levels of the socio-ecological model accompanied with an example of an item from the questionnaire.
## 2.3. Intrapersonal Level
The following sociodemographic variables were examined as part of the intrapersonal level: age, sex, family situation, children, neighborhood, country of birth, native language, educational level, educational level partner, profession, profession partner, net family income per month, smoking, and medication intake [34]. The combination of the net family income per month, educational level (yourself and partner) and profession (yourself and partner) provide an estimation of the socio-economic status of the participant. Other explanatory variables within the intrapersonal level are based on the integrated behavioral change (IBC) model [35]. The IBC model combines psychological factors from different behavior change theories including the TPB, SDT, Dual System Theory, and Social Cognitive Theory (SCT)) [35]. This resulted in the following psychological constructs being included in the questionnaire: autonomous motivation (Theory: SDT), attitude (Theory: TPB, SCT), internal behavioral control such as habits, routines (Theory: Dual System Theory), self-efficacy/perceived behavioral control (Theory: SCT, TPB, SDT) and external behavioral control such as barriers and facilitators (Theory: Dual System Theory) [35].
## 2.3.1. Interpersonal Level
The interpersonal level represents the social environment and contains three constructs, i.e., subjective norm (Theory: TPB), social modeling (Theory: SCT) and social support (Theory: SDT) [25,26].
## 2.3.2. Physical Environmental Level
The physical environmental factors reflect participants’ perceived environment and are structured within four constructs: the sleep environment within the home environment, electronic devices within the home environment, the neighborhood and the work environment [25,26].
## 2.4. Procedure
This questionnaire was built in REDCap, which is a secure, web-based software platform for data collection and management developed by Vanderbilt University (Nashville, TN, USA) [36]. This electronic data capture tool is hosted by the Health Innovation and Research Institute of Ghent University Hospital [36]. A digital version of the questionnaire was completed online by the participants. All questionnaires were completed in Dutch. The participants had to fill in the questionnaire twice, once at baseline (Timepoint T1; test) and once 14 days later (Timepoint T2; retest), which is a recommended time frame to assess test–retest reliability [37,38]. Two days prior to T2, a reminder was sent to fill in the questionnaire for the second time on the 14th day.
## 2.5. Statistical Analyses
All sample characteristics were categorical variables which are expressed as a percentage of the total sample size, except age and the time interval between T1 and T2, which is expressed as a mean with standard deviation. The test–retest reliability was examined calculating the ICC and the respective $95\%$ confidence intervals. The ICC and their $95\%$ confidence intervals were calculated based on a single measurement, absolute agreement, and two-way mixed effects [39]. An ICC higher than 0.90 represented excellent reliability, an ICC between 0.75 and 0.90 represented a good reliability, an ICC between 0.50 and 0.75 represented a moderate reliability, and an ICC lower than 0.5 represented a poor reliability [39]. The internal consistency between items within constructs was assessed by using the Cronbach’s Alpha Coefficient. A Cronbach’s Alpha Coefficient higher than 0.70 indicated sufficient homogeneity among items [40]. If the Cronbach’s Alpha Coefficient of a construct was below 0.70, the homogeneity among items was considered insufficient. Consequently, an evaluation of deleting specific items within the construct was done to check the homogeneity again [40]. If the homogeneity could not be improved, it was recommended to interpret every item separately with exclusion of items identified as poor reliable. All statistical tests were calculated using SPSS statistical package version 27 [41].
## 3.1. Sample Characteristics
Table 1 gives an overview of the sample characteristics. Of the 40 participants who consented to participate, everyone completed the questionnaire at T1 and a total of 35 participants filled in the questionnaire at both time points. The reason for drop out was lack of time to fill in the questionnaire at T2. The 35 included participants had a mean age of 42.94 years (±16.07), and 60 percent were women. All participants were native Dutch speakers. The socio-economic status of the participants was high, as most participants had a high educational level (diploma higher then secondary school), a net family income of >2000 EUR/month, and were employed. The average time interval between T1 and T2 was 15.82 days (±2.00). The average response time to fill in the online questionnaire was subjectively reported as ranging from 30 min to 50 min.
## 3.2. Test–Retest Reliability
The total questionnaire contains 266 items, consisting of 14 items on general information, 70 items on physical activity, 102 items on sedentary behavior, 45 items on sleep and 35 items on the physical environment. Table 2 provides a detailed description of the ICC-range per construct in the questionnaire. Table S1 in the Supplementary Material provides the test–retest reliability of each questionnaire item separately, i.e., ICC, lower bound and upper bound (see Supplementary Material Table S1).
All the sociodemographic items showed good to excellent reliability, i.e., 14 items ($100.00\%$). Of the 70 items regarding PA, 40 items ($57.14\%$) showed a moderate to good test–retest reliability, and 30 items ($42.86\%$) showed poor reliability. Within the intrapersonal level, the number of items with moderate to good reliability were one item within the autonomous motivation construct ($50.00\%$), three items within the attitude construct ($30.00\%$), three items within the facilitators construct ($21.43\%$), one item within the internal behavioral control construct ($50.00\%$), eight items within the self-efficacy construct ($80.00\%$), and 11 items within the barriers construct ($68.75\%$). Within the interpersonal level, all three constructs showed a moderate to good reliability for most of their items, i.e., subjective norm (four items, $80.00\%$), social modeling (five items, $100.00\%$), social support (four items, $66.66\%$).
Out of the 102 items regarding SB, 71 ($69.61\%$) items had a good to excellent test–retest reliability and 31 items ($30.39\%$) showed poor reliability. Within the intrapersonal level, the number of items with moderate to good reliability were one item within the autonomous motivation construct ($50.00\%$), three items within the construct on attitude regarding a long sitting period ($60.00\%$), two items within the attitude regarding interrupting sitting construct ($40.00\%$), seven items within the facilitators construct ($87.50\%$), three items within the internal behavioral control construct ($60.00\%$), nine items within the self-efficacy construct ($52.94\%$), and nine items within barriers construct ($56.25\%$). In addition, each of these overall constructs was divided into subconstructs based on the active living domains. See Table 2 for the reliability levels of each of these subconstructs.
Within the interpersonal level, all three constructs showed a moderate to good reliability for a majority of items, i.e., subjective norm (eight items, $66.67\%$), social modeling (18 items, $90.00\%$), social support (11 items, $91.67\%$). Again, each of these overall constructs was divided into subconstructs based on the active living domains. See Table 2 for the ICC and internal consistency of these subconstructs.
Of the forty-five items on sleep, 30 items ($66.67\%$) were classified as moderate to good reliable and 15 items ($33.33\%$) as poor reliable. Within the intrapersonal level, the items with moderate to good reliability corresponded to five items within the attitude construct ($50.00\%$), three items within the facilitators construct ($50.00\%$), two items within the internal behavioral control construct ($100.00\%$), four items within the self-efficacy construct ($66.67\%$), and 10 items within barriers construct ($83.33\%$). All items within autonomous motivation had a poor reliability ($100.00\%$). Within the interpersonal level, all three constructs showed a moderate to good reliability for most of their items, i.e., subjective norm (two items, $100.00\%$), social modeling (three items, $100.00\%$), and social support (one item, $50.00\%$).
Last, the physical environmental level contains four constructs. All items within the electronic devices within the home environment showed a moderate to good reliability (10 items, $100.00\%$). The sleep environment within the home environment contained six items with a moderate to good reliability ($85.71\%$). The construct neighborhood consisted of 12 items with a moderate to excellent reliability ($92.31\%$) and one item with a poor reliability ($7.69\%$). Finally, all work environment items (five items, $100.00\%$) showed good to excellent reliability.
## 3.3. Internal Consistency
Table 2 represents all Cronbach’s Alpha Coefficients per construct. All PA constructs, except for autonomous motivation (α = 0.585), social modeling (α = 0.561) and social support (α = 0.623), showed sufficient homogeneity among items (α > 0.700). All SB constructs, except for attitude regarding long sitting periods (α = 0.695), internal behavioral control (α = 0.389), self-efficacy regarding sitting during household tasks (α = 0.533), subjective norm regarding passive transport (α = 0.638), and social support regarding sitting during household tasks (α = 0.645), demonstrated a sufficient homogeneity among items (α > 0.700). Additionally, all sleep constructs, except for social support (α = 0.687), resulted in a sufficient homogeneity among items (α > 0.700). Last, the neighborhood construct and the work environment construct showed a sufficient homogeneity among items (α > 0.700). The sleep environment and electronic devices within the home environment resulted in low Cronbach’s Alpha Coefficients (α = 0.526, α = 0.664). No improvement in homogeneity among items was achieved by deleting items within any of the above-mentioned constructs.
## 4. Discussion
The main aim of this study was to assess the test–retest reliability and the internal consistency of a questionnaire on explanatory variables of 24-h movement behaviors among adults. Overall, this study showed a moderate to excellent test–retest reliability for items belonging to different constructs and a good internal consistency among these constructs.
Seventy-one percent of all explanatory items showed a moderate to excellent reliability (188 out of 266 items). However, 29 percent of the items had a poor reliability. This could possibly be explained by biases linked with test–retest reliability research, i.e., “recall bias” and the “question behavior effect”. Recall bias, also known as response bias, refers to various conditions that lead to participants inaccurately responding to questions [38]. To overcome this bias, the time period of test–retest reliability studies should be optimal [37,38]. When this time period is too short, the respondents will remember their answers to questions [37,38]. When the period is too long, participants’ behavior may have changed over time. However, the time interval in this study, i.e., 14 days, is generally considered to be adequate [37,38]. Additionally, it is possible that participants focus more on their lifestyle behaviors within a period of 14 days, as they are more aware of these behaviors as a result of filling in the questionnaire. This awareness might induce subsequent behavior change, which is called the “question behavior effect” [42,43]. This can potentially explain the poor reliability scores, as participants think about their lifestyle behaviors and change their minds and feelings after filling in the questionnaire for the first time [42,43]. Moreover, a meta-analysis by Wilding et al. [ 2016] showed stronger “Question Behavior Effects” for the promotion of protective behaviors than for reducing risk behaviors [42]. This could be a possible explanation why questions regarding PA showed a higher number of low-reliability items compared to questions regarding SB or sleep. Questions on PA can be interpreted as promoting protective behavior (i.e., promotion of being active), whereas questions on SB or sleep can be interpreted as reducing risky behavior (i.e., reducing inactive behavior, reducing inconsistency in sleeping patterns).
Almost all explanatory constructs showed a good homogeneity. This means that items related to a specific construct can be combined and summed up in a single construct score. For some constructs, an insufficient Cronbach’s Alpha Coefficient was found, which indicates that caution should be taken when combining the items into one overall construct (i.e., autonomous motivation regarding PA, social modeling regarding PA, social support regarding PA, attitude regarding long sitting periods, internal behavioral control regarding sedentary behavior, self-efficacy regarding sitting time during household tasks, social support regarding limiting sedentary time during household tasks, social support regarding sleep, electronic devices and sleep environment in the home environment). A possible solution for dealing with these lower homogeneity scores is to delete specific items within each construct to create a better homogeneity among items [40]. However, deleting items within constructs did not improve the homogeneity scores, so none of the items were deleted. Another possibility is to combine subconstructs within the overall construct. For example, self-efficacy regarding limiting SB has different subconstructs for each active living domain, i.e., work, household, leisure time, and transport. When combining all items into the overarching “self-efficacy regarding limiting SB construct” there is sufficient homogeneity between items. Nevertheless, this will create a loss in detailed information on active living domains [40]. In order to not lose this information, every question can be interpreted separately, except for the items with poor reliability, which should not be used. For example, the “internal behavior control” construct regarding SB had a low homogeneity among items (α = 0.398). Deleting items within this construct did not improve the homogeneity of the construct. Therefore, it is recommended to not combine the items in an overall “internal behavior control” construct regarding SB, but to only use the items with moderate, good or excellent reliability as separate indicators of “internal behavior control”. It is suggested to do further research on constructs where the majority of items had a poor reliability. A recommendation could be to set up focus groups to test if these items and constructs are understandable and correctly interpreted by the target group.
One might question whether it is necessary to develop another questionnaire, since some questionnaires already exist that assess explanatory variables of lifestyle behaviors in isolation [12,15]. There are questionnaires to measure the explanatory variables of PA and SB, such as the DPAQ, and the Sedentary Behavior Change Questionnaire [12,15]. The DPAQ is a questionnaire with a good discriminant validity, test–retest reliability and a reasonable to good internal consistency for most determinant areas [12]. This questionnaire is based on the theoretical domains framework (TDF) which is a practical guide to identifying determinants to explain current behaviors [12,44,45]. Nevertheless, this theoretical framework is a pragmatic framework and does not rely upon a behavior change theory where the relation between determinant constructs and behavior intention is lacking [12,46]. However, using behavior change techniques out of the TDF as mediators to translate the behavior change theory into practice seems promising for promoting motivation for behavior change [23,47]. The Sedentary Behavior Change *Questionnaire is* a questionnaire [2019] based on the constructs of the SCT (i.e., self-efficacy, outcome expectations, goals setting, planning, and barriers) [15]. Moreover, this questionnaire is not optimally transferrable among other groups, as it was developed within a group of adults with multiple sclerosis [15]. This questionnaire reported some preliminary support for structural validity and internal consistency [15]. Additionally, there are different questionnaires on explanatory variables of sleep such as the Sleep Practices and Attitudes Questionnaire, or the Dysfunctional Beliefs and Attitudes about Sleep Scale (specific for insomnia), and the Sleep Beliefs Scale [48,49,50]. Most of these questionnaires investigate explanatory variables, which often lacks the link with a behavior change theory [48,49,50]. Moreover, none of these questionnaires addressed multiple behaviors such as 24-h movement behaviors, whereas this newly developed questionnaire does.
As the evidence on 24-h movement behavior paradigm is rapidly expanding, new challenges exist regarding the inclusion of this paradigm into health promotion research. Therefore, the main strength of this study is that this is the first questionnaire assessing the explanatory variables of all behaviors from the perspective of an entire day based on a theoretical framework, i.e., the socio-ecological model in combination of the IBC model embedded within the intrapersonal level. The IBC model is a behavior change model that integrates different psychological constructs from different behavior change theories, i.e., TPB, SCT, Dual System theory, and SDT [35]. The strength of using this theoretical framework is the ability to focus on the most important key constructs for behavior change [35]. Moreover, it showed a good fit with predicting PA behavior within a group of adults and older adults [20]. The key constructs of the TPB, SCT, SDT were mostly positively correlated with behavior intention [22,23,47]. However, in these behavior change theories, the behavior change intentions were often not sufficient to prompt effective changes, which can be explained by the weak links between key constructs or a disrupted link between intention and behavior change, also known as the intention behavior gap [35,51,52]. To bridge this gap, the IBC model integrated some constructs from the previously mentioned behavior change theories (TPB, SDT, SCT) with constructs of the Dual System theory (motivational phase, volitional phase) [20,35,52]. Additionally, as this newly developed questionnaire is a comprehensive assessment of different explanatory variables for all 24-h movement behaviors, it is possible to select a specific behavior of interest in combination with the construct of interest (e.g., self-efficacy regarding PA). However, it remains recommended to use the questionnaire in its entirety, as it provides the most valuable insights into behaviors across a total day. Additionally, this questionnaire has its fundaments in a behavior change theory that can lay the foundation for personalizing interventions in the future [53].
This study also has some limitations. First, this study used a convenience sampling method within the researchers’ network. Moreover, one of the inclusion criteria was formulated as working for at least 50 percent, because behaviors at work were addressed by questions in this questionnaire. This might not be representative for lower SES groups. Further studies are required to test this questionnaire within a group with lower SES. Second, this questionnaire was developed in Dutch and conducted in Flanders (Belgium). There is an English translation of questions available in the Supplementary Materials; however, this English translation has not been tested. Third, this study was conducted within a general healthy adult population. Although, it is possible to add and test disease-specific items to each construct. For example, a question addressing barriers to performing PA among participants with diabetes may take a form such as “controlling my glucose level is a barrier to performing physical activity”. Last, this questionnaire included some poor-reliability questions as well as constructs with low homogeneity among items. It is recommended to use these questions and constructs with caution. Supplementary File provides a detailed description of each question with the corresponding ICC, and the lower and upper bound. This creates the opportunity to use questions or constructs independently of the total questionnaire, as well as to adjust or further develop poor reliable questions and constructs. An additional recommendation for future research is to combine the assessment of this questionnaire with an objective assessment of 24-h movement behaviors (e.g., using an accelerometer). By collecting adults’ 24-h movement behaviors, these behaviors can be associated with the explanatory variables to provide a detailed assessment of lifestyle behaviors and to provide direction for the development of tailored interventions. Combining both objective and subjective assessment methods will provide the most complete information as both methods measure different aspects of behaviors (i.e., objectively measured behaviors, context, and explanatory variables).
## 5. Conclusions
This newly developed questionnaire on explanatory variables for 24-h movement behavior showed a moderate to excellent test–retest reliability for a majority of items and a good homogeneity among items for a majority of constructs. This comprehensive questionnaire, which collects a broad range of explanatory variables of all behaviors performed in a 24-h day, might be a valuable tool for future research to improve the understanding of adults’ 24-h movement behaviors. Hence, constructs with poor-reliability questions should be interpreted with caution, and it is recommended that, for constructs with an insufficient homogeneity among items, the items should be interpreted separately. Further research is recommended to fine tune poor-reliability questions or insufficiently homogenous constructs, as well as to adapt and test this questionnaire for use in clinical populations.
## References
1. Itani O., Jike M., Watanabe N., Kaneita Y.. **Short sleep duration and health outcomes: A systematic review, meta-analysis, and meta-regression**. *Sleep Med.* (2017.0) **32** 246-256. DOI: 10.1016/j.sleep.2016.08.006
2. Jike M., Itani O., Watanabe N., Buysse D.J., Kaneita Y.. **Long sleep duration and health outcomes: A systematic review, meta-analysis and meta-regression**. *Sleep Med. Rev.* (2018.0) **39** 25-36. DOI: 10.1016/j.smrv.2017.06.011
3. Ekelund U., Brown W.J., Steene-Johannessen J., Fagerland M.W., Owen N., Powell K.E., Bauman A.E., Lee I.M.. **Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850,060 participants**. *Br. J. Sport. Med.* (2019.0) **53** 886-894. DOI: 10.1136/bjsports-2017-098963
4. Kraus W.E., Powell K.E., Haskell W.L., Janz K.F., Campbell W.W., Jakicic J.M., Troiano R.P., Sprow K., Torres A., Piercy K.L.. **Physical Activity, All-Cause and Cardiovascular Mortality, and Cardiovascular Disease**. *Med. Sci. Sport. Exerc.* (2019.0) **51** 1270-1281. DOI: 10.1249/MSS.0000000000001939
5. Battista F., Ermolao A., van Baak M.A., Beaulieu K., Blundell J.E., Busetto L., Carraça E.V., Encantado J., Dicker D., Farpour-Lambert N.. **Effect of exercise on cardiometabolic health of adults with overweight or obesity: Focus on blood pressure, insulin resistance, and intrahepatic fat—A systematic review and meta-analysis**. *Obes. Rev.* (2021.0) **22** e13269. DOI: 10.1111/obr.13269
6. Dumuid D., Pedišić Ž., Palarea-Albaladejo J., Martín-Fernández J.A., Hron K., Olds T., Filzmoser P., Hron K., Martín-Fernández J.A., Palarea-Albaladejo J.. **Compositional data analysis in time-use epidemiology**. *Advances in Compositional Data Analysis: Festschrift in Honour of Vera Pawlowsky-Glahn* (2021.0) 383-404. DOI: 10.1007/978-3-030-71175-7_20
7. Ross R., Chaput J.P., Giangregorio L.M., Janssen I., Saunders T.J., Kho M.E., Poitras V.J., Tomasone J.R., El-Kotob R., McLaughlin E.C.. **Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: An integration of physical activity, sedentary behaviour, and sleep**. *Appl. Physiol. Nutr. Metab.* (2020.0) **45** S57-S102. DOI: 10.1139/apnm-2020-0467
8. Janssen I., Clarke A.E., Carson V., Chaput J.P., Giangregorio L.M., Kho M.E., Poitras V.J., Ross R., Saunders T.J., Ross-White A.. **A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults**. *Appl. Physiol. Nutr. Metab.* (2020.0) **45** S248-S257. DOI: 10.1139/apnm-2020-0160
9. Chaput J.P., Dutil C., Featherstone R., Ross R., Giangregorio L., Saunders T.J., Janssen I., Poitras V.J., Kho M.E., Ross-White A.. **Sleep timing, sleep consistency, and health in adults: A systematic review**. *Appl. Physiol. Nutr. Metab.* (2020.0) **45** S232-S247. DOI: 10.1139/apnm-2020-0032
10. Rossen J., Von Rosen P., Johansson U.B., Brismar K., Hagströmer M.. **Associations of physical activity and sedentary behavior with cardiometabolic biomarkers in prediabetes and type 2 diabetes: A compositional data analysis**. *Phys. Sportsmed.* (2020.0) **48** 222-228. DOI: 10.1080/00913847.2019.1684811
11. Swindell N., Rees P., Fogelholm M., Drummen M., MacDonald I., Martinez J.A., Navas-Carretero S., Handjieva-Darlenska T., Boyadjieva N., Bogdanov G.. **Compositional analysis of the associations between 24-h movement behaviours and cardio-metabolic risk factors in overweight and obese adults with pre-diabetes from the PREVIEW study: Cross-sectional baseline analysis**. *Int. J. Behav. Nutr. Phys. Act.* (2020.0) **17** 29. DOI: 10.1186/s12966-020-00936-5
12. Taylor N., Lawton R., Conner M.. **Development and initial validation of the determinants of physical activity questionnaire**. *Int. J. Behav. Nutr. Phys. Act.* (2013.0) **10** 74. DOI: 10.1186/1479-5868-10-74
13. Eldredge L.K.B., Markham C.M., Ruiter R.A., Fernández M.E., Kok G., Parcel G.S.. *Planning Health Promotion Programs: An Intervention Mapping Approach* (2016.0)
14. Oluka O.C., Nie S., Sun Y.. **Quality assessment of TPB-based questionnaires: A systematic review**. *PLoS ONE* (2014.0) **9**. DOI: 10.1371/journal.pone.0094419
15. Motl R.W., Sasaki J.E., Cederberg K.L., Jeng B.. **Social-cognitive theory variables as correlates of sedentary behavior in multiple sclerosis: Preliminary evidence**. *Disabil. Health J.* (2019.0) **12** 622-627. DOI: 10.1016/j.dhjo.2019.05.002
16. Duan Y., Shang B., Liang W., Du G., Yang M., Rhodes R.E.. **Effects of eHealth-Based Multiple Health Behavior Change Interventions on Physical Activity, Healthy Diet, and Weight in People with Noncommunicable Diseases: Systematic Review and Meta-analysis**. *J. Med. Internet Res.* (2021.0) **23** e23786. DOI: 10.2196/23786
17. Gardner B., Smith L., Lorencatto F., Hamer M., Biddle S.J.. **How to reduce sitting time? A review of behaviour change strategies used in sedentary behaviour reduction interventions among adults**. *Health Psychol. Rev.* (2016.0) **10** 89-112. DOI: 10.1080/17437199.2015.1082146
18. Connell L.E., Carey R.N., de Bruin M., Rothman A.J., Johnston M., Kelly M.P., Michie S.. **Links Between Behavior Change Techniques and Mechanisms of Action: An Expert Consensus Study**. *Ann. Behav. Med.* (2019.0) **53** 708-720. DOI: 10.1093/abm/kay082
19. Curran F., Blake C., Cunningham C., Perrotta C., van der Ploeg H., Matthews J., O’Donoghue G.. **Efficacy, characteristics, behavioural models and behaviour change strategies, of non-workplace interventions specifically targeting sedentary behaviour; a systematic review and meta-analysis of randomised control trials in healthy ambulatory adults**. *PLoS ONE* (2021.0) **16**. DOI: 10.1371/journal.pone.0256828
20. Galli F., Chirico A., Mallia L., Girelli L., De Laurentiis M., Lucidi F., Giordano A., Botti G.. **Active lifestyles in older adults: An integrated predictive model of physical activity and exercise**. *Oncotarget* (2018.0) **9** 25402-25413. DOI: 10.18632/oncotarget.25352
21. Hagger M.S., Chatzisarantis N.L.. **Integrating the theory of planned behaviour and self-determination theory in health behaviour: A meta-analysis**. *Br. J. Health Psychol.* (2009.0) **14** 275-302. DOI: 10.1348/135910708X373959
22. Young M.D., Plotnikoff R.C., Collins C.E., Callister R., Morgan P.J.. **Social cognitive theory and physical activity: A systematic review and meta-analysis**. *Obes. Rev.* (2014.0) **15** 983-995. DOI: 10.1111/obr.12225
23. Gillison F.B., Rouse P., Standage M., Sebire S.J., Ryan R.M.. **A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective**. *Health Psychol. Rev.* (2019.0) **13** 110-130. DOI: 10.1080/17437199.2018.1534071
24. Rodrigues F., Bento T., Cid L., Pereira Neiva H., Teixeira D., Moutão J., Almeida Marinho D., Monteiro D.. **Can Interpersonal Behavior Influence the Persistence and Adherence to Physical Exercise Practice in Adults? A Systematic Review**. *Front. Psychol.* (2018.0) **9** 2141. DOI: 10.3389/fpsyg.2018.02141
25. Sallis J., Cervero R., Ascher W., Henderson K., Kraft M., Kerr J.. **An Ecological Approach to Creating More Physically Active Communities**. *Annu. Rev. Public Health* (2006.0) **27** 297-322. DOI: 10.1146/annurev.publhealth.27.021405.102100
26. Bronfenbrenner U.. **Toward an experimental ecology of human development**. *Am. Psychol.* (1977.0) **32** 513-531. DOI: 10.1037/0003-066X.32.7.513
27. Lee Y., Park S.. **Understanding of Physical Activity in Social Ecological Perspective: Application of Multilevel Model**. *Front. Psychol.* (2021.0) **12** 622929. DOI: 10.3389/fpsyg.2021.622929
28. Mullane S.L., Toledo M.J.L., Rydell S.A., Feltes L.H., Vuong B., Crespo N.C., Pereira M.A., Buman M.P.. **Social ecological correlates of workplace sedentary behavior**. *Int. J. Behav. Nutr. Phys. Act.* (2017.0) **14** 117. DOI: 10.1186/s12966-017-0576-x
29. Grandner M.A.. **Chapter 5—Social-ecological model of sleep health**. *Sleep and Health* (2019.0) 45-53. DOI: 10.1016/B978-0-12-815373-4.00005-8
30. Bujang M.A., Baharum N.. **A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: A review**. *Arch. Orofac. Sci.* (2017.0) **12** 1-11
31. Yurdugül H.. **Minimum Sample Size for Cronbach’s Coefficient Alpha: A Monte-Carlo Study**. *Acettepe Univ. J. Educ.* (2008.0) **35** 397-405
32. Bujang M.A., Omar E.D., Baharum N.A.. **A Review on Sample Size Determination for Cronbach’s Alpha Test: A Simple Guide for Researchers**. *Malays. J. Med. Sci.* (2018.0) **25** 85-99. DOI: 10.21315/mjms2018.25.6.9
33. McLeroy K.R., Bibeau D., Steckler A., Glanz K.. **An ecological perspective on health promotion programs**. *Health Educ. Q.* (1988.0) **15** 351-377. DOI: 10.1177/109019818801500401
34. Pinchevsky Y., Butkow N., Raal F.J., Chirwa T., Rothberg A.. **Demographic and Clinical Factors Associated with Development of Type 2 Diabetes: A Review of the Literature**. *Int. J. Gen. Med.* (2020.0) **13** 121-129. DOI: 10.2147/IJGM.S226010
35. Hagger M.S., Chatzisarantis N.L.D.. **An Integrated Behavior Change Model for Physical Activity**. *Exerc. Sport Sci. Rev.* (2014.0) **42** 62-69. DOI: 10.1249/JES.0000000000000008
36. Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G.. **Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support**. *J. Biomed. Inform.* (2009.0) **42** 377-381. DOI: 10.1016/j.jbi.2008.08.010
37. Park M.S., Kang K.J., Jang S.J., Lee J.Y., Chang S.J.. **Evaluating test-retest reliability in patient-reported outcome measures for older people: A systematic review**. *Int. J. Nurs. Stud.* (2018.0) **79** 58-69. DOI: 10.1016/j.ijnurstu.2017.11.003
38. Streiner D.L., Norman G.R., Cairney J.. *Health Measurement Scales: A Practical Guide to Their Development and Use* (2014.0). DOI: 10.1093/med/9780199685219.001.0001
39. Koo T.K., Li M.Y.. **A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research**. *J. Chiropr. Med.* (2016.0) **15** 155-163. DOI: 10.1016/j.jcm.2016.02.012
40. Taber K.S.. **The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education**. *Res. Sci. Educ.* (2018.0) **48** 1273-1296. DOI: 10.1007/s11165-016-9602-2
41. 41.
IBM Corp
Released 2020. IBM SPSS Statistics for WindowsVersion 27.0IBM CorpArmonk, NY, USA. *Released 2020. IBM SPSS Statistics for Windows*
42. Wilding S., Conner M., Sandberg T., Prestwich A., Lawton R., Wood C., Miles E., Godin G., Sheeran P.. **The question-behaviour effect: A theoretical and methodological review and meta-analysis**. *Eur. Rev. Soc. Psychol.* (2016.0) **27** 196-230. DOI: 10.1080/10463283.2016.1245940
43. Wood C., Conner M., Miles E., Sandberg T., Taylor N., Godin G., Sheeran P.. **The Impact of Asking Intention or Self-Prediction Questions on Subsequent Behavior: A Meta-Analysis**. *Pers. Soc. Psychol. Rev.* (2016.0) **20** 245-268. DOI: 10.1177/1088868315592334
44. Phillips C.J., Marshall A.P., Chaves N.J., Jankelowitz S.K., Lin I.B., Loy C.T., Rees G., Sakzewski L., Thomas S., To T.P.. **Experiences of using the Theoretical Domains Framework across diverse clinical environments: A qualitative study**. *J. Multidiscip. Healthc.* (2015.0) **8** 139-146. DOI: 10.2147/jmdh.S78458
45. Cane J., O’Connor D., Michie S.. **Validation of the theoretical domains framework for use in behaviour change and implementation research**. *Implement. Sci.* (2012.0) **7** 37. DOI: 10.1186/1748-5908-7-37
46. Ajzen I.. **The theory of planned behavior**. *Organ. Behav. Hum. Decis. Process.* (1991.0) **50** 179-211. DOI: 10.1016/0749-5978(91)90020-T
47. Senkowski V., Gannon C., Branscum P.. **Behavior Change Techniques Used in Theory of Planned Behavior Physical Activity Interventions among Older Adults: A Systematic Review**. *J. Aging Phys. Act.* (2019.0) **27** 746-754. DOI: 10.1123/japa.2018-0103
48. Grandner M.A., Jackson N., Gooneratne N.S., Patel N.P.. **The development of a questionnaire to assess sleep-related practices, beliefs, and attitudes**. *Behav. Sleep Med.* (2014.0) **12** 123-142. DOI: 10.1080/15402002.2013.764530
49. Edinger J.D., Wohlgemuth W.K.. **Psychometric comparisons of the standard and abbreviated DBAS-10 versions of the dysfunctional beliefs and attitudes about sleep questionnaire**. *Sleep Med.* (2001.0) **2** 493-500. DOI: 10.1016/S1389-9457(01)00078-8
50. Adan A., Fabbri M., Natale V., Prat G.. **Sleep Beliefs Scale (SBS) and circadian typology**. *J. Sleep Res.* (2006.0) **15** 125-132. DOI: 10.1111/j.1365-2869.2006.00509.x
51. Rhodes R.E., Boudreau P., Josefsson K.W., Ivarsson A.. **Mediators of physical activity behaviour change interventions among adults: A systematic review and meta-analysis**. *Health Psychol. Rev.* (2021.0) **15** 272-286. DOI: 10.1080/17437199.2019.1706614
52. Rhodes R.E., Cox A., Sayar R.. **What Predicts the Physical Activity Intention-Behavior Gap? A Systematic Review**. *Ann. Behav. Med.* (2022.0) **56** 1-20. DOI: 10.1093/abm/kaab044
53. Hardeman W., Houghton J., Lane K., Jones A., Naughton F.. **A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity**. *Int. J. Behav. Nutr. Phys. Act.* (2019.0) **16** 31. DOI: 10.1186/s12966-019-0792-7
|
---
title: The Mediating Effect of Central Obesity on the Association between Dietary
Quality, Dietary Inflammation Level and Low-Grade Inflammation-Related Serum Inflammatory
Markers in Adults
authors:
- Shuai Zhang
- Xuebin Yang
- Limei E
- Xiaofei Zhang
- Hongru Chen
- Xiubo Jiang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001533
doi: 10.3390/ijerph20053781
license: CC BY 4.0
---
# The Mediating Effect of Central Obesity on the Association between Dietary Quality, Dietary Inflammation Level and Low-Grade Inflammation-Related Serum Inflammatory Markers in Adults
## Abstract
To date, few studies have explored the role of central obesity on the association between diet quality, measured by the health eating index (HEI), inflammatory eating index (DII), and low-grade inflammation-related serum inflammatory markers. In this paper, we use the data from the 2015–2018 National Health and Nutrition Examination Survey (NHANES) to explore this. Dietary intakes were measured during two 24-h dietary recall interviews and using USDA Food Pattern Equivalence Database (FPED) dietary data. Serum inflammatory markers were obtained from NHANES Laboratory Data. Generalized structural equation models (GSEMs) were used to explore the mediating relationship. Central obesity plays a significant mediating role in the association between HEI-2015 and high-sensitivity C-reactive protein (hs-CRP), mediating $26.87\%$ of the associations between the two; it also mediates $15.24\%$ of the associations between DII and hs-CRP. Central obesity plays a mediating role in $13.98\%$ of the associations between HEI-2015 and white blood cells (WBC); it also mediates $10.83\%$ of the associations between DII and WBC. Our study suggests that central obesity plays a mediating role in the association of dietary quality with low-grade inflammation-related serum inflammatory markers (hs-CRP and WBC).
## 1. Introduction
Growing evidence shows that low levels of chronic systemic inflammation are associated with a number of chronic diseases, including cardiovascular disease, cancer, chronic kidney disease and neurodevelopmental disorders [1,2,3]. Dietary nutrition is a key variable affecting chronic inflammation, mainly because daily food intake is a good indicator of inflammatory potential [4].
Recent studies have linked different types of food to chronic inflammation. A previous study has shown that when added sugars are consumed, fat cells release pro-inflammatory cytokines that trigger inflammation [5]. Recent studies have also found an inverse association between increased vegetable and fruit intake and serum CRP levels [6,7]. The results of a cross-sectional study in India showed that a $1\%$ reduction in dietary saturated fatty acid (SFA) intake was associated with a 0.14 g/L reduction in plasma hs-CRP, after adjusting for relevant variables [8]. Although the relationship between individual foods or individual nutrients and chronic inflammation has been discussed, in recent years, there has been a growing recognition that different combinations of food components may interact in complex ways that are better explained by dietary patterns.
Dietary intake can modulate cancer and is a promising means of reducing the risk of chronic diseases and metabolic dysfunction [9,10,11,12]. Healthy eating index (HEI) score is a measure of dietary quality, which represents the degree to which the Dietary Guidelines for Americans (DGA) are followed [13,14]. Dietary inflammatory index (DII) score is a measure of dietary inflammatory potential based on the overall inflammatory characteristics of dietary components [15,16]. Several studies [9,12,16] have shown that both HEI and DII are associated with inflammatory markers.
Obesity is described as a chronic low-grade inflammatory state, and visceral fat is known to secrete a number of inflammatory markers [17,18]. The increased secretion of adipokine in people with obesity may lead to chronic low-grade inflammation and oxidative stress, which may induce the development of chronic diseases [19]. Studies [20,21,22,23,24] have shown that DII and HEI score were associated with central obesity. However, the underlying mechanism between these diet scores and chronic systemic inflammation remains unclear. Therefore, it is necessary to explore the pathways and intrinsic associations between dietary scores and inflammation. Thus, changes of body weight under different dietary conditions may lead to changes in inflammatory markers in the body.
Taken together, the above evidence suggests that central obesity may be a causal chain between dietary scores and chronic inflammation. However, to the best of our knowledge, no study to date has investigated whether central obesity mediates the relationship between diet score and inflammatory markers. Therefore, the aim of this study was to explore the relationship between DII, HEI and the level of inflammatory markers, and to further explore whether this relationship is mediated by obesity.
## 2.1. Data Source and Study Sample
The data of this study were obtained from National Health and Nutrition Examination Survey (NHANE) multi-stage large sample database. NHANES is a cross-sectional study conducted by the National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC) that provides data from a nationally representative survey of the health and nutrition status of the non-institutional United States (U.S.) population. It follows complex multi-stage sampling design, investigation including face to face interviews at home (population, social economy, diet, and health related issues), in the center of the flow check health checks (medical and physiological measurement) and laboratory test (exposure biomarkers and end). One cycle in NHANES includes data collected by two years.
Data from NHANES, from the years 2015–2016 and 2017–2018, were selected for this study, which included a total of 19,225 participants. 7377 participants were under the age of 18 years old and 1314 lacked data for BMI and waist circumference. 731 participants lacked the data for high-sensitivity C-reactive protein (hs-CRP), white blood cells (WBC), and neutrophil to lymphocyte ratio (NLR). 1095 participants with abnormal values that could not reflect the state of low-grade inflammation well (hs-CRP ≥ 10 mg/L [25] and WBC >11 × 109 cells/L [26]) were excluded. Thus, a total of 8157 participants were enrolled in our study (Figure 1).
## 2.2. Central Obesity
Central obesity was defined by waist circumference, which was defined as waist circumference ≥ 102 cm in men and ≥ 88 cm in women. Although BMI is a common indicator of obesity in general, it does not reflect differences in the distribution of body fat between individuals [27].
## 2.3. Dietary Score
In this study, two different types of dietary scores were selected. We used the healthy eating index (HEI) score to represent dietary quality in participants. The dietary inflammatory index (DII) score was used to represent inflammatory dietary index.
## 2.3.1. Healthy Eating Index (HEI)
We use the HEI score that was designed and recommended by the United States Department of Agriculture (USDA) to measure an individual’s adherence to the dietary guidelines for Americans (DGA) [28]. The HEI-2015 score is the latest version diet index based on the HEI. The maximum score of HEI-2015 is 100. This index consists of 13 components, which can be divided into 9 adequacy components (total vegetables, greens and beans, total fruits, whole fruits, whole grains, dairy, total protein foods, seafood, plant proteins, and fatty acids) and 4 moderation components (sodium, refined grains, saturated fats, and added sugars). The more adequacy components are consumed, the higher the score, while the fewer moderation components are consumed, the higher the score.
NHANES individual food questionnaire data and Food Patterns Equivalents Database (FPED) dietary data were used to estimate food supply to determine the HEI-2015 score. Each food was classified according to the USDA food code. Finally, the recommended SAS code was used to calculate the HEI-2015 score [29].
## 2.3.2. Dietary Inflammatory Index (DII)
DII score is an indicator of the inflammatory potential of foods and can be used in all populations where dietary data can be collected. DII calculations involved 45 dietary parameters, including a variety of macro and micronutrients, flavonoids, flavorings, and other bioactive compounds, each of which correlated with inflammatory effect scores. Then, DII scores were calculated as a standardization of the world database, which contains the mean and standard deviation of food intake parameters from 11 countries around the world. In NHANES, 45 dietary parameters and 28 inflammatory parameters (carbohydrate, protein, cholesterol, iron, zinc, magnesium, selenium, fiber, fat, monounsaturated fatty acids, caffeine, n-3 polyunsaturated fatty acid, n-6 polyunsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acid, alcohol, vitamin A, vitamin B1, vitamin B2, vitamin B6, vitamin B12, vitamin B6, vitamin B12, beta-carotene, vitamin C, vitamin D, vitamin E, folic acid, energy) can be used for DII score calculation. Previous studies have shown no change in DII’s ability to predict inflammation when the available food parameters are reduced, compared with a complete study with 45 parameters [15,30]. DII scores > 0 indicates that the individual’s diet has a pro-inflammatory effect; DII scores < 0 indicates that the diet has anti-inflammatory effects. The specific DII calculation process is shown in Figure 2.
## 2.4. Serum Inflammatory Marker
Serum inflammatory markers were obtained from NHANES laboratory data. Three different inflammatory markers were selected, including hs-CRP, WBC, and NLR.
CRP is a sensitive marker of systemic inflammation, tissue damage and infection in clinical practice [31]. It is one of the sensitive but non-specific inflammatory indicators. Compared with simply calculated CRP, hs-CRP can reflect the current level of cardiovascular disease risk in individuals without inflammatory conditions. In NHANES, CRP was quantified by latex-enhanced turbidimetry. Because laboratories, instruments, and methods varied between the two periods we explored, weighted Deming regression provided by NHANES was used to compare the two [32]. The equation is as follows: Forward (applicable to DxC 660i values ≤ 23 mg/L): Y (Cobas 6000) = 0.8695 ($95\%$ CI: 0.8419 to 0.8971) ∗ X (DxC 660i) + 0.2954 ($95\%$ CI: 0.2786 to 0.3121) The NHANES performed complete blood cell counts (CBC) in duplicate for all study participants over one year of age. Blood samples were obtained by venipuncture into EDTA tubes and analyzed on a Coulter®DXH800 analyzer. Counts of WBC and their subtypes were obtained using a UNICEL DXH800 analyzer.
## 2.5. Sensitive Analysis
To make our results more representative, we performed a sensitivity analysis. BMI is the simplest and broadest anthropometric measure of general obesity [BMI = weight (kg)/height (m)2]. According to World Health Organization standards, general obesity is defined as BMI ≥ 30 kg/m2. Individuals with general obesity were included in the study as a sensitive analysis.
## 2.6. Covariates
Recent epidemiological studies have shown that dietary over-nutrition and institutionally driven declines in physical activity may be important factors influencing obesity [33,34]. Obesity is associated with an increased risk of diabetes, according to a meta-analysis involving 18 prospective studies [35]. Previous prospective studies have found that the development of high blood pressure is proportional to the level of obesity [36]. A cross-sectional study of 499,504 adults found that smoking was associated with an increased risk of obesity [37]. In addition, since dietary patterns and the state of obesity may vary by race and generation, we further adjusted for relevant demographic variables.
Trained NHANES investigators obtained demographic information from participants living in sample areas. To control for the effect of potential confounders, the following covariates were included: age (18–39, 40–59, >60), sex (men, women), race/ethnicity (Mexican American, Other Hispanic, non-Hispanic White, non-Hispanic Black, and Other Race), education of household referent (less than high school, high school, more than high school), ratio of family income to poverty, marital status (married/living with partner, widowed/divorced/separated/never married), work activity (vigorous activity, moderate activity, and other), recreational activity (vigorous activity, moderate activity, and other), smoking (never smoker, former smoker: lifetime intake of more than 100 cigarettes but current serum cotinine does not reach the threshold, current smoker: lifetime intake of more than 100 cigarettes and current serum cotinine reach the threshold), diabetes (self-reported whether they have ever been diagnosed with diabetes by a doctor), and hypertension (yes: systolic blood pressure ≥ 130 or diastolic blood pressure ≥ 80, or no). The threshold for serum cotinine, which is used to distinguish former smokers and current smokers, were for non-Hispanic white > 4.85 ng/mL, non-Hispanic Black > 5.92 ng/mL, Mexican American > 0.84 ng/mL and other > 3.08 ng/mL [38].
## 2.7. Statistical Analysis
The analysis was performed using Stata version 12.0 (Stata Corporation, College Station, TX, USA) and SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). Due to the complex sampling design, all analyses were adjusted for survey design and weight variables. Since this study combined NHANES data from 2 periods, a new sample weight (the original 2-year sample weight divided by 2) was constructed according to the NHANES analysis guidelines before analysis. Classified variables were described by percentage, and the basic characteristics of continuous variables were described by mean and standard deviation. Student’s t-test and rank sum test are used to analyze differences between continuous data, and chi-square test is used to analyze differences between classified data. The normality of each clinical biomarker was assessed based on visual inspection of the normogram and assessment of skewness and kurtosis measurements. If the results were not normal, they were naturally log-transformed. Residuals of the predicted values were plotted and assessed for normality. To better fit the model, both hs-CRP and NLR were naturally log-transformed.
All statistical analyses were based on the survey design and weighted variables adjusted to account for the complex sample design and to ensure nationally representative estimates. Multiple linear regression and multiple logistic regression analyses were used to explore the association between diet scores and obesity according to the type of study data. *Weighted* generalized structural equation models (GSEMs) were used to explore the mediating effect of central obesity on diet score and low-grade inflammation. We performed a sensitivity analysis on people with general obesity. The mediation model is constructed and analyzed by a causal diagram (Figure 3). All p values reported were two-sided; $p \leq 0.05$ was statistically significant.
## 3. Results
Table 1 shows the baseline characteristics of participants in terms of obesity and central obesity. A total of 8157 members were included in our study. The proportion of central obesity was $56.1\%$. Central obesity was found in $40.4\%$ of men and 59.6 of women, and women were more likely to be more obese than men. There were statistically significant differences between people with central obesity or without central obesity in gender, age, race, education level, PIR, smoking, diabetes, work activities, recreational activities, and hypertension.
Table 2 shows the results of studies on the relationship between dietary score, general obesity, and central obesity on inflammatory markers. A multiple linear regression model was used to conduct statistical analysis on the results. The results in all adjusted models show that increased DII score was associated with the risk of hs-CRP and WBC (βhs-CRP = 0.046, $95\%$CI: 0.025, 0.068; βWBC = 0.058, $95\%$CI: 0.012, 0.103). Increased HEI score was associated with reduced risk of hs-CRP and WBC (βhs-CRP = −0.006, $95\%$CI: −0.009, −0.004; βWBC = −0.010, $95\%$CI: −0.015, −0.005). Increased risk of general obesity was associated with the increased level of hs-CRP and WBC (βhs-CRP = 0.650, $95\%$CI: 0.591, 0.709; βWBC = 0.502, $95\%$CI: 0.377, 0.627). The increased risk of central obesity was associated with the increased level of hs-CRP and WBC (βhs-CRP = 0.661, $95\%$CI: 0.592, 0.730; βWBC = 0.582, $95\%$CI: 0.440, 0.724). The association between NLR and both types of obesity and dietary score were not statistically significant ($p \leq 0.05$).
Table 3 shows the correlation between two different dietary scores and two types of obesity. Multiple logistic regression models were used to analyze the data. The results all adjusted model shows that increased HEI score was adversely associated with the risk of both types of obesity (ORgeneral obesity = 0.980, $95\%$CI: 0.975, 0.985; ORcentral obesity = 0.987, $95\%$ CI: 0.980, 0.994). Increased DII scores was associated with the risk of both two types of obesity (ORgeneral obesity = 1.066, $95\%$CI: 1.008, 1.127; ORcentral obesity = 1.055, $95\%$ CI: 1.002, 1.110). The association between changes in NLR and two types of obesity was not statistically significant ($p \leq 0.05$).
Table 4 shows the mediating effect analysis of central obesity on the relationship between dietary pattern and inflammatory markers in serum. The results showed that the mediating effect regression coefficient of central obesity on the relationship between DII score, HEI score and hs-CRP was statistically significant (βDII = 0.007, $95\%$CI: 0.001, 0.014; βHEI = −0.002, $95\%$CI: −0.003,−0.001), accounting for $15.24\%$ and $26.87\%$ of the total effect, respectively. The mediating effect of central obesity influence on the relationship between DII score, HEI score, and WBC was statistically significant (βDII = 0.006, $95\%$CI: 0.000009, 0.012; βHEI = −0.001, $95\%$CI: −0.002,−0.0005), accounting for $10.83\%$ and $13.98\%$ of the total effect, respectively.
Table 5 shows the mediating effect analysis of general obesity on the relationship between dietary pattern and inflammatory markers in serum, which was analyzed by using generalized structural equations. The sensitive analysis shows that the mediating effect still significant ($p \leq 0.05$).
## 4. Discussion
In this study, data from two periods of NHANES, 2015–2016 and 2017–2018, were used to analyze the mediating effect of central obesity on the relationship between dietary scores and low-grade inflammation-related serum inflammatory markers. We found that both DII and HEI-2015 dietary scores were associated with central obesity in US adults. In addition, central obesity partially mediated the association between dietary score and low-grade inflammation-related serum inflammatory markers. In the association of DII with hs-CRP and WBC, central obesity mediated $15.24\%$ and $10.83\%$, respectively. Among the associations of HEI-2015 with hs-CRP and WBC, central obesity mediated $26.87\%$ and $13.98\%$, respectively.
The effect of dietary quality and dietary inflammation index may be mediated partly by central obesity. We found that HEI-2015 score was negatively correlated with hs-CRP and WBC, and DII score was positively correlated with these two serum inflammatory markers. Hs-CRP and WBC were positively associated with obesity and central obesity, respectively. A prospective study found that dietary patterns are associated with pro-inflammatory and anti-inflammatory characteristics of gut microbial bacteria [39]. A British twin cohort showed that dietary quality was associated with methylation of 24 CpG sites, several of which were associated with adiposity, inflammation, and glucose abnormalities [40]. Alterations in HEI are associated with altered expression of genes that are markers of inflammation [41,42], and the effects of diet in regulating inflammation are thought to be due to complex interactions between food and biologically active nutrients [12]. A cross-sectional study of 20,823 adults at Moli-Sani constructed an INFLA composite score (CRP, white blood cell count, and NLR) that was positively associated with DII score [43].
Therefore, positive and effective diet for individuals with obesity may help to better maintain state of inflammation, and consequently avoid the occurrence and development of other complications. Dietary recommendations from the DGA can help people reduce their risk of obesity and low-grade inflammation. Several studies [44,45] have shown that a diet high in vegetables and fruits is inversely associated with inflammatory markers, while a diet high in meat, low in vegetables and omega-3 fatty acids, and high in refined carbohydrates, added sugars, saturated and trans fatty acids tends to be positively associated with inflammatory markers. The most obvious change from the 2015–2020 version of the DGA is the explicit limit on added sugars. Intake of added sugars, such as sucrose and high fructose corn syrup, has increased over the past hundred years and is strongly associated with increases in obesity, metabolic syndrome, and diabetes [46].
We found that HEI-2015 was significantly negatively associated with obesity. DII was significantly correlated with of obesity, and two different types of obesity were significantly correlated with hs-CRP and WBC levels. Obesity is a chronic, low-grade inflammatory state, and there may be several reasons why obesity leads to increased levels of inflammatory markers. Fat cells enhance insulin resistance and metabolic disorders, thereby promoting inflammation by increasing levels of CRP and other inflammatory markers [47]. Macrophages are reported to be the source of adipose tissue-derived proteins [48]. In individuals with abdominal obesity, an increase in the number of macrophages infiltrating visceral adipose tissue suggests that adipose tissue itself is a source and site of inflammation [49].
Based on this evidence, we found that dietary patterns and diet quality may influence low-level inflammation through obesity. Therefore, dietary intervention for obese individuals to improve their dietary quality can effectively avoid the risk of inflammation and prevent its complications. Of course, central obesity may not be the only mediator between diet quality and low-grade inflammation. Other factors, such as hypertension and diabetes, have also been strongly linked to diet quality and inflammation, and these need to be further verified in subsequent studies.
Our study has several advantages. First of all, the sample size we selected was large enough, and because the coverage of NHANES was very wide, the sample representation was very good. Second, we weighted the data throughout the study, helping to extrapolate our results to the entire U.S. population. Third, we conducted correlation analysis before mediation analysis to improve the credibility of the results.
At the same time, the disadvantages of the research should not be ignored. First of all, this is a cross-sectional study that cannot determine the causal relationship, and further prospective studies are needed to explore it. Second, the absence of data on the study population may lead to selection bias and affect the results of the whole study. Third, we use a limited set to work with in NHANES; thus, we could not further adjust for variables such as genetic factors or microbial factors that also have an impact. Finally, we used dietary review data and limited food groups to construct dietary scores, particularly DII, which may have influenced our results.
## 5. Conclusions
In conclusion, the results of this study suggest that better dietary quality can influence the state of central obesity, which can reduce the level of inflammation. It should be noted that dietary quality and dietary inflammatory factors may have important implications for the prevention of the level of inflammation, which should be further explored in prospective studies.
## References
1. McKibben R.A., Margolick J.B., Grinspoon S., Li X.H., Palella F.J., Kingsley L.A., Witt M.D., George R.T., Jacobson L.P., Budoff M.. **Elevated Levels of Monocyte Activation Markers Are Associated With Subclinical Atherosclerosis in Men With and Those Without HIV Infection**. *J. Infect. Dis.* (2015.0) **211** 1219-1228. DOI: 10.1093/infdis/jiu594
2. Glezeva N., Voon V., Watson C., Horgan S., McDonald K., Ledwidge M., Baugh J.. **Exaggerated Inflammation and Monocytosis Associate With Diastolic Dysfunction in Heart Failure With Preserved Ejection Fraction: Evidence of M2 Macrophage Activation in Disease Pathogenesis**. *J. Card. Fail.* (2015.0) **21** 167-177. DOI: 10.1016/j.cardfail.2014.11.004
3. Dandona P., Aljada A., Chaudhuri A., Mohanty P., Garg R.. **Metabolic syndrome—A comprehensive perspective based on interactions between obesity, diabetes, and inflammation**. *Circulation* (2015.0) **111** 1448-1454. DOI: 10.1161/01.CIR.0000158483.13093.9D
4. Aleksandrova K., Koelman L., Rodrigues C.E.. **Dietary patterns and biomarkers of oxidative stress and inflammation: A systematic review of observational and intervention studies**. *Redox Biol.* (2021.0) **42** 101869. DOI: 10.1016/j.redox.2021.101869
5. Stefan N., Birkenfeld A.L., Schulze M.B.. **Global pandemics interconnected—Obesity, impaired metabolic health and COVID-19**. *Nat. Rev. Endocrinol* (2021.0) **17** 135-149. DOI: 10.1038/s41574-020-00462-1
6. Mahoney S.E., Loprinzi P.D.. **Influence of flavonoid-rich fruit and vegetable intake on diabetic retinopathy and diabetes-related biomarkers**. *J. Diabetes Complicat.* (2014.0) **28** 767-771. DOI: 10.1016/j.jdiacomp.2014.06.011
7. Kenfield S.A., Dupre N., Richman E.L., Stampfer M.J., Chan J.M.. **Giovannucci EL Mediterranean Diet and Prostate Cancer Risk and Mortality in the Health Professionals Follow-up Study**. *Eur. Urol.* (2014.0) **65** 887-894. DOI: 10.1016/j.eururo.2013.08.009
8. Arya S., Isharwal S., Misra A., Pandey R.M., Rastogi K., Vikram N.K., Dhingra V., Chatterjee A., Sharma R., Luthra K.. **C-reactive protein and dietary nutrients in urban Asian Indian adolescents and young adults**. *Nutrition* (2006.0) **22** 865-871. DOI: 10.1016/j.nut.2006.05.002
9. Ahluwalia N., Andreeva V.A., Kesse-Guyot E., Hercberg S.. **Dietary patterns, inflammation and the metabolic syndrome**. *Diabetes Metab.* (2013.0) **39** 99-110. DOI: 10.1016/j.diabet.2012.08.007
10. Kirwan A.M., Lenighan Y.M., O'Reilly M.E., McGillicuddy F.C., Roche H.M.. **Nutritional modulation of metabolic inflammation**. *Biochem. Soc. Trans.* (2017.0) **45** 979-985. DOI: 10.1042/BST20160465
11. Andersen C.J., Fernandez M.L.. **Dietary strategies to reduce metabolic syndrome**. *Rev. Endocr. Metab. Disord.* (2013.0) **14** 241-254. DOI: 10.1007/s11154-013-9251-y
12. Calle M.C., Andersen C.J.. **Assessment of Dietary Patterns Represents a Potential, Yet Variable, Measure of Inflammatory Status: A Review and Update**. *Dis. Markers* (2019.0) **2019** 3102870. DOI: 10.1155/2019/3102870
13. **Department of Agriculture. 2015–2020 Dietary Guidelines for Americans. 8th Edition. December 2015**
14. Jessri M., Lou W.Y., L'Abbe M.R.. **The 2015 Dietary Guidelines for Americans is associated with a more nutrient-dense diet and a lower risk of obesity**. *Am. J. Clin. Nutr.* (2016.0) **104** 1378-1392. DOI: 10.3945/ajcn.116.132647
15. Shivappa N., Steck S.E., Hurley T.G., Hussey J.R., Hebert J.R.. **Designing and developing a literature-derived, population-based dietary inflammatory index**. *Public Health Nutr.* (2014.0) **17** 1689-1696. DOI: 10.1017/S1368980013002115
16. Cavicchia P.P., Steck S.E., Hurley T.G., Hussey J.R., Ma Y., Ockene I.S., Hebert J.R.. **A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein**. *J. Nutr.* (2009.0) **139** 2365-2372. DOI: 10.3945/jn.109.114025
17. Maurizi G., Della Guardia L., Maurizi A., Poloni A.. **Adipocytes properties and crosstalk with immune system in obesity-related inflammation**. *J. Cell Physiol.* (2018.0) **233** 88-97. DOI: 10.1002/jcp.25855
18. Berg A.H., Scherer P.E.. **Adipose tissue, inflammation, and cardiovascular disease**. *Circ. Res.* (2005.0) **96** 939-949. DOI: 10.1161/01.RES.0000163635.62927.34
19. Kopp W.. **How Western Diet And Lifestyle Drive The Pandemic Of Obesity And Civilization Diseases**. *Diabetes Metab Syndr. Obes.* (2019.0) **12** 2221-2236. DOI: 10.2147/DMSO.S216791
20. Navarro P., Shivappa N., Hebert J.R., Mehegan J., Murrin C.M., Kelleher C.C., Phillips C.M.. **Lifeways Cross-Generation Cohort S Predictors of the dietary inflammatory index in children and associations with childhood weight status: A longitudinal analysis in the Lifeways Cross-Generation Cohort Study**. *Clin. Nutr.* (2020.0) **39** 2169-2179. DOI: 10.1016/j.clnu.2019.09.004
21. Ruiz-Canela M., Zazpe I., Shivappa N., Hebert J.R., Sanchez-Tainta A., Corella D., Salas-Salvado J., Fito M., Lamuela-Raventos R.M., Rekondo J.. **Martinez-Gonzalez MA Dietary inflammatory index and anthropometric measures of obesity in a population sample at high cardiovascular risk from the PREDIMED (PREvencion con DIeta MEDiterranea) trial**. *Br. J. Nutr.* (2015.0) **113** 984-995. DOI: 10.1017/S0007114514004401
22. Farhangi M.A., Vajdi M.. **The association between dietary inflammatory index and risk of central obesity in adults: An updated systematic review and meta-analysis**. *Int. J. Vitam. Nutr. Res.* (2020.0) **90** 535-552. DOI: 10.1024/0300-9831/a000648
23. Asghari G., Mirmiran P., Yuzbashian E., Azizi F.. **A systematic review of diet quality indices in relation to obesity**. *Br. J. Nutr.* (2017.0) **117** 1055-1065. DOI: 10.1017/S0007114517000915
24. Tande D.L., Magel R., Strand B.N.. **Healthy Eating Index and abdominal obesity**. *Public Health Nutr.* (2010.0) **13** 208-214. DOI: 10.1017/S1368980009990723
25. Kushner I., Rzewnicki D., Samols D.. **What does minor elevation of C-reactive protein signify?**. *Am. J. Med.* (2006.0) **119** 166.e17-166.e28. DOI: 10.1016/j.amjmed.2005.06.057
26. Asadollahi K., Beeching N.J., Gill G.V.. **Leukocytosis as a predictor for non-infective mortality and morbidity**. *QJM Int. J. Med.* (2010.0) **103** 285-292. DOI: 10.1093/qjmed/hcp182
27. **Obesity: Preventing and Managing the Global Epidemic: Report of a WHO consultation. World Health Organization. 2000. WHO Technical Report Series. 894, 252**
28. **National Cancer Institute Division of Cancer Control & Population Sciences Overview & Background of The Healthy Eating Index**
29. **National Cancer Institute Division of Cancer Control & Population Sciences Healthy Eating Index SAS Code**
30. Ryu S., Shivappa N., Veronese N., Kang M., Mann J.R., Hebert J.R., Wirth M.D., Loprinzi P.D.. **Secular trends in Dietary Inflammatory Index among adults in the United States, 1999–2014**. *Eur. J. Clin. Nutr.* (2019.0) **73** 1343-1351. DOI: 10.1038/s41430-018-0378-5
31. Wolfgang K., Malte S., Margit F., Hans-Günther F., Hannelore L., Angela D., Winston L.H., Mark B., Pepys M.D.. **C-Reactive Protein, a Sensitive Marker of Inflammation, Predicts Future Risk of Coronary Heart Disease in Initially Healthy Middle-Aged Men: Results From the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) Augsburg Cohort Study, 1984 to 1992**. *Circulation* (1999.0) **99** 237-242. PMID: 9892589
32. **Survey NHaNE 2017–2018 Data Documentation, Codebook, and Frequencies High-Sensitivity C-Reactive Protein (HSCRP_J)**
33. McAllister E.J., Dhurandhar N.V., Keith S.W., Aronne L.J., Barger J., Baskin M., Benca R.M., Biggio J., Boggiano M.M., Eisenmann J.C.. **Ten Putative Contributors to the Obesity Epidemic**. *Crit. Rev. Food Sci. Nutr.* (2009.0) **49** 868-913. DOI: 10.1080/10408390903372599
34. Keith S.W., Redden D.T., Katzmarzyk P.T., Boggiano M.M., Hanlon E.C., Benca R.M., Ruden D., Pietrobelli A., Barger J.L., Fontaine K.R.. **Putative contributors to the secular increase in obesity: Exploring the roads less traveled**. *Int. J. Obes.* (2006.0) **30** 1585-1594. DOI: 10.1038/sj.ijo.0803326
35. Abdullah A., Peeters A., de Courten M., Stoelwinder J.. **The magnitude of association between overweight and obesity and the risk of diabetes: A meta-analysis of prospective cohort studies**. *Diabetes Res. Clin. Pract.* (2010.0) **89** 309-319. DOI: 10.1016/j.diabres.2010.04.012
36. Kannel W.B., Brand N., Skinner Jr J.J., Dawber T.R., McNamara P.M.. **The relation of adiposity to blood pressure and development of hypertension. The Framingham study**. *Ann. Intern. Med.* (1967.0) **67** 48-59. DOI: 10.7326/0003-4819-67-1-48
37. Dare S., Mackay D.F., Pell J.P.. **Relationship between Smoking and Obesity: A Cross-Sectional Study of 499,504 Middle-Aged Adults in the UK General Population**. *PLoS ONE* (2015.0) **10**. DOI: 10.1371/journal.pone.0123579
38. Parikh N.S., Chatterjee A., Diaz I., Merkler A.E., Murthy S.B., Iadecola C., Navi B.B., Kamel H.. **Trends in Active Cigarette Smoking Among Stroke Survivors in the United States, 1999 to 2018**. *Stroke* (2020.0) **51** 1656-1661. DOI: 10.1161/STROKEAHA.120.029084
39. Bolte L.A., Vila A.V., Imhann F., Collij V., Gacesa R., Peters V., Wijmenga C., Kurilshikov A., Campmans-Kuijpers M.J.E., Fu J.. **Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome**. *Gut* (2021.0) **70** 1287-1298. DOI: 10.1136/gutjnl-2020-322670
40. Do W.L., Whitsel E.A., Costeira R., Masachs O.M., Le Roy C.I., Bell J.T., Staimez L.R., Stein A.D., Smith A.K., Horvath S.. **Epigenome-wide association study of diet quality in the Women's Health Initiative and TwinsUK cohort**. *Int. J. Epidemiol* (2021.0) **50** 675-684. DOI: 10.1093/ije/dyaa215
41. Marques-Rocha J.L., Milagro F.I., Mansego M.L., Zulet M.A., Bressan J., Martinez J.A.. **Expression of inflammation-related miRNAs in white blood cells from subjects with metabolic syndrome after 8 wk of following a Mediterranean diet-based weight loss program**. *Nutrition* (2016.0) **32** 48-55. DOI: 10.1016/j.nut.2015.06.008
42. Shivappa N., Hebert J.R., Rietzschel E.R., De Buyzere M.L., Langlois M., Debruyne E., Marcos A., Huybrechts I.. **Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study**. *Br. J. Nutr.* (2015.0) **113** 665-671. DOI: 10.1017/S000711451400395X
43. Shivappa N., Bonaccio M., Hebert J.R., Di Castelnuovo A., Costanzo S., Ruggiero E., Pounis G., Donati M.B., de Gaetano G., Iacoviello L.. **Association of proinflammatory diet with low-grade inflammation: Results from the Moli-sani study**. *Nutrition* (2018.0) **54** 182-188. DOI: 10.1016/j.nut.2018.04.004
44. Barbaresko J., Koch M., Schulze M.B., Nothlings U.. **Dietary pattern analysis and biomarkers of low-grade inflammation: A systematic literature review**. *Nutr. Rev.* (2013.0) **71** 511-527. DOI: 10.1111/nure.12035
45. Koebnick C., Black M.H., Wu J., Shu Y.H., MacKay A.W., Watanabe R.M., Buchanan T.A., Xiang A.H.. **A diet high in sugar-sweetened beverage and low in fruits and vegetables is associated with adiposity and a pro-inflammatory adipokine profile**. *Br. J. Nutr.* (2018.0) **120** 1230-1239. DOI: 10.1017/S0007114518002726
46. Johnson R.J., Nakagawa T., Sanchez-Lozada L.G., Shafiu M., Sundaram S., Le M., Ishimoto T., Sautin Y.Y., Lanaspa M.A.. **Sugar, uric acid, and the etiology of diabetes and obesity**. *Diabetes* (2013.0) **62** 3307-3315. DOI: 10.2337/db12-1814
47. Gregor M.F., Hotamisligil G.S.. **Inflammatory Mechanisms in Obesity**. *Annu. Rev. Immunol.* (2011.0) **29** 415-445. DOI: 10.1146/annurev-immunol-031210-101322
48. Weisberg S.P., McCann D., Desai M., Rosenbaum M., Leibel R.L., Ferrante A.W.. **Obesity is associated with macrophage accumulation in adipose tissue**. *J. Clin. Investig.* (2003.0) **112** 1796-1808. DOI: 10.1172/JCI200319246
49. Bruun J.M., Lihn A.S., Pedersen S.B., Richelsen B.. **Monocyte chemoattractant protein-1 release is higher in visceral than subcutaneous human adipose tissue (AT): Implication of macrophages resident in the AT**. *J. Clin. Endocrinol. Metab.* (2005.0) **90** 2282-2289. DOI: 10.1210/jc.2004-1696
|
---
title: Association between Metabolic Syndrome Status and Daily Physical Activity Measured
by a Wearable Device in Japanese Office Workers
authors:
- Yukako Yamaga
- Thomas Svensson
- Ung-il Chung
- Akiko Kishi Svensson
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001536
doi: 10.3390/ijerph20054315
license: CC BY 4.0
---
# Association between Metabolic Syndrome Status and Daily Physical Activity Measured by a Wearable Device in Japanese Office Workers
## Abstract
[1] Background: This study examined the cross-sectional association between metabolic syndrome (MetS) status classified into three groups and daily physical activity (PA; step count and active minutes) using a wearable device in Japanese office workers. [ 2] Methods: This secondary analysis used data from 179 participants in the intervention group of a randomized controlled trial for 3 months. Individuals who had received an annual health check-up and had MetS or were at a high risk of MetS based on Japanese guidelines were asked to use a wearable device and answer questionnaires regarding their daily life for the entire study period. Multilevel mixed-effects logistic regression models adjusted for covariates associated with MetS and PA were used to estimate associations. A sensitivity analysis investigated the associations between MetS status and PA level according to the day of the week. [ 3] Results: Compared to those with no MetS, those with MetS were not significantly associated with PA, while those with pre-MetS were inversely associated with PA [step count Model 3: OR = 0.60; $95\%$ CI: 0.36, 0.99; active minutes Model 3: OR = 0.62; $95\%$ CI: 0.40, 0.96]. In the sensitivity analysis, day of the week was an effect modifier for both PA ($p \leq 0.001$). [ 4] Conclusions: Compared to those with no MetS, those with pre-MetS, but not MetS, showed significantly lower odds of reaching their daily recommended PA level. Our findings suggest that the day of the week could be a modifier for the association between MetS and PA. Further research with longer study periods and larger sample sizes are needed to confirm our results.
## 1. Introduction
Metabolic syndrome (MetS) is a global epidemic and growing public health concern. Although the definition of and criteria for MetS vary among health organizations in different countries, it is a major risk factor contributing to cardiovascular disease and type 2 diabetes mellitus [1], and leads to higher mortality [1,2]. While the biological and pathophysiological mechanisms of MetS remain an active area of research, it is clearly a cluster of health conditions that include obesity, dyslipidemia, hypertension, hyperglycemia, and insulin resistance, which are intricately associated with lifestyle factors such as lower physical activity (PA) [3,4,5], imbalanced nutrition [6], and genetics and epigenetics [6,7]. While estimations of its prevalence vary, more than one quarter of the world’s population, or approximately one billion people, are estimated to have MetS [7]. Therefore, public education, the awareness of health hazards caused by MetS, and appropriate measures to assess MetS are expected to limit the impact on society [7].
Recent studies have suggested that PA may play an important role in improving health and preventing MetS. In fact, greater levels and duration of PA are linked to a significant reduction in MetS prevalence [5]. In 2020, the World Health Organization (WHO) updated their guidelines on PA and sedentary behavior for different population groups to enhance health outcomes [8]. In Japan, the Ministry of Health, Labour and Welfare of Japan (MHLW) has issued the Exercise and Physical Activity Reference for Health Promotion (EPAR) 2013, which is a revision of the 2006 guidelines [9]. However, according to the 2019 Japanese National Health and Nutrition Survey (NHNS), the average step count of those aged 20–64 years old was below the target value [10]. Although studies that have examined the effect of PA interventions have shown improvements in hyperlipidemia [11], hypertension [12] and diabetes mellitus [13], much of the evidence on the beneficial effects of PA on metabolic risk in population-based studies have been obtained using self-reported questionnaires [3,4], which are limited by potential recall bias.
Consumer wearable devices have risen in popularity over the past several years, as they allow individuals to monitor various health parameters, such as PA [14], sleep duration [15], heart rate [16], and energy consumption. The availability and use of wearable devices is expected to increase, as they can encourage better health behaviors and help individuals manage their health through daily monitoring [14]. Additionally, as consumer wearable devices can capture various PA-related parameters, they are also useful for researchers in gathering data in free-living conditions. This can enhance investigations into multiple conditions regarding PA level, although challenges related to the reliability and validity of different devices and models remain an issue [17,18].
Given this background, the aim of this study was to examine the association between MetS and objectively measured PA levels. We studied this by: [1] measuring PA levels using a wearable device and comparing them with PA reference values in Japan, and determining MetS status based on data derived from annual health checkup (AHC) data and MetS criteria in Japan [19]; and [2] examining the potential effect modification of the day of the week (weekday or weekend/holiday) in sensitivity analysis. We hypothesized that MetS and pre-MetS would be inversely associated with objectively measured PA-level.
## 2.1. Study Design
Participants were full-time professionals, managerial, or clerical workers recruited from five companies, each with more than 1000 employees located in the Tokyo metropolitan area in Japan. The original study was a randomized controlled trial (RCT) of a smartphone lifestyle intervention application. Participants eligible for the RCT were employees that had completed the annual health check-up (AHC) and had been categorized with MetS or were considered to be at risk of MetS based on their AHC results. The participants of the original RCT were recruited among 7437 eligible employees. A total of 272 participants were enrolled and randomized into 2 arms: 181 were in the intervention group and 91 were in the control group. Participants in the intervention group were asked to use a wearable device 24 h per day and a dedicated smartphone application for the entire 3 months of the study. Participants in the control group completed a questionnaire using a dedicated smartphone application. All participants received detailed information about the study both in writing and face-to-face. Information provided to study participants included, but was not limited to, ethical considerations, the purpose of the study, and the right to discontinue at any time without penalty. All participants understood that participation was entirely voluntary and provided written informed consent. The study was approved by the Ethics Committee of the School of Engineering, the University of Tokyo (approval number: KE18-44). Participants in the intervention group were gifted the wearable device as an incentive if they completed the final questionnaire at the end of the study. Participants in the control group were gifted the wearable device at the end of the study.
## 2.2.1. Wearable Device
The Fitbit Versa (FV), a consumer wearable device manufactured by Fitbit Inc. (San Francisco, CA, USA, https://www.fitbit.com (accessed on 19 April 2022), was used in this study. The FV connects to a dedicated smartphone application using Bluetooth technology and is designed to measure daily information such as step count, duration of PA, heart rate, sleep time, distance, and consumed calories. These data are automatically uploaded to the application when the device is synchronized to the smartphone application via Bluetooth. In this study, participants were instructed to wear the FV for 24 h per day except when the device was charging or they were bathing. Recent studies evaluating the accuracy of consumer wearable devices compared with research-grade devices indicated that Fitbit was examined the most frequently [17] and had sufficient accuracy to measure step count [20] and energy expenditure showing the equivalence zone [21].
## 2.2.2. Questionnaires
Participants in both arms of the study were asked to complete questionnaires through a smartphone application at the start and end of the study. The intervention arm additionally answered daily questions every morning and afternoon during the study period. Questions originally established and asked at the start and end of the study aimed to assess participants’ lifestyle, psychological and physical condition, medical history, family history, socio-economic status, smoking, alcohol, diet, exercise, sleep status, working condition, stress, and health awareness. Daily questions were related to subjective sleep assessment, alcohol consumption, smoking, diet, exercise, and stress level, which are reflective of each participant’s behavior and health condition on the corresponding night (for sleep) and day (for the remaining lifestyle factors).
## 2.3. Study Population
We conducted a secondary analysis of data from the intervention group of a 90-day randomized controlled trial conducted between 3 December 2018 and 2 March 2019. As the present study aimed to examine the association of MetS prevalence with free-living PA in Japanese office workers, only the intervention group (179 participants with 16,110 observations) was eligible for analysis (Figure 1). Two participants originally randomized to the intervention group declined to participate. Due to the characteristics of the dataset, we excluded any participants who did not complete the final questionnaire ($$n = 1$$; 90 observations) and participants whose exposure (MetS) status could not be classified ($$n = 11$$; 990 observations) due to missing information on waist circumference (WC). Participants and observations were further excluded if they had missing values on daily step count ($$n = 2$$; 182 observations), unreasonable values on daily step count (daily step count < 1000: $$n = 0$$; 1136 observations), and missing values on very active minutes ($$n = 0$$; 4 observations), fairly active minutes ($$n = 0$$; 8 observations), lightly active minutes ($$n = 0$$; 8 observations), and sedentary minutes ($$n = 0$$; 2 observations). Lastly, we excluded those with missing values on total sleep time (TST; $$n = 0$$; 2510 observations), unreasonable values on TST (0 min: $$n = 0$$; 1 observation), and missing values on daily alcohol consumption ($$n = 2$$; 2914 observations). As a result, 163 participants with 8265 observations were included in the present analysis.
## 2.4. Metabolic Syndrome Status (Main Exposure)
Participants were classified into three categories of MetS (no-MetS, pre-MetS, and MetS) using AHC data according to the criteria outlined by the Japanese Society of Internal Medicine in 2005 [19]. The prevalence of MetS was determined using the obligatory criterion for WC (≥85 cm in men and ≥90 cm in women) in addition to at least 2 non-obligatory criteria: triglycerides (TG) ≥ 150 mg/dL and/or high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL; systolic blood pressure (SBP) ≥ 130 mmHg and/or diastolic blood pressure (DBP) ≥85 mmHg; or fasting blood glucose (FBG) ≥ 110 mg/dL. Participants who met the obligatory criterion for WC but met only 1 non-obligatory criterion were categorized as having pre-MetS. On the other hand, participants who did not meet the obligatory criterion for WC were categorized as no-MetS [22].
## 2.5. Physical Activity (Main Outcome)
We used 2 main outcome measures, daily step count and active minutes, to represent participants’ PA. Both outcome measures were obtained from the wearable device and were used as continuous outcome variables in the main analyses. For step count, we excluded any missing values ($$n = 2$$; 182 observations) and any daily observations with fewer than 1000 steps ($$n = 0$$; 1136 observations), as such low daily counts were considered improbable and would most likely represent inadequate use of the wearable device.
As reference values for daily step count are suggested in the EPAR (9000 steps in men and 8500 steps in women aged 18–64 years old), we established a binary variable based on these cutoffs (0: did not achieve the reference daily steps, 1: achieved the reference daily steps).
The EPAR also suggests a reference PA intensity as 60 min of moderate-to-vigorous intensity physical activity (MVPA) based on the recommended minimum PA per day for people aged 18–64 years old (23 metabolic equivalents (METs) h/week) [9]. Activity measures obtained from the FV were classified into four PA intensity categories [23,24]: [1] sedentary active minutes (<1.5 METs), [2] lightly active minutes (1.5–3.0 METs), [3] fairly active minutes (3.0–6.0 METs), and [4] very active minutes (>6.0 METs). As fairly active minutes and very active minutes are likely comparable to the MVPA recommended by the EPAR (equivalent to >3 METs), we combined these categories into a single variable called active minutes [24]. We additionally established a binary variable based on the cutoff for active minutes (0: did not achieve the reference PA intensity (<3 METs h/day), 1: achieved the reference PA intensity (≥3 METs h/day)).
## 2.6. Covariates
Covariates were determined based on their known or suspected association with the exposure and outcomes. Covariates were related to participants’ demographic information, lifestyle, socio-economic situation, and general health awareness, and were obtained from four main sources: [1] the study’s baseline questionnaire, [2] daily questions, [3] annual health check-up data, and [4] the wearable device. Demographic information included sex and age (continuous); lifestyle factors included eating habits with self-reported balanced food intake (yes/no), daily alcohol consumption (ethanol intake <20 g or ≥20 g as per the MHLW’s recommended daily limit [25]); TST (continuous, hours); smoking status (non-smoker, past smoker, current smoker <20 cigarettes per day, or current smoker ≥20 cigarettes per day); self-reported hours of overtime work in the month preceding the start of the study (continuous, hours); and living arrangement (living alone/living with someone). Socio-economic status was categorized according to self-reported annual income (<10 million JPY or ≥11 million JPY). We constructed a variable to represent heath awareness using five self-reported items from the study’s baseline questionnaire to obtain an overall composite score. The items were: [1] “How often (per week) do you exercise (physically demanding sports/physical activity) for at least 30 min?” ( 0: no exercise, 1: 1–2 days, 2: ≥3 days), [2] “How often do you walk or do equivalent physical activity for an hour or more?” ( 0: never, 1: <2 times, 2: ≥3 times per week), [3] “Do you consciously take opportunities to move your body, such as taking the stairs instead of the elevator?” ( 0: no, 1: yes), [4] “Do you regularly measure your weight?” ( 0: no, 1: yes), and [5] “Do you have a dental check-up at least once a year?” ( 0: no, 1: yes). The total score of the composite variable ranged between 0–7 points, with higher scores indicating greater health awareness. Finally, we used the item, “How do you feel about your current health status?” to represent participants’ self-rated health status (SRH), which ranged from 0–5 points (very poor, poor, fair, good, or very good). To determine the day of the week during the study, we created a binary variable (weekdays or weekends/holidays) to classify days according to the Japanese calendar.
## 2.7. Statistical Analysis
Baseline characteristics according to MetS classification were compared using the chi-squared test for categorical variables, the Kruskal–Wallis test for continuous variables and repeated-measures variables (i.e., variables collected daily using the daily questionnaire: daily alcohol intake), or via the wearable device (step count, active minutes, and TST).
Associations between MetS status and PA level (step count and active minutes) based on the EPAR reference values were estimated using multilevel mixed-effects logistic regression models by considering $95\%$ confidence intervals (CI) while accounting for goodness of fit. Model 1 included age and sex as fixed effects. Model 2 additionally included the day of the week (weekday or weekend/holiday), and eating habit, smoking, overtime hours, living arrangement, socio-economic status (all fixed effects), alcohol consumption and TST (random effects) as lifestyle factors. Moreover, because participants’ PA may be influenced by health awareness and daily behaviors, we adjusted for health awareness and SRH [26] as fixed effects in Model 3. All mixed models were analyzed using the unstructured covariance matrix by reviewing the results of the Akaike Information Criterion (AIC) to evaluate goodness of fit for Model 2 and Model 3.
The sensitivity analysis investigated the association between MetS status and PA level by stratifying the main analyses according to the day of the week (weekday or weekend/holiday). We investigated the effect modification of the day of the week using the likelihood ratio test by comparing a multivariable model that included the interaction term between the day of the week and MetS with a multivariable model without this term.
All statistical analyses were performed using Stata/MP version 16.1 (Stata Corp LLC, College Station, TX, USA). A two-tailed p-value < 0.05 was considered statistically significant.
## 3.1. Main Analyses
The 163 study participants (151 men and 12 women) included in the analyses had a median age (interquartile range [iqr]) of 44.2 [12.0] years. Of these, $31.8\%$ of men ($\frac{48}{151}$) and $25.0\%$ of women ($\frac{3}{12}$) were classified as having MetS. Table 1 shows the baseline characteristics according to MetS status. Median age [iqr] was highest in the MetS group (47.0 [10.0] years, $$p \leq 0.012$$). Although no significant differences were found, the MetS group had a high health awareness score (3.0 [3.0] points, $$p \leq 0.65$$), the highest proportion of participants indicating they considered the nutritional balance of their meals ($70.6\%$, $\frac{36}{51}$, $$p \leq 0.90$$), and the highest median step count (11,195 [5262] steps/day, $p \leq 0.001$) and active minutes (56 [60.2] mins/day, $p \leq 0.001$) among the groups.
Table 2 shows the association between MetS status and daily PA using Japanese PA reference values for the respective outcomes. Compared to the no-MetS group, the MetS group showed an inverse, albeit non-significant, association with step count in Model 1 (OR = 0.81; $95\%$ CI: 0.47, 1.37). Following adjustment for additional covariates, the MetS group remained non-significantly and inversely associated with step count in Model 2 (OR = 0.86; CI: 0.50, 1.49) and Model 3 (OR = 0.81; $95\%$ CI: 0.47, 1.40). Similarly, compared with the no-MetS group, the pre-MetS group showed an inverse, albeit non-significant, association with step count in Model 1 (OR = 0.63; $95\%$ CI: 0.38, 1.04) and Model 2 (OR = 0.60; $95\%$ CI: 0.36, 1.01). However, it showed a significant inverse association in the fully-adjusted model (Model 3: OR = 0.60; $95\%$ CI: 0.36, 0.99).
In relation to active minutes, the MetS group showed a non-significant association in Model 1 (OR = 0.94; $95\%$ CI: 0.57, 1.54), Model 2 (OR = 1.02; $95\%$ CI: 0.63, 1.64), and Model 3 (OR = 0.94; $95\%$ CI: 0.59, 1.49) compared to the no-MetS group. In contrast, the pre-MetS group showed an inverse, albeit non-significant, association with active minutes in Model 1 (OR = 0.63; $95\%$ CI: 0.39, 1.01) compared to the no-MetS group. Following a further adjustment for covariates, however, the pre-MetS group showed a significant inverse association with active minutes in Model 2 (OR = 0.61; $95\%$ CI: 0.39, 0.97) and Model 3 (OR = 0.62; $95\%$ CI: 0.40, 0.96).
## 3.2. Sensitivity Analysis
Table 3 shows the main analyses stratified by the day of the week (i.e., weekday or weekend/holiday) for the association between MetS status and PA level. The analyses showed a significant difference in step count between weekdays and weekends/holidays in the MetS group (likelihood ratio test: $p \leq 0.001$). Compared to the weekday step count in the no-MetS group, MetS status was slightly positively, albeit non-significantly, associated with weekday step count in all models (Model 3: OR = 1.03; $95\%$ CI: 0.59, 1.79). Meanwhile, compared to the weekday step count in the no-MetS group, pre-MetS status was inversely and non-significantly associated with weekday step count in Model 1 (OR = 0.61; $95\%$ CI: 0.35, 1.06), but significantly and inversely associated with weekday step count in Model 2 (OR = 0.57; $95\%$ CI: 0.33, 0.96) and Model 3 (OR = 0.57; $95\%$ CI: 0.34, 0.95). When compared to the weekday step count in the no-MetS group, all MetS groups showed significantly inverse associations with weekend/holiday step count in all models (no-MetS Model 3: OR = 0.35; $95\%$ CI: 0.27, 0.44; MetS Model 3: OR = 0.21; $95\%$ CI: 0.12, 0.36; pre-MetS Model 3: OR = 0.23; $95\%$ CI: 0.13, 0.38).
The analyses also showed a significant difference in active minutes between weekdays and weekends/holidays in the MetS group (likelihood ratio test: $p \leq 0.001$). Compared to the weekday active minutes in the no-MetS group, MetS status was slightly positively, albeit non-significantly, associated with weekday active minutes in all models (Model 3: OR = 1.09; $95\%$ CI: 0.68, 1.75). Meanwhile, compared to the weekday active minutes in the no-MetS group, pre-MetS status was inversely and non-significantly associated with weekday active minutes in Model 1 (OR = 0.62; $95\%$ CI: 0.38, 1.00), but inversely and significantly associated with weekday active minutes in Model 2 (OR = 0.60; $95\%$ CI: 0.38, 0.96) and Model 3 (OR = 0.61; $95\%$ CI: 0.39, 0.95). While no-MetS status was slightly positively albeit non-significantly associated with weekend/holiday active minutes in all models (Model 3: OR = 1.12; $95\%$ CI: 0.91, 1.38), those in the MetS and pre-MetS groups showed a non-significant inverse association with weekend/holiday active minutes in all models (MetS Model 3: OR = 0.76; $95\%$ CI: 0.47, 1.24; pre-MetS Model 3: OR = 0.71; $95\%$ CI: 0.45, 1.12) compared to the weekday active minutes in the no-MetS group.
## 4.1. Main Analysis
The present study aimed to examine the association between MetS status and PA level, as represented by step count and active minutes, using Japanese PA reference values recommended by EPAR. The main analysis indicated that the pre-MetS and MetS groups showed comparable associations with both step count and active minutes, respectively, when compared to the no-MetS group. Participants with MetS showed an inverse, albeit non-significant, association with both step count and active minutes compared to those with no MetS. Meanwhile, individuals with pre-MetS showed significant inverse associations with both step count and active minutes. In fact, compared to individuals without MetS, those with pre-MetS had approximately $40\%$ lower odds of reaching their daily recommended step count and active minutes.
Our finding that MetS did not show a significant inverse association with PA deviates from the primary hypothesis. The following may explain these results. First, in Japan, insurers have been required to provide AHCs to insured employees aged 40 to 74 under the Japanese National Health Screening and Intervention Program since 2008. Employees classified as having MetS are requested to receive intensive health guidance from health professionals, which includes a follow-up consultation for 3–6 months [27], while those with a lower risk of developing lifestyle-related diseases are asked to undergo motivational health guidance with a professional’s advice. Some participants with MetS were likely aware of their health risk and engaged in positive health behaviors to improve their condition with active health support. In fact, we found that those with MetS, although not significant, had higher health awareness scores than the no-MetS group, more frequently indicated they intended to consume a balanced diet, and had higher median PA levels compared to the other MetS groups during the study period. Hence, participants with MetS may have comparatively higher health awareness [28]; in addition, the intervention itself may have re-affirmed health awareness and health-conscious behaviors in those with MetS in this study. To eliminate contingency and more precisely evaluate PA, a study of longer duration is needed in the future. Second, the study was conducted in winter, from December 2018 to March 2019. Many Japanese companies close for several days over the New Year’s holiday period, and some participants may adopt irregular lifestyle patterns or behaviors during this period. Our finding that PA levels varied among MetS individuals may be related to when the study was conducted, which may have affected the results. Characteristic seasonal behaviors [29] may thus need to be considered when researching PA. Third, while we used the reference values recommended by the EPAR to determine PA level (9000 steps/day in men, 8500 steps/day in women or 60 min MVPA/day, about 23 METs h/week), according to a suggestion by Kim et al. [ 30], an even higher PA level (26 METs h/week) may be more appropriate for the middle-aged Japanese population in order to reduce the risk of MetS. Finally, our participants were office workers in large enterprises with over 1000 employees, had relatively high incomes, and were predominantly men ($92.6\%$). Because gender effects are often observed in the analysis of PA and MetS [30], the demographics and characteristics of this study population may have attenuated the associations between PA and MetS prevalence.
Conversely, pre-MetS, considered a transitional pathway to MetS, showed a significant inverse association with PA level, presenting approximately $40\%$ lower odds of reaching the daily recommended PA level than the no-MetS group. This finding indicates that participants with pre-MetS had significantly lower PA levels than those with no MetS, and were unlikely to achieve the current PA guidelines. A notable difference between the MetS and the pre-MetS groups may be whether individuals are receiving active health support with follow-up consultation. As the pre-MetS group exhibited significantly lowest odds among the MetS groups, those with pre-MetS would benefit from increased guidance to prevent the onset of MetS. Furthermore, we also found a significant association between pre-MetS and PA level only after adjusting for health awareness in addition to lifestyle factors. This finding suggests that health awareness is an important covariate for the association between MetS and PA.
Given the non-significant associations between MetS and PA level, our results were inconsistent with previous studies that reported a clear inverse association between PA and MetS. However, it should be noted that the design of the present study differed from that of previous studies in that it examined the main exposure of MetS according to three categories and adjusted for a different selection of covariates. A study by Ko et al., which examined the association between PA and MetS in male white collar workers [31], reported that the prevalence of MetS was significantly lower ($p \leq 0.05$) in the high PA group ($14.3\%$) than in the low PA group ($25.2\%$), and that the latter had a higher risk of MetS than the former. Another study by Xu et al. [ 32] reported that high PA was significantly inversely associated with MetS after adjusting for age, sex, ethnicity, and current smoking in older adults with obesity. Importantly, these studies used questionnaires, namely the International Physical Activity Questionnaire (IPAQ) or Global Physical Activity Questionnaire (GPAQ), to assess the PA level, and a different index of MetS criteria. A systematic review and meta-analysis by Oliveira et al. [ 3] noted that studies of MetS in adolescents using self-reported assessments did not indicate a significant association, and suggested using objective measure instruments such as accelerometers. A study that objectively measured PA using a triaxial accelerometer and MetS index in Japan by Kim et al. [ 30] reported that low levels of PA were significantly positively associated with MetS compared with high levels of PA, with a significantly higher risk observed in middle-aged Japanese men, but not women. Similarly, Sagawa et al. [ 33] reported that a higher step count (≥10,000 steps/day) in middle-aged Japanese men objectively measured by a pedometer was inversely associated with MetS.
When measuring PA in free-living conditions, the use of wearable devices may avoid recall bias by participants. However, that proprietary algorithms are not disclosed leaves a level of uncertainty [17,24]. In fact, validation studies that compared wearable devices with gold standard instruments indicate that individual wearable devices are prone to variability in their measurement of specific PA parameters [18]. For example, findings of variability in MVPA estimations, which increase along with the MVPA volume [24,34], by Fitbit devices under free-living conditions compared to a gold standard have been attributed to the systematic bias related to differences in the algorithms of each device [24,35]. Although some limitations remain, the capabilities and accuracy of wearable devices for obtaining objective measures will likely increase with each software update and technological upgrade, which, when incorporated, should lead to their optimal usage in research.
Another explanation for our results could be that activity levels differ between weekdays and weekends/holidays. We conducted a sensitivity analysis with the hypothesis that the day of the week could be an effect modifier for the association between MetS status and PA level. Indeed, we found that the interaction between the day of the week and MetS status was highly significant for both step count and active minutes. Thus, the fact that the study period included several days over the New Year’s holidays could have contributed substantially if PA differed according to the day of the week.
## 4.2. Sensitivity Analysis
Our sensitivity analysis indicated that while all MetS groups had significantly lower step counts on weekends/holidays, active minutes did not significantly change in any group. Although the associations were not significant, participants with MetS were likely to meet the reference PA level on weekdays and have slightly higher PA than those with no MetS, but had decreased PA on weekends/holidays, with greater disparity observed by the day of the week in those with MetS. We speculate that most of the PA conducted by individuals with MetS may come from their weekday commute, leading to less activity on weekends/holidays. In fact, a study that examined the effect of sitting time on MetS in Japanese workers reported that a high proportion of sitting time in leisure time during holidays may increase the risk of MetS [36]. We also found a significant effect modification by the day of the week, with the step count of all MetS groups being lower on weekends/holidays than on weekdays. However, while the number of active minutes on weekends/holidays was lower than that on weekdays for those with MetS, it was unchanged for those with pre-MetS and higher for those with no MetS. Given that active minutes also provide information on the intensity of the PA, our results suggest that the modifying effect of the day of the week is greatest for those with MetS, who on weekends/holidays have a lower step count and fewer active minutes (i.e., less time spent in MVPA). In contrast, although those with no MetS had a lower step count on weekends/holidays, they spent more time in MVPA. We assume that those with no MetS and pre-MetS may conduct more intense PA, such as running, because of their lower step count on weekends/holidays. Our findings illustrate the important difference and characteristic pattern of PA among those with different MetS status by the day of the week according to current guidelines for PA.
Some studies have shown that those with MetS have lower energy expenditure in leisure time physical activity (LTPA) than those with no MetS [37], and that increased LTPA is significantly inversely associated with MetS [38]. In addition to leisure time, our findings suggest that the day of the week is another factor affecting the association between MetS and PA. Education to enhance health awareness and campaigns that promote PA on weekends/holidays might be effective public health strategies for preventing the onset of MetS. In particular, consistent PA regardless of the day of the week should be recommended for those with or at risk of MetS.
## 4.3. Limitations and Strengths
This study has a few limitations. First, as this was a cross-sectional analysis, we could not determine the causal associations between MetS and PA. Second, the cross-sectional analysis does not take into account the temporal change in activity for the 3 exposure groups during the 3-month study period. Third, the definition of pre-MetS and MetS relies on a combination of specific components (i.e., waist circumference, lipids, blood pressure, and blood glucose). Consequently, this may result in the heterogeneity of participants within the pre-MetS and MetS groups, respectively. However, the decomposition of the pre-MetS and MetS groups into component-specific classifications are beyond the scope of the present study. Moreover, the definitions of pre-MetS and MetS are in accord with Japanese national guidelines. Finally, the results of this study may not be generalizable to the *Japanese* general population given that the majority of participants were male office workers in Tokyo.
Despite these limitations, this study has several strengths. First, to our knowledge, this was the first analysis to use 3 MetS categories, namely no-MetS, pre-MetS and MetS, to investigate the association of MetS status with PA. Second, we examined real-world data objectively measured using a wearable device instead of relying on questionnaires to assess PA level. Third, this study also used anthropometric and biomarker data from AHCs. Finally, we investigated the impact of weekday/weekend PA level, which was made possible by using the wearable device.
## 5. Conclusions
Individuals with MetS did not show a significant inverse association with PA level compared to those with no MetS. In contrast, individuals with pre-MetS had significantly lower odds of reaching the step count and active minutes recommended by the Japanese Exercise and Physical Activity Reference for Health Promotion guidelines. Moreover, the day of the week (i.e., weekday vs weekend/holiday) was a modifier of the association between MetS status and PA level. We suggest that, in addition to those with MetS, those with pre-MetS would benefit from support strategies that encourage increased PA to reduce the risk of MetS onset. Continuous efforts to promote consistent PA including on weekends/holidays is also needed. Further research using longitudinal analyses with longer study periods, larger sample sizes, and numbers from both genders is needed to confirm our findings.
## References
1. Wilson P.W., D’Agostino R.B., Parise H., Sullivan L., Meigs J.B.. **Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus**. *Circulation* (2005.0) **112** 3066-3072. DOI: 10.1161/CIRCULATIONAHA.105.539528
2. Yu W.W., Randhawa A.K., Blair S.N., Sui X., Kuk J.L.. **Age- and sex- specific all-cause mortality risk greatest in metabolic syndrome combinations with elevated blood pressure from 7 U.S. cohorts**. *PLoS ONE* (2019.0) **14**. DOI: 10.1371/journal.pone.0218307
3. Oliveira R.G., Guedes D.P.. **Physical Activity, Sedentary Behavior, Cardiorespiratory Fitness and Metabolic Syndrome in Adolescents: Systematic Review and Meta-Analysis of Observational Evidence**. *PLoS ONE* (2016.0) **11**. DOI: 10.1371/journal.pone.0168503
4. Myers J., Kokkinos P., Nyelin E.. **Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome**. *Nutrients* (2019.0) **11**. DOI: 10.3390/nu11071652
5. Gaesser G.A.. **Exercise for prevention and treatment of cardiovascular disease, type 2 diabetes, and metabolic syndrome**. *Curr. Diabetes Rep.* (2007.0) **7** 14-19. DOI: 10.1007/s11892-007-0004-8
6. Xu H., Li X., Adams H., Kubena K., Guo S.. **Etiology of Metabolic Syndrome and Dietary Intervention**. *Int. J. Mol. Sci.* (2018.0) **20**. DOI: 10.3390/ijms20010128
7. Saklayen M.G.. **The Global Epidemic of the Metabolic Syndrome**. *Curr. Hypertens. Rep.* (2018.0) **20** 12. DOI: 10.1007/s11906-018-0812-z
8. Bull F.C., Al-Ansari S.S., Biddle S., Borodulin K., Buman M.P., Cardon G., Carty C., Chaput J.P., Chastin S., Chou R.. **World Health Organization 2020 guidelines on physical activity and sedentary behaviour**. *Br. J. Sports Med.* (2020.0) **54** 1451-1462. DOI: 10.1136/bjsports-2020-102955
9. 9.
Ministry of Health Labour and Welfare
Exercise and Physical Activity Reference for Health Promotion (EPAR) 2013
National Institute of Health and Nutrition
Ministry of Health Labour and WelfareTokyo, Japan2013. *Exercise and Physical Activity Reference for Health Promotion (EPAR) 2013* (2013.0)
10. **The National Health and Nutrition Survey (NHNS) Japan, 2019. (In Japanese)**
11. Mach F., Baigent C., Catapano A.L., Koskinas K.C., Casula M., Badimon L., Chapman M.J., De Backer G.G., Delgado V., Ference B.A.. **2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk**. *Eur. Heart. J.* (2020.0) **41** 111-188. DOI: 10.1093/eurheartj/ehz455
12. Carey R.M., Muntner P., Bosworth H.B., Whelton P.K.. **Prevention and Control of Hypertension: JACC Health Promotion Series**. *J. Am. Coll. Cardiol.* (2018.0) **72** 1278-1293. DOI: 10.1016/j.jacc.2018.07.008
13. Kirwan J.P., Sacks J., Nieuwoudt S.. **The essential role of exercise in the management of type 2 diabetes**. *Clevel. Clin. J. Med.* (2017.0) **84** S15-S21. DOI: 10.3949/ccjm.84.s1.03
14. Brickwood K.J., Watson G., O’Brien J., Williams A.D.. **Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis**. *JMIR Mhealth Uhealth* (2019.0) **7** e11819. DOI: 10.2196/11819
15. Chinoy E.D., Cuellar J.A., Huwa K.E., Jameson J.T., Watson C.H., Bessman S.C., Hirsch D.A., Cooper A.D., Drummond S.P.A., Markwald R.R.. **Performance of Seven Consumer Sleep-Tracking Devices Compared with Polysomnography**. *Sleep* (2020.0) **44** zsaa291. DOI: 10.1093/sleep/zsaa291
16. Nelson B.W., Allen N.B.. **Accuracy of Consumer Wearable Heart Rate Measurement during an Ecologically Valid 24-Hour Period: Intraindividual Validation Study**. *JMIR Mhealth Uhealth* (2019.0) **7** e10828. DOI: 10.2196/10828
17. Fuller D., Colwell E., Low J., Orychock K., Tobin M.A., Simango B., Buote R., Van Heerden D., Luan H., Cullen K.. **Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review**. *JMIR Mhealth Uhealth* (2020.0) **8** e18694. DOI: 10.2196/18694
18. Rosenberger M.E., Buman M.P., Haskell W.L., McConnell M.V., Carstensen L.L.. **Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices**. *Med. Sci. Sports Exerc.* (2016.0) **48** 457-465. DOI: 10.1249/MSS.0000000000000778
19. Matsuzawa Y.. **Metabolic syndrome--definition and diagnostic criteria in Japan**. *J. Atheroscler. Thromb.* (2005.0) **12** 301. DOI: 10.5551/jat.12.301
20. Case M.A., Burwick H.A., Volpp K.G., Patel M.S.. **Accuracy of smartphone applications and wearable devices for tracking physical activity data**. *JAMA* (2015.0) **313** 625-626. DOI: 10.1001/jama.2014.17841
21. Lee J.M., Kim Y., Welk G.J.. **Validity of consumer-based physical activity monitors**. *Med. Sci. Sports Exerc.* (2014.0) **46** 1840-1848. DOI: 10.1249/MSS.0000000000000287
22. **The National Health and Nutrition Survey. (In Japanese)**
23. Carpenter C., Yang C.H., West D.. **A Comparison of Sedentary Behavior as Measured by the Fitbit and ActivPAL in College Students**. *Int. J. Environ. Res. Public Health* (2021.0) **18**. DOI: 10.3390/ijerph18083914
24. Semanik P., Lee J., Pellegrini C.A., Song J., Dunlop D.D., Chang R.W.. **Comparison of Physical Activity Measures Derived From the Fitbit Flex and the ActiGraph GT3X+ in an Employee Population with Chronic Knee Symptoms**. *ACR Open Rheumatol.* (2020.0) **2** 48-52. DOI: 10.1002/acr2.11099
25. **Health Japan 21 (Kenko Nippon 21) Alcohol Section. (In Japanese)**
26. Noh J.W., Chang Y., Park M., Kwon Y.D., Ryu S.. **Self-rated health and the risk of incident type 2 diabetes mellitus: A cohort study**. *Sci. Rep.* (2019.0) **9** 3697. DOI: 10.1038/s41598-019-40090-y
27. **The Standard of National Health Screening and Intervention Program. (In Japanese)**
28. Owei I., Umekwe N., Ceesay F., Dagogo-Jack S.. **Awareness of Prediabetes Status and Subsequent Health Behavior, Body Weight, and Blood Glucose Levels**. *J. Am. Board Fam. Med.* (2019.0) **32** 20-27. DOI: 10.3122/jabfm.2019.01.180242
29. Garriga A., Sempere-Rubio N., Molina-Prados M.J., Faubel R.. **Impact of Seasonality on Physical Activity: A Systematic Review**. *Int. J. Environ. Res. Public Health* (2021.0) **19**. DOI: 10.3390/ijerph19010002
30. Kim J., Tanabe K., Yokoyama N., Zempo H., Kuno S.. **Association between physical activity and metabolic syndrome in middle-aged Japanese: A cross-sectional study**. *BMC Public Health* (2011.0) **11**. DOI: 10.1186/1471-2458-11-624
31. Ko K.J., Kim E.H., Baek U.H., Gang Z., Kang S.J.. **The relationship between physical activity levels and metabolic syndrome in male white-collar workers**. *J. Phys. Ther. Sci.* (2016.0) **28** 3041-3046. DOI: 10.1589/jpts.28.3041
32. Xu F., Cohen S.A., Lofgren I.E., Greene G.W., Delmonico M.J., Greaney M.L.. **The Association between Physical Activity and Metabolic Syndrome in Older Adults with Obesity**. *J. Frailty Aging* (2019.0) **8** 27-32. DOI: 10.14283/jfa.2018.34
33. Sagawa N., Rockette-Wagner B., Azuma K., Ueshima H., Hisamatsu T., Takamiya T., El-Saed A., Miura K., Kriska A., Sekikawa A.. **Physical activity levels in American and Japanese men from the ERA-JUMP Study and associations with metabolic syndrome**. *J. Sport Health Sci.* (2020.0) **9** 170-178. DOI: 10.1016/j.jshs.2019.09.007
34. Redenius N., Kim Y., Byun W.. **Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity**. *BMC Med. Res. Methodol.* (2019.0) **19**. DOI: 10.1186/s12874-019-0668-1
35. Degroote L., De Bourdeaudhuij I., Verloigne M., Poppe L., Crombez G.. **The Accuracy of Smart Devices for Measuring Physical Activity in Daily Life: Validation Study**. *JMIR Mhealth Uhealth* (2018.0) **6** e10972. DOI: 10.2196/10972
36. So R., Matsuo T.. **The Effect of Domain-Specific Sitting Time and Exercise Habits on Metabolic Syndrome in Japanese Workers: A Cross-Sectional Study**. *Int. J. Environ. Res. Public Health* (2020.0) **17**. DOI: 10.3390/ijerph17113883
37. Gallardo-Alfaro L., Bibiloni M.D.M., Mateos D., Ugarriza L., Tur J.A.. **Leisure-Time Physical Activity and Metabolic Syndrome in Older Adults**. *Int. J. Environ. Res. Public Health* (2019.0) **16**. DOI: 10.3390/ijerph16183358
38. Huang J.H., Li R.H., Huang S.L., Sia H.K., Lee S.S., Wang W.H., Tang F.C.. **Relationships between different types of physical activity and metabolic syndrome among Taiwanese workers**. *Sci. Rep.* (2017.0) **7** 13735. DOI: 10.1038/s41598-017-13872-5
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.